






"""simple docstring"""

import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    BartForSequenceClassification,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    TapexTokenizer,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.17.0.dev0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/text-classification/requirements.txt''')

lowercase    			=       logging.getLogger(__name__)

@dataclass
class 						A_							:
				UpperCAmelCase__        =				field(
				    default='''tab_fact'''					,							metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}  )
				UpperCAmelCase__        =				field(
				    default='''tab_fact'''					,							metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}					,							)
				UpperCAmelCase__        =				field(
				    default=1_0_2_4					,							metadata={
				        '''help''': (
				            '''The maximum total input sequence length after tokenization. Sequences longer '''
				            '''than this will be truncated, sequences shorter will be padded.'''
				        )
				    }					,							)
				UpperCAmelCase__        =				field(
				    default=snake_case_					,							metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}  )
				UpperCAmelCase__        =				field(
				    default=snake_case_					,							metadata={
				        '''help''': (
				            '''Whether to pad all samples to `max_seq_length`. '''
				            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
				        )
				    }					,							)
				UpperCAmelCase__        =				field(
				    default=snake_case_					,							metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
				            '''value if set.'''
				        )
				    }					,							)
				UpperCAmelCase__        =				field(
				    default=snake_case_					,							metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
				            '''value if set.'''
				        )
				    }					,							)
				UpperCAmelCase__        =				field(
				    default=snake_case_					,							metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
				            '''value if set.'''
				        )
				    }					,							)
				UpperCAmelCase__        =				field(
				    default=snake_case_					,							metadata={'''help''': '''A csv or a json file containing the training data.'''}  )
				UpperCAmelCase__        =				field(
				    default=snake_case_					,							metadata={'''help''': '''A csv or a json file containing the validation data.'''}  )
				UpperCAmelCase__        =				field(default=snake_case_					,							metadata={'''help''': '''A csv or a json file containing the test data.'''}  )







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[int]:
										if self.dataset_name is not None:
																pass
										elif self.train_file is None or self.validation_file is None:
																raise ValueError("Need either a GLUE task, a training/validation file or a dataset name."	)
										else:
																__magic_name__            =     self.train_file.split("."	)[-1]
																assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
																__magic_name__            =     self.validation_file.split("."	)[-1]
																assert (
																    validation_extension == train_extension
																), "`validation_file` should have the same extension (csv or json) as `train_file`."





@dataclass
class 						A_							:
				UpperCAmelCase__        =				field(
				    default=snake_case_					,							metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}  )
				UpperCAmelCase__        =				field(
				    default=snake_case_					,							metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
				UpperCAmelCase__        =				field(
				    default=snake_case_					,							metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}  )
				UpperCAmelCase__        =				field(
				    default=snake_case_					,							metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					,							)
				UpperCAmelCase__        =				field(
				    default=snake_case_					,							metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}					,							)
				UpperCAmelCase__        =				field(
				    default='''main'''					,							metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}					,							)
				UpperCAmelCase__        =				field(
				    default=snake_case_					,							metadata={
				        '''help''': (
				            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
				            '''with private models).'''
				        )
				    }					,							)






def _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
						if len(sys.argv   ) == 2 and sys.argv[1].endswith(".json"   ):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												__magic_name__      ,    __magic_name__      ,    __magic_name__            =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
						else:
												__magic_name__      ,    __magic_name__      ,    __magic_name__            =     parser.parse_args_into_dataclasses()

						# Setup logging
						logging.basicConfig(
						    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,						datefmt="%m/%d/%Y %H:%M:%S" ,						handlers=[logging.StreamHandler(sys.stdout   )] ,						)

						__magic_name__            =     training_args.get_process_log_level()
						logger.setLevel(__lowerCamelCase   )
						datasets.utils.logging.set_verbosity(__lowerCamelCase   )
						transformers.utils.logging.set_verbosity(__lowerCamelCase   )
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()

						# Log on each process the small summary:
						logger.warning(
						    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
						    + f'''distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}'''   )
						logger.info(f'''Training/evaluation parameters {training_args}'''   )

