'''simple docstring'''

import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						import PIL

						from transformers import FlavaImageProcessor
						from transformers.image_utils import PILImageResampling
						from transformers.models.flava.image_processing_flava import (
						    FLAVA_CODEBOOK_MEAN,
						    FLAVA_CODEBOOK_STD,
						    FLAVA_IMAGE_MEAN,
						    FLAVA_IMAGE_STD,
						)
else:
						A_	   = A_	   = A_	   = A_	   = None




class 		SCREAMING_SNAKE_CASE_					(unittest.TestCase      ):



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_=7				,			snake_case_=3				,			snake_case_=30				,			snake_case_=400				,			snake_case_=True				,			snake_case_=None				,			snake_case_=True				,			snake_case_=None				,			snake_case_=None				,			snake_case_=True				,			snake_case_=1 / 255				,			snake_case_=True				,			snake_case_=FLAVA_IMAGE_MEAN				,			snake_case_=FLAVA_IMAGE_STD				,			snake_case_=14				,			snake_case_=75				,			snake_case_=None				,			snake_case_=16				,			snake_case_=0.3				,			snake_case_=None				,			snake_case_=True				,			snake_case_=None				,			snake_case_=None				,			snake_case_=True				,			snake_case_=None				,			snake_case_=True				,			snake_case_=True				,			snake_case_=FLAVA_CODEBOOK_MEAN				,			snake_case_=FLAVA_CODEBOOK_STD				,			):
									snake_case :       Optional[int]				=    size if size is not None else {"height": 224, "width": 224}
									snake_case :       int				=    crop_size if crop_size is not None else {"height": 224, "width": 224}
									snake_case :       Union[str, Any]				=    codebook_size if codebook_size is not None else {"height": 112, "width": 112}
									snake_case :       Optional[int]				=    codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}

									snake_case :       int				=    parent
									snake_case :       Any				=    batch_size
									snake_case :       Tuple				=    num_channels
									snake_case :       Any				=    do_resize
									snake_case :       Optional[Any]				=    do_rescale
									snake_case :       Optional[Any]				=    rescale_factor
									snake_case :       Union[str, Any]				=    min_resolution
									snake_case :       Dict				=    max_resolution
									snake_case :       Dict				=    size
									snake_case :       Union[str, Any]				=    resample if resample is not None else PILImageResampling.BICUBIC
									snake_case :       List[str]				=    do_normalize
									snake_case :       List[str]				=    image_mean
									snake_case :       Tuple				=    image_std
									snake_case :       Any				=    do_center_crop
									snake_case :       List[Any]				=    crop_size

									snake_case :       List[str]				=    input_size_patches
									snake_case :       List[Any]				=    total_mask_patches
									snake_case :       Any				=    mask_group_max_patches
									snake_case :       List[str]				=    mask_group_min_patches
									snake_case :       Optional[Any]				=    mask_group_min_aspect_ratio
									snake_case :       Optional[Any]				=    mask_group_max_aspect_ratio

									snake_case :       List[Any]				=    codebook_do_resize
									snake_case :       Optional[int]				=    codebook_size
									snake_case :       str				=    codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
									snake_case :       Dict				=    codebook_do_center_crop
									snake_case :       Any				=    codebook_crop_size
									snake_case :       Dict				=    codebook_do_map_pixels
									snake_case :       str				=    codebook_do_normalize
									snake_case :       List[Any]				=    codebook_image_mean
									snake_case :       int				=    codebook_image_std



			def   lowerCamelCase (							self					):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "resample": self.resample,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "input_size_patches": self.input_size_patches,
									    "total_mask_patches": self.total_mask_patches,
									    "mask_group_max_patches": self.mask_group_max_patches,
									    "mask_group_min_patches": self.mask_group_min_patches,
									    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
									    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
									    "codebook_do_resize": self.codebook_do_resize,
									    "codebook_size": self.codebook_size,
									    "codebook_resample": self.codebook_resample,
									    "codebook_do_center_crop": self.codebook_do_center_crop,
									    "codebook_crop_size": self.codebook_crop_size,
									    "codebook_do_map_pixels": self.codebook_do_map_pixels,
									    "codebook_do_normalize": self.codebook_do_normalize,
									    "codebook_image_mean": self.codebook_image_mean,
									    "codebook_image_std": self.codebook_image_std,
									}



			def   lowerCamelCase (							self					):
									return (self.size["height"], self.size["width"])



			def   lowerCamelCase (							self					):
									return (
									    (self.input_size_patches, self.input_size_patches)
									    if not isinstance(self.input_size_patches				,			snake_case_					)
									    else self.input_size_patches
									)






			def   lowerCamelCase (							self					):
									return (self.codebook_size["height"], self.codebook_size["width"])







