

'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


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

lowerCamelCase				:   Dict      				=  {
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}







class 							A__					( A__						):
  A__					=       'blenderbot'
  A__					=       ['past_key_values']
  A__					=       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





  def __init__(	self   :			str							,      _a   :			Optional[int]=8008							,      _a   :			Union[str, Any]=128							,      _a   :			List[str]=2							,      _a   :			Optional[int]=1_0240							,      _a   :			Union[str, Any]=32							,      _a   :			Optional[Any]=24							,      _a   :			Optional[Any]=1_0240							,      _a   :			List[str]=32							,      _a   :			List[str]=0.0							,      _a   :			List[Any]=0.0							,      _a   :			Union[str, Any]=True							,      _a   :			List[Any]=True							,      _a   :			Optional[int]="gelu"							,      _a   :			Optional[Any]=2560							,      _a   :			Tuple=0.1							,      _a   :			Optional[int]=0.0							,      _a   :			Tuple=0.0							,      _a   :			Dict=0.02							,      _a   :			Tuple=1							,      _a   :			str=False							,      _a   :			Union[str, Any]=0							,      _a   :			str=1							,      _a   :			List[str]=2							,      _a   :			str=3							,      _a   :			Union[str, Any]=2							,      **_a   :			Dict							,      )			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=vocab_size
       _SCREAMING_SNAKE_CASE												=max_position_embeddings
       _SCREAMING_SNAKE_CASE												=d_model
       _SCREAMING_SNAKE_CASE												=encoder_ffn_dim
       _SCREAMING_SNAKE_CASE												=encoder_layers
       _SCREAMING_SNAKE_CASE												=encoder_attention_heads
       _SCREAMING_SNAKE_CASE												=decoder_ffn_dim
       _SCREAMING_SNAKE_CASE												=decoder_layers
       _SCREAMING_SNAKE_CASE												=decoder_attention_heads
       _SCREAMING_SNAKE_CASE												=dropout
       _SCREAMING_SNAKE_CASE												=attention_dropout
       _SCREAMING_SNAKE_CASE												=activation_dropout
       _SCREAMING_SNAKE_CASE												=activation_function
       _SCREAMING_SNAKE_CASE												=init_std
       _SCREAMING_SNAKE_CASE												=encoder_layerdrop
       _SCREAMING_SNAKE_CASE												=decoder_layerdrop
       _SCREAMING_SNAKE_CASE												=use_cache
       _SCREAMING_SNAKE_CASE												=encoder_layers
       _SCREAMING_SNAKE_CASE												=scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           pad_token_id=_a							,      bos_token_id=_a							,      eos_token_id=_a							,      is_encoder_decoder=_a							,      decoder_start_token_id=_a							,      encoder_no_repeat_ngram_size=_a							,      forced_eos_token_id=_a							,      **_a							,      )







class 							A__					( A__						):





  @property
  def 		A						(	self   :			Tuple				)			->					Mapping[str, Mapping[int, str]]:





       '''simple docstring'''






       if self.task in ["default", "seq2seq-lm"]:
            _SCREAMING_SNAKE_CASE												=OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]				)
            if self.use_past:
                 _SCREAMING_SNAKE_CASE												={0: 'batch'}
                 _SCREAMING_SNAKE_CASE												={0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                 _SCREAMING_SNAKE_CASE												={0: 'batch', 1: 'decoder_sequence'}
                 _SCREAMING_SNAKE_CASE												={0: 'batch', 1: 'decoder_sequence'}
            if self.use_past:
                 self.fill_with_past_key_values_(_a							,      direction='inputs'				)
       elif self.task == "causal-lm":
            _SCREAMING_SNAKE_CASE												=OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]				)
            if self.use_past:
                 _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=self.num_layers
                 for i in range(_a				):
                      _SCREAMING_SNAKE_CASE												={0: 'batch', 2: 'past_sequence + sequence'}
                      _SCREAMING_SNAKE_CASE												={0: 'batch', 2: 'past_sequence + sequence'}
       else:
            _SCREAMING_SNAKE_CASE												=OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                    ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                    ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                ]				)

       return common_inputs





  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
  def 		A						(	self   :			Tuple				)			->					Mapping[str, Mapping[int, str]]:





