
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	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase = None	,					) ->						int:
    __lowercase		:			Any	    =       {}

    if train_file is not None:
        __lowercase		:			int	    =       [train_file]
    if eval_file is not None:
        __lowercase		:			Tuple	    =       [eval_file]
    if test_file is not None:
        __lowercase		:			Dict	    =       [test_file]

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

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

    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		:			Any	    =       labelaid[ex[label_name]]
            yield (d, label)

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

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

    __lowercase		:			List[str]	    =       (
        tf.data.Dataset.from_generator(
            __UpperCAmelCase	,					({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		:			List[str]	    =       train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

    __lowercase		:			Any	    =       (
        tf.data.Dataset.from_generator(
            __UpperCAmelCase	,					({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		:			Dict	    =       val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

    __lowercase		:			List[Any]	    =       (
        tf.data.Dataset.from_generator(
            __UpperCAmelCase	,					({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		:			Dict	    =       test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

    return train_ds, val_ds, test_ds, labelaid


__lowerCAmelCase					:			List[Any]            =		logging.getLogger(__name__)


@dataclass
class 						__lowerCAmelCase	:



      """simple docstring"""







      A__	:			int        =    field(metadata={'''help''': '''Which column contains the label'''}      )
      A__	:			str        =    field(default=_snake_case , metadata={'''help''': '''The path of the training file'''}      )
      A__	:			Optional[str]        =    field(default=_snake_case , metadata={'''help''': '''The path of the development file'''}      )
      A__	:			Optional[str]        =    field(default=_snake_case , metadata={'''help''': '''The path of the test file'''}      )
      A__	:			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.'''
              )
          } , )
      A__	:			bool        =    field(
          default=_snake_case , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}      )






@dataclass
class 						__lowerCAmelCase	:



      """simple docstring"""







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






def     UpperCAmelCase_  (						) ->						Dict:
    __lowercase		:			Dict	    =       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
    __lowercase    ,      __lowercase    ,      __lowercase		:			List[str]	    =       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    ,      __lowercase    ,      __lowercase    ,      __lowercase		:			List[Any]	    =       get_tfds(
        train_file=data_args.train_file	,					eval_file=data_args.dev_file	,					test_file=data_args.test_file	,					tokenizer=__UpperCAmelCase	,					label_column_id=data_args.label_column_id	,					max_seq_length=data_args.max_seq_length	,					)

    __lowercase		:			Tuple	    =       AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path	,					num_labels=len(__UpperCAmelCase	)	,					labelaid=__UpperCAmelCase	,					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=__UpperCAmelCase	,					cache_dir=model_args.cache_dir	,					)

    def compute_metrics(__lowerCAmelCase	) -> Dict:
        __lowercase		:			int	    =       np.argmax(p.predictions	,					axis=1	)

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

    # Initialize our Trainer
    __lowercase		:			List[str]	    =       TFTrainer(
        model=__UpperCAmelCase	,					args=__UpperCAmelCase	,					train_dataset=__UpperCAmelCase	,					eval_dataset=__UpperCAmelCase	,					compute_metrics=__UpperCAmelCase	,					)

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

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

        with open(__UpperCAmelCase	,					'''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(__UpperCAmelCase	)

    return results


if __name__ == "__main__":
 main()




import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
 import torch

 from transformers import AutoModelForCausalLM


@require_torch
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       Union[str, Any]				):
          __lowercase		:			Optional[Any]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Any	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[str]	    =       -1

          __lowercase		:			Optional[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			Tuple	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			Optional[int]	    =       tokenizer.decode(greedy_ids[0]				)

          with CaptureStdout() as cs:
              __lowercase		:			Optional[Any]	    =       TextStreamer(_snake_case				)
              model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				,       streamer=_snake_case				)
          # The greedy text should be printed to stdout, except for the final "\n" in the streamer
          __lowercase		:			List[Any]	    =       cs.out[:-1]

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Optional[int]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Optional[Any]	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[str]	    =       -1

          __lowercase		:			Tuple	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			List[Any]	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			Optional[Any]	    =       tokenizer.decode(greedy_ids[0]				)

          __lowercase		:			int	    =       TextIteratorStreamer(_snake_case				)
          __lowercase		:			int	    =       {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
          __lowercase		:			Optional[Any]	    =       Thread(target=model.generate				,       kwargs=_snake_case				)
          thread.start()
          __lowercase		:			Optional[int]	    =       ''''''
          for new_text in streamer:
              streamer_text += new_text

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			Dict	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			int	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[Any]	    =       -1

          __lowercase		:			List[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			str	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			List[Any]	    =       greedy_ids[:, input_ids.shape[1] :]
          __lowercase		:			Optional[Any]	    =       tokenizer.decode(new_greedy_ids[0]				)

          with CaptureStdout() as cs:
              __lowercase		:			int	    =       TextStreamer(_snake_case				,       skip_prompt=_snake_case				)
              model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				,       streamer=_snake_case				)
          # The greedy text should be printed to stdout, except for the final "\n" in the streamer
          __lowercase		:			int	    =       cs.out[:-1]

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       Any				):
          # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
          # with actual models -- the dummy models' tokenizers are not aligned with their models, and
          # `skip_special_tokens=True` has no effect on them
          __lowercase		:			Dict	    =       AutoTokenizer.from_pretrained('''distilgpt2'''				)
          __lowercase		:			List[str]	    =       AutoModelForCausalLM.from_pretrained('''distilgpt2'''				).to(_snake_case				)
          __lowercase		:			str	    =       -1

          __lowercase		:			Dict	    =       torch.ones((1, 5)				,       device=_snake_case				).long() * model.config.bos_token_id
          with CaptureStdout() as cs:
              __lowercase		:			Optional[Any]	    =       TextStreamer(_snake_case				,       skip_special_tokens=_snake_case				)
              model.generate(_snake_case				,       max_new_tokens=1				,       do_sample=_snake_case				,       streamer=_snake_case				)

          # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
          # re-tokenized, must only contain one token
          __lowercase		:			Dict	    =       cs.out[:-1]  # Remove the final "\n"
          __lowercase		:			Any	    =       tokenizer(_snake_case				,       return_tensors='''pt'''				)
          self.assertEqual(streamer_text_tokenized.input_ids.shape				,       (1, 1)				)




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			List[str]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Union[str, Any]	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			Dict	    =       -1

          __lowercase		:			List[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			Tuple	    =       TextIteratorStreamer(_snake_case				,       timeout=0.0_01				)
          __lowercase		:			Tuple	    =       {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
          __lowercase		:			Optional[int]	    =       Thread(target=model.generate				,       kwargs=_snake_case				)
          thread.start()

          # The streamer will timeout after 0.001 seconds, so an exception will be raised
          with self.assertRaises(_snake_case				):
              __lowercase		:			List[Any]	    =       ''''''
              for new_text in streamer:
                  streamer_text += new_text


