


'''simple docstring'''

import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase__				     =		{
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
lowercase__				     =		{
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
lowercase__				     =		{
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

lowercase__				     =		{
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
lowercase__				     =		{
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
lowercase__				     =		{
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


lowercase__				     =		{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
lowercase__				     =		{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
lowercase__				     =		{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				VOCAB_FILES_NAMES
     UpperCAmelCase 					=				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase 					=				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase 					=				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				VOCAB_FILES_NAMES
     UpperCAmelCase 					=				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase 					=				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase 					=				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







lowercase__				     =		collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

lowercase__				     =		collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


lowercase__				     =		R"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "





@add_start_docstrings(lowerCamelCase__ )
class 						__SCREAMING_SNAKE_CASE					:






     def __call__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    BatchEncoding:
          if titles is None and texts is None:
               return super().__call__(
                   __UpperCamelCase     ,							padding=__UpperCamelCase     ,							truncation=__UpperCamelCase     ,							max_length=__UpperCamelCase     ,							return_tensors=__UpperCamelCase     ,							return_attention_mask=__UpperCamelCase     ,							**__UpperCamelCase     ,							)
          elif titles is None or texts is None:
               _a						=		titles if texts is None else texts
               return super().__call__(
                   __UpperCamelCase     ,							__UpperCamelCase     ,							padding=__UpperCamelCase     ,							truncation=__UpperCamelCase     ,							max_length=__UpperCamelCase     ,							return_tensors=__UpperCamelCase     ,							return_attention_mask=__UpperCamelCase     ,							**__UpperCamelCase     ,							)
          _a						=		titles if not isinstance(__UpperCamelCase     ,							__UpperCamelCase		) else [titles]
          _a						=		texts if not isinstance(__UpperCamelCase     ,							__UpperCamelCase		) else [texts]
          _a						=		len(__UpperCamelCase		)
          _a						=		questions if not isinstance(__UpperCamelCase     ,							__UpperCamelCase		) else [questions] * n_passages
          if len(__UpperCamelCase		) != len(__UpperCamelCase		):
               raise ValueError(
                   f"There should be as many titles than texts but got {len(__UpperCamelCase		)} titles and {len(__UpperCamelCase		)} texts."		)
          _a						=		super().__call__(__UpperCamelCase     ,							__UpperCamelCase     ,							padding=__UpperCamelCase     ,							truncation=__UpperCamelCase		)["input_ids"]
          _a						=		super().__call__(__UpperCamelCase     ,							add_special_tokens=__UpperCamelCase     ,							padding=__UpperCamelCase     ,							truncation=__UpperCamelCase		)["input_ids"]
          _a						=		{
              "input_ids": [
                  (encoded_question_and_title + encoded_text)[:max_length]
                  if max_length is not None and truncation
                  else encoded_question_and_title + encoded_text
                  for encoded_question_and_title, encoded_text in zip(__UpperCamelCase     ,							__UpperCamelCase		)
              ]
          }
          if return_attention_mask is not False:
               _a						=		[]
               for input_ids in encoded_inputs["input_ids"]:
                    attention_mask.append([int(input_id != self.pad_token_id		) for input_id in input_ids]		)
               _a						=		attention_mask
          return self.pad(__UpperCamelCase     ,							padding=__UpperCamelCase     ,							max_length=__UpperCamelCase     ,							return_tensors=__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = 16     ,							__UpperCamelCase = 64     ,							__UpperCamelCase = 4     ,							)       ->    List[DPRSpanPrediction]:
          _a						=		reader_input["input_ids"]
          _a			,			_a			,			_a						=		reader_output[:3]
          _a						=		len(__UpperCamelCase		)
          _a						=		sorted(range(__UpperCamelCase		)     ,							reverse=__UpperCamelCase     ,							key=relevance_logits.__getitem__		)
          _a						=		[]
          for doc_id in sorted_docs:
               _a						=		list(input_ids[doc_id]		)
               # assuming question & title information is at the beginning of the sequence
               _a						=		sequence_ids.index(self.sep_token_id     ,							2		) + 1  # second sep id
               if sequence_ids[-1] == self.pad_token_id:
                    _a						=		sequence_ids.index(self.pad_token_id		)
               else:
                    _a						=		len(__UpperCamelCase		)