       '''simple docstring'''






       if self.task in ["default", "seq2seq-lm"]:
            _SCREAMING_SNAKE_CASE												=super().outputs
       else:
            _SCREAMING_SNAKE_CASE												=super(_a							,      self				).outputs
            if self.use_past:
                 _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=self.num_layers
                 for i in range(_a				):
                      _SCREAMING_SNAKE_CASE												={0: 'batch', 2: 'past_sequence + sequence'}
                      _SCREAMING_SNAKE_CASE												={0: 'batch', 2: 'past_sequence + sequence'}
       return common_outputs





  def 		A						(	self   :			int							,      _a   :			PreTrainedTokenizer							,      _a   :			int = -1							,      _a   :			int = -1							,      _a   :			bool = False							,      _a   :			Optional[TensorType] = None							,      )			->					Mapping[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _a							,      _a							,      _a							,      _a							,      _a				)
       # Generate decoder inputs
       _SCREAMING_SNAKE_CASE												=seq_length if not self.use_past else 1
       _SCREAMING_SNAKE_CASE												=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _a							,      _a							,      _a							,      _a							,      _a				)
       _SCREAMING_SNAKE_CASE												={f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
       _SCREAMING_SNAKE_CASE												=dict(**_a							,      **_a				)

       if self.use_past:
            if not is_torch_available():
                 raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'				)
            else:
                 import torch
            _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=common_inputs['input_ids'].shape
            _SCREAMING_SNAKE_CASE												=common_inputs['decoder_input_ids'].shape[1]
            _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=self.num_attention_heads
            _SCREAMING_SNAKE_CASE												=(
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            _SCREAMING_SNAKE_CASE												=decoder_seq_length
            _SCREAMING_SNAKE_CASE												=(
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )
            _SCREAMING_SNAKE_CASE												=torch.cat(
                [common_inputs['decoder_attention_mask'], torch.ones(_a							,      _a				)]							,      dim=1				)
            _SCREAMING_SNAKE_CASE												=[]
            _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=self.num_layers

            for _ in range(_a				):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(_a				),
                         torch.zeros(_a				),
                         torch.zeros(_a				),
                         torch.zeros(_a				),
                     )				)
       return common_inputs





  def 		A						(	self   :			Dict							,      _a   :			PreTrainedTokenizer							,      _a   :			int = -1							,      _a   :			int = -1							,      _a   :			bool = False							,      _a   :			Optional[TensorType] = None							,      )			->					Mapping[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _a							,      _a							,      _a							,      _a							,      _a				)

       if self.use_past:
            if not is_torch_available():
                 raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'				)
            else:
                 import torch
            _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=common_inputs['input_ids'].shape
            _SCREAMING_SNAKE_CASE												=seqlen
            _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=self.num_layers
            _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=self.num_attention_heads
            _SCREAMING_SNAKE_CASE												=(
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            _SCREAMING_SNAKE_CASE												=common_inputs['attention_mask'].dtype
            _SCREAMING_SNAKE_CASE												=torch.cat(
                [common_inputs['attention_mask'], torch.ones(_a							,      _a							,      dtype=_a				)]							,      dim=1				)
            _SCREAMING_SNAKE_CASE												=[
                (torch.zeros(_a				), torch.zeros(_a				)) for _ in range(_a				)
            ]
       return common_inputs





  def 		A						(	self   :			Optional[int]							,      _a   :			PreTrainedTokenizer							,      _a   :			int = -1							,      _a   :			int = -1							,      _a   :			bool = False							,      _a   :			Optional[TensorType] = None							,      )			->					Mapping[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=compute_effective_axis_dimension(
           _a							,      fixed_dimension=OnnxConfig.default_fixed_batch							,      num_token_to_add=0				)

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       _SCREAMING_SNAKE_CASE												=tokenizer.num_special_tokens_to_add(_a				)
       _SCREAMING_SNAKE_CASE												=compute_effective_axis_dimension(
           _a							,      fixed_dimension=OnnxConfig.default_fixed_sequence							,      num_token_to_add=_a				)

       # Generate dummy inputs according to compute batch and sequence
       _SCREAMING_SNAKE_CASE												=[' '.join([tokenizer.unk_token]				) * seq_length] * batch_size
       _SCREAMING_SNAKE_CASE												=dict(tokenizer(_a							,      return_tensors=_a				)				)
       return common_inputs





  def 		A						(	self   :			Optional[Any]							,      _a   :			PreTrainedTokenizer							,      _a   :			int = -1							,      _a   :			int = -1							,      _a   :			bool = False							,      _a   :			Optional[TensorType] = None							,      )			->					Mapping[str, Any]:





       '''simple docstring'''






       if self.task in ["default", "seq2seq-lm"]:
            _SCREAMING_SNAKE_CASE												=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                _a							,      batch_size=_a							,      seq_length=_a							,      is_pair=_a							,      framework=_a				)

       elif self.task == "causal-lm":
            _SCREAMING_SNAKE_CASE												=self._generate_dummy_inputs_for_causal_lm(
                _a							,      batch_size=_a							,      seq_length=_a							,      is_pair=_a							,      framework=_a				)
       else:
            _SCREAMING_SNAKE_CASE												=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _a							,      batch_size=_a							,      seq_length=_a							,      is_pair=_a							,      framework=_a				)

       return common_inputs





  def 		A						(	self   :			Optional[Any]							,      _a   :			int							,      _a   :			Tuple							,      _a   :			str							,      _a   :			int				)			->					List[Any]:





       '''simple docstring'''






       if self.task in ["default", "seq2seq-lm"]:
            _SCREAMING_SNAKE_CASE												=super()._flatten_past_key_values_(_a							,      _a							,      _a							,      _a				)
       else:
            _SCREAMING_SNAKE_CASE												=super(_a							,      self				)._flatten_past_key_values_(
                _a							,      _a							,      _a							,      _a				)





  def 		A						(	self   :			Dict							,      _a   :			Mapping[str, Mapping[int, str]]							,      _a   :			str				)			->					List[str]:





       '''simple docstring'''






       if direction not in ["inputs", "outputs"]:
            raise ValueError(f"direction must either be \"inputs\" or \"outputs\", but {direction} was given"				)

       _SCREAMING_SNAKE_CASE												='past_key_values' if direction == 'inputs' else 'present'
       _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=self.num_layers

       _SCREAMING_SNAKE_CASE												='past_encoder_sequence'
       _SCREAMING_SNAKE_CASE												='past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

       for i in range(_a				):
            _SCREAMING_SNAKE_CASE												={0: 'batch', 2: decoder_sequence}
            _SCREAMING_SNAKE_CASE												={0: 'batch', 2: decoder_sequence}
            _SCREAMING_SNAKE_CASE												={0: 'batch', 2: encoder_sequence}
            _SCREAMING_SNAKE_CASE												={0: 'batch', 2: encoder_sequence}



'''simple docstring'''
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:
     lowerCamelCase				:   Optional[Any]      				=  False


if is_vision_available():
     from PIL import Image
else:







     class 							A__					:





       @staticmethod
       def 		A						(	*_a   :			Any							,      **_a   :			str				)			->					Optional[int]:





            '''simple docstring'''






            pass







@is_pipeline_test
@require_vision
class 							A__					( unittest.TestCase						):
  A__					=       MODEL_FOR_VISION_2_SEQ_MAPPING
  A__					=       TF_MODEL_FOR_VISION_2_SEQ_MAPPING





  def 		A						(	self   :			List[str]							,      _a   :			List[Any]							,      _a   :			Any							,      _a   :			Dict				)			->					Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model=_a							,      tokenizer=_a							,      image_processor=_a				)
       _SCREAMING_SNAKE_CASE												=[
           Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				),
           './tests/fixtures/tests_samples/COCO/000000039769.png',
       ]
       return pipe, examples





  def 		A						(	self   :			str							,      _a   :			int							,      _a   :			Tuple				)			->					Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(
           _a							,      [
               [{'generated_text': ANY(_a				)}],
               [{'generated_text': ANY(_a				)}],
           ]							,      )





  @require_tf
  def 		A						(	self   :			Any				)			->					Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='hf-internal-testing/tiny-random-vit-gpt2'							,      framework='tf'				)
       _SCREAMING_SNAKE_CASE												='./tests/fixtures/tests_samples/COCO/000000039769.png'

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(
           _a							,      [
               {
                   'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
               },
           ]							,      )

       _SCREAMING_SNAKE_CASE												=pipe([image, image]				)
       self.assertEqual(
           _a							,      [
               [
                   {
                       'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                   }
               ],
               [
                   {
                       'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                   }
               ],
           ]							,      )

       _SCREAMING_SNAKE_CASE												=pipe(_a							,      max_new_tokens=1				)
       self.assertEqual(
           _a							,      [{'generated_text': 'growth'}]							,      )





