






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A_       :Union[str, Any]       =						logging.get_logger(__name__)


A_       :Tuple       =						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

A_       :str       =						{
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

A_       :Optional[Any]       =						{
    '''allenai/longformer-base-4096''': 4096,
    '''allenai/longformer-large-4096''': 4096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}






@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 							A     (       )     ->						Tuple:
					__UpperCamelCase      :					Optional[int]       		=(
					    list(range(ord('!'					)							,ord('~'					) + 1					)					) + list(range(ord('¡'					)							,ord('¬'					) + 1					)					) + list(range(ord('®'					)							,ord('ÿ'					) + 1					)					)
					)
					__UpperCamelCase      :					List[Any]       		=bs[:]
					__UpperCamelCase      :					Optional[int]       		=0
					for b in range(2**8					):
										if b not in bs:
															bs.append(snake_case__					)
															cs.append(2**8 + n					)
															n += 1
					__UpperCamelCase      :					str       		=[chr(snake_case__					) for n in cs]
					return dict(zip(snake_case__							,snake_case__					)					)






def 							A     (       a_					)     ->						int:
					__UpperCamelCase      :					str       		=set()
					__UpperCamelCase      :					Optional[int]       		=word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)					)
										__UpperCamelCase      :					Optional[int]       		=char
					return pairs




class 				__A     (			__lowerCAmelCase ):





				"""simple docstring"""







				UpperCamelCase__ :      str			 =VOCAB_FILES_NAMES
				UpperCamelCase__ :      int			 =PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase__ :      Union[str, Any]			 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase__ :      Union[str, Any]			 =['''input_ids''', '''attention_mask''']


				def __init__( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__="replace"							,  lowerCamelCase__="<s>"							,  lowerCamelCase__="</s>"							,  lowerCamelCase__="</s>"							,  lowerCamelCase__="<s>"							,  lowerCamelCase__="<unk>"							,  lowerCamelCase__="<pad>"							,  lowerCamelCase__="<mask>"							,  lowerCamelCase__=False							,  **lowerCamelCase__							,  ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_    ) else bos_token
									__UpperCamelCase      :					int       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_    ) else eos_token
									__UpperCamelCase      :					Optional[int]       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_    ) else sep_token
									__UpperCamelCase      :					List[str]       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_    ) else cls_token
									__UpperCamelCase      :					Tuple       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_    ) else unk_token
									__UpperCamelCase      :					Optional[Any]       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_    ) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									__UpperCamelCase      :					List[str]       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_    ) else mask_token

									super().__init__(
									    errors=lowerCAmelCase_							,  bos_token=lowerCAmelCase_							,  eos_token=lowerCAmelCase_							,  unk_token=lowerCAmelCase_							,  sep_token=lowerCAmelCase_							,  cls_token=lowerCAmelCase_							,  pad_token=lowerCAmelCase_							,  mask_token=lowerCAmelCase_							,  add_prefix_space=lowerCAmelCase_							,  **lowerCAmelCase_							,  )

									with open(lowerCAmelCase_							,  encoding='utf-8'    ) as vocab_handle:
														__UpperCamelCase      :					Tuple       		=json.load(lowerCAmelCase_    )
									__UpperCamelCase      :					Union[str, Any]       		={v: k for k, v in self.encoder.items()}
									__UpperCamelCase      :					Any       		=errors  # how to handle errors in decoding
									__UpperCamelCase      :					List[Any]       		=bytes_to_unicode()
									__UpperCamelCase      :					int       		={v: k for k, v in self.byte_encoder.items()}
									with open(lowerCAmelCase_							,  encoding='utf-8'    ) as merges_handle:
														__UpperCamelCase      :					str       		=merges_handle.read().split('\n'    )[1:-1]
									__UpperCamelCase      :					Optional[Any]       		=[tuple(merge.split()    ) for merge in bpe_merges]
									__UpperCamelCase      :					List[Any]       		=dict(zip(lowerCAmelCase_							,  range(len(lowerCAmelCase_    )    )    )    )
									__UpperCamelCase      :					Optional[int]       		={}
									__UpperCamelCase      :					str       		=add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									__UpperCamelCase      :					Optional[int]       		=re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'    )


				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									return len(self.encoder    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									return dict(self.encoder							,  **self.added_tokens_encoder    )


				def       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									if token in self.cache:
														return self.cache[token]
									__UpperCamelCase      :					List[Any]       		=tuple(lowerCAmelCase_    )
									__UpperCamelCase      :					Tuple       		=get_pairs(lowerCAmelCase_    )

									if not pairs:
														return token

									while True:
														__UpperCamelCase      :					int       		=min(lowerCAmelCase_							,  key=lambda lowerCamelCase__    : self.bpe_ranks.get(lowerCAmelCase_							,  float('inf'    )    )    )
														if bigram not in self.bpe_ranks:
																			break
														__UpperCamelCase		,				__UpperCamelCase      :					List[Any]       		=bigram
														__UpperCamelCase      :					Any       		=[]
														__UpperCamelCase      :					Any       		=0
														while i < len(lowerCAmelCase_    ):
																			try:
																								__UpperCamelCase      :					Optional[Any]       		=word.index(lowerCAmelCase_							,  lowerCAmelCase_    )
																			except ValueError:
																								new_word.extend(word[i:]    )
																								break
																			else:
																								new_word.extend(word[i:j]    )
																								__UpperCamelCase      :					int       		=j

