code: 
  
  
  
  
  
  from typing import Dict, List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  _lowercase		:     int 					=logging.get_logger(__name__)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  					_a			:	List[Any]							=				['input_ids', 'attention_mask']
  
  
  
  					def __init__(		self    :    Dict					,			lowerCamelCase    :    Dict="[PAD]"					,			lowerCamelCase    :    List[str]="[BOS]"					,			lowerCamelCase    :    str="[EOS]"					,			lowerCamelCase    :    Tuple="[MASK]"					,			lowerCamelCase    :    List[Any]="[CLS]"					,			lowerCamelCase    :    Optional[int]="[SEP]"					,			lowerCamelCase    :    List[str]=20_48					,			**lowerCamelCase    :    str					,			):
  							lowerCamelCase_  :    int   	=			AddedToken(lowerCamelCase					,			lstrip=lowerCamelCase					,			rstrip=lowerCamelCase	) if isinstance(lowerCamelCase					,			lowerCamelCase	) else pad_token
  							lowerCamelCase_  :    int   	=			AddedToken(lowerCamelCase					,			lstrip=lowerCamelCase					,			rstrip=lowerCamelCase	) if isinstance(lowerCamelCase					,			lowerCamelCase	) else bos_token
  							lowerCamelCase_  :    Tuple   	=			AddedToken(lowerCamelCase					,			lstrip=lowerCamelCase					,			rstrip=lowerCamelCase	) if isinstance(lowerCamelCase					,			lowerCamelCase	) else eos_token
  							lowerCamelCase_  :    List[Any]   	=			AddedToken(lowerCamelCase					,			lstrip=lowerCamelCase					,			rstrip=lowerCamelCase	) if isinstance(lowerCamelCase					,			lowerCamelCase	) else mask_token
  							lowerCamelCase_  :    Optional[Any]   	=			AddedToken(lowerCamelCase					,			lstrip=lowerCamelCase					,			rstrip=lowerCamelCase	) if isinstance(lowerCamelCase					,			lowerCamelCase	) else cls_token
  							lowerCamelCase_  :    List[Any]   	=			AddedToken(lowerCamelCase					,			lstrip=lowerCamelCase					,			rstrip=lowerCamelCase	) if isinstance(lowerCamelCase					,			lowerCamelCase	) else sep_token
  
  							super().__init__(
  							    pad_token=lowerCamelCase					,			bos_token=lowerCamelCase					,			eos_token=lowerCamelCase					,			mask_token=lowerCamelCase					,			cls_token=lowerCamelCase					,			sep_token=lowerCamelCase					,			model_max_length=lowerCamelCase					,			**lowerCamelCase					,			)
  
  							lowerCamelCase_  :    str   	=			2**8  # utf is 8 bits
  
  							# define special tokens dict
  							lowerCamelCase_  :    Dict[str, int]   	=			{
  							    self.pad_token: 0,
  							    self.bos_token: 1,
  							    self.eos_token: 2,
  							    self.mask_token: 3,
  							    self.cls_token: 4,
  							    self.sep_token: 5,
  							}
  							lowerCamelCase_  :    Optional[int]   	=			len(self.special_tokens_encoder	)
  							lowerCamelCase_  :    Dict[int, str]   	=			{v: k for k, v in self.special_tokens_encoder.items()}
  
  
  
  					def      __a    (		self    :    List[str]	):
  							lowerCamelCase_  :    Dict   	=			self.special_tokens_encoder.copy()
  							vocab.update(self.added_tokens_encoder	)
  							for i in range(self._utf_vocab_size	):
  									lowerCamelCase_  :    Tuple   	=			chr(lowerCamelCase	)
  									lowerCamelCase_  :    List[str]   	=			i + len(self.special_tokens_encoder	)
  							return vocab
  
  
  
  					@property
  					def      __a    (		self    :    Optional[Any]	):
  							return self._utf_vocab_size + self._num_special_tokens
  
  
  
  					def      __a    (		self    :    Tuple					,			lowerCamelCase    :    List[int]					,			lowerCamelCase    :    Optional[List[int]] = None					,			lowerCamelCase    :    bool = False	):
  							if already_has_special_tokens:
  									return super().get_special_tokens_mask(
  									    token_ids_a=lowerCamelCase					,			token_ids_a=lowerCamelCase					,			already_has_special_tokens=lowerCamelCase	)
  