						# Detecting last checkpoint.
						__magic_name__            =     None
						if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
												__magic_name__            =     get_last_checkpoint(training_args.output_dir   )
												if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
																		raise ValueError(
																		    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
																		    "Use --overwrite_output_dir to overcome."   )
												elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
																		logger.info(
																		    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
																		    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."   )

    # Set seed before initializing model.
						set_seed(training_args.seed   )

						# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
						# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
						#
						# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
						#
						# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
						# single column. You can easily tweak this behavior (see below)
						#
						# In distributed training, the load_dataset function guarantee that only one local process can concurrently
						# download the dataset.
						if data_args.dataset_name is not None:
												# Downloading and loading a dataset from the hub.
												__magic_name__            =     load_dataset(
												    data_args.dataset_name ,						data_args.dataset_config_name ,						cache_dir=model_args.cache_dir   )
						else:
												# Loading a dataset from your local files.
												# CSV/JSON training and evaluation files are needed.
												__magic_name__            =     {"train": data_args.train_file, "validation": data_args.validation_file}

												# Get the test dataset: you can provide your own CSV/JSON test file (see below)
												# when you use `do_predict` without specifying a GLUE benchmark task.
												if training_args.do_predict:
																		if data_args.test_file is not None:
																								__magic_name__            =     data_args.train_file.split("."   )[-1]
																								__magic_name__            =     data_args.test_file.split("."   )[-1]
																								assert (
																								    test_extension == train_extension
																								), "`test_file` should have the same extension (csv or json) as `train_file`."
																								__magic_name__            =     data_args.test_file
																		else:
																								raise ValueError("Need either a GLUE task or a test file for `do_predict`."   )

												for key in data_files.keys():
																		logger.info(f'''load a local file for {key}: {data_files[key]}'''   )

												if data_args.train_file.endswith(".csv"   ):
																		# Loading a dataset from local csv files
																		__magic_name__            =     load_dataset("csv" ,						data_files=__lowerCamelCase ,						cache_dir=model_args.cache_dir   )
												else:
																		# Loading a dataset from local json files
																		__magic_name__            =     load_dataset("json" ,						data_files=__lowerCamelCase ,						cache_dir=model_args.cache_dir   )
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
						__magic_name__            =     raw_datasets["train"].features["label"].names
						__magic_name__            =     len(__lowerCamelCase   )

						# Load pretrained model and tokenizer
						#
						# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.
						__magic_name__            =     AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,						num_labels=__lowerCamelCase ,						cache_dir=model_args.cache_dir ,						revision=model_args.model_revision ,						use_auth_token=True if model_args.use_auth_token else None ,						)
						# load tapex tokenizer
						__magic_name__            =     TapexTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,						cache_dir=model_args.cache_dir ,						use_fast=model_args.use_fast_tokenizer ,						revision=model_args.model_revision ,						use_auth_token=True if model_args.use_auth_token else None ,						add_prefix_space=__lowerCamelCase ,						)
						__magic_name__            =     BartForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path ,						from_tf=bool(".ckpt" in model_args.model_name_or_path   ) ,						config=__lowerCamelCase ,						cache_dir=model_args.cache_dir ,						revision=model_args.model_revision ,						use_auth_token=True if model_args.use_auth_token else None ,						)

						# Padding strategy
						if data_args.pad_to_max_length:
												__magic_name__            =     "max_length"
						else:
												# We will pad later, dynamically at batch creation, to the max sequence length in each batch
												__magic_name__            =     False

						# Some models have set the order of the labels to use, so let's make sure we do use it.
						__magic_name__            =     {"Refused": 0, "Entailed": 1}
						__magic_name__            =     {0: "Refused", 1: "Entailed"}

						if data_args.max_seq_length > tokenizer.model_max_length:
												logger.warning(
												    f'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
												    f'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''   )
						__magic_name__            =     min(data_args.max_seq_length ,						tokenizer.model_max_length   )

						def preprocess_tabfact_function(__lowerCamelCase:str   ):
												# Tokenize the texts
												def _convert_table_text_to_pandas(__lowerCamelCase:int   ):
																		__magic_name__            =     [_table_row.split("#"   ) for _table_row in _table_text.strip("\n"   ).split("\n"   )]
																		__magic_name__            =     pd.DataFrame.from_records(_table_content[1:] ,						columns=_table_content[0]   )
																		return _table_pd

