


"""simple docstring"""


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast







@require_vision
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Any:
							A						   =   tempfile.mkdtemp()

							A						   =   BlipImageProcessor()
							A						   =   BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'      )

							A						   =   BlipProcessor(A_  ,A_      )

							processor.save_pretrained(self.tmpdirname      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,**A_     :      Dict      )     ->   Dict:
							return AutoProcessor.from_pretrained(self.tmpdirname  ,**A_      ).tokenizer


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,**A_     :      Tuple      )     ->   Tuple:
							return AutoProcessor.from_pretrained(self.tmpdirname  ,**A_      ).image_processor


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Optional[Any]:
							shutil.rmtree(self.tmpdirname      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Tuple:
							A						   =   [np.random.randint(255  ,size=(3, 30, 400)  ,dtype=np.uinta      )]

							A						   =   [Image.fromarray(np.moveaxis(A_  ,0  ,-1      )      ) for x in image_inputs]

							return image_inputs


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Tuple:
							A						   =   BlipProcessor(tokenizer=self.get_tokenizer()  ,image_processor=self.get_image_processor()      )
							processor.save_pretrained(self.tmpdirname      )

							A						   =   self.get_tokenizer(bos_token='(BOS)'  ,eos_token='(EOS)'      )
							A						   =   self.get_image_processor(do_normalize=A_  ,padding_value=1.0      )

							A						   =   BlipProcessor.from_pretrained(
							    self.tmpdirname  ,bos_token='(BOS)'  ,eos_token='(EOS)'  ,do_normalize=A_  ,padding_value=1.0      )

							self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer_add_kwargs.get_vocab()      )
							self.assertIsInstance(processor.tokenizer  ,A_      )

							self.assertEqual(processor.image_processor.to_json_string()  ,image_processor_add_kwargs.to_json_string()      )
							self.assertIsInstance(processor.image_processor  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   List[str]:
							A						   =   self.get_image_processor()
							A						   =   self.get_tokenizer()

							A						   =   BlipProcessor(tokenizer=A_  ,image_processor=A_      )

							A						   =   self.prepare_image_inputs()

							A						   =   image_processor(A_  ,return_tensors='np'      )
							A						   =   processor(images=A_  ,return_tensors='np'      )

							for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()  ,input_processor[key].sum()  ,delta=1e-2      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   List[Any]:
							A						   =   self.get_image_processor()
							A						   =   self.get_tokenizer()

							A						   =   BlipProcessor(tokenizer=A_  ,image_processor=A_      )

							A						   =   'lower newer'

							A						   =   processor(text=A_      )

							A						   =   tokenizer(A_  ,return_token_type_ids=A_      )

							for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]  ,encoded_processor[key]      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.get_image_processor()
							A						   =   self.get_tokenizer()

							A						   =   BlipProcessor(tokenizer=A_  ,image_processor=A_      )

							A						   =   'lower newer'
							A						   =   self.prepare_image_inputs()

							A						   =   processor(text=A_  ,images=A_      )

							self.assertListEqual(list(inputs.keys()      )  ,['pixel_values', 'input_ids', 'attention_mask']      )

							# test if it raises when no input is passed
							with pytest.raises(A_      ):
								processor()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Any:
							A						   =   self.get_image_processor()
							A						   =   self.get_tokenizer()

							A						   =   BlipProcessor(tokenizer=A_  ,image_processor=A_      )

							A						   =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							A						   =   processor.batch_decode(A_      )
							A						   =   tokenizer.batch_decode(A_      )

							self.assertListEqual(A_  ,A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Any:
							A						   =   self.get_image_processor()
							A						   =   self.get_tokenizer()

							A						   =   BlipProcessor(tokenizer=A_  ,image_processor=A_      )

							A						   =   'lower newer'
							A						   =   self.prepare_image_inputs()

							A						   =   processor(text=A_  ,images=A_      )

							# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
							self.assertListEqual(list(inputs.keys()      )  ,['pixel_values', 'input_ids', 'attention_mask']      )

"""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


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{'''vocab_file''': '''spm_char.model'''}

_lowercase							   =				{
    '''vocab_file''': {
        '''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
        '''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
        '''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
    }
}

_lowercase							   =				{
    '''microsoft/speecht5_asr''': 10_24,
    '''microsoft/speecht5_tts''': 10_24,
    '''microsoft/speecht5_vc''': 10_24,
}







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Optional[Any]      =    VOCAB_FILES_NAMES
						_lowerCamelCase:    List[Any]      =    PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:    str      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:    Tuple      =    ['''input_ids''', '''attention_mask''']


						def __init__( self     :      List[str]  ,A_     :      int  ,A_     :      List[str]="<s>"  ,A_     :      Optional[Any]="</s>"  ,A_     :      Optional[Any]="<unk>"  ,A_     :      str="<pad>"  ,A_     :      Optional[Dict[str, Any]] = None  ,**A_     :      List[str]  ,)     ->   None:
							A						   =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=A_  ,eos_token=A_  ,unk_token=A_  ,pad_token=A_  ,sp_model_kwargs=self.sp_model_kwargs  ,**A_  ,)

							A						   =   vocab_file

							A						   =   spm.SentencePieceProcessor(**self.sp_model_kwargs      )
							self.sp_model.Load(A_      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   str:
							return self.sp_model.get_piece_size()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Optional[Any]:
							A						   =   {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     :      str      )     ->   Any:
							A						   =   self.__dict__.copy()
							A						   =   None
							return state


						def __setstate__( self     :      Optional[int]  ,A_     :      str      )     ->   Tuple:
							A						   =   d

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

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


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Union[str, Any]      )     ->   Union[str, Any]:
							return self.sp_model.piece_to_id(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      Dict      )     ->   List[Any]:
							A						   =   self.sp_model.IdToPiece(A_      )
							return token


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple  ,A_     :      Optional[Any]      )     ->   List[str]:
							A						   =   []
							A						   =   ''
							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
									A						   =   []
								else:
									current_sub_tokens.append(A_      )
							out_string += self.sp_model.decode(A_      )
							return out_string.strip()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Dict  ,A_     :      Optional[int]=None      )     ->   List[int]:
							if token_ids_a is None:
								return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      List[int]  ,A_     :      Optional[List[int]] = None  ,A_     :      bool = False      )     ->   List[int]:
							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_  ,token_ids_a=A_  ,already_has_special_tokens=A_      )

							A						   =   [1]
							if token_ids_a is None:
								return ([0] * len(A_      )) + suffix_ones
							return ([0] * len(A_      )) + ([0] * len(A_      )) + suffix_ones




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      str  ,A_     :      Optional[str] = None      )     ->   Tuple[str]:
							if not os.path.isdir(A_      ):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
								return
							A						   =   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:
									A						   =   self.sp_model.serialized_model_proto()
									fi.write(A_      )

							return (out_vocab_file,)