  							# normal case: some special tokens
  							if token_ids_a is None:
  									return [1] + [0] * len(lowerCamelCase	) + [1]
  							return [1] + ([0] * len(lowerCamelCase	)) + [1] + ([0] * len(lowerCamelCase	)) + [1]
  
  
  
  					def      __a    (		self    :    Union[str, Any]					,			lowerCamelCase    :    List[int]					,			lowerCamelCase    :    Optional[List[int]] = None	):
  							if token_ids_a is None:
  									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  							else:
  									return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
  
  
  
  					def      __a    (		self    :    Optional[int]					,			lowerCamelCase    :    str	):
  							lowerCamelCase_  :    Any   	=			[chr(lowerCamelCase	) for i in text.encode('utf-8'	)]
  							return tokens
  
  
  
  					def      __a    (		self    :    List[Any]					,			lowerCamelCase    :    Tuple	):
  							if token in self.special_tokens_encoder:
  									lowerCamelCase_  :    List[str]   	=			self.special_tokens_encoder[token]
  							elif token in self.added_tokens_encoder:
  									lowerCamelCase_  :    Tuple   	=			self.added_tokens_encoder[token]
  							elif len(lowerCamelCase	) != 1:
  									lowerCamelCase_  :    str   	=			self.unk_token_id
  							else:
  									lowerCamelCase_  :    Tuple   	=			ord(lowerCamelCase	) + self._num_special_tokens
  							return token_id
  
  
  
  					def      __a    (		self    :    Tuple					,			lowerCamelCase    :    Any	):
  							if index in self.special_tokens_decoder:
  									lowerCamelCase_  :    Any   	=			self.special_tokens_decoder[index]
  							elif index in self.added_tokens_decoder:
  									lowerCamelCase_  :    List[Any]   	=			self.added_tokens_decoder[index]
  							else:
  									lowerCamelCase_  :    Union[str, Any]   	=			chr(index - self._num_special_tokens	)
  							return token
  
  
  
  					def      __a    (		self    :    Optional[Any]					,			lowerCamelCase    :    Any	):
  							lowerCamelCase_  :    Union[str, Any]   	=			b''
  							for token in tokens:
  									if token in self.special_tokens_decoder:
  											lowerCamelCase_  :    Optional[int]   	=			self.special_tokens_decoder[token].encode('utf-8'	)
  									elif token in self.added_tokens_decoder:
  											lowerCamelCase_  :    Any   	=			self.special_tokens_decoder[token].encode('utf-8'	)
  									elif token in self.special_tokens_encoder:
  											lowerCamelCase_  :    Optional[Any]   	=			token.encode('utf-8'	)
  									elif token in self.added_tokens_encoder:
  											lowerCamelCase_  :    str   	=			token.encode('utf-8'	)
  									else:
  											lowerCamelCase_  :    Tuple   	=			bytes([ord(lowerCamelCase	)]	)
  									bstring += tok_string
  							lowerCamelCase_  :    str   	=			bstring.decode('utf-8'					,			errors='replace'	)
  							return string
  
  
  
  
  
  					def      __a    (		self    :    int					,			lowerCamelCase    :    str					,			lowerCamelCase    :    Optional[str] = None	):
  							return ()
  
style_context: 
  
  
  
  
  
  import unittest
  
  import requests
  
  from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
  from transformers.pipelines import pipeline
  from transformers.testing_utils import (
      is_pipeline_test,
      is_torch_available,
      require_tf,
      require_torch,
      require_vision,
      slow,
  )
  
  from .test_pipelines_common import ANY
  
  
  if is_torch_available():
  		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
  else:
  		_lowercase		:     Dict 					=False
  
  
  if is_vision_available():
  		from PIL import Image
  else:
  
  
  
  
  
  
  		class 	UpperCamelCase_     :
  
  
  
  							@staticmethod
  							def      __a    (		*lowerCamelCase    :    Any					,			**lowerCamelCase    :    List[Any]	):
  									pass
  
  
  
  
  
  
  @is_pipeline_test
  @require_vision
  class 	UpperCamelCase_     (     unittest.TestCase       ):
  					_a			:	int							=				MODEL_FOR_VISION_2_SEQ_MAPPING
  					_a			:	int							=				TF_MODEL_FOR_VISION_2_SEQ_MAPPING
  