												__magic_name__            =     examples["statement"]
												__magic_name__            =     list(map(_convert_table_text_to_pandas ,						examples["table_text"]   )   )
												__magic_name__            =     tokenizer(__lowerCamelCase ,						__lowerCamelCase ,						padding=__lowerCamelCase ,						max_length=__lowerCamelCase ,						truncation=__lowerCamelCase   )

												__magic_name__            =     examples["label"]
												return result

						with training_args.main_process_first(desc="dataset map pre-processing"   ):
												__magic_name__            =     raw_datasets.map(
												    __lowerCamelCase ,						batched=__lowerCamelCase ,						load_from_cache_file=not data_args.overwrite_cache ,						desc="Running tokenizer on dataset" ,						)
						if training_args.do_train:
												if "train" not in raw_datasets:
																		raise ValueError("--do_train requires a train dataset"   )
												__magic_name__            =     raw_datasets["train"]
												if data_args.max_train_samples is not None:
																		__magic_name__            =     train_dataset.select(range(data_args.max_train_samples   )   )

						if training_args.do_eval:
												if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
																		raise ValueError("--do_eval requires a validation dataset"   )
												__magic_name__            =     raw_datasets["validation"]
												if data_args.max_eval_samples is not None:
																		__magic_name__            =     eval_dataset.select(range(data_args.max_eval_samples   )   )

						if training_args.do_predict or data_args.test_file is not None:
												if "test" not in raw_datasets and "test_matched" not in raw_datasets:
																		raise ValueError("--do_predict requires a test dataset"   )
												__magic_name__            =     raw_datasets["test"]
												if data_args.max_predict_samples is not None:
																		__magic_name__            =     predict_dataset.select(range(data_args.max_predict_samples   )   )

    # Log a few random samples from the training set:
						if training_args.do_train:
												for index in random.sample(range(len(__lowerCamelCase   )   ) ,						3   ):
																		logger.info(f'''Sample {index} of the training set: {train_dataset[index]}.'''   )

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
						def compute_metrics(__lowerCamelCase:EvalPrediction   ):
												__magic_name__            =     p.predictions[0] if isinstance(p.predictions ,						__lowerCamelCase   ) else p.predictions
												__magic_name__            =     np.argmax(__lowerCamelCase ,						axis=1   )
												return {"accuracy": (preds == p.label_ids).astype(np.floataa   ).mean().item()}

						# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
						if data_args.pad_to_max_length:
												__magic_name__            =     default_data_collator
						elif training_args.fpaa:
												__magic_name__            =     DataCollatorWithPadding(__lowerCamelCase ,						pad_to_multiple_of=8   )
						else:
												__magic_name__            =     None

						# Initialize our Trainer
						__magic_name__            =     Trainer(
						    model=__lowerCamelCase ,						args=__lowerCamelCase ,						train_dataset=train_dataset if training_args.do_train else None ,						eval_dataset=eval_dataset if training_args.do_eval else None ,						compute_metrics=__lowerCamelCase ,						tokenizer=__lowerCamelCase ,						data_collator=__lowerCamelCase ,						)

						# Training
						if training_args.do_train:
												__magic_name__            =     None
												if training_args.resume_from_checkpoint is not None:
																		__magic_name__            =     training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		__magic_name__            =     last_checkpoint
												__magic_name__            =     trainer.train(resume_from_checkpoint=__lowerCamelCase   )
												__magic_name__            =     train_result.metrics
												__magic_name__            =     (
												    data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCamelCase   )
												)
												__magic_name__            =     min(__lowerCamelCase ,						len(__lowerCamelCase   )   )

												trainer.save_model()  # Saves the tokenizer too for easy upload

												trainer.log_metrics("train" ,						__lowerCamelCase   )
												trainer.save_metrics("train" ,						__lowerCamelCase   )
												trainer.save_state()

