


"""simple docstring"""


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A  :     Dict    					=    logging.get_logger(__name__)


A  :     Dict    					=    "▁"

A  :     Dict    					=    {"vocab_file": "spiece.model"}

A  :     Any    					=    {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

A  :     List[str]    					=    {
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					List[Any]          =VOCAB_FILES_NAMES
							__UpperCAmelCase		:					Union[str, Any]          =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase		:					List[Any]          =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase		:					Tuple          =["""input_ids""", """attention_mask"""]

							def __init__(  self					,     __a					,     __a="</s>"					,     __a="<unk>"					,     __a=[]					,     __a = None					,     **__a					,     ):
										__lowerCAmelCase       					=    {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    eos_token=__a					,     unk_token=__a					,     additional_special_tokens=__a					,     sp_model_kwargs=self.sp_model_kwargs					,     **__a					,     )

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

							@property
							def     snake_case       (  self			):
										return self.sp_model.get_piece_size()

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    {self.convert_ids_to_tokens(__a			): i for i in range(self.vocab_size			)}
										vocab.update(self.added_tokens_encoder			)
										return vocab

							def __getstate__(  self			):
										__lowerCAmelCase       					=    self.__dict__.copy()
										__lowerCAmelCase       					=    None
										return state

							def __setstate__(  self					,     __a			):
										__lowerCAmelCase       					=    d

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

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

							def     snake_case       (  self					,     __a			):
										return self.sp_model.encode(__a					,     out_type=__a			)

							def     snake_case       (  self					,     __a			):
										return self.sp_model.piece_to_id(__a			)

							def     snake_case       (  self					,     __a			):
										if index < self.sp_model.get_piece_size():
													__lowerCAmelCase       					=    self.sp_model.IdToPiece(__a			)
										return token

							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    []
										__lowerCAmelCase       					=    ""
										for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																out_string += self.sp_model.decode(__a			) + token
																__lowerCAmelCase       					=    []
													else:
																current_sub_tokens.append(__a			)
										out_string += self.sp_model.decode(__a			)
										return out_string.strip()



							def     snake_case       (  self					,     __a					,     __a = None			):
										if not os.path.isdir(__a			):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
													return
										__lowerCAmelCase       					=    os.path.join(
										    __a					,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

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

										return (out_vocab_file,)








"""simple docstring"""


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 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self					,     __a			):
										for model_result in results.values():
													for batch_size, sequence_length in zip(model_result["bs"]					,     model_result["ss"]			):
																__lowerCAmelCase       					=    model_result["result"][batch_size][sequence_length]
																self.assertIsNotNone(__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     eager_mode=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sgugger/tiny-distilbert-classification"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     only_pretrain_model=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

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

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

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

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result			)
										self.check_results_dict_not_empty(results.memory_train_result			)

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

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "patrickvonplaten/t5-tiny-random"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     configs=[config]			)
										__lowerCAmelCase       					=    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     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     use_xla=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase       					=    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					,     )
													__lowerCAmelCase       					=    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     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"

										def _check_summary_is_not_empty(__a			):
													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:
													__lowerCAmelCase       					=    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					,     )
													__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
													__lowerCAmelCase       					=    benchmark.run()
													_check_summary_is_not_empty(result.inference_summary			)
													self.assertTrue(Path(os.path.join(__a					,     "log.txt"			)			).exists()			)




