





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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ = None ,							)				->						int:
    SCREAMING_SNAKE_CASE__											=			{}

    if train_file is not None:
        SCREAMING_SNAKE_CASE__											=			[train_file]
    if eval_file is not None:
        SCREAMING_SNAKE_CASE__											=			[eval_file]
    if test_file is not None:
        SCREAMING_SNAKE_CASE__											=			[test_file]

    SCREAMING_SNAKE_CASE__											=			datasets.load_dataset('''csv''' ,							data_files=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			list(ds[list(files.keys()	)[0]].features.keys()	)
    SCREAMING_SNAKE_CASE__											=			features_name.pop(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			list(set(ds[list(files.keys()	)[0]][label_name]	)	)
    SCREAMING_SNAKE_CASE__											=			{label: i for i, label in enumerate(lowerCAmelCase_	)}
    SCREAMING_SNAKE_CASE__											=			tokenizer.model_input_names
    SCREAMING_SNAKE_CASE__											=			{}

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

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

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

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

    SCREAMING_SNAKE_CASE__											=			(
        tf.data.Dataset.from_generator(
            lowerCAmelCase_ ,							({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:
        SCREAMING_SNAKE_CASE__											=			train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

    SCREAMING_SNAKE_CASE__											=			(
        tf.data.Dataset.from_generator(
            lowerCAmelCase_ ,							({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:
        SCREAMING_SNAKE_CASE__											=			val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

    SCREAMING_SNAKE_CASE__											=			(
        tf.data.Dataset.from_generator(
            lowerCAmelCase_ ,							({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:
        SCREAMING_SNAKE_CASE__											=			test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

    return train_ds, val_ds, test_ds, labelaid


_A					:				Any				=			logging.getLogger(__name__)



@dataclass
class 					__snake_case	:


    '''simple docstring'''







    lowerCamelCase__    :		int				=	field(metadata={"""help""": """Which column contains the label"""}    )
    lowerCamelCase__    :		str				=	field(default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """The path of the training file"""}    )
    lowerCamelCase__    :		Optional[str]				=	field(default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """The path of the development file"""}    )
    lowerCamelCase__    :		Optional[str]				=	field(default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """The path of the test file"""}    )
    lowerCamelCase__    :		int				=	field(
        default=1_2_8			,     metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )
        }			,     )
    lowerCamelCase__    :		bool				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )





@dataclass
class 					__snake_case	:


    '''simple docstring'''







    lowerCamelCase__    :		str				=	field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    )
    lowerCamelCase__    :		Optional[str]				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
    lowerCamelCase__    :		Optional[str]				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
    lowerCamelCase__    :		bool				=	field(default=__SCREAMING_SNAKE_CASE			,     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.
    lowerCamelCase__    :		Optional[str]				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,     )





def 							__snake_case							(    )				->						List[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.
    SCREAMING_SNAKE_CASE__											=			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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.

    SCREAMING_SNAKE_CASE__											=			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 ,							)

    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			get_tfds(
        train_file=data_args.train_file ,							eval_file=data_args.dev_file ,							test_file=data_args.test_file ,							tokenizer=lowerCAmelCase_ ,							label_column_id=data_args.label_column_id ,							max_seq_length=data_args.max_seq_length ,							)

    SCREAMING_SNAKE_CASE__											=			AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path ,							num_labels=len(lowerCAmelCase_	) ,							labelaid=lowerCAmelCase_ ,							idalabel={id: label for label, id in labelaid.items()} ,							finetuning_task='''text-classification''' ,							cache_dir=model_args.cache_dir ,							)

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

    def compute_metrics(lowerCAmelCase_	) -> Dict:
        SCREAMING_SNAKE_CASE__											=			np.argmax(p.predictions ,							axis=1	)

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

    # Initialize our Trainer
    SCREAMING_SNAKE_CASE__											=			TFTrainer(
        model=lowerCAmelCase_ ,							args=lowerCAmelCase_ ,							train_dataset=lowerCAmelCase_ ,							eval_dataset=lowerCAmelCase_ ,							compute_metrics=lowerCAmelCase_ ,							)

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

    # Evaluation
    SCREAMING_SNAKE_CASE__											=			{}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''	)
        SCREAMING_SNAKE_CASE__											=			trainer.evaluate()
        SCREAMING_SNAKE_CASE__											=			os.path.join(training_args.output_dir ,							'''eval_results.txt'''	)

        with open(lowerCAmelCase_ ,							'''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(lowerCAmelCase_	)

    return results


if __name__ == "__main__":
  main()






'''simple docstring'''






import os
import re
import shutil
import sys
import tempfile
import unittest

import black


A_			      =		os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
A_			      =		"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"






class        lowercase_      (						unittest.TestCase							):