						# Evaluation
						if training_args.do_eval:
												logger.info("*** Evaluate ***"   )

												__magic_name__            =     trainer.evaluate(eval_dataset=__lowerCamelCase   )
												__magic_name__            =     data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCamelCase   )
												__magic_name__            =     min(__lowerCamelCase ,						len(__lowerCamelCase   )   )

												trainer.log_metrics("eval" ,						__lowerCamelCase   )
												trainer.save_metrics("eval" ,						__lowerCamelCase   )

						if training_args.do_predict:
												logger.info("*** Predict ***"   )

												# Removing the `label` columns because it contains -1 and Trainer won't like that.
												__magic_name__            =     predict_dataset.remove_columns("label"   )
												__magic_name__            =     trainer.predict(__lowerCamelCase ,						metric_key_prefix="predict"   ).predictions
												__magic_name__            =     np.argmax(__lowerCamelCase ,						axis=1   )

												__magic_name__            =     os.path.join(training_args.output_dir ,						"predict_results_tabfact.txt"   )
												if trainer.is_world_process_zero():
																		with open(__lowerCamelCase ,						"w"   ) as writer:
																								logger.info("***** Predict Results *****"   )
																								writer.write("index\tprediction\n"   )
																								for index, item in enumerate(__lowerCamelCase   ):
																														__magic_name__            =     label_list[item]
																														writer.write(f'''{index}\t{item}\n'''   )

						__magic_name__            =     {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}

						if training_args.push_to_hub:
												trainer.push_to_hub(**__lowerCamelCase   )
						else:
												trainer.create_model_card(**__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:Dict   ):

						'''simple docstring'''






						main()


if __name__ == "__main__":
			main()











"""simple docstring"""

from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor

class 						A_							:







				def __init__(							self							:						List[str]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						Dict=1_3						,    __lowerCamelCase							:						int=3_0						,    __lowerCamelCase							:						List[Any]=2						,    __lowerCamelCase							:						List[Any]=3						,    __lowerCamelCase							:						Optional[int]=True						,    __lowerCamelCase							:						Optional[Any]=True						,    __lowerCamelCase							:						int=3_2						,    __lowerCamelCase							:						int=2						,    __lowerCamelCase							:						Optional[Any]=4						,    __lowerCamelCase							:						List[str]=3_7						,    __lowerCamelCase							:						List[str]="gelu"						,    __lowerCamelCase							:						Tuple=0.1						,    __lowerCamelCase							:						int=0.1						,    __lowerCamelCase							:						Optional[int]=1_0						,    __lowerCamelCase							:						Tuple=0.02						,    __lowerCamelCase							:						Any=3						,    __lowerCamelCase							:						Union[str, Any]=0.6						,    __lowerCamelCase							:						Optional[int]=None						,    )				->					Union[str, Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     image_size
										__magic_name__            =     patch_size
										__magic_name__            =     num_channels
										__magic_name__            =     is_training
										__magic_name__            =     use_labels
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size
										__magic_name__            =     hidden_act
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     type_sequence_label_size
										__magic_name__            =     initializer_range
										__magic_name__            =     mask_ratio
										__magic_name__            =     scope

										# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
										# (we add 1 for the [CLS] token)
										__magic_name__            =     (image_size // patch_size) ** 2
										__magic_name__            =     int(math.ceil((1 - mask_ratio) * (num_patches + 1)	)	)







				def     _snake_case					(							self							:						Optional[Any]	)				->					List[Any]:
										__magic_name__            =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

										__magic_name__            =     None
										if self.use_labels:
																__magic_name__            =     ids_tensor([self.batch_size]						,    self.type_sequence_label_size	)

										__magic_name__            =     self.get_config()

										return config, pixel_values, labels







				def     _snake_case					(							self							:						Optional[int]	)				->					Dict:
										return ViTMAEConfig(
										    image_size=self.image_size						,    patch_size=self.patch_size						,    num_channels=self.num_channels						,    hidden_size=self.hidden_size						,    num_hidden_layers=self.num_hidden_layers						,    num_attention_heads=self.num_attention_heads						,    intermediate_size=self.intermediate_size						,    decoder_hidden_size=self.hidden_size						,    decoder_num_hidden_layers=self.num_hidden_layers						,    decoder_num_attention_heads=self.num_attention_heads						,    decoder_intermediate_size=self.intermediate_size						,    hidden_act=self.hidden_act						,    hidden_dropout_prob=self.hidden_dropout_prob						,    attention_probs_dropout_prob=self.attention_probs_dropout_prob						,    is_decoder=__lowerCamelCase						,    initializer_range=self.initializer_range						,    mask_ratio=self.mask_ratio						,    )







