


'''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


_UpperCAmelCase				: Union[str, Any]							    =				logging.get_logger(__name__)


_UpperCAmelCase				: Dict							    =				"""▁"""

_UpperCAmelCase				: Any							    =				{"""vocab_file""": """spiece.model"""}

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

_UpperCAmelCase				: List[str]							    =				{
    """google/reformer-crime-and-punishment""": 5_2_4_2_8_8,
}


class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		List[Any]		=	VOCAB_FILES_NAMES
				__UpperCamelCase    :		List[str]		=	PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase    :		List[Any]		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase    :		Any		=	['input_ids', 'attention_mask']




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

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

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




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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				{self.convert_ids_to_tokens(__lowercase		): 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       ,			__lowercase		):
										__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       ,			__lowercase		):
										return self.sp_model.encode(__lowercase       ,			out_type=__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										return self.sp_model.piece_to_id(__lowercase		)




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




				def 					_snake_case  (self       ,			__lowercase		):
										__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(__lowercase		) + token
																						__lowerCAmelCase							       =				[]
																else:
																						current_sub_tokens.append(__lowercase		)
										out_string += self.sp_model.decode(__lowercase		)
										return out_string.strip()




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

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

										return (out_vocab_file,)





'''simple docstring'''

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_keras_nlp_available():
	from transformers.models.gpta import TFGPTaTokenizer


_UpperCAmelCase				: str							    =				["""gpt2"""]
_UpperCAmelCase				: Optional[int]							    =				"""gpt2"""

if is_tf_available():


	class       a__				( tf.Module      ):







					"""simple docstring"""



					def __init__(self       ,			__lowercase		):
											super().__init__()
											__lowerCAmelCase							       =				tokenizer
											__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
											__lowerCAmelCase							       =				TFGPTaLMHeadModel.from_config(__lowercase		)




					@tf.function(input_signature=(tf.TensorSpec((None,)       ,			tf.string       ,			name='''text'''		),)		)
					def 					_snake_case  (self       ,			__lowercase		):
											__lowerCAmelCase							       =				self.tokenizer(__lowercase		)
											__lowerCAmelCase							       =				tokenized['''input_ids'''].to_tensor()

											__lowerCAmelCase							       =				tf.cast(input_ids_dense > 0       ,			tf.intaa		)
											# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

											__lowerCAmelCase							       =				self.model(input_ids=__lowercase       ,			attention_mask=__lowercase		)['''logits''']

											return outputs


@require_tf
@require_keras_nlp
class       a__				( unittest.TestCase      ):







				"""simple docstring"""



				def 					_snake_case  (self		):
										super().setUp()

										__lowerCAmelCase							       =				[GPTaTokenizer.from_pretrained(__lowercase		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
										__lowerCAmelCase							       =				[TFGPTaTokenizer.from_pretrained(__lowercase		) for checkpoint in TOKENIZER_CHECKPOINTS]
										assert len(self.tokenizers		) == len(self.tf_tokenizers		)

										__lowerCAmelCase							       =				[
										    '''This is a straightforward English test sentence.''',
										    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
										    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
										    '''And some much more rare Chinese: 齉 堃 齉堃''',
										    '''Je vais aussi écrire en français pour tester les accents''',
										    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
										]
										__lowerCAmelCase							       =				list(zip(self.test_sentences       ,			self.test_sentences[::-1]		)		)




				def 					_snake_case  (self		):
										for tokenizer, tf_tokenizer in zip(self.tokenizers       ,			self.tf_tokenizers		):
																for test_inputs in self.test_sentences:
																						__lowerCAmelCase							       =				tokenizer([test_inputs]       ,			return_tensors='''tf'''		)
																						__lowerCAmelCase							       =				tf_tokenizer([test_inputs]		)

																						for key in python_outputs.keys():
																												# convert them to numpy to avoid messing with ragged tensors
																												__lowerCAmelCase							       =				python_outputs[key].numpy()
																												__lowerCAmelCase							       =				tf_outputs[key].numpy()

																												self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape		)		)
																												self.assertTrue(tf.reduce_all(tf.cast(__lowercase       ,			tf.intaa		) == tf_outputs_values		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				tf.function(__lowercase		)
																for test_inputs in self.test_sentences:
																						__lowerCAmelCase							       =				tf.constant(__lowercase		)
																						__lowerCAmelCase							       =				compiled_tokenizer(__lowercase		)
																						__lowerCAmelCase							       =				tf_tokenizer(__lowercase		)

																						for key in eager_outputs.keys():
																												self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				ModelToSave(tokenizer=__lowercase		)
																__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																__lowerCAmelCase							       =				model.serving(__lowercase		)  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																						__lowerCAmelCase							       =				Path(__lowercase		) / '''saved.model'''
																						tf.saved_model.save(__lowercase       ,			__lowercase       ,			signatures={'''serving_default''': model.serving}		)
																						__lowerCAmelCase							       =				tf.saved_model.load(__lowercase		)
																__lowerCAmelCase							       =				loaded_model.signatures['''serving_default'''](__lowercase		)['''output_0''']
																# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																self.assertTrue(tf.reduce_all(out == loaded_output		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																__lowerCAmelCase							       =				tf_tokenizer(__lowercase		)  # Build model with some sample inputs

																__lowerCAmelCase							       =				tf_tokenizer.get_config()
																__lowerCAmelCase							       =				TFGPTaTokenizer.from_config(__lowercase		)
																__lowerCAmelCase							       =				model_from_config(__lowercase		)

																for key in from_config_output.keys():
																						self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																# for the test to run
																__lowerCAmelCase							       =				12_31_23

																for max_length in [3, 5, 10_24]:
																						__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																						__lowerCAmelCase							       =				tf_tokenizer(__lowercase       ,			max_length=__lowercase		)

																						__lowerCAmelCase							       =				out['''input_ids'''].numpy().shape[1]

																						assert out_length == max_length

