

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

import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


SCREAMING_SNAKE_CASE_        =      logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.14.0', 'To fix: pip install -r examples/pytorch/audio-classification/requirements.txt')

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  np.ndarray  ,      lowerCAmelCase:  float  ,      lowerCAmelCase:  int = 1_6000 )   ->       Any:
				_UpperCAmelCase       :		List[str] 		=    int(round(sample_rate * max_length ) )
				if len(lowerCAmelCase ) <= sample_length:
								return wav
				_UpperCAmelCase       :		Tuple 		=    randint(0  ,      len(lowerCAmelCase ) - sample_length - 1 )
				return wav[random_offset : random_offset + sample_length]

@dataclass
class 				a  :
						_lowercase    						=			field(default=UpperCAmelCase							, metadata={"help": "Name of a dataset from the datasets package"}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "A file containing the training audio paths and labels."}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "A file containing the validation audio paths and labels."}				)
						_lowercase    						=			field(
						    default="train"							, metadata={
						        "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
						    }							, )
						_lowercase    						=			field(
						    default="validation"							, metadata={
						        "help": (
						            "The name of the training data set split to use (via the datasets library). Defaults to 'validation'"
						        )
						    }							, )
						_lowercase    						=			field(
						    default="audio"							, metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"}							, )
						_lowercase    						=			field(
						    default="label"							, metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of training examples to this "
						            "value if set."
						        )
						    }							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
						            "value if set."
						        )
						    }							, )
						_lowercase    						=			field(
						    default=2_0							, metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."}							, )







@dataclass
class 				a  :
						_lowercase    						=			field(
						    default="facebook/wav2vec2-base"							, metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "Pretrained config name or path if not the same as model_name"}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"}				)
						_lowercase    						=			field(
						    default="main"							, metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "Name or path of preprocessor config."}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "Whether to freeze the feature encoder layers of the model."}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "Whether to generate an attention mask in the feature extractor."}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={
						        "help": (
						            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
						            "with private models)."
						        )
						    }							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "Whether to freeze the feature extractor layers of the model."}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}							, )


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										if not self.freeze_feature_extractor and self.freeze_feature_encoder:
														warnings.warn(
														    "The argument `--freeze_feature_extractor` is deprecated and "
														    "will be removed in a future version. Use `--freeze_feature_encoder`"
														    "instead. Setting `freeze_feature_encoder==True`."						,					A_						,					)
										if self.freeze_feature_extractor and not self.freeze_feature_encoder:
														raise ValueError(
														    "The argument `--freeze_feature_extractor` is deprecated and "
														    "should not be used in combination with `--freeze_feature_encoder`."
														    "Only make use of `--freeze_feature_encoder`."	)







def 							__SCREAMING_SNAKE_CASE							(				)   ->       Union[str, Any]:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				_UpperCAmelCase       :		Optional[int] 		=    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.
								_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Tuple 		=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
				else:
								_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry("run_audio_classification"  ,      lowerCAmelCase  ,      lowerCAmelCase )

				# 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 )]  ,      )

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				_UpperCAmelCase       :		Dict 		=    training_args.get_process_log_level()
				logger.setLevel(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}' )

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

				# Detecting last checkpoint.
				_UpperCAmelCase       :		Dict 		=    None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								_UpperCAmelCase       :		Any 		=    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 train from scratch." )
								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." )

    # Initialize our dataset and prepare it for the audio classification task.
				_UpperCAmelCase       :		Optional[int] 		=    DatasetDict()
				_UpperCAmelCase       :		int 		=    load_dataset(
				    data_args.dataset_name  ,      data_args.dataset_config_name  ,      split=data_args.train_split_name  ,      use_auth_token=True if model_args.use_auth_token else None  ,      )
				_UpperCAmelCase       :		Optional[int] 		=    load_dataset(
				    data_args.dataset_name  ,      data_args.dataset_config_name  ,      split=data_args.eval_split_name  ,      use_auth_token=True if model_args.use_auth_token else None  ,      )

				if data_args.audio_column_name not in raw_datasets["train"].column_names:
								raise ValueError(
								    F'--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '
								    "Make sure to set `--audio_column_name` to the correct audio column - one of "
								    F'{", ".join(raw_datasets["train"].column_names )}.' )

				if data_args.label_column_name not in raw_datasets["train"].column_names:
								raise ValueError(
								    F'--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '
								    "Make sure to set `--label_column_name` to the correct text column - one of "
								    F'{", ".join(raw_datasets["train"].column_names )}.' )

				# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
				# transformer outputs in the classifier, but it doesn't always lead to better accuracy
				_UpperCAmelCase       :		Dict 		=    AutoFeatureExtractor.from_pretrained(
				    model_args.feature_extractor_name or model_args.model_name_or_path  ,      return_attention_mask=model_args.attention_mask  ,      cache_dir=model_args.cache_dir  ,      revision=model_args.model_revision  ,      use_auth_token=True if model_args.use_auth_token else None  ,      )

				# `datasets` takes care of automatically loading and resampling the audio,
				# so we just need to set the correct target sampling rate.
				_UpperCAmelCase       :		Dict 		=    raw_datasets.cast_column(
				    data_args.audio_column_name  ,      datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )

				_UpperCAmelCase       :		int 		=    feature_extractor.model_input_names[0]

				def train_transforms(lowerCAmelCase:  List[Any] ):
								_UpperCAmelCase       :		Tuple 		=    []
								for audio in batch[data_args.audio_column_name]:
												_UpperCAmelCase       :		Union[str, Any] 		=    random_subsample(
												    audio["array"]  ,      max_length=data_args.max_length_seconds  ,      sample_rate=feature_extractor.sampling_rate )
												subsampled_wavs.append(lowerCAmelCase )
								_UpperCAmelCase       :		Dict 		=    feature_extractor(lowerCAmelCase  ,      sampling_rate=feature_extractor.sampling_rate )
								_UpperCAmelCase       :		List[Any] 		=    {model_input_name: inputs.get(lowerCAmelCase )}
								_UpperCAmelCase       :		Optional[int] 		=    list(batch[data_args.label_column_name] )

								return output_batch

				def val_transforms(lowerCAmelCase:  str ):
								_UpperCAmelCase       :		Dict 		=    [audio["array"] for audio in batch[data_args.audio_column_name]]
								_UpperCAmelCase       :		str 		=    feature_extractor(lowerCAmelCase  ,      sampling_rate=feature_extractor.sampling_rate )
								_UpperCAmelCase       :		int 		=    {model_input_name: inputs.get(lowerCAmelCase )}
								_UpperCAmelCase       :		Any 		=    list(batch[data_args.label_column_name] )

								return output_batch

				# Prepare label mappings.
				# We'll include these in the model's config to get human readable labels in the Inference API.
				_UpperCAmelCase       :		Optional[Any] 		=    raw_datasets["train"].features[data_args.label_column_name].names
				_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    {}, {}
				for i, label in enumerate(lowerCAmelCase ):
								_UpperCAmelCase       :		Dict 		=    str(lowerCAmelCase )
								_UpperCAmelCase       :		Optional[int] 		=    label

				# Load the accuracy metric from the datasets package
				_UpperCAmelCase       :		Dict 		=    evaluate.load("accuracy" )

				# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
				# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
				def compute_metrics(lowerCAmelCase:  Union[str, Any] ):
								_UpperCAmelCase       :		Tuple 		=    np.argmax(eval_pred.predictions  ,      axis=1 )
								return metric.compute(predictions=lowerCAmelCase  ,      references=eval_pred.label_ids )

				_UpperCAmelCase       :		Any 		=    AutoConfig.from_pretrained(
				    model_args.config_name or model_args.model_name_or_path  ,      num_labels=len(lowerCAmelCase )  ,      labelaid=lowerCAmelCase  ,      idalabel=lowerCAmelCase  ,      finetuning_task="audio-classification"  ,      cache_dir=model_args.cache_dir  ,      revision=model_args.model_revision  ,      use_auth_token=True if model_args.use_auth_token else None  ,      )
				_UpperCAmelCase       :		str 		=    AutoModelForAudioClassification.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  ,      ignore_mismatched_sizes=model_args.ignore_mismatched_sizes  ,      )