																			if word[i] == first and i < len(lowerCAmelCase_    ) - 1 and word[i + 1] == second:
																								new_word.append(first + second    )
																								i += 2
																			else:
																								new_word.append(word[i]    )
																								i += 1
														__UpperCamelCase      :					int       		=tuple(lowerCAmelCase_    )
														__UpperCamelCase      :					List[Any]       		=new_word
														if len(lowerCAmelCase_    ) == 1:
																			break
														else:
																			__UpperCamelCase      :					Union[str, Any]       		=get_pairs(lowerCAmelCase_    )
									__UpperCamelCase      :					Optional[int]       		=' '.join(lowerCAmelCase_    )
									__UpperCamelCase      :					Union[str, Any]       		=word
									return word


				def       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[Any]       		=[]
									for token in re.findall(self.pat							,  lowerCAmelCase_    ):
														__UpperCamelCase      :					Tuple       		=''.join(
														    self.byte_encoder[b] for b in token.encode('utf-8'    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
														bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase_    ).split(' '    )    )
									return bpe_tokens


				def       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									return self.encoder.get(lowerCAmelCase_							,  self.encoder.get(self.unk_token    )    )


				def       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									return self.decoder.get(lowerCAmelCase_    )


				def       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		=''.join(lowerCAmelCase_    )
									__UpperCamelCase      :					Optional[int]       		=bytearray([self.byte_decoder[c] for c in text]    ).decode('utf-8'							,  errors=self.errors    )
									return text


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__ = None    ):




									"""simple docstring"""

									if not os.path.isdir(lowerCAmelCase_    ):
														logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
														return
									__UpperCamelCase      :					Union[str, Any]       		=os.path.join(
									    lowerCAmelCase_							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
									__UpperCamelCase      :					Dict       		=os.path.join(
									    lowerCAmelCase_							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']    )

									with open(lowerCAmelCase_							,  'w'							,  encoding='utf-8'    ) as f:
														f.write(json.dumps(self.encoder							,  indent=2							,  sort_keys=lowerCAmelCase_							,  ensure_ascii=lowerCAmelCase_    ) + '\n'    )

									__UpperCamelCase      :					Dict       		=0
									with open(lowerCAmelCase_							,  'w'							,  encoding='utf-8'    ) as writer:
														writer.write('#version: 0.2\n'    )
														for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,  key=lambda lowerCamelCase__    : kv[1]    ):
																			if index != token_index:
																								logger.warning(
																								    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																								    ' Please check that the tokenizer is not corrupted!'    )
																								__UpperCamelCase      :					Optional[Any]       		=token_index
																			writer.write(' '.join(lowerCAmelCase_    ) + '\n'    )
																			index += 1

									return vocab_file, merge_file


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__ = None    ):




									"""simple docstring"""

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


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__ = None							,  lowerCamelCase__ = False    ):




									"""simple docstring"""

									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       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__ = None    ):




									"""simple docstring"""

									__UpperCamelCase      :					Union[str, Any]       		=[self.sep_token_id]
									__UpperCamelCase      :					List[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 + sep + token_ids_a + sep    ) * [0]







				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__=False							,  **lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		=kwargs.pop('add_prefix_space'							,  self.add_prefix_space    )
									if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase_    ) > 0 and not text[0].isspace()):
														__UpperCamelCase      :					List[Any]       		=' ' + text
									return (text, kwargs)


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,
        BertTokenizerFast,
        BlipImageProcessor,
        GPTaTokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )



@require_vision
class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  tempfile.mkdtemp()

        _A        =  BlipImageProcessor()
        _A        =  GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""							)
        _A        =  BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)

        _A        =  InstructBlipProcessor(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)

        processor.save_pretrained(self.tmpdirname							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).tokenizer



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Dict:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).image_processor



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Union[str, Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).qformer_tokenizer



 def      UpperCAmelCase							(					self							)  ->					int:
        shutil.rmtree(self.tmpdirname							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  [np.random.randint(2_55		,				size=(3, 30, 4_00)		,				dtype=np.uinta							)]

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

        return image_inputs



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  InstructBlipProcessor(
            tokenizer=self.get_tokenizer()		,				image_processor=self.get_image_processor()		,				qformer_tokenizer=self.get_qformer_tokenizer()		,				)
        processor.save_pretrained(self.tmpdirname							)

        _A        =  self.get_tokenizer(bos_token="""(BOS)"""		,				eos_token="""(EOS)"""							)
        _A        =  self.get_image_processor(do_normalize=lowerCAmelCase_		,				padding_value=1.0							)

        _A        =  InstructBlipProcessor.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_							)
        self.assertIsInstance(processor.qformer_tokenizer		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  self.prepare_image_inputs()

        _A        =  image_processor(lowerCAmelCase_		,				return_tensors="""np"""							)
        _A        =  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							)  ->					Dict:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""

        _A        =  processor(text=lowerCAmelCase_							)

        _A        =  tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)
        _A        =  qformer_tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)

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

        for key in encoded_tokens_qformer.keys():
               self.assertListEqual(encoded_tokens_qformer[key]		,				encoded_processor["""qformer_""" + key]							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""
        _A        =  self.prepare_image_inputs()

        _A        =  processor(text=lowerCAmelCase_		,				images=lowerCAmelCase_							)

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

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



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

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

        _A        =  processor.batch_decode(lowerCAmelCase_							)
        _A        =  tokenizer.batch_decode(lowerCAmelCase_							)

        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""
        _A        =  self.prepare_image_inputs()

        _A        =  processor(text=lowerCAmelCase_		,				images=lowerCAmelCase_							)

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