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Optional[int]	)				->					Optional[int]:
										__magic_name__            =     TFViTMAEModel(config=__lowerCamelCase	)
										__magic_name__            =     model(__lowerCamelCase						,    training=__lowerCamelCase	)
										self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)	)







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						str	)				->					Union[str, Any]:
										__magic_name__            =     TFViTMAEForPreTraining(__lowerCamelCase	)
										__magic_name__            =     model(__lowerCamelCase						,    training=__lowerCamelCase	)
										# expected sequence length = num_patches
										__magic_name__            =     (self.image_size // self.patch_size) ** 2
										__magic_name__            =     self.patch_size**2 * self.num_channels
										self.parent.assertEqual(result.logits.shape						,    (self.batch_size, num_patches, expected_num_channels)	)

										# test greyscale images
										__magic_name__            =     1
										__magic_name__            =     TFViTMAEForPreTraining(__lowerCamelCase	)

										__magic_name__            =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
										__magic_name__            =     model(__lowerCamelCase						,    training=__lowerCamelCase	)
										__magic_name__            =     self.patch_size**2
										self.parent.assertEqual(result.logits.shape						,    (self.batch_size, num_patches, expected_num_channels)	)







				def     _snake_case					(							self							:						Tuple	)				->					Optional[Any]:
										__magic_name__            =     self.prepare_config_and_inputs()
										((__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__))            =     config_and_inputs
										__magic_name__            =     {"pixel_values": pixel_values}
										return config, inputs_dict





@require_tf
class 						A_							(      snake_case_					,							snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
				UpperCAmelCase__        =				{'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False







				def     _snake_case					(							self							:						Any	)				->					int:
										__magic_name__            =     TFViTMAEModelTester(self	)
										__magic_name__            =     ConfigTester(self						,    config_class=__lowerCamelCase						,    has_text_modality=__lowerCamelCase						,    hidden_size=3_7	)







				def     _snake_case					(							self							:						Dict	)				->					Any:
										self.config_tester.run_common_tests()







				@unittest.skip(reason="ViTMAE does not use inputs_embeds"	)
				def     _snake_case					(							self							:						Any	)				->					Optional[Any]:
										pass







				def     _snake_case					(							self							:						Optional[Any]	)				->					Tuple:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																__magic_name__            =     model_class(__lowerCamelCase	)
																self.assertIsInstance(model.get_input_embeddings()						,    (tf.keras.layers.Layer)	)
																__magic_name__            =     model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(__lowerCamelCase						,    tf.keras.layers.Layer	)	)







				def     _snake_case					(							self							:						Optional[Any]	)				->					Optional[Any]:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     inspect.signature(model.call	)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																__magic_name__            =     [*signature.parameters.keys()]

																__magic_name__            =     ["pixel_values"]
																self.assertListEqual(arg_names[:1]						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						int	)				->					Union[str, Any]:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowerCamelCase	)







				def     _snake_case					(							self							:						str	)				->					Any:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase	)







				def     _snake_case					(							self							:						str	)				->					List[Any]:
										# make the mask reproducible
										np.random.seed(2	)

										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

										__magic_name__            =     int((config.image_size // config.patch_size) ** 2	)
										__magic_name__            =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

										for model_class in self.all_model_classes:
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)

																__magic_name__            =     model(__lowerCamelCase						,    noise=__lowerCamelCase	)

																__magic_name__            =     copy.deepcopy(self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)	)
																__magic_name__            =     model(**__lowerCamelCase						,    noise=__lowerCamelCase	)
																__magic_name__            =     outputs_dict[0].numpy()
																__magic_name__            =     outputs_keywords[0].numpy()

																self.assertLess(np.sum(np.abs(output_dict - output_keywords	)	)						,    1e-6	)







				def     _snake_case					(							self							:						int	)				->					Union[str, Any]:
										# make the mask reproducible
										np.random.seed(2	)