  
  
  					def      __a    (		self    :    int					,			lowerCamelCase    :    Union[str, Any]					,			lowerCamelCase    :    Optional[int]					,			lowerCamelCase    :    Union[str, Any]	):
  							lowerCamelCase_  :    List[str]   	=			pipeline('image-to-text'					,			model=lowerCamelCase					,			tokenizer=lowerCamelCase					,			image_processor=lowerCamelCase	)
  							lowerCamelCase_  :    str   	=			[
  							    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	),
  							    './tests/fixtures/tests_samples/COCO/000000039769.png',
  							]
  							return pipe, examples
  
  
  
  					def      __a    (		self    :    List[Any]					,			lowerCamelCase    :    Any					,			lowerCamelCase    :    List[str]	):
  							lowerCamelCase_  :    str   	=			pipe(lowerCamelCase	)
  							self.assertEqual(
  							    lowerCamelCase					,			[
  							        [{'generated_text': ANY(lowerCamelCase	)}],
  							        [{'generated_text': ANY(lowerCamelCase	)}],
  							    ]					,			)
  
  
  
  					@require_tf
  					def      __a    (		self    :    Union[str, Any]	):
  							lowerCamelCase_  :    Tuple   	=			pipeline('image-to-text'					,			model='hf-internal-testing/tiny-random-vit-gpt2'					,			framework='tf'	)
  							lowerCamelCase_  :    Optional[int]   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'
  
  							lowerCamelCase_  :    Optional[int]   	=			pipe(lowerCamelCase	)
  							self.assertEqual(
  							    lowerCamelCase					,			[
  							        {
  							            'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
  							        },
  							    ]					,			)
  
  							lowerCamelCase_  :    List[Any]   	=			pipe([image, image]	)
  							self.assertEqual(
  							    lowerCamelCase					,			[
  							        [
  							            {
  							                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
  							            }
  							        ],
  							        [
  							            {
  							                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
  							            }
  							        ],
  							    ]					,			)
  
  							lowerCamelCase_  :    str   	=			pipe(lowerCamelCase					,			max_new_tokens=1	)
  							self.assertEqual(
  							    lowerCamelCase					,			[{'generated_text': 'growth'}]					,			)
  
  
  
  					@require_torch
  					def      __a    (		self    :    Tuple	):
  							lowerCamelCase_  :    Union[str, Any]   	=			pipeline('image-to-text'					,			model='hf-internal-testing/tiny-random-vit-gpt2'	)
  							lowerCamelCase_  :    Any   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'
  
  							lowerCamelCase_  :    int   	=			pipe(lowerCamelCase	)
  							self.assertEqual(
  							    lowerCamelCase					,			[
  							        {
  							            'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
  							        },
  							    ]					,			)
  
  							lowerCamelCase_  :    Tuple   	=			pipe([image, image]	)
  							self.assertEqual(
  							    lowerCamelCase					,			[
  							        [
  							            {
  							                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
  							            }
  							        ],
  							        [
  							            {
  							                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
  							            }
  							        ],
  							    ]					,			)
  
  
  
  					@require_torch
  					def      __a    (		self    :    Dict	):
  							lowerCamelCase_  :    Any   	=			pipeline('image-to-text'					,			model='hf-internal-testing/tiny-random-BlipForConditionalGeneration'	)
  							lowerCamelCase_  :    List[Any]   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'
  							lowerCamelCase_  :    List[Any]   	=			'a photo of'
  
  							lowerCamelCase_  :    Dict   	=			pipe(lowerCamelCase					,			prompt=lowerCamelCase	)
  							self.assertTrue(outputs[0]['generated_text'].startswith(lowerCamelCase	)	)
  
  
  
  					@slow
  					@require_torch
  					def      __a    (		self    :    Union[str, Any]	):
  							lowerCamelCase_  :    List[str]   	=			pipeline('image-to-text'					,			model='ydshieh/vit-gpt2-coco-en'	)
  							lowerCamelCase_  :    Any   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'
  
  							lowerCamelCase_  :    str   	=			pipe(lowerCamelCase	)
  							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]	)
  
  							lowerCamelCase_  :    Union[str, Any]   	=			pipe([image, image]	)
  							self.assertEqual(
  							    lowerCamelCase					,			[
  							        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
  							        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
  							    ]					,			)
  
