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 				__UpperCamelCase   (    self      :       int,_A      :       List[Any]    ):







							"""simple docstring"""






							for model_result in results.values():
									for batch_size, sequence_length in zip(model_result["bs"],model_result["ss"]    ):
											SCREAMING_SNAKE_CASE_  :		Any				      =    model_result["result"][batch_size][sequence_length]
											self.assertIsNotNone(_A    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],eager_mode=_A,multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    TensorFlowBenchmark(_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result    )
							self.check_results_dict_not_empty(results.memory_inference_result    )

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    "sgugger/tiny-distilbert-classification"
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,only_pretrain_model=_A,)
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    TensorFlowBenchmark(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result    )
							self.check_results_dict_not_empty(results.memory_inference_result    )

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmark(_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result    )
							self.check_results_dict_not_empty(results.memory_inference_result    )

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AutoConfig.from_pretrained(_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],eager_mode=_A,multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    TensorFlowBenchmark(_A,[config]    )
							SCREAMING_SNAKE_CASE_  :		int				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result    )
							self.check_results_dict_not_empty(results.memory_inference_result    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    AutoConfig.from_pretrained(_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmark(_A,[config]    )
							SCREAMING_SNAKE_CASE_  :		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 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Dict				      =    TensorFlowBenchmark(_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result    )
							self.check_results_dict_not_empty(results.memory_train_result    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		Any				      =    AutoConfig.from_pretrained(_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    TensorFlowBenchmark(_A,[config]    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result    )
							self.check_results_dict_not_empty(results.memory_train_result    )

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    "patrickvonplaten/t5-tiny-random"
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AutoConfig.from_pretrained(_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    TensorFlowBenchmark(_A,configs=[config]    )
							SCREAMING_SNAKE_CASE_  :		str				      =    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 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],use_xla=_A,multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    TensorFlowBenchmark(_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result    )
							self.check_results_dict_not_empty(results.memory_inference_result    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






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






					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    "sshleifer/tiny-gpt2"

							def _check_summary_is_not_empty(_A      :       Optional[Any]    ):
									self.assertTrue(hasattr(_A,"sequential"    )    )
									self.assertTrue(hasattr(_A,"cumulative"    )    )
									self.assertTrue(hasattr(_A,"current"    )    )
									self.assertTrue(hasattr(_A,"total"    )    )

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








"""simple docstring"""






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


UpperCAmelCase_   : Optional[int]   			= logging.get_logger(__name__)

UpperCAmelCase_   : Dict   			= {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase_   : Dict   			= {
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

UpperCAmelCase_   : List[str]   			= {
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  VOCAB_FILES_NAMES
      __UpperCamelCase	 =  PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase	 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase	 =  ["input_ids", "attention_mask"]
      __UpperCamelCase	 =  GPTaTokenizer
      def __init__(						self     :  Optional[int]						,	lowercase_     :  int=None						,	lowercase_     :  List[str]=None						,	lowercase_     :  Union[str, Any]=None						,	lowercase_     :  Tuple="<|endoftext|>"						,	lowercase_     :  str="<|endoftext|>"						,	lowercase_     :  Dict="<|endoftext|>"						,	lowercase_     :  Tuple=False						,	**lowercase_     :  Optional[int]						,	):




          '''simple docstring'''


          super().__init__(
              lowercase_						,	lowercase_						,	tokenizer_file=lowercase_						,	unk_token=lowercase_						,	bos_token=lowercase_						,	eos_token=lowercase_						,	add_prefix_space=lowercase_						,	**lowercase_						,	)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		kwargs.pop('''add_bos_token'''						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
          if pre_tok_state.get('''add_prefix_space'''						,	lowercase_) != add_prefix_space:
              SCREAMING_SNAKE_CASE_      :  int    						=		getattr(lowercase_						,	pre_tok_state.pop('''type'''))
              SCREAMING_SNAKE_CASE_      :  str    						=		add_prefix_space
              SCREAMING_SNAKE_CASE_      :  Dict    						=		pre_tok_class(**lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		add_prefix_space
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	*lowercase_     :  List[Any]						,	**lowercase_     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		kwargs.get('''is_split_into_words'''						,	lowercase_)
          assert self.add_prefix_space or not is_split_into_words, (
              F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	*lowercase_     :  List[str]						,	**lowercase_     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		kwargs.get('''is_split_into_words'''						,	lowercase_)

          assert self.add_prefix_space or not is_split_into_words, (
              F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  str						,	lowercase_     :  Optional[str] = None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self._tokenizer.model.save(lowercase_						,	name=lowercase_)
          return tuple(lowercase_)



      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  "Conversation"):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		[]
          for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowercase_						,	add_special_tokens=lowercase_) + [self.eos_token_id])

          if len(lowercase_) > self.model_max_length:
              SCREAMING_SNAKE_CASE_      :  Any    						=		input_ids[-self.model_max_length :]
          return input_ids