										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

										__magic_name__            =     int((config.image_size // config.patch_size) ** 2	)
										__magic_name__            =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

										def prepare_numpy_arrays(__lowerCamelCase							:						Tuple	):
																__magic_name__            =     {}
																for k, v in inputs_dict.items():
																						if tf.is_tensor(__lowerCamelCase	):
																												__magic_name__            =     v.numpy()
																						else:
																												__magic_name__            =     np.array(__lowerCamelCase	)

																return inputs_np_dict

										for model_class in self.all_model_classes:
																__magic_name__            =     model_class(__lowerCamelCase	)

																__magic_name__            =     self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)
																__magic_name__            =     prepare_numpy_arrays(__lowerCamelCase	)

																__magic_name__            =     model(__lowerCamelCase						,    noise=__lowerCamelCase	)
																__magic_name__            =     model(**__lowerCamelCase						,    noise=__lowerCamelCase	)
																self.assert_outputs_same(__lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Dict	)				->					List[Any]:
										# make masks reproducible
										np.random.seed(2	)

										__magic_name__            =     int((tf_model.config.image_size // tf_model.config.patch_size) ** 2	)
										__magic_name__            =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
										__magic_name__            =     tf.constant(__lowerCamelCase	)

										# Add `noise` argument.
										# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
										__magic_name__            =     tf_noise

										super().check_pt_tf_models(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					List[str]:
										# make mask reproducible
										np.random.seed(2	)

										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

										__magic_name__            =     {
										    module_member
										    for model_class in self.all_model_classes
										    for module in (import_module(model_class.__module__	),)
										    for module_member_name in dir(__lowerCamelCase	)
										    if module_member_name.endswith("MainLayer"	)
										    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
										    and module_member_name[: -len("MainLayer"	)] == model_class.__name__[: -len("Model"	)]
										    for module_member in (getattr(__lowerCamelCase						,    __lowerCamelCase	),)
										    if isinstance(__lowerCamelCase						,    __lowerCamelCase	)
										    and tf.keras.layers.Layer in module_member.__bases__
										    and getattr(__lowerCamelCase						,    "_keras_serializable"						,    __lowerCamelCase	)
										}

										__magic_name__            =     int((config.image_size // config.patch_size) ** 2	)
										__magic_name__            =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
										__magic_name__            =     tf.convert_to_tensor(__lowerCamelCase	)
										inputs_dict.update({"noise": noise}	)

										for main_layer_class in tf_main_layer_classes:
																__magic_name__            =     main_layer_class(__lowerCamelCase	)

																__magic_name__            =     {
																    name: tf.keras.Input(tensor.shape[1:]						,    dtype=tensor.dtype	) for name, tensor in inputs_dict.items()
																}

																__magic_name__            =     tf.keras.Model(__lowerCamelCase						,    outputs=main_layer(__lowerCamelCase	)	)
																__magic_name__            =     model(__lowerCamelCase	)

																with tempfile.TemporaryDirectory() as tmpdirname:
																						__magic_name__            =     os.path.join(__lowerCamelCase						,    "keras_model.h5"	)
																						model.save(__lowerCamelCase	)
																						__magic_name__            =     tf.keras.models.load_model(
																						    __lowerCamelCase						,    custom_objects={main_layer_class.__name__: main_layer_class}	)
																						assert isinstance(__lowerCamelCase						,    tf.keras.Model	)
																						__magic_name__            =     model(__lowerCamelCase	)
																						self.assert_outputs_same(__lowerCamelCase						,    __lowerCamelCase	)







				@slow
				def     _snake_case					(							self							:						Union[str, Any]	)				->					str:
										# make mask reproducible
										np.random.seed(2	)