				# freeze the convolutional waveform encoder
				if model_args.freeze_feature_encoder:
								model.freeze_feature_encoder()

				if training_args.do_train:
								if data_args.max_train_samples is not None:
												_UpperCAmelCase       :		Any 		=    (
												    raw_datasets["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
												)
								# Set the training transforms
								raw_datasets["train"].set_transform(lowerCAmelCase  ,      output_all_columns=lowerCAmelCase )

				if training_args.do_eval:
								if data_args.max_eval_samples is not None:
												_UpperCAmelCase       :		Tuple 		=    (
												    raw_datasets["eval"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
												)
								# Set the validation transforms
								raw_datasets["eval"].set_transform(lowerCAmelCase  ,      output_all_columns=lowerCAmelCase )

				# Initialize our trainer
				_UpperCAmelCase       :		List[str] 		=    Trainer(
				    model=lowerCAmelCase  ,      args=lowerCAmelCase  ,      train_dataset=raw_datasets["train"] if training_args.do_train else None  ,      eval_dataset=raw_datasets["eval"] if training_args.do_eval else None  ,      compute_metrics=lowerCAmelCase  ,      tokenizer=lowerCAmelCase  ,      )

				# Training
				if training_args.do_train:
								_UpperCAmelCase       :		str 		=    None
								if training_args.resume_from_checkpoint is not None:
												_UpperCAmelCase       :		List[Any] 		=    training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												_UpperCAmelCase       :		Dict 		=    last_checkpoint
								_UpperCAmelCase       :		Dict 		=    trainer.train(resume_from_checkpoint=lowerCAmelCase )
								trainer.save_model()
								trainer.log_metrics("train"  ,      train_result.metrics )
								trainer.save_metrics("train"  ,      train_result.metrics )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								_UpperCAmelCase       :		List[str] 		=    trainer.evaluate()
								trainer.log_metrics("eval"  ,      lowerCAmelCase )
								trainer.save_metrics("eval"  ,      lowerCAmelCase )

				# Write model card and (optionally) push to hub
				_UpperCAmelCase       :		int 		=    {
				    "finetuned_from": model_args.model_name_or_path,
				    "tasks": "audio-classification",
				    "dataset": data_args.dataset_name,
				    "tags": ["audio-classification"],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**lowerCAmelCase )
				else:
								trainer.create_model_card(**lowerCAmelCase )


if __name__ == "__main__":
					main()





import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    MODEL_MAPPING,
					    BeitForImageClassification,
					    BeitForMaskedImageModeling,
					    BeitForSemanticSegmentation,
					    BeitModel,
					)
					from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					import PIL
					from PIL import Image

					from transformers import BeitImageProcessor

class 				a  :


						def __init__(       self						,					A_						,					A_=100						,					A_=13						,					A_=30						,					A_=2						,					A_=3						,					A_=True						,					A_=True						,					A_=32						,					A_=4						,					A_=4						,					A_=37						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=10						,					A_=0.02						,					A_=3						,					A_=None						,					A_=[0, 1, 2, 3]						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    parent
										_UpperCAmelCase       :		Any 		=    100
										_UpperCAmelCase       :		Optional[Any] 		=    batch_size
										_UpperCAmelCase       :		Any 		=    image_size
										_UpperCAmelCase       :		int 		=    patch_size
										_UpperCAmelCase       :		Dict 		=    num_channels
										_UpperCAmelCase       :		int 		=    is_training
										_UpperCAmelCase       :		Union[str, Any] 		=    use_labels
										_UpperCAmelCase       :		Optional[Any] 		=    hidden_size
										_UpperCAmelCase       :		str 		=    num_hidden_layers
										_UpperCAmelCase       :		Tuple 		=    num_attention_heads
										_UpperCAmelCase       :		List[str] 		=    intermediate_size
										_UpperCAmelCase       :		List[str] 		=    hidden_act
										_UpperCAmelCase       :		List[Any] 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Any 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Dict 		=    type_sequence_label_size
										_UpperCAmelCase       :		Optional[Any] 		=    initializer_range
										_UpperCAmelCase       :		Optional[int] 		=    scope
										_UpperCAmelCase       :		Any 		=    out_indices
										_UpperCAmelCase       :		List[str] 		=    num_labels

										# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										_UpperCAmelCase       :		Any 		=    (image_size // patch_size) ** 2
										_UpperCAmelCase       :		str 		=    num_patches + 1


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

										_UpperCAmelCase       :		Optional[Any] 		=    None
										_UpperCAmelCase       :		str 		=    None
										if self.use_labels:
														_UpperCAmelCase       :		Union[str, Any] 		=    ids_tensor([self.batch_size]						,					self.type_sequence_label_size	)
														_UpperCAmelCase       :		Optional[int] 		=    ids_tensor([self.batch_size, self.image_size, self.image_size]						,					self.num_labels	)

										_UpperCAmelCase       :		Tuple 		=    self.get_config()

										return config, pixel_values, labels, pixel_labels


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return BeitConfig(
										    vocab_size=self.vocab_size						,					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						,					hidden_act=self.hidden_act						,					hidden_dropout_prob=self.hidden_dropout_prob						,					attention_probs_dropout_prob=self.attention_probs_dropout_prob						,					is_decoder=A_						,					initializer_range=self.initializer_range						,					out_indices=self.out_indices						,					)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    BeitModel(config=A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		int 		=    model(A_	)
										self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    BeitForMaskedImageModeling(config=A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		Any 		=    model(A_	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length - 1, self.vocab_size)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.type_sequence_label_size
										_UpperCAmelCase       :		Optional[int] 		=    BeitForImageClassification(A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		List[str] 		=    model(A_						,					labels=A_	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.type_sequence_label_size)	)

										# test greyscale images
										_UpperCAmelCase       :		Optional[Any] 		=    1
										_UpperCAmelCase       :		Tuple 		=    BeitForImageClassification(A_	)
										model.to(A_	)
										model.eval()

										_UpperCAmelCase       :		int 		=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
										_UpperCAmelCase       :		Dict 		=    model(A_						,					labels=A_	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.type_sequence_label_size)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    self.num_labels
										_UpperCAmelCase       :		Dict 		=    BeitForSemanticSegmentation(A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		str 		=    model(A_	)
										self.parent.assertEqual(
										    result.logits.shape						,					(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
										_UpperCAmelCase       :		List[Any] 		=    model(A_						,					labels=A_	)
										self.parent.assertEqual(
										    result.logits.shape						,					(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    self.prepare_config_and_inputs()
										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    config_and_inputs
										_UpperCAmelCase       :		int 		=    {"pixel_values": pixel_values}
										return config, inputs_dict







@require_torch
class 				a  (	UpperCAmelCase							, UpperCAmelCase							, unittest.TestCase				):
						_lowercase    						=			(
						    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						_lowercase    						=			(
						    {
						        "feature-extraction": BeitModel,
						        "image-classification": BeitForImageClassification,
						        "image-segmentation": BeitForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						_lowercase    						=			False
						_lowercase    						=			False
						_lowercase    						=			False


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    BeitModelTester(self	)
										_UpperCAmelCase       :		List[Any] 		=    ConfigTester(self						,					config_class=A_						,					has_text_modality=A_						,					hidden_size=37	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						@unittest.skip(reason="BEiT does not use inputs_embeds"	)
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										pass


						@require_torch_multi_gpu
						@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"	)
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										pass


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_UpperCAmelCase       :		Union[str, Any] 		=    model_class(A_	)
														self.assertIsInstance(model.get_input_embeddings()						,					(nn.Module)	)
														_UpperCAmelCase       :		Tuple 		=    model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(A_						,					nn.Linear	)	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_UpperCAmelCase       :		List[str] 		=    model_class(A_	)
														_UpperCAmelCase       :		Tuple 		=    inspect.signature(model.forward	)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_UpperCAmelCase       :		int 		=    [*signature.parameters.keys()]

														_UpperCAmelCase       :		int 		=    ["pixel_values"]
														self.assertListEqual(arg_names[:1]						,					A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										if not self.model_tester.is_training:
														return

