

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

import sentencepiece as spm

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


lowerCAmelCase							:  int   	=						logging.get_logger(__name__)

lowerCAmelCase							:  Tuple   	=						{"""vocab_file""": """sentencepiece.model"""}

lowerCAmelCase							:  int   	=						{
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowerCAmelCase							:  Any   	=						{
    """google/rembert""": 256,
}



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[Any]										=     VOCAB_FILES_NAMES
			_UpperCAmelCase   :							int										=     PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase   :							Tuple										=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



			def __init__( self  :     List[Any]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Optional[int]=False   ,						lowerCAmelCase__  :     Union[str, Any]=True   ,						lowerCAmelCase__  :     Any=True   ,						lowerCAmelCase__  :     List[str]="[CLS]"   ,						lowerCAmelCase__  :     Tuple="[SEP]"   ,						lowerCAmelCase__  :     List[str]="[UNK]"   ,						lowerCAmelCase__  :     Union[str, Any]="[SEP]"   ,						lowerCAmelCase__  :     List[Any]="[PAD]"   ,						lowerCAmelCase__  :     int="[CLS]"   ,						lowerCAmelCase__  :     Optional[Any]="[MASK]"   ,						**lowerCAmelCase__  :     Tuple   ,						):
									super().__init__(
									    do_lower_case=lowerCAmelCase__   ,						remove_space=lowerCAmelCase__   ,						keep_accents=lowerCAmelCase__   ,						bos_token=lowerCAmelCase__   ,						eos_token=lowerCAmelCase__   ,						unk_token=lowerCAmelCase__   ,						sep_token=lowerCAmelCase__   ,						pad_token=lowerCAmelCase__   ,						cls_token=lowerCAmelCase__   ,						mask_token=lowerCAmelCase__   ,						**lowerCAmelCase__   ,						)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       do_lower_case
									SCREAMING_SNAKE_CASE_: Any  		=       remove_space
									SCREAMING_SNAKE_CASE_: Tuple  		=       keep_accents
									SCREAMING_SNAKE_CASE_: int  		=       vocab_file

									SCREAMING_SNAKE_CASE_: List[Any]  		=       spm.SentencePieceProcessor()
									self.sp_model.Load(lowerCAmelCase__)



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									return len(self.sp_model)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       {self.convert_ids_to_tokens(lowerCAmelCase__): i for i in range(self.vocab_size)}
									vocab.update(self.added_tokens_encoder)
									return vocab



			def __getstate__( self  :     int):
									SCREAMING_SNAKE_CASE_: int  		=       self.__dict__.copy()
									SCREAMING_SNAKE_CASE_: Any  		=       None
									return state



			def __setstate__( self  :     str   ,						lowerCAmelCase__  :     Any):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       d
									SCREAMING_SNAKE_CASE_: Dict  		=       spm.SentencePieceProcessor()
									self.sp_model.Load(self.vocab_file)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     List[str]=False):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.sp_model.EncodeAsPieces(lowerCAmelCase__)
									return pieces



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Any):
									return self.sp_model.PieceToId(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Tuple):
									return self.sp_model.IdToPiece(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.sp_model.decode_pieces(lowerCAmelCase__)
									return out_string



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_: Dict  		=       [self.sep_token_id]
									SCREAMING_SNAKE_CASE_: str  		=       [self.cls_token_id]
									if token_ids_a is None:
															return cls + token_ids_a + sep
									return cls + token_ids_a + sep + token_ids_a + sep



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None   ,						lowerCAmelCase__  :     bool = False):

									if already_has_special_tokens:
															if token_ids_a is not None:
																					raise ValueError(
																					    "You should not supply a second sequence if the provided sequence of "
																					    "ids is already formatted with special tokens for the model.")
															return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

									if token_ids_a is not None:
															return [1] + ([0] * len(lowerCAmelCase__)) + [1] + ([0] * len(lowerCAmelCase__)) + [1]
									return [1] + ([0] * len(lowerCAmelCase__)) + [1]



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_: Any  		=       [self.sep_token_id]
									SCREAMING_SNAKE_CASE_: str  		=       [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) * [0] + len(token_ids_a + sep) * [1]




			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[str] = None):
									if not os.path.isdir(lowerCAmelCase__):
															logger.error("Vocabulary path ({}) should be a directory".format(lowerCAmelCase__))
															return
									SCREAMING_SNAKE_CASE_: Any  		=       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__):
															copyfile(self.vocab_file   ,						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": ""}])






