



'''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 						_UpperCamelCase  (							unittest.TestCase    ):


			'''simple docstring'''

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		tempfile.mkdtemp()

					__SCREAMING_SNAKE_CASE       :		List[str]           =		BlipImageProcessor()
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""						)

					__SCREAMING_SNAKE_CASE       :		Tuple           =		BlipProcessor(lowerCAmelCase__							,     lowerCAmelCase__						)

					processor.save_pretrained(self.tmpdirname						)

			def UpperCamelCase__					(							self     : Union[str, Any]							,     **lowerCAmelCase__     : int						):





					"""simple docstring"""






					return AutoProcessor.from_pretrained(self.tmpdirname							,     **lowerCAmelCase__						).tokenizer

			def UpperCamelCase__					(							self     : List[str]							,     **lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






					return AutoProcessor.from_pretrained(self.tmpdirname							,     **lowerCAmelCase__						).image_processor

			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					shutil.rmtree(self.tmpdirname						)

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		[np.random.randint(2_5_5							,     size=(3, 3_0, 4_0_0)							,     dtype=np.uinta						)]

					__SCREAMING_SNAKE_CASE       :		List[str]           =		[Image.fromarray(np.moveaxis(lowerCAmelCase__							,     0							,     -1						)						) for x in image_inputs]

					return image_inputs

			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		BlipProcessor(tokenizer=self.get_tokenizer()							,     image_processor=self.get_image_processor()						)
					processor.save_pretrained(self.tmpdirname						)

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.get_tokenizer(bos_token="""(BOS)"""							,     eos_token="""(EOS)"""						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self.get_image_processor(do_normalize=lowerCAmelCase__							,     padding_value=1.0						)

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		BlipProcessor.from_pretrained(
					    self.tmpdirname							,     bos_token="""(BOS)"""							,     eos_token="""(EOS)"""							,     do_normalize=lowerCAmelCase__							,     padding_value=1.0						)

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

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

			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		self.get_image_processor()
					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.get_tokenizer()

					__SCREAMING_SNAKE_CASE       :		Any           =		BlipProcessor(tokenizer=lowerCAmelCase__							,     image_processor=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.prepare_image_inputs()

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		image_processor(lowerCAmelCase__							,     return_tensors="""np"""						)
					__SCREAMING_SNAKE_CASE       :		int           =		processor(images=lowerCAmelCase__							,     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 UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.get_image_processor()
					__SCREAMING_SNAKE_CASE       :		Dict           =		self.get_tokenizer()

					__SCREAMING_SNAKE_CASE       :		int           =		BlipProcessor(tokenizer=lowerCAmelCase__							,     image_processor=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		List[str]           =		"""lower newer"""

					__SCREAMING_SNAKE_CASE       :		Any           =		processor(text=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Tuple           =		tokenizer(lowerCAmelCase__							,     return_token_type_ids=lowerCAmelCase__						)

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

			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.get_image_processor()
					__SCREAMING_SNAKE_CASE       :		Any           =		self.get_tokenizer()

					__SCREAMING_SNAKE_CASE       :		Any           =		BlipProcessor(tokenizer=lowerCAmelCase__							,     image_processor=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		"""lower newer"""
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.prepare_image_inputs()

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		processor(text=lowerCAmelCase__							,     images=lowerCAmelCase__						)

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

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

			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.get_image_processor()
					__SCREAMING_SNAKE_CASE       :		int           =		self.get_tokenizer()

					__SCREAMING_SNAKE_CASE       :		List[str]           =		BlipProcessor(tokenizer=lowerCAmelCase__							,     image_processor=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__SCREAMING_SNAKE_CASE       :		int           =		processor.batch_decode(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		tokenizer.batch_decode(lowerCAmelCase__						)

					self.assertListEqual(lowerCAmelCase__							,     lowerCAmelCase__						)



			def UpperCamelCase__					(							self     : Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		self.get_image_processor()
					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.get_tokenizer()

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		BlipProcessor(tokenizer=lowerCAmelCase__							,     image_processor=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		"""lower newer"""
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.prepare_image_inputs()

					__SCREAMING_SNAKE_CASE       :		Any           =		processor(text=lowerCAmelCase__							,     images=lowerCAmelCase__						)

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

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


SCREAMING_SNAKE_CASE							:Optional[int]							 =				logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE							:Tuple							 =				{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

SCREAMING_SNAKE_CASE							:List[Any]							 =				{
    'camembert-base': 512,
}

SCREAMING_SNAKE_CASE							:List[str]							 =				'▁'





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     ["input_ids", "attention_mask"]






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

					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

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






	def 				UpperCamelCase_  (       self					: int							,A					: List[int]							,A					: Optional[List[int]] = None ):

					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__A   			= [self.cls_token_id]
					__A   			= [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep






	def 				UpperCamelCase_  (       self					: Dict							,A					: List[int]							,A					: Optional[List[int]] = None							,A					: bool = False ):
					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=A							,token_ids_a=A							,already_has_special_tokens=A )

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






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: List[int]							,A					: Optional[List[int]] = None ):
					__A   			= [self.sep_token_id]
					__A   			= [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 				UpperCamelCase_  (       self					: Dict ):
					return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def 				UpperCamelCase_  (       self					: Any							,A					: str ):
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Dict ):
					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					elif self.sp_model.PieceToId(A ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
					return self.fairseq_offset + self.sp_model.PieceToId(A )






	def 				UpperCamelCase_  (       self					: Dict							,A					: Tuple ):
					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 				UpperCamelCase_  (       self					: Optional[Any]							,A					: Dict ):
					__A   			= []
					__A   			= ""
					__A   			= 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(A ) + token
													__A   			= True
													__A   			= []
									else:
													current_sub_tokens.append(A )
													__A   			= False
					out_string += self.sp_model.decode(A )
					return out_string.strip()






	def __getstate__(       self					: Dict ):
					__A   			= self.__dict__.copy()
					__A   			= None
					return state






	def __setstate__(       self					: Union[str, Any]							,A					: Any ):
					__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 				UpperCamelCase_  (       self					: Any							,A					: str							,A					: Optional[str] = None ):
					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,)