										_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    self.model_tester.prepare_config_and_inputs_for_common()
										_UpperCAmelCase       :		str 		=    True

										for model_class in self.all_model_classes:
														# we don't test BeitForMaskedImageModeling
														if model_class in [*get_values(A_	), BeitForMaskedImageModeling]:
																		continue

														_UpperCAmelCase       :		Union[str, Any] 		=    model_class(A_	)
														model.to(A_	)
														model.train()
														_UpperCAmelCase       :		str 		=    self._prepare_for_class(A_						,					A_						,					return_labels=A_	)
														_UpperCAmelCase       :		Optional[Any] 		=    model(**A_	).loss
														loss.backward()


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    self.model_tester.prepare_config_and_inputs_for_common()
										if not self.model_tester.is_training:
														return

										_UpperCAmelCase       :		Tuple 		=    False
										_UpperCAmelCase       :		Any 		=    True

										for model_class in self.all_model_classes:
														# we don't test BeitForMaskedImageModeling
														if (
														    model_class in [*get_values(A_	), BeitForMaskedImageModeling]
														    or not model_class.supports_gradient_checkpointing
														):
																		continue

														_UpperCAmelCase       :		Optional[Any] 		=    model_class(A_	)
														model.gradient_checkpointing_enable()
														model.to(A_	)
														model.train()
														_UpperCAmelCase       :		Union[str, Any] 		=    self._prepare_for_class(A_						,					A_						,					return_labels=A_	)
														_UpperCAmelCase       :		Optional[Any] 		=    model(**A_	).loss
														loss.backward()


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		List[Any] 		=    self.model_tester.prepare_config_and_inputs_for_common()

										_UpperCAmelCase       :		Optional[Any] 		=    _config_zero_init(A_	)
										for model_class in self.all_model_classes:
														_UpperCAmelCase       :		Tuple 		=    model_class(config=A_	)
														for name, param in model.named_parameters():
																		# we skip lambda parameters as these require special initial values
																		# determined by config.layer_scale_init_value
																		if "lambda" in name:
																						continue
																		if param.requires_grad:
																						self.assertIn(
																						    ((param.data.mean() * 1e9).round() / 1e9).item()						,					[0.0, 1.0]						,					msg=f'Parameter {name} of model {model_class} seems not properly initialized'						,					)





						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase       :		Dict 		=    BeitModel.from_pretrained(A_	)
														self.assertIsNotNone(A_	)







def 							__SCREAMING_SNAKE_CASE							(				)   ->       Any:
				_UpperCAmelCase       :		Optional[int] 		=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
				return image





@require_torch
@require_vision
class 				a  (	unittest.TestCase				):


						@cached_property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"	) if is_vision_available() else None


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"	).to(A_	)

										_UpperCAmelCase       :		Dict 		=    self.default_image_processor
										_UpperCAmelCase       :		Optional[Any] 		=    prepare_img()
										_UpperCAmelCase       :		Any 		=    image_processor(images=A_						,					return_tensors="pt"	).pixel_values.to(A_	)

										# prepare bool_masked_pos
										_UpperCAmelCase       :		Dict 		=    torch.ones((1, 196)						,					dtype=torch.bool	).to(A_	)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase       :		List[str] 		=    model(pixel_values=A_						,					bool_masked_pos=A_	)
										_UpperCAmelCase       :		Optional[int] 		=    outputs.logits

										# verify the logits
										_UpperCAmelCase       :		Optional[int] 		=    torch.Size((1, 196, 8192)	)
										self.assertEqual(logits.shape						,					A_	)

										_UpperCAmelCase       :		Tuple 		=    torch.tensor(
										    [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]	).to(A_	)

										self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]						,					A_						,					atol=1e-2	)	)


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"	).to(A_	)

										_UpperCAmelCase       :		str 		=    self.default_image_processor
										_UpperCAmelCase       :		Union[str, Any] 		=    prepare_img()
										_UpperCAmelCase       :		List[Any] 		=    image_processor(images=A_						,					return_tensors="pt"	).to(A_	)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase       :		Tuple 		=    model(**A_	)
										_UpperCAmelCase       :		Dict 		=    outputs.logits