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   str	      =    tempfile.mkdtemp()
           os.makedirs(os.path.join(self.transformer_dir     ,							'models/bert/'			)			)
           snake_case__       :   Optional[Any]	      =    self.transformer_dir
           shutil.copy(
               os.path.join(__lowerCamelCase     ,							'src/transformers/models/bert/modeling_bert.py'			)     ,							os.path.join(self.transformer_dir     ,							'models/bert/modeling_bert.py'			)     ,							)





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Union[str, Any]	      =    'src/transformers'
           shutil.rmtree(self.transformer_dir			)





     def 			_lowerCAmelCase      (     self     :					Tuple     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					List[Any]=None			):
           snake_case__       :   Optional[int]	      =    comment + F"\nclass {class_name}(nn.Module):\n" + class_code
           if overwrite_result is not None:
                 snake_case__       :   List[Any]	      =    comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
           snake_case__       :   Optional[int]	      =    black.Mode(target_versions={black.TargetVersion.PYaa}     ,							line_length=119			)
           snake_case__       :   Tuple	      =    black.format_str(__lowerCamelCase     ,							mode=__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    os.path.join(self.transformer_dir     ,							'new_code.py'			)
           with open(__lowerCamelCase     ,							'w'     ,							newline='\n'			) as f:
                 f.write(__lowerCamelCase			)
           if overwrite_result is None:
                 self.assertTrue(len(check_copies.is_copy_consistent(__lowerCamelCase			)			) == 0			)
           else:
                 check_copies.is_copy_consistent(f.name     ,							overwrite=__lowerCamelCase			)
                 with open(__lowerCamelCase     ,							'r'			) as f:
                       self.assertTrue(f.read()     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   List[str]	      =    check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'			)
           self.assertEqual(__lowerCamelCase     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Any			):
           # Base copy consistency
           self.check_copy_consistency(
               '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'     ,							'BertLMPredictionHead'     ,							REFERENCE_CODE + '\n'     ,							)

           # With no empty line at the end
           self.check_copy_consistency(
               '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'     ,							'BertLMPredictionHead'     ,							__lowerCamelCase     ,							)

           # Copy consistency with rename
           self.check_copy_consistency(
               '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'     ,							'TestModelLMPredictionHead'     ,							re.sub('Bert'     ,							'TestModel'     ,							__lowerCamelCase			)     ,							)

           # Copy consistency with a really long name
           snake_case__       :   Union[str, Any]	      =    'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
           self.check_copy_consistency(
               F"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"     ,							F"{long_class_name}LMPredictionHead"     ,							re.sub('Bert'     ,							__lowerCamelCase     ,							__lowerCamelCase			)     ,							)

           # Copy consistency with overwrite
           self.check_copy_consistency(
               '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'     ,							'TestModelLMPredictionHead'     ,							__lowerCamelCase     ,							overwrite_result=re.sub('Bert'     ,							'TestModel'     ,							__lowerCamelCase			)     ,							)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   List[str]	      =    check_copies.LOCALIZED_READMES['README_zh-hans.md']

           snake_case__       :   List[str]	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
               ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
               ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
               ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
               ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
               ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
               ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
               ' method has been applied to compress GPT2 into'
               ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
               ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
               ' Multilingual BERT into'
               ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
               ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
               ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
               ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
               ' Luong, Quoc V. Le, Christopher D. Manning.'
           )
           snake_case__       :   Union[str, Any]	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
               ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
               ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
               ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
           )
           snake_case__       :   Tuple	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
               ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
               ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
               ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
               ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
               ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
               ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
               ' method has been applied to compress GPT2 into'
               ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
               ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
               ' Multilingual BERT into'
               ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
               ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
               ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
               ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
               ' Christopher D. Manning 发布。\n'
           )

           snake_case__      ,							snake_case__       :   Optional[Any]	      =    check_copies.convert_to_localized_md(
               __lowerCamelCase     ,							__lowerCamelCase     ,							localized_readme['format_model_list']			)

           self.assertFalse(__lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     ,							__lowerCamelCase			)

           snake_case__      ,							snake_case__       :   Any	      =    check_copies.convert_to_localized_md(
               __lowerCamelCase     ,							__lowerCamelCase     ,							localized_readme['format_model_list']			)

           # Check whether the number of models is equal to README.md after conversion.
           self.assertTrue(__lowerCamelCase			)

           snake_case__       :   Optional[Any]	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
               ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
               ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
               ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
           )
           snake_case__       :   int	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
               ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
               ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
               ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
           )
           snake_case__       :   List[str]	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
               ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
               ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
               ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
           )

           snake_case__      ,							snake_case__       :   Any	      =    check_copies.convert_to_localized_md(
               __lowerCamelCase     ,							__lowerCamelCase     ,							localized_readme['format_model_list']			)

           # Check if the model link is synchronized.
           self.assertEqual(__lowerCamelCase     ,							__lowerCamelCase			)