@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase							,				unittest.TestCase      ):



			"""simple docstring"""






			__magic_name__ :				List[Any]   							=       FlavaImageProcessor if is_vision_available() else None
			__magic_name__ :				List[Any]   							=       None



			def   lowerCamelCase (							self					):
									snake_case :       Optional[int]				=    FlavaImageProcessingTester(self					)



			@property
			def   lowerCamelCase (							self					):
									return self.image_processor_tester.prepare_image_processor_dict()



			def   lowerCamelCase (							self					):
									snake_case :       str				=    self.image_processing_class(**self.image_processor_dict					)
									self.assertTrue(hasattr(snake_case_				,			"image_mean"					)					)
									self.assertTrue(hasattr(snake_case_				,			"image_std"					)					)
									self.assertTrue(hasattr(snake_case_				,			"do_normalize"					)					)
									self.assertTrue(hasattr(snake_case_				,			"do_resize"					)					)
									self.assertTrue(hasattr(snake_case_				,			"resample"					)					)
									self.assertTrue(hasattr(snake_case_				,			"crop_size"					)					)
									self.assertTrue(hasattr(snake_case_				,			"do_center_crop"					)					)
									self.assertTrue(hasattr(snake_case_				,			"do_rescale"					)					)
									self.assertTrue(hasattr(snake_case_				,			"rescale_factor"					)					)
									self.assertTrue(hasattr(snake_case_				,			"masking_generator"					)					)
									self.assertTrue(hasattr(snake_case_				,			"codebook_do_resize"					)					)
									self.assertTrue(hasattr(snake_case_				,			"codebook_size"					)					)
									self.assertTrue(hasattr(snake_case_				,			"codebook_resample"					)					)
									self.assertTrue(hasattr(snake_case_				,			"codebook_do_center_crop"					)					)
									self.assertTrue(hasattr(snake_case_				,			"codebook_crop_size"					)					)
									self.assertTrue(hasattr(snake_case_				,			"codebook_do_map_pixels"					)					)
									self.assertTrue(hasattr(snake_case_				,			"codebook_do_normalize"					)					)
									self.assertTrue(hasattr(snake_case_				,			"codebook_image_mean"					)					)
									self.assertTrue(hasattr(snake_case_				,			"codebook_image_std"					)					)



			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    self.image_processing_class.from_dict(self.image_processor_dict					)
									self.assertEqual(image_processor.size				,			{"height": 224, "width": 224}					)
									self.assertEqual(image_processor.crop_size				,			{"height": 224, "width": 224}					)
									self.assertEqual(image_processor.codebook_size				,			{"height": 112, "width": 112}					)
									self.assertEqual(image_processor.codebook_crop_size				,			{"height": 112, "width": 112}					)

									snake_case :       str				=    self.image_processing_class.from_dict(
									    self.image_processor_dict				,			size=42				,			crop_size=84				,			codebook_size=33				,			codebook_crop_size=66					)
									self.assertEqual(image_processor.size				,			{"height": 42, "width": 42}					)
									self.assertEqual(image_processor.crop_size				,			{"height": 84, "width": 84}					)
									self.assertEqual(image_processor.codebook_size				,			{"height": 33, "width": 33}					)
									self.assertEqual(image_processor.codebook_crop_size				,			{"height": 66, "width": 66}					)



			def   lowerCamelCase (							self					):
									pass



			def   lowerCamelCase (							self					):
									# Initialize image_processing
									snake_case :       Optional[Any]				=    self.image_processing_class(**self.image_processor_dict					)
									# create random PIL images
									snake_case :       List[Any]				=    prepare_image_inputs(self.image_processor_tester				,			equal_resolution=snake_case_					)
									for image in image_inputs:
															self.assertIsInstance(snake_case_				,			PIL.Image.Image					)

									# Test not batched input
									snake_case :       int				=    image_processing(image_inputs[0]				,			return_tensors="pt"					)

									# Test no bool masked pos
									self.assertFalse("bool_masked_pos" in encoded_images					)

									snake_case				,					snake_case :       Union[str, Any]				=    self.image_processor_tester.get_expected_image_size()

									self.assertEqual(
									    encoded_images.pixel_values.shape				,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,			)

									# Test batched
									snake_case :       List[str]				=    image_processing(snake_case_				,			return_tensors="pt"					)
									snake_case				,					snake_case :       int				=    self.image_processor_tester.get_expected_image_size()

