import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_    :		str							=	logging.get_logger(__name__)

lowerCamelCase_    :		Tuple							=	{'vocab_file': 'sentencepiece.bpe.model'}

lowerCamelCase_    :		Union[str, Any]							=	{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

lowerCamelCase_    :		List[str]							=	{
    'camembert-base': 512,
}

lowerCamelCase_    :		int							=	'▁'


class 						a__							(       __snake_case		):
   A__    :       Dict        =      VOCAB_FILES_NAMES
   A__    :       Optional[int]        =      PRETRAINED_VOCAB_FILES_MAP
   A__    :       Tuple        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__    :       Optional[int]        =      ['input_ids', 'attention_mask']



   def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase="<s>"				,						UpperCAmelCase="</s>"				,						UpperCAmelCase="</s>"				,						UpperCAmelCase="<s>"				,						UpperCAmelCase="<unk>"				,						UpperCAmelCase="<pad>"				,						UpperCAmelCase="<mask>"				,						UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   None:
        __a		 =       AddedToken(_snake_case				,						lstrip=_snake_case				,						rstrip=_snake_case   ) if isinstance(_snake_case				,						_snake_case   ) else mask_token

        __a		 =       {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=_snake_case				,						eos_token=_snake_case				,						unk_token=_snake_case				,						sep_token=_snake_case				,						cls_token=_snake_case				,						pad_token=_snake_case				,						mask_token=_snake_case				,						additional_special_tokens=_snake_case				,						sp_model_kwargs=self.sp_model_kwargs				,						**_snake_case				,						)
        __a		 =       spm.SentencePieceProcessor(**self.sp_model_kwargs   )
        self.sp_model.Load(str(_snake_case   )   )
        __a		 =       vocab_file
        # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
        # sentencepiece vocabulary (this is the case for <s> and </s>
        __a		 =       {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
        __a		 =       len(self.fairseq_tokens_to_ids   )
        __a		 =       len(self.sp_model   ) + len(self.fairseq_tokens_to_ids   )
        __a		 =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}



   def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   List[int]:




        if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __a		 =       [self.cls_token_id]
        __a		 =       [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep



   def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = False   )			->   List[int]:




        if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_snake_case				,						token_ids_a=_snake_case				,						already_has_special_tokens=_snake_case   )

        if token_ids_a is None:
             return [1] + ([0] * len(_snake_case   )) + [1]
        return [1] + ([0] * len(_snake_case   )) + [1, 1] + ([0] * len(_snake_case   )) + [1]



   def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   List[int]:
        __a		 =       [self.sep_token_id]
        __a		 =       [self.cls_token_id]

        if token_ids_a is None:
             return len(cls + token_ids_a + sep   ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



   @property
   def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
        return len(self.fairseq_tokens_to_ids   ) + len(self.sp_model   )



   def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
        __a		 =       {self.convert_ids_to_tokens(_snake_case   ): i for i in range(self.vocab_size   )}
        vocab.update(self.added_tokens_encoder   )
        return vocab



   def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[str]:
        return self.sp_model.encode(_snake_case				,						out_type=_snake_case   )



   def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Union[str, Any]:




        if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
        elif self.sp_model.PieceToId(_snake_case   ) == 0:
             # Convert sentence piece unk token to fairseq unk token index
             return self.unk_token_id
        return self.fairseq_offset + self.sp_model.PieceToId(_snake_case   )



   def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[Any]:




        if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset   )



   def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Dict:
        __a		 =       []
        __a		 =       ''
        __a		 =       False
        for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                  if not prev_is_special:
                       out_string += " "
                  out_string += self.sp_model.decode(_snake_case   ) + token
                  __a		 =       True
                  __a		 =       []
             else:
                  current_sub_tokens.append(_snake_case   )
                  __a		 =       False
        out_string += self.sp_model.decode(_snake_case   )
        return out_string.strip()



   def __getstate__(						self   )			->   List[Any]:
        __a		 =       self.__dict__.copy()
        __a		 =       None
        return state



   def __setstate__(						self				,						UpperCAmelCase   )			->   Optional[int]:
        __a		 =       d

        # for backward compatibility
        if not hasattr(self				,						'sp_model_kwargs'   ):
             __a		 =       {}

        __a		 =       spm.SentencePieceProcessor(**self.sp_model_kwargs   )
        self.sp_model.Load(self.vocab_file   )







   def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Tuple[str]:




        if not os.path.isdir(_snake_case   ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
             return
        __a		 =       os.path.join(
            _snake_case				,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

        if os.path.abspath(self.vocab_file   ) != os.path.abspath(_snake_case   ) and os.path.isfile(self.vocab_file   ):
             copyfile(self.vocab_file				,						_snake_case   )
        elif not os.path.isfile(self.vocab_file   ):
             with open(_snake_case				,						'wb'   ) as fi:
                  __a		 =       self.sp_model.serialized_model_proto()
                  fi.write(_snake_case   )

        return (out_vocab_file,)





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf

	from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments


@require_tf
class 						a__							(       unittest.TestCase		):



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Optional[Any]:
								for model_result in results.values():
													for batch_size, sequence_length in zip(model_result['bs']				,						model_result['ss']   ):
																		__a		 =       model_result['result'][batch_size][sequence_length]
																		self.assertIsNotNone(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sgugger/tiny-distilbert-classification'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						only_pretrain_model=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result   )
								self.check_results_dict_not_empty(results.memory_train_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result   )
								self.check_results_dict_not_empty(results.memory_train_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       'patrickvonplaten/t5-tiny-random'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						configs=[config]   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'   )   ) == 0				,						'Cannot do xla on CPU.'   )
			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						use_xla=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       'sshleifer/tiny-gpt2'
								with tempfile.TemporaryDirectory() as tmp_dir:
													__a		 =       TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,						inference=UpperCAmelCase				,						save_to_csv=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						inference_time_csv_file=os.path.join(UpperCAmelCase				,						'inf_time.csv'   )				,						inference_memory_csv_file=os.path.join(UpperCAmelCase				,						'inf_mem.csv'   )				,						env_info_csv_file=os.path.join(UpperCAmelCase				,						'env.csv'   )				,						multi_process=UpperCAmelCase				,						)
													__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
													benchmark.run()
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'inf_time.csv'   )   ).exists()   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'inf_mem.csv'   )   ).exists()   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'env.csv'   )   ).exists()   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       'sshleifer/tiny-gpt2'

								def _check_summary_is_not_empty(UpperCAmelCase   ):
													self.assertTrue(hasattr(UpperCAmelCase				,						'sequential'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'cumulative'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'current'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'total'   )   )

								with tempfile.TemporaryDirectory() as tmp_dir:
													__a		 =       TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						log_filename=os.path.join(UpperCAmelCase				,						'log.txt'   )				,						log_print=UpperCAmelCase				,						trace_memory_line_by_line=UpperCAmelCase				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
													__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
													__a		 =       benchmark.run()
													_check_summary_is_not_empty(result.inference_summary   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'log.txt'   )   ).exists()   )




