

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


a		=logging.get_logger(__name__)

a		={"""vocab_file""": """sentencepiece.bpe.model"""}

a		={
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

a		={
    """camembert-base""": 512,
}

a		="""▁"""






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Optional[Any]										=				VOCAB_FILES_NAMES
							_UpperCAmelCase   :  List[Any]										=				PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase   :  Tuple										=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase   :  int										=				['''input_ids''', '''attention_mask''']
							def __init__(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      List[str]="<s>"       ,SCREAMING_SNAKE_CASE__		:      List[str]="</s>"       ,SCREAMING_SNAKE_CASE__		:      List[str]="</s>"       ,SCREAMING_SNAKE_CASE__		:      int="<s>"       ,SCREAMING_SNAKE_CASE__		:      List[Any]="<unk>"       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]="<pad>"       ,SCREAMING_SNAKE_CASE__		:      Optional[int]="<mask>"       ,SCREAMING_SNAKE_CASE__		:      Dict=["<s>NOTUSED", "</s>NOTUSED"]       ,SCREAMING_SNAKE_CASE__		:      Optional[Dict[str, Any]] = None       ,**SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,):
								# Mask token behave like a normal word, i.e. include the space before it
								__lowerCamelCase    :    Tuple 	=			AddedToken(SCREAMING_SNAKE_CASE__       ,lstrip=SCREAMING_SNAKE_CASE__       ,rstrip=SCREAMING_SNAKE_CASE__) if isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__) else mask_token

								__lowerCamelCase    :    Dict 	=			{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=SCREAMING_SNAKE_CASE__       ,eos_token=SCREAMING_SNAKE_CASE__       ,unk_token=SCREAMING_SNAKE_CASE__       ,sep_token=SCREAMING_SNAKE_CASE__       ,cls_token=SCREAMING_SNAKE_CASE__       ,pad_token=SCREAMING_SNAKE_CASE__       ,mask_token=SCREAMING_SNAKE_CASE__       ,additional_special_tokens=SCREAMING_SNAKE_CASE__       ,sp_model_kwargs=self.sp_model_kwargs       ,**SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    Dict 	=			spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(str(SCREAMING_SNAKE_CASE__))
								__lowerCamelCase    :    Dict 	=			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>
								__lowerCamelCase    :    Dict 	=			{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
								__lowerCamelCase    :    Optional[int] 	=			len(self.fairseq_tokens_to_ids)
								__lowerCamelCase    :    List[Any] 	=			len(self.sp_model) + len(self.fairseq_tokens_to_ids)
								__lowerCamelCase    :    int 	=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      List[int]       ,SCREAMING_SNAKE_CASE__		:      Optional[List[int]] = None):

								if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__lowerCamelCase    :    List[Any] 	=			[self.cls_token_id]
								__lowerCamelCase    :    Tuple 	=			[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      List[int]       ,SCREAMING_SNAKE_CASE__		:      Optional[List[int]] = None       ,SCREAMING_SNAKE_CASE__		:      bool = False):
								if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=SCREAMING_SNAKE_CASE__       ,token_ids_a=SCREAMING_SNAKE_CASE__       ,already_has_special_tokens=SCREAMING_SNAKE_CASE__)

								if token_ids_a is None:
									return [1] + ([0] * len(SCREAMING_SNAKE_CASE__)) + [1]
								return [1] + ([0] * len(SCREAMING_SNAKE_CASE__)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__)) + [1]
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      List[int]       ,SCREAMING_SNAKE_CASE__		:      Optional[List[int]] = None):
								__lowerCamelCase    :    Tuple 	=			[self.sep_token_id]
								__lowerCamelCase    :    Any 	=			[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    lowerCAmelCase					(		self		:      Tuple):
								return len(self.fairseq_tokens_to_ids) + len(self.sp_model)
							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    Union[str, Any] 	=			{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__): i for i in range(self.vocab_size)}
								vocab.update(self.added_tokens_encoder)
								return vocab
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      str):
								return self.sp_model.encode(SCREAMING_SNAKE_CASE__       ,out_type=SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      str):
								if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(SCREAMING_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(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Union[str, 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    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
								__lowerCamelCase    :    Optional[Any] 	=			[]
								__lowerCamelCase    :    Optional[Any] 	=			''
								__lowerCamelCase    :    List[Any] 	=			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(SCREAMING_SNAKE_CASE__) + token
										__lowerCamelCase    :    int 	=			True
										__lowerCamelCase    :    Dict 	=			[]
									else:
										current_sub_tokens.append(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Tuple 	=			False
								out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__)
								return out_string.strip()
							def __getstate__(		self		:      int):
								__lowerCamelCase    :    Optional[Any] 	=			self.__dict__.copy()
								__lowerCamelCase    :    Any 	=			None
								return state
							def __setstate__(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      int):
								__lowerCamelCase    :    Any 	=			d

