


"""simple docstring"""



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def       lowerCamelCase					(a_						:str				,				a_						:str				,				a_						:str				,				a_						:PreTrainedTokenizer				,				a_						:int				,				a_						:Optional[int] = None				,				)     ->							int:
							lowercase		:Dict       							=				{}

							if train_file is not None:
														lowercase		:Union[str, Any]       							=				[train_file]
							if eval_file is not None:
														lowercase		:str       							=				[eval_file]
							if test_file is not None:
														lowercase		:Dict       							=				[test_file]

							lowercase		:Optional[Any]       							=				datasets.load_dataset('''csv'''				,				data_files=a_)
							lowercase		:int       							=				list(ds[list(files.keys())[0]].features.keys())
							lowercase		:Dict       							=				features_name.pop(a_)
							lowercase		:str       							=				list(set(ds[list(files.keys())[0]][label_name]))
							lowercase		:List[str]       							=				{label: i for i, label in enumerate(a_)}
							lowercase		:Any       							=				tokenizer.model_input_names
							lowercase		:Optional[Any]       							=				{}

							if len(a_) == 1:
														for k in files.keys():
																					lowercase		:Dict       							=				ds[k].map(
																					    lambda a_: tokenizer.batch_encode_plus(
																					        example[features_name[0]]				,				truncation=a_				,				max_length=a_				,				padding='''max_length''')				,				batched=a_				,				)
							elif len(a_) == 2:
														for k in files.keys():
																					lowercase		:Optional[Any]       							=				ds[k].map(
																					    lambda a_: tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]])				,				truncation=a_				,				max_length=a_				,				padding='''max_length'''				,				)				,				batched=a_				,				)

							def gen_train():
														for ex in transformed_ds[datasets.Split.TRAIN]:
																					lowercase		:Any       							=				{k: v for k, v in ex.items() if k in input_names}
																					lowercase		:List[str]       							=				labelaid[ex[label_name]]
																					yield (d, label)

							def gen_val():
														for ex in transformed_ds[datasets.Split.VALIDATION]:
																					lowercase		:Optional[Any]       							=				{k: v for k, v in ex.items() if k in input_names}
																					lowercase		:Tuple       							=				labelaid[ex[label_name]]
																					yield (d, label)

							def gen_test():
														for ex in transformed_ds[datasets.Split.TEST]:
																					lowercase		:Dict       							=				{k: v for k, v in ex.items() if k in input_names}
																					lowercase		:Optional[int]       							=				labelaid[ex[label_name]]
																					yield (d, label)

							lowercase		:List[str]       							=				(
							    tf.data.Dataset.from_generator(
							        a_				,				({k: tf.intaa for k in input_names}, tf.intaa)				,				({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))				,				)
							    if datasets.Split.TRAIN in transformed_ds
							    else None
							)

							if train_ds is not None:
														lowercase		:Union[str, Any]       							=				train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))

							lowercase		:Optional[int]       							=				(
							    tf.data.Dataset.from_generator(
							        a_				,				({k: tf.intaa for k in input_names}, tf.intaa)				,				({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))				,				)
							    if datasets.Split.VALIDATION in transformed_ds
							    else None
							)

							if val_ds is not None:
														lowercase		:List[Any]       							=				val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION])))

							lowercase		:Optional[Any]       							=				(
							    tf.data.Dataset.from_generator(
							        a_				,				({k: tf.intaa for k in input_names}, tf.intaa)				,				({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))				,				)
							    if datasets.Split.TEST in transformed_ds
							    else None
							)

							if test_ds is not None:
														lowercase		:int       							=				test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST])))

							return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase      		=			logging.getLogger(__name__)
@dataclass
class 					__magic_name__		:
							__A			:      int        =   field(metadata={"help": "Which column contains the label"}     )
							__A			:      str        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the training file"}     )
							__A			:      Optional[str]        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the development file"}     )
							__A			:      Optional[str]        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the test file"}     )
							__A			:      int        =   field(
							    default=1_28			,						metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }			,						)
							__A			:      bool        =   field(
							    default=__UpperCAmelCase			,						metadata={"help": "Overwrite the cached training and evaluation sets"}     )







@dataclass
class 					__magic_name__		:
							__A			:      str        =   field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}     )
							__A			:      Optional[str]        =   field(
							    default=__UpperCAmelCase			,						metadata={"help": "Pretrained config name or path if not the same as model_name"}     )
							__A			:      Optional[str]        =   field(
							    default=__UpperCAmelCase			,						metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}     )
							__A			:      bool        =   field(default=__UpperCAmelCase			,						metadata={"help": "Set this flag to use fast tokenization."}     )
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							__A			:      Optional[str]        =   field(
							    default=__UpperCAmelCase			,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,						)







def       lowerCamelCase					()     ->							Dict:
							# 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.
							lowercase		:Dict       							=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
							lowercase		:Union[str, Any]       							=				parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir)
							    and os.listdir(training_args.output_dir)
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    ''' --overwrite_output_dir to overcome.''')