               _a						=		self._get_best_spans(
                   start_logits=start_logits[doc_id][passage_offset:sequence_len]     ,							end_logits=end_logits[doc_id][passage_offset:sequence_len]     ,							max_answer_length=__UpperCamelCase     ,							top_spans=__UpperCamelCase     ,							)
               for start_index, end_index in best_spans:
                    start_index += passage_offset
                    end_index += passage_offset
                    nbest_spans_predictions.append(
                        DPRSpanPrediction(
                            span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]     ,							relevance_score=relevance_logits[doc_id]     ,							doc_id=__UpperCamelCase     ,							start_index=__UpperCamelCase     ,							end_index=__UpperCamelCase     ,							text=self.decode(sequence_ids[start_index : end_index + 1]		)     ,							)		)
               if len(__UpperCamelCase		) >= num_spans:
                    break
          return nbest_spans_predictions[:num_spans]






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							)       ->    List[DPRSpanPrediction]:
          _a						=		[]
          for start_index, start_score in enumerate(__UpperCamelCase		):
               for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]		):
                    scores.append(((start_index, start_index + answer_length), start_score + end_score)		)
          _a						=		sorted(__UpperCamelCase     ,							key=lambda __UpperCamelCase		: x[1]     ,							reverse=__UpperCamelCase		)
          _a						=		[]
          for (start_index, end_index), score in scores:
               if start_index > end_index:
                    raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]"		)
               _a						=		end_index - start_index + 1
               if length > max_answer_length:
                    raise ValueError(f"Span is too long: {length} > {max_answer_length}"		)
               if any(
                   start_index <= prev_start_index <= prev_end_index <= end_index
                   or prev_start_index <= start_index <= end_index <= prev_end_index
                   for (prev_start_index, prev_end_index) in chosen_span_intervals		):
                    continue
               chosen_span_intervals.append((start_index, end_index)		)

               if len(__UpperCamelCase		) == top_spans:
                    break
          return chosen_span_intervals







@add_end_docstrings(lowerCamelCase__ )
class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ,			lowerCamelCase__ ):
     UpperCAmelCase 					=				VOCAB_FILES_NAMES
     UpperCAmelCase 					=				READER_PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase 					=				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase 					=				READER_PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase 					=				['''input_ids''', '''attention_mask''']





'''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 __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: PreTrainedTokenizer      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: Optional[int] = None      ,  )      ->     Tuple:







     '''simple docstring'''






     _a						=		{}

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

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

     if len(__lowerCamelCase				) == 1:
          for k in files.keys():
               _a						=		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():
               _a						=		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]:
               _a						=		{k: v for k, v in ex.items() if k in input_names}
               _a						=		labelaid[ex[label_name]]
               yield (d, label)

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

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

     _a						=		(
         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:
          _a						=		train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

     _a						=		(
         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:
          _a						=		val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

     _a						=		(
         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:
          _a						=		test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

     return train_ds, val_ds, test_ds, labelaid


lowercase__				     =		logging.getLogger(__name__)





@dataclass
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				field(metadata={'''help''': '''Which column contains the label'''} )
     UpperCAmelCase 					=				field(default=lowerCamelCase__ ,			metadata={'''help''': '''The path of the training file'''} )
     UpperCAmelCase 					=				field(default=lowerCamelCase__ ,			metadata={'''help''': '''The path of the development file'''} )
     UpperCAmelCase 					=				field(default=lowerCamelCase__ ,			metadata={'''help''': '''The path of the test file'''} )
     UpperCAmelCase 					=				field(
         default=128 ,			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=lowerCamelCase__ ,			metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )







@dataclass
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				field(
         metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
     UpperCAmelCase 					=				field(
         default=lowerCamelCase__ ,			metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
     UpperCAmelCase 					=				field(
         default=lowerCamelCase__ ,			metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
     UpperCAmelCase 					=				field(default=lowerCamelCase__ ,			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.
     UpperCAmelCase 					=				field(
         default=lowerCamelCase__ ,			metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,			)







def __UpperCamelCase   (			)      ->     str:







     '''simple docstring'''






     _a						=		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
     _a			,			_a			,			_a						=		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.

     _a						=		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      ,  )

     _a			,			_a			,			_a			,			_a						=		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      ,  )

     _a						=		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():
          _a						=		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		: EvalPrediction				) -> Dict:
          _a						=		np.argmax(p.predictions      ,  axis=1				)

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

     # Initialize our Trainer
     _a						=		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
     _a						=		{}
     if training_args.do_eval:
          logger.info("*** Evaluate ***"				)
          _a						=		trainer.evaluate()
          _a						=		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()

