





"""simple docstring"""




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						: List[Any]     	=			logging.get_logger(__name__)

a						: Optional[int]     	=			{'''vocab_file''': '''sentencepiece.bpe.model'''}

a						: Optional[Any]     	=			{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

a						: Optional[int]     	=			{
    '''camembert-base''': 512,
}

a						: Union[str, Any]     	=			'''▁'''


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					int      					=VOCAB_FILES_NAMES
							lowerCamelCase   :					List[str]      					=PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase   :					int      					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase   :					str      					=["""input_ids""", """attention_mask"""]






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__="<s>"      ,					lowerCAmelCase__="</s>"      ,					lowerCAmelCase__="</s>"      ,					lowerCAmelCase__="<s>"      ,					lowerCAmelCase__="<unk>"      ,					lowerCAmelCase__="<pad>"      ,					lowerCAmelCase__="<mask>"      ,					lowerCAmelCase__=["<s>NOTUSED", "</s>NOTUSED"]      ,					lowerCAmelCase__ = None      ,					**lowerCAmelCase__      ,					)	-> None:
								# Mask token behave like a normal word, i.e. include the space before it
								a      :		List[Any]	=				AddedToken(lowerCAmelCase__      ,					lstrip=lowerCAmelCase__      ,					rstrip=lowerCAmelCase__      ) if isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ) else mask_token

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

								super().__init__(
								    bos_token=lowerCAmelCase__      ,					eos_token=lowerCAmelCase__      ,					unk_token=lowerCAmelCase__      ,					sep_token=lowerCAmelCase__      ,					cls_token=lowerCAmelCase__      ,					pad_token=lowerCAmelCase__      ,					mask_token=lowerCAmelCase__      ,					additional_special_tokens=lowerCAmelCase__      ,					sp_model_kwargs=self.sp_model_kwargs      ,					**lowerCAmelCase__      ,					)
								a      :		str	=				spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(str(lowerCAmelCase__      )      )
								a      :		List[str]	=				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>
								a      :		List[str]	=				{"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
								a      :		List[str]	=				len(self.fairseq_tokens_to_ids      )
								a      :		List[Any]	=				len(self.sp_model      ) + len(self.fairseq_tokens_to_ids      )
								a      :		Any	=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> List[int]:

								if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								a      :		List[str]	=				[self.cls_token_id]
								a      :		Any	=				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = False      )	-> List[int]:
								if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=lowerCAmelCase__      ,					token_ids_a=lowerCAmelCase__      ,					already_has_special_tokens=lowerCAmelCase__      )

								if token_ids_a is None:
									return [1] + ([0] * len(lowerCAmelCase__      )) + [1]
								return [1] + ([0] * len(lowerCAmelCase__      )) + [1, 1] + ([0] * len(lowerCAmelCase__      )) + [1]






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> List[int]:
								a      :		Optional[Any]	=				[self.sep_token_id]
								a      :		Tuple	=				[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     __a   (   self      )	-> Union[str, Any]:
								return len(self.fairseq_tokens_to_ids      ) + len(self.sp_model      )






							def     __a   (   self      )	-> Optional[int]:
								a      :		List[str]	=				{self.convert_ids_to_tokens(lowerCAmelCase__      ): i for i in range(self.vocab_size      )}
								vocab.update(self.added_tokens_encoder      )
								return vocab






							def     __a   (   self      ,					lowerCAmelCase__      )	-> List[str]:
								return self.sp_model.encode(lowerCAmelCase__      ,					out_type=lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(lowerCAmelCase__      ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> 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     __a   (   self      ,					lowerCAmelCase__      )	-> List[Any]:
								a      :		Union[str, Any]	=				[]
								a      :		str	=				""
								a      :		Tuple	=				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(lowerCAmelCase__      ) + token
										a      :		str	=				True
										a      :		Union[str, Any]	=				[]
									else:
										current_sub_tokens.append(lowerCAmelCase__      )
										a      :		Tuple	=				False
								out_string += self.sp_model.decode(lowerCAmelCase__      )
								return out_string.strip()






							def __getstate__(   self      )	-> Tuple:
								a      :		str	=				self.__dict__.copy()
								a      :		Union[str, Any]	=				None
								return state






							def __setstate__(   self      ,					lowerCAmelCase__      )	-> str:
								a      :		Union[str, Any]	=				d

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

								a      :		Tuple	=				spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(self.vocab_file      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> Tuple[str]:
								if not os.path.isdir(lowerCAmelCase__      ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
									return
								a      :		int	=				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__      ) and os.path.isfile(self.vocab_file      ):
									copyfile(self.vocab_file      ,					lowerCAmelCase__      )
								elif not os.path.isfile(self.vocab_file      ):
									with open(lowerCAmelCase__      ,					"wb"      ) as fi:
										a      :		Optional[Any]	=				self.sp_model.serialized_model_proto()
										fi.write(lowerCAmelCase__      )

								return (out_vocab_file,)





import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
						import torch



@is_pipeline_test
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[Any]										=     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			_UpperCAmelCase   :							str										=     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: Any  		=       TextaTextGenerationPipeline(model=lowerCAmelCase__   ,						tokenizer=lowerCAmelCase__)
									return generator, ["Something to write", "Something else"]



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Any):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator("Something there")
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ANY(lowerCAmelCase__)}])
									# These are encoder decoder, they don't just append to incoming string
									self.assertFalse(outputs[0]["generated_text"].startswith("Something there"))

									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator(["This is great !", "Something else"]   ,						num_return_sequences=2   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									    ]   ,						)

									SCREAMING_SNAKE_CASE_: Dict  		=       generator(
									    ["This is great !", "Something else"]   ,						num_return_sequences=2   ,						batch_size=2   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									    ]   ,						)

									with self.assertRaises(lowerCAmelCase__):
															generator(4)



			@require_torch
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       pipeline("text2text-generation"   ,						model="patrickvonplaten/t5-tiny-random"   ,						framework="pt")
									# do_sample=False necessary for reproducibility
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       generator("Something there"   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ""}])

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       3
									SCREAMING_SNAKE_CASE_: Any  		=       generator(
									    "Something there"   ,						num_return_sequences=lowerCAmelCase__   ,						num_beams=lowerCAmelCase__   ,						)
									SCREAMING_SNAKE_CASE_: Any  		=       [
									    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
									    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
									    {"generated_text": ""},
									]
									self.assertEqual(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: int  		=       generator("This is a test"   ,						do_sample=lowerCAmelCase__   ,						num_return_sequences=2   ,						return_tensors=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        {"generated_token_ids": ANY(torch.Tensor)},
									        {"generated_token_ids": ANY(torch.Tensor)},
									    ]   ,						)
									SCREAMING_SNAKE_CASE_: str  		=       generator.model.config.eos_token_id
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       "<pad>"
									SCREAMING_SNAKE_CASE_: Tuple  		=       generator(
									    ["This is a test", "This is a second test"]   ,						do_sample=lowerCAmelCase__   ,						num_return_sequences=2   ,						batch_size=2   ,						return_tensors=lowerCAmelCase__   ,						)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [
									            {"generated_token_ids": ANY(torch.Tensor)},
									            {"generated_token_ids": ANY(torch.Tensor)},
									        ],
									        [
									            {"generated_token_ids": ANY(torch.Tensor)},
									            {"generated_token_ids": ANY(torch.Tensor)},
									        ],
									    ]   ,						)




			@require_tf
			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       pipeline("text2text-generation"   ,						model="patrickvonplaten/t5-tiny-random"   ,						framework="tf")
									# do_sample=False necessary for reproducibility
									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator("Something there"   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ""}])