										# verify the logits
										_UpperCAmelCase       :		List[str] 		=    torch.Size((1, 1000)	)
										self.assertEqual(logits.shape						,					A_	)

										_UpperCAmelCase       :		int 		=    torch.tensor([-1.23_85, -1.09_87, -1.01_08]	).to(A_	)

										self.assertTrue(torch.allclose(logits[0, :3]						,					A_						,					atol=1e-4	)	)

										_UpperCAmelCase       :		List[Any] 		=    281
										self.assertEqual(logits.argmax(-1	).item()						,					A_	)


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"	).to(
										    A_	)

										_UpperCAmelCase       :		str 		=    self.default_image_processor
										_UpperCAmelCase       :		List[str] 		=    prepare_img()
										_UpperCAmelCase       :		List[Any] 		=    image_processor(images=A_						,					return_tensors="pt"	).to(A_	)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase       :		Any 		=    model(**A_	)
										_UpperCAmelCase       :		List[Any] 		=    outputs.logits

										# verify the logits
										_UpperCAmelCase       :		List[str] 		=    torch.Size((1, 21841)	)
										self.assertEqual(logits.shape						,					A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    torch.tensor([1.68_81, -0.27_87, 0.59_01]	).to(A_	)

										self.assertTrue(torch.allclose(logits[0, :3]						,					A_						,					atol=1e-4	)	)

										_UpperCAmelCase       :		List[str] 		=    2396
										self.assertEqual(logits.argmax(-1	).item()						,					A_	)


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"	)
										_UpperCAmelCase       :		str 		=    model.to(A_	)

										_UpperCAmelCase       :		int 		=    BeitImageProcessor(do_resize=A_						,					size=640						,					do_center_crop=A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    load_dataset("hf-internal-testing/fixtures_ade20k"						,					split="test"	)
										_UpperCAmelCase       :		Any 		=    Image.open(ds[0]["file"]	)
										_UpperCAmelCase       :		int 		=    image_processor(images=A_						,					return_tensors="pt"	).to(A_	)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase       :		Dict 		=    model(**A_	)
										_UpperCAmelCase       :		Optional[int] 		=    outputs.logits

										# verify the logits
										_UpperCAmelCase       :		str 		=    torch.Size((1, 150, 160, 160)	)
										self.assertEqual(logits.shape						,					A_	)

										_UpperCAmelCase       :		str 		=    version.parse(PIL.__version__	) < version.parse("9.0.0"	)

										if is_pillow_less_than_a:
														_UpperCAmelCase       :		int 		=    torch.tensor(
														    [
														        [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
														        [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
														        [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
														    ]						,					device=A_						,					)
										else:
														_UpperCAmelCase       :		Any 		=    torch.tensor(
														    [
														        [[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
														        [[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
														        [[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
														    ]						,					device=A_						,					)

										self.assertTrue(torch.allclose(logits[0, :3, :3, :3]						,					A_						,					atol=1e-4	)	)





						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"	)
										_UpperCAmelCase       :		Dict 		=    model.to(A_	)

										_UpperCAmelCase       :		Dict 		=    BeitImageProcessor(do_resize=A_						,					size=640						,					do_center_crop=A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    load_dataset("hf-internal-testing/fixtures_ade20k"						,					split="test"	)
										_UpperCAmelCase       :		List[str] 		=    Image.open(ds[0]["file"]	)
										_UpperCAmelCase       :		Union[str, Any] 		=    image_processor(images=A_						,					return_tensors="pt"	).to(A_	)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase       :		List[Any] 		=    model(**A_	)

										_UpperCAmelCase       :		Tuple 		=    outputs.logits.detach().cpu()

										_UpperCAmelCase       :		str 		=    image_processor.post_process_semantic_segmentation(outputs=A_						,					target_sizes=[(500, 300)]	)
										_UpperCAmelCase       :		Dict 		=    torch.Size((500, 300)	)
										self.assertEqual(segmentation[0].shape						,					A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    image_processor.post_process_semantic_segmentation(outputs=A_	)
										_UpperCAmelCase       :		Union[str, Any] 		=    torch.Size((160, 160)	)
										self.assertEqual(segmentation[0].shape						,					A_	)