									# Test no bool masked pos
									self.assertFalse("bool_masked_pos" in encoded_images					)

									self.assertEqual(
									    encoded_images.pixel_values.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )				,			)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_					):
									# Initialize image_processing
									snake_case :       Dict				=    self.image_processing_class(**self.image_processor_dict					)
									# create random tensors
									snake_case :       Tuple				=    prepare_image_inputs(self.image_processor_tester				,			equal_resolution=snake_case_				,			**snake_case_					)
									for image in image_inputs:
															self.assertIsInstance(snake_case_				,			snake_case_					)

									# Test not batched input
									snake_case :       str				=    image_processing(image_inputs[0]				,			return_tensors="pt"					)

									snake_case				,					snake_case :       Optional[int]				=    self.image_processor_tester.get_expected_image_size()
									self.assertEqual(
									    encoded_images.pixel_values.shape				,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,			)

									snake_case :       str				=    image_processing(snake_case_				,			return_image_mask=snake_case_				,			return_tensors="pt"					)

									snake_case				,					snake_case :       Optional[int]				=    self.image_processor_tester.get_expected_image_size()
									self.assertEqual(
									    encoded_images.pixel_values.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )				,			)

									snake_case				,					snake_case :       Tuple				=    self.image_processor_tester.get_expected_mask_size()
									self.assertEqual(
									    encoded_images.bool_masked_pos.shape				,			(
									        self.image_processor_tester.batch_size,
									        expected_height,
									        expected_width,
									    )				,			)

									# Test batched
									snake_case :       List[str]				=    image_processing(snake_case_				,			return_tensors="pt"					).pixel_values

									snake_case				,					snake_case :       Optional[Any]				=    self.image_processor_tester.get_expected_image_size()
									self.assertEqual(
									    encoded_images.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )				,			)

									# Test masking
									snake_case :       Any				=    image_processing(snake_case_				,			return_image_mask=snake_case_				,			return_tensors="pt"					)

									snake_case				,					snake_case :       Optional[Any]				=    self.image_processor_tester.get_expected_image_size()
									self.assertEqual(
									    encoded_images.pixel_values.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )				,			)

									snake_case				,					snake_case :       Optional[Any]				=    self.image_processor_tester.get_expected_mask_size()
									self.assertEqual(
									    encoded_images.bool_masked_pos.shape				,			(
									        self.image_processor_tester.batch_size,
									        expected_height,
									        expected_width,
									    )				,			)



			def   lowerCamelCase (							self					):
									self._test_call_framework(np.ndarray				,			prepare_kwargs={"numpify": True}					)



			def   lowerCamelCase (							self					):
									self._test_call_framework(torch.Tensor				,			prepare_kwargs={"torchify": True}					)



			def   lowerCamelCase (							self					):
									# Initialize image_processing
									random.seed(1_234					)
									snake_case :       Union[str, Any]				=    self.image_processing_class(**self.image_processor_dict					)
									snake_case :       Optional[Any]				=    prepare_image_inputs(self.image_processor_tester				,			equal_resolution=snake_case_				,			torchify=snake_case_					)

									# Test not batched input
									snake_case :       Any				=    image_processing(image_inputs[0]				,			return_image_mask=snake_case_				,			return_tensors="pt"					)
									self.assertEqual(encoded_images.bool_masked_pos.sum().item()				,			75					)






			def   lowerCamelCase (							self					):
									# Initialize image_processing
									snake_case :       str				=    self.image_processing_class(**self.image_processor_dict					)
									# create random PIL images
									snake_case :       Any				=    prepare_image_inputs(self.image_processor_tester				,			equal_resolution=snake_case_					)
									for image in image_inputs:
															self.assertIsInstance(snake_case_				,			PIL.Image.Image					)

									# Test not batched input
									snake_case :       Dict				=    image_processing(image_inputs[0]				,			return_codebook_pixels=snake_case_				,			return_tensors="pt"					)
									snake_case				,					snake_case :       List[str]				=    self.image_processor_tester.get_expected_codebook_image_size()
									self.assertEqual(
									    encoded_images.codebook_pixel_values.shape				,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,			)

									# Test batched
									snake_case :       Union[str, Any]				=    image_processing(snake_case_				,			return_codebook_pixels=snake_case_				,			return_tensors="pt"					)
									snake_case				,					snake_case :       int				=    self.image_processor_tester.get_expected_codebook_image_size()
									self.assertEqual(
									    encoded_images.codebook_pixel_values.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )				,			)






'''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:
						A_	   = False


if is_vision_available():
						from PIL import Image
else:




						class 		SCREAMING_SNAKE_CASE_					:



									"""simple docstring"""






									@staticmethod
									def   lowerCamelCase (							*snake_case_				,			**snake_case_					):
															pass




