"""simple docstring"""






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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_xlnet import XLNetTokenizer
else:
       __lowerCAmelCase    :       Optional[Any]					     =None


__lowerCAmelCase    :       int					     =logging.get_logger(__name__)

__lowerCAmelCase    :       str					     ={"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__lowerCAmelCase    :       Tuple					     ={
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

__lowerCAmelCase    :       Optional[Any]					     ={
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

__lowerCAmelCase    :       Dict					     ="""▁"""

# Segments (not really needed)
__lowerCAmelCase    :       int					     =0
__lowerCAmelCase    :       Any					     =1
__lowerCAmelCase    :       Any					     =2
__lowerCAmelCase    :       Optional[int]					     =3
__lowerCAmelCase    :       Dict					     =4





class  _A						(       snake_case_	):
      snake_case__			:   int							  =	VOCAB_FILES_NAMES
      snake_case__			:   List[Any]							  =	PRETRAINED_VOCAB_FILES_MAP
      snake_case__			:   List[Any]							  =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__			:   Optional[Any]							  =	'left'
      snake_case__			:   List[Any]							  =	XLNetTokenizer

      def __init__(						self		,       __lowerCAmelCase=None		,       __lowerCAmelCase=None		,       __lowerCAmelCase=False		,       __lowerCAmelCase=True		,       __lowerCAmelCase=False		,       __lowerCAmelCase="<s>"		,       __lowerCAmelCase="</s>"		,       __lowerCAmelCase="<unk>"		,       __lowerCAmelCase="<sep>"		,       __lowerCAmelCase="<pad>"		,       __lowerCAmelCase="<cls>"		,       __lowerCAmelCase="<mask>"		,       __lowerCAmelCase=["<eop>", "<eod>"]		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            lowercase														=	AddedToken(__lowerCAmelCase		,       lstrip=__lowerCAmelCase		,       rstrip=__lowerCAmelCase						) if isinstance(__lowerCAmelCase		,       __lowerCAmelCase						) else mask_token

            super().__init__(
                vocab_file=__lowerCAmelCase		,       tokenizer_file=__lowerCAmelCase		,       do_lower_case=__lowerCAmelCase		,       remove_space=__lowerCAmelCase		,       keep_accents=__lowerCAmelCase		,       bos_token=__lowerCAmelCase		,       eos_token=__lowerCAmelCase		,       unk_token=__lowerCAmelCase		,       sep_token=__lowerCAmelCase		,       pad_token=__lowerCAmelCase		,       cls_token=__lowerCAmelCase		,       mask_token=__lowerCAmelCase		,       additional_special_tokens=__lowerCAmelCase		,       **__lowerCAmelCase		,       )

            lowercase														=	3
            lowercase														=	do_lower_case
            lowercase														=	remove_space
            lowercase														=	keep_accents
            lowercase														=	vocab_file
            lowercase														=	False if not self.vocab_file else True

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



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

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



            lowercase														=	[self.sep_token_id]
            lowercase														=	[2]

            if token_ids_a is None:
                  return len(token_ids_a + sep						) * [0] + cls_segment_id
            return len(token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1] + cls_segment_id







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



            if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                      """tokenizer."""						)

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

            if os.path.abspath(self.vocab_file						) != os.path.abspath(__lowerCAmelCase						):
                  copyfile(self.vocab_file		,       __lowerCAmelCase						)

            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 		_lowercase						(					unittest.TestCase       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							Union[str, Any]			)   ->						Optional[int]:

  """simple docstring"""





  for model_result in results.values():
   for batch_size, sequence_length in zip(model_result['bs']   ,      model_result['ss']			):
    UpperCamelCase_					:						Optional[Any] =					model_result['result'][batch_size][sequence_length]
    self.assertIsNotNone(snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Dict =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      eager_mode=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						List[str] =					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	:							str			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					'sgugger/tiny-distilbert-classification'
  UpperCamelCase_					:						int =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      only_pretrain_model=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						Tuple =					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	:							Tuple			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						str =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Any =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						Optional[int] =					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	:							Tuple			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						str =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      eager_mode=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						Optional[Any] =					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[Any]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						int =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						Any =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						Tuple =					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	:							Tuple			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Optional[int] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						str =					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	:							Any			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						List[str] =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						int =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						List[Any] =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						int =					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	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'patrickvonplaten/t5-tiny-random'
  UpperCamelCase_					:						List[str] =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case   ,      configs=[config]			)
  UpperCamelCase_					:						Optional[Any] =					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	:							Optional[int]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						int =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Union[str, Any] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      use_xla=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Union[str, Any] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						List[str] =					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	:							List[str]			)   ->						Optional[int]:

  """simple docstring"""





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


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					'sshleifer/tiny-gpt2'

  def _check_summary_is_not_empty(snake_case	:							Union[str, Any]			):
   self.assertTrue(hasattr(snake_case   ,      'sequential'			)			)
   self.assertTrue(hasattr(snake_case   ,      'cumulative'			)			)
   self.assertTrue(hasattr(snake_case   ,      'current'			)			)
   self.assertTrue(hasattr(snake_case   ,      'total'			)			)

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



