'''simple docstring'''

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor




class 		SCREAMING_SNAKE_CASE_					(unittest.TestCase      ):



			"""simple docstring"""






			@property
			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    1
									snake_case :       Any				=    3
									snake_case :       Optional[int]				=    8
									snake_case :       Tuple				=    8

									snake_case :       Dict				=    torch.rand((batch_size, num_channels, height, width)					)

									return sample



			def   lowerCamelCase (							self				,			snake_case_					):
									if isinstance(image[0]				,			PIL.Image.Image					):
															return np.stack([np.array(snake_case_					) for i in image]				,			axis=0					)
									elif isinstance(snake_case_				,			torch.Tensor					):
															return image.cpu().numpy().transpose(0				,			2				,			3				,			1					)
									return image



			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    VaeImageProcessor(do_resize=snake_case_				,			do_normalize=snake_case_					)

									snake_case :       Optional[int]				=    self.dummy_sample
									snake_case :       List[Any]				=    self.to_np(snake_case_					)

									for output_type in ["pt", "np", "pil"]:
															snake_case :       List[Any]				=    image_processor.postprocess(
															    image_processor.preprocess(snake_case_					)				,			output_type=snake_case_				,			)
															snake_case :       Optional[Any]				=    self.to_np(snake_case_					)
															snake_case :       int				=    (input_np * 255).round() if output_type == "pil" else input_np
															assert (
															    np.abs(in_np - out_np					).max() < 1e-6
															), F"""decoded output does not match input for output_type {output_type}"""



			def   lowerCamelCase (							self					):
									snake_case :       int				=    VaeImageProcessor(do_resize=snake_case_				,			do_normalize=snake_case_					)
									snake_case :       Optional[int]				=    self.dummy_sample.cpu().numpy().transpose(0				,			2				,			3				,			1					)

									for output_type in ["pt", "np", "pil"]:
															snake_case :       str				=    image_processor.postprocess(image_processor.preprocess(snake_case_					)				,			output_type=snake_case_					)

															snake_case :       List[Any]				=    self.to_np(snake_case_					)
															snake_case :       Optional[int]				=    (input_np * 255).round() if output_type == "pil" else input_np
															assert (
															    np.abs(in_np - out_np					).max() < 1e-6
															), F"""decoded output does not match input for output_type {output_type}"""



			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    VaeImageProcessor(do_resize=snake_case_				,			do_normalize=snake_case_					)

									snake_case :       Optional[Any]				=    self.dummy_sample.cpu().numpy().transpose(0				,			2				,			3				,			1					)
									snake_case :       Any				=    image_processor.numpy_to_pil(snake_case_					)

									for output_type in ["pt", "np", "pil"]:
															snake_case :       int				=    image_processor.postprocess(image_processor.preprocess(snake_case_					)				,			output_type=snake_case_					)
															for i, o in zip(snake_case_				,			snake_case_					):
																					snake_case :       int				=    np.array(snake_case_					)
																					snake_case :       Optional[int]				=    self.to_np(snake_case_					) if output_type == "pil" else (self.to_np(snake_case_					) * 255).round()
																					assert (
																					    np.abs(in_np - out_np					).max() < 1e-6
																					), F"""decoded output does not match input for output_type {output_type}"""



			def   lowerCamelCase (							self					):
									snake_case :       int				=    VaeImageProcessor(do_resize=snake_case_				,			do_normalize=snake_case_					)

									snake_case :       int				=    self.dummy_sample
									snake_case :       Optional[int]				=    input_pt_ad.squeeze(0					)

									snake_case :       int				=    image_processor.postprocess(
									    image_processor.preprocess(snake_case_					)				,			output_type="np"				,			)
									snake_case :       Union[str, Any]				=    image_processor.postprocess(
									    image_processor.preprocess(snake_case_					)				,			output_type="np"				,			)

									snake_case :       List[Any]				=    self.to_np(self.dummy_sample					)
									snake_case :       List[str]				=    input_np_ad.squeeze(0					)

									snake_case :       List[str]				=    image_processor.postprocess(
									    image_processor.preprocess(snake_case_					)				,			output_type="np"				,			)
									snake_case :       List[str]				=    image_processor.postprocess(
									    image_processor.preprocess(snake_case_					)				,			output_type="np"				,			)

									assert np.abs(out_pt_ad - out_pt_ad					).max() < 1e-6
									assert np.abs(out_np_ad - out_np_ad					).max() < 1e-6






			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    VaeImageProcessor(do_resize=snake_case_				,			do_normalize=snake_case_					)

									snake_case :       Any				=    self.dummy