										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

										__magic_name__            =     int((config.image_size // config.patch_size) ** 2	)
										__magic_name__            =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

										for model_class in self.all_model_classes:
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)
																__magic_name__            =     model(__lowerCamelCase						,    noise=__lowerCamelCase	)

																if model_class.__name__ == "TFViTMAEModel":
																						__magic_name__            =     outputs.last_hidden_state.numpy()
																						__magic_name__            =     0
																else:
																						__magic_name__            =     outputs.logits.numpy()
																						__magic_name__            =     0

																with tempfile.TemporaryDirectory() as tmpdirname:
																						model.save_pretrained(__lowerCamelCase						,    saved_model=__lowerCamelCase	)
																						__magic_name__            =     model_class.from_pretrained(__lowerCamelCase	)
																						__magic_name__            =     model(__lowerCamelCase						,    noise=__lowerCamelCase	)

																						if model_class.__name__ == "TFViTMAEModel":
																												__magic_name__            =     after_outputs["last_hidden_state"].numpy()
																												__magic_name__            =     0
																						else:
																												__magic_name__            =     after_outputs["logits"].numpy()
																												__magic_name__            =     0

																						__magic_name__            =     np.amax(np.abs(out_a - out_a	)	)
																						self.assertLessEqual(__lowerCamelCase						,    1e-5	)







				def     _snake_case					(							self							:						List[str]	)				->					Optional[Any]:
										# make mask reproducible
										np.random.seed(2	)

										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

										__magic_name__            =     int((config.image_size // config.patch_size) ** 2	)
										__magic_name__            =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

										for model_class in self.all_model_classes:
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)

																__magic_name__            =     model(__lowerCamelCase						,    noise=__lowerCamelCase	)
																__magic_name__            =     model.get_config()
																# make sure that returned config is jsonifiable, which is required by keras
																json.dumps(__lowerCamelCase	)
																__magic_name__            =     model_class.from_config(model.get_config()	)
																# make sure it also accepts a normal config
																__magic_name__            =     model_class.from_config(model.config	)
																__magic_name__            =     new_model(__lowerCamelCase	)  # Build model
																new_model.set_weights(model.get_weights()	)
																__magic_name__            =     new_model(__lowerCamelCase						,    noise=__lowerCamelCase	)

																self.assert_outputs_same(__lowerCamelCase						,    __lowerCamelCase	)







				@unittest.skip(
				    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."	)
				def     _snake_case					(							self							:						str	)				->					List[Any]:
										pass







				@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"	)
				def     _snake_case					(							self							:						Any	)				->					List[str]:
										pass







				@slow
				def     _snake_case					(							self							:						List[Any]	)				->					Any:
										__magic_name__            =     TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"	)
										self.assertIsNotNone(__lowerCamelCase	)






def _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
						return image


@require_tf
@require_vision
class 						A_							(      unittest.TestCase  ):







				@cached_property
				def     _snake_case					(							self							:						Union[str, Any]	)				->					Union[str, Any]:
										return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"	) if is_vision_available() else None







				@slow
				def     _snake_case					(							self							:						Union[str, Any]	)				->					Union[str, Any]:
										# make random mask reproducible across the PT and TF model
										np.random.seed(2	)

										__magic_name__            =     TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"	)

										__magic_name__            =     self.default_image_processor
										__magic_name__            =     prepare_img()
										__magic_name__            =     image_processor(images=__lowerCamelCase						,    return_tensors="tf"	)

										# prepare a noise vector that will be also used for testing the TF model
										# (this way we can ensure that the PT and TF models operate on the same inputs)
										__magic_name__            =     ViTMAEConfig()
										__magic_name__            =     int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2	)
										__magic_name__            =     np.random.uniform(size=(1, num_patches)	)

										# forward pass
										__magic_name__            =     model(**__lowerCamelCase						,    noise=__lowerCamelCase	)

										# verify the logits
										__magic_name__            =     tf.convert_to_tensor([1, 1_9_6, 7_6_8]	)
										self.assertEqual(outputs.logits.shape						,    __lowerCamelCase	)

										__magic_name__            =     tf.convert_to_tensor(
										    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]	)

										tf.debugging.assert_near(outputs.logits[0, :3, :3]						,    __lowerCamelCase						,    atol=1e-4	)