  
  
  					@slow
  					@require_torch
  					def      __a    (		self    :    List[Any]	):
  							lowerCamelCase_  :    int   	=			pipeline('image-to-text'					,			model='Salesforce/blip-image-captioning-base'	)
  							lowerCamelCase_  :    Tuple   	=			'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
  							lowerCamelCase_  :    List[Any]   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)
  
  							lowerCamelCase_  :    Optional[Any]   	=			pipe(lowerCamelCase	)
  							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a pink pokemon pokemon with a blue shirt and a blue shirt'}]	)
  
  
  
  					@slow
  					@require_torch
  					def      __a    (		self    :    List[str]	):
  							lowerCamelCase_  :    Optional[Any]   	=			pipeline('image-to-text'					,			model='microsoft/git-base-coco'	)
  							lowerCamelCase_  :    str   	=			'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
  							lowerCamelCase_  :    Dict   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)
  
  							lowerCamelCase_  :    Optional[int]   	=			pipe(lowerCamelCase	)
  							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a cartoon of a purple character.'}]	)
  
  
  
  					@slow
  					@require_torch
  					def      __a    (		self    :    List[str]	):
  							lowerCamelCase_  :    List[Any]   	=			pipeline('image-to-text'					,			model='Salesforce/blip-image-captioning-base'	)
  							lowerCamelCase_  :    int   	=			'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
  							lowerCamelCase_  :    List[str]   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)
  
  							lowerCamelCase_  :    int   	=			'a photography of'
  
  							lowerCamelCase_  :    Tuple   	=			pipe(lowerCamelCase					,			prompt=lowerCamelCase	)
  							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a photography of a volcano'}]	)
  
  							with self.assertRaises(lowerCamelCase	):
  									lowerCamelCase_  :    Union[str, Any]   	=			pipe([image, image]					,			prompt=[prompt, prompt]	)
  
  
  
  					@slow
  					@require_torch
  					def      __a    (		self    :    int	):
  							lowerCamelCase_  :    Optional[int]   	=			pipeline('image-to-text'					,			model='microsoft/git-base-coco'	)
  							lowerCamelCase_  :    int   	=			'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
  							lowerCamelCase_  :    Optional[Any]   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)
  
  							lowerCamelCase_  :    Optional[int]   	=			'a photo of a'
  
  							lowerCamelCase_  :    Optional[int]   	=			pipe(lowerCamelCase					,			prompt=lowerCamelCase	)
  							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a photo of a tent with a tent and a tent in the background.'}]	)
  
  							with self.assertRaises(lowerCamelCase	):
  									lowerCamelCase_  :    List[Any]   	=			pipe([image, image]					,			prompt=[prompt, prompt]	)
  
  
  
  					@unittest.skipIf(
  					    not is_torch_greater_or_equal_than_1_11					,			reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'	)
  					@slow
  					@require_torch
  					def      __a    (		self    :    Any	):
  							lowerCamelCase_  :    Union[str, Any]   	=			pipeline('image-to-text'					,			model='google/pix2struct-ai2d-base'	)
  							lowerCamelCase_  :    List[str]   	=			'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
  							lowerCamelCase_  :    Tuple   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)
  
  							lowerCamelCase_  :    str   	=			'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'
  
  							lowerCamelCase_  :    Union[str, Any]   	=			pipe(lowerCamelCase					,			prompt=lowerCamelCase	)
  							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'ash cloud'}]	)
  
  							with self.assertRaises(lowerCamelCase	):
  									lowerCamelCase_  :    Optional[Any]   	=			pipe([image, image]					,			prompt=[prompt, prompt]	)
  
  
  
  
  
  					@slow
  					@require_tf
  					def      __a    (		self    :    Dict	):
  							lowerCamelCase_  :    List[str]   	=			pipeline('image-to-text'					,			model='ydshieh/vit-gpt2-coco-en'					,			framework='tf'	)
  							lowerCamelCase_  :    Dict   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'
  
  							lowerCamelCase_  :    Optional[Any]   	=			pipe(lowerCamelCase	)
  							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]	)
  
  							lowerCamelCase_  :    List[Any]   	=			pipe([image, image]	)
  							self.assertEqual(
  							    lowerCamelCase					,			[
  							        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
  							        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
  							    ]					,			)
  