  @require_torch
  def 		A						(	self   :			List[str]				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='hf-internal-testing/tiny-random-vit-gpt2'				)
       _SCREAMING_SNAKE_CASE												='./tests/fixtures/tests_samples/COCO/000000039769.png'

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(
           _a							,      [
               {
                   'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
               },
           ]							,      )

       _SCREAMING_SNAKE_CASE												=pipe([image, image]				)
       self.assertEqual(
           _a							,      [
               [
                   {
                       'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                   }
               ],
               [
                   {
                       'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                   }
               ],
           ]							,      )





  @require_torch
  def 		A						(	self   :			Union[str, Any]				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='hf-internal-testing/tiny-random-BlipForConditionalGeneration'				)
       _SCREAMING_SNAKE_CASE												='./tests/fixtures/tests_samples/COCO/000000039769.png'
       _SCREAMING_SNAKE_CASE												='a photo of'

       _SCREAMING_SNAKE_CASE												=pipe(_a							,      prompt=_a				)
       self.assertTrue(outputs[0]['generated_text'].startswith(_a				)				)





  @slow
  @require_torch
  def 		A						(	self   :			Any				)			->					List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='ydshieh/vit-gpt2-coco-en'				)
       _SCREAMING_SNAKE_CASE												='./tests/fixtures/tests_samples/COCO/000000039769.png'

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]				)

       _SCREAMING_SNAKE_CASE												=pipe([image, image]				)
       self.assertEqual(
           _a							,      [
               [{'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]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='Salesforce/blip-image-captioning-base'				)
       _SCREAMING_SNAKE_CASE												='https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
       _SCREAMING_SNAKE_CASE												=Image.open(requests.get(_a							,      stream=_a				).raw				)

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a pink pokemon pokemon with a blue shirt and a blue shirt'}]				)





  @slow
  @require_torch
  def 		A						(	self   :			str				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='microsoft/git-base-coco'				)
       _SCREAMING_SNAKE_CASE												='https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
       _SCREAMING_SNAKE_CASE												=Image.open(requests.get(_a							,      stream=_a				).raw				)

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a cartoon of a purple character.'}]				)





  @slow
  @require_torch
  def 		A						(	self   :			Any				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='Salesforce/blip-image-captioning-base'				)
       _SCREAMING_SNAKE_CASE												='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
       _SCREAMING_SNAKE_CASE												=Image.open(requests.get(_a							,      stream=_a				).raw				)

       _SCREAMING_SNAKE_CASE												='a photography of'

       _SCREAMING_SNAKE_CASE												=pipe(_a							,      prompt=_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a photography of a volcano'}]				)

       with self.assertRaises(_a				):
            _SCREAMING_SNAKE_CASE												=pipe([image, image]							,      prompt=[prompt, prompt]				)





  @slow
  @require_torch
  def 		A						(	self   :			str				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='microsoft/git-base-coco'				)
       _SCREAMING_SNAKE_CASE												='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
       _SCREAMING_SNAKE_CASE												=Image.open(requests.get(_a							,      stream=_a				).raw				)

       _SCREAMING_SNAKE_CASE												='a photo of a'

       _SCREAMING_SNAKE_CASE												=pipe(_a							,      prompt=_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a photo of a tent with a tent and a tent in the background.'}]				)

       with self.assertRaises(_a				):
            _SCREAMING_SNAKE_CASE												=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   :			List[Any]				)			->					Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='google/pix2struct-ai2d-base'				)
       _SCREAMING_SNAKE_CASE												='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
       _SCREAMING_SNAKE_CASE												=Image.open(requests.get(_a							,      stream=_a				).raw				)

       _SCREAMING_SNAKE_CASE												='What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'

       _SCREAMING_SNAKE_CASE												=pipe(_a							,      prompt=_a				)
       self.assertEqual(_a							,      [{'generated_text': 'ash cloud'}]				)

       with self.assertRaises(_a				):
            _SCREAMING_SNAKE_CASE												=pipe([image, image]							,      prompt=[prompt, prompt]				)





  @slow
  @require_tf
  def 		A						(	self   :			List[str]				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='ydshieh/vit-gpt2-coco-en'							,      framework='tf'				)
       _SCREAMING_SNAKE_CASE												='./tests/fixtures/tests_samples/COCO/000000039769.png'

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]				)

       _SCREAMING_SNAKE_CASE												=pipe([image, image]				)
       self.assertEqual(
           _a							,      [
               [{'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 '}],
           ]							,      )