@is_pipeline_test
@require_vision
class 		SCREAMING_SNAKE_CASE_					(unittest.TestCase      ):



			"""simple docstring"""






			__magic_name__ :				Optional[int]   							=       MODEL_FOR_VISION_2_SEQ_MAPPING
			__magic_name__ :				Optional[int]   							=       TF_MODEL_FOR_VISION_2_SEQ_MAPPING



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       str				=    pipeline("image-to-text"				,			model=snake_case_				,			tokenizer=snake_case_				,			image_processor=snake_case_					)
									snake_case :       List[Any]				=    [
									    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]
									return pipe, examples



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_					):
									snake_case :       Union[str, Any]				=    pipe(snake_case_					)
									self.assertEqual(
									    snake_case_				,			[
									        [{"generated_text": ANY(snake_case_					)}],
									        [{"generated_text": ANY(snake_case_					)}],
									    ]				,			)



			@require_tf
			def   lowerCamelCase (							self					):
									snake_case :       Tuple				=    pipeline("image-to-text"				,			model="hf-internal-testing/tiny-random-vit-gpt2"				,			framework="tf"					)
									snake_case :       Union[str, Any]				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"

									snake_case :       Dict				=    pipe(snake_case_					)
									self.assertEqual(
									    snake_case_				,			[
									        {
									            "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									        },
									    ]				,			)

									snake_case :       Tuple				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									    ]				,			)

									snake_case :       List[Any]				=    pipe(snake_case_				,			max_new_tokens=1					)
									self.assertEqual(
									    snake_case_				,			[{"generated_text": "growth"}]				,			)



			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    pipeline("image-to-text"				,			model="hf-internal-testing/tiny-random-vit-gpt2"					)
									snake_case :       Tuple				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"

									snake_case :       Any				=    pipe(snake_case_					)
									self.assertEqual(
									    snake_case_				,			[
									        {
									            "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									        },
									    ]				,			)

									snake_case :       str				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									    ]				,			)



			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Tuple				=    pipeline("image-to-text"				,			model="hf-internal-testing/tiny-random-BlipForConditionalGeneration"					)
									snake_case :       Dict				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"
									snake_case :       List[Any]				=    "a photo of"

									snake_case :       List[str]				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertTrue(outputs[0]["generated_text"].startswith(snake_case_					)					)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    pipeline("image-to-text"				,			model="ydshieh/vit-gpt2-coco-en"					)
									snake_case :       Union[str, Any]				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"

									snake_case :       Tuple				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]					)

									snake_case :       Tuple				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [{"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   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    pipeline("image-to-text"				,			model="Salesforce/blip-image-captioning-base"					)
									snake_case :       Any				=    "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
									snake_case :       Union[str, Any]				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Optional[Any]				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a pink pokemon pokemon with a blue shirt and a blue shirt"}]					)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Tuple				=    pipeline("image-to-text"				,			model="microsoft/git-base-coco"					)
									snake_case :       Tuple				=    "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
									snake_case :       Tuple				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Any				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a cartoon of a purple character."}]					)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    pipeline("image-to-text"				,			model="Salesforce/blip-image-captioning-base"					)
									snake_case :       int				=    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
									snake_case :       Tuple				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       str				=    "a photography of"

									snake_case :       int				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a photography of a volcano"}]					)

									with self.assertRaises(snake_case_					):
															snake_case :       List[Any]				=    pipe([image, image]				,			prompt=[prompt, prompt]					)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    pipeline("image-to-text"				,			model="microsoft/git-base-coco"					)
									snake_case :       Optional[Any]				=    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
									snake_case :       List[str]				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Any				=    "a photo of a"

									snake_case :       Dict				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a photo of a tent with a tent and a tent in the background."}]					)

									with self.assertRaises(snake_case_					):
															snake_case :       List[str]				=    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   lowerCamelCase (							self					):
									snake_case :       List[str]				=    pipeline("image-to-text"				,			model="google/pix2struct-ai2d-base"					)
									snake_case :       Optional[int]				=    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
									snake_case :       str				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Any				=    "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"

									snake_case :       str				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "ash cloud"}]					)

									with self.assertRaises(snake_case_					):
															snake_case :       List[str]				=    pipe([image, image]				,			prompt=[prompt, prompt]					)






			@slow
			@require_tf
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    pipeline("image-to-text"				,			model="ydshieh/vit-gpt2-coco-en"				,			framework="tf"					)
									snake_case :       Union[str, Any]				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"

									snake_case :       Any				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]					)

									snake_case :       Optional[int]				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [{"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 "}],
									    ]				,			)