							# Setup logging
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''				,				datefmt='''%m/%d/%Y %H:%M:%S'''				,				level=logging.INFO				,				)
							logger.info(
							    F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1)}, """
							    F"""16-bits training: {training_args.fpaa}""")
							logger.info(F"""Training/evaluation parameters {training_args}""")

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							lowercase		:Optional[int]       							=				AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,				cache_dir=model_args.cache_dir				,				)

							lowercase		:int       							=				get_tfds(
							    train_file=data_args.train_file				,				eval_file=data_args.dev_file				,				test_file=data_args.test_file				,				tokenizer=a_				,				label_column_id=data_args.label_column_id				,				max_seq_length=data_args.max_seq_length				,				)

							lowercase		:Optional[int]       							=				AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path				,				num_labels=len(a_)				,				labelaid=a_				,				idalabel={id: label for label, id in labelaid.items()}				,				finetuning_task='''text-classification'''				,				cache_dir=model_args.cache_dir				,				)

							with training_args.strategy.scope():
														lowercase		:int       							=				TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path				,				from_pt=bool('''.bin''' in model_args.model_name_or_path)				,				config=a_				,				cache_dir=model_args.cache_dir				,				)

							def compute_metrics(a_						:EvalPrediction) -> Dict:
														lowercase		:Tuple       							=				np.argmax(p.predictions				,				axis=1)

														return {"acc": (preds == p.label_ids).mean()}

							# Initialize our Trainer
							lowercase		:Any       							=				TFTrainer(
							    model=a_				,				args=a_				,				train_dataset=a_				,				eval_dataset=a_				,				compute_metrics=a_				,				)

							# Training
							if training_args.do_train:
														trainer.train()
														trainer.save_model()
														tokenizer.save_pretrained(training_args.output_dir)

							# Evaluation
							lowercase		:Union[str, Any]       							=				{}
							if training_args.do_eval:
														logger.info('''*** Evaluate ***''')
														lowercase		:str       							=				trainer.evaluate()
														lowercase		:int       							=				os.path.join(training_args.output_dir				,				'''eval_results.txt''')

														with open(a_				,				'''w''') as writer:
																					logger.info('''***** Eval results *****''')

																					for key, value in result.items():
																												logger.info(F"""  {key} = {value}""")
																												writer.write(F"""{key} = {value}\n""")

																					results.update(a_)

							return results


if __name__ == "__main__":
	main()





"""simple docstring"""



import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()
class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      List[str]        =   VQModel
       __A			:      Any        =   "sample"






       @property
       def  __snake_case						(   self  :    int  ,     snake_case__  :    int=(3_2, 3_2)				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				4
              lowercase		:Tuple       							=				3

              lowercase		:List[str]       							=				floats_tensor((batch_size, num_channels) + sizes				).to(snake_case__				)

              return {"sample": image}






       @property
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              return (3, 3_2, 3_2)






       @property
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              return (3, 3_2, 3_2)






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				{
                  '''block_out_channels''': [3_2, 6_4],
                  '''in_channels''': 3,
                  '''out_channels''': 3,
                  '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                  '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                  '''latent_channels''': 3,
              }
              lowercase		:Dict       							=				self.dummy_input
              return init_dict, inputs_dict






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase							,  lowercase		:Optional[int]       							=				VQModel.from_pretrained('''fusing/vqgan-dummy'''  ,     output_loading_info=snake_case__				)
              self.assertIsNotNone(snake_case__				)
              self.assertEqual(len(loading_info['''missing_keys''']				)  ,     0				)

              model.to(snake_case__				)
              lowercase		:List[str]       							=				model(**self.dummy_input				)

              assert image is not None, "Make sure output is not None"







       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				VQModel.from_pretrained('''fusing/vqgan-dummy'''				)
              model.to(snake_case__				).eval()

              torch.manual_seed(0				)
              if torch.cuda.is_available():
                     torch.cuda.manual_seed_all(0				)

              lowercase		:List[Any]       							=				torch.randn(1  ,     model.config.in_channels  ,     model.config.sample_size  ,     model.config.sample_size				)
              lowercase		:int       							=				image.to(snake_case__				)
              with torch.no_grad():
                     lowercase		:str       							=				model(snake_case__				).sample

              lowercase		:List[Any]       							=				output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              lowercase		:Tuple       							=				torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43]				)
              # fmt: on
              self.assertTrue(torch.allclose(snake_case__  ,     snake_case__  ,     atol=1e-3				)				)