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

								__lowerCamelCase    :    int 	=			spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(self.vocab_file)


							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[str] = None):
								if not os.path.isdir(SCREAMING_SNAKE_CASE__):
									logger.error(F"Vocabulary path ({save_directory}) should be a directory")
									return
								__lowerCamelCase    :    Optional[int] 	=			os.path.join(
								    SCREAMING_SNAKE_CASE__       ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

								if os.path.abspath(self.vocab_file) != os.path.abspath(SCREAMING_SNAKE_CASE__) and os.path.isfile(self.vocab_file):
									copyfile(self.vocab_file       ,SCREAMING_SNAKE_CASE__)
								elif not os.path.isfile(self.vocab_file):
									with open(SCREAMING_SNAKE_CASE__       ,'wb') as fi:
										__lowerCamelCase    :    Optional[Any] 	=			self.sp_model.serialized_model_proto()
										fi.write(SCREAMING_SNAKE_CASE__)

								return (out_vocab_file,)







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


a		=["""gpt2"""]
a		="""gpt2"""

if is_tf_available():






					class 	A_		(	tf.Module    ):
												def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Tuple):
													super().__init__()
													__lowerCamelCase    :    List[Any] 	=			tokenizer
													__lowerCamelCase    :    str 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
													__lowerCamelCase    :    Tuple 	=			TFGPTaLMHeadModel.from_config(SCREAMING_SNAKE_CASE__)


												@tf.function(input_signature=(tf.TensorSpec((None,)       ,tf.string       ,name='text'),))
												def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
													__lowerCamelCase    :    str 	=			self.tokenizer(SCREAMING_SNAKE_CASE__)
													__lowerCamelCase    :    Dict 	=			tokenized['input_ids'].to_tensor()

													__lowerCamelCase    :    List[Any] 	=			tf.cast(input_ids_dense > 0       ,tf.intaa)
													# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

													__lowerCamelCase    :    Union[str, Any] 	=			self.model(input_ids=SCREAMING_SNAKE_CASE__       ,attention_mask=SCREAMING_SNAKE_CASE__)['logits']

													return outputs






@require_tf
@require_keras_nlp
class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      List[str]):
								super().setUp()

								__lowerCamelCase    :    str 	=			[GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								__lowerCamelCase    :    List[Any] 	=			[TFGPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__) for checkpoint in TOKENIZER_CHECKPOINTS]
								assert len(self.tokenizers) == len(self.tf_tokenizers)

								__lowerCamelCase    :    Optional[int] 	=			[
								    '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[str] 	=			list(zip(self.test_sentences       ,self.test_sentences[::-1]))
							def    lowerCAmelCase					(		self		:      Optional[int]):
								for tokenizer, tf_tokenizer in zip(self.tokenizers       ,self.tf_tokenizers):
									for test_inputs in self.test_sentences:
										__lowerCamelCase    :    Union[str, Any] 	=			tokenizer([test_inputs]       ,return_tensors='tf')
										__lowerCamelCase    :    Dict 	=			tf_tokenizer([test_inputs])

										for key in python_outputs.keys():
											# convert them to numpy to avoid messing with ragged tensors
											__lowerCamelCase    :    List[str] 	=			python_outputs[key].numpy()
											__lowerCamelCase    :    Optional[Any] 	=			tf_outputs[key].numpy()

											self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
											self.assertTrue(tf.reduce_all(tf.cast(SCREAMING_SNAKE_CASE__       ,tf.intaa) == tf_outputs_values))
							@slow
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Dict 	=			tf.function(SCREAMING_SNAKE_CASE__)
									for test_inputs in self.test_sentences:
										__lowerCamelCase    :    Any 	=			tf.constant(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			compiled_tokenizer(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__)

										for key in eager_outputs.keys():
											self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
							@slow
							def    lowerCAmelCase					(		self		:      str):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Any 	=			ModelToSave(tokenizer=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Tuple 	=			tf.convert_to_tensor([self.test_sentences[0]])
									__lowerCamelCase    :    str 	=			model.serving(SCREAMING_SNAKE_CASE__)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
										__lowerCamelCase    :    Optional[Any] 	=			Path(SCREAMING_SNAKE_CASE__) / 'saved.model'
										tf.saved_model.save(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,signatures={'serving_default': model.serving})
										__lowerCamelCase    :    Tuple 	=			tf.saved_model.load(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			loaded_model.signatures['serving_default'](SCREAMING_SNAKE_CASE__)['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    lowerCAmelCase					(		self		:      int):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Optional[int] 	=			tf.convert_to_tensor([self.test_sentences[0]])
									__lowerCamelCase    :    Optional[Any] 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__)  # Build model with some sample inputs

									__lowerCamelCase    :    str 	=			tf_tokenizer.get_config()
									__lowerCamelCase    :    List[str] 	=			TFGPTaTokenizer.from_config(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			model_from_config(SCREAMING_SNAKE_CASE__)

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


							@slow
							def    lowerCAmelCase					(		self		:      Dict):
								for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									__lowerCamelCase    :    List[Any] 	=			1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
										__lowerCamelCase    :    Dict 	=			tf.convert_to_tensor([self.test_sentences[0]])
										__lowerCamelCase    :    Union[str, Any] 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__       ,max_length=SCREAMING_SNAKE_CASE__)

										__lowerCamelCase    :    List[Any] 	=			out['input_ids'].numpy().shape[1]

										assert out_length == max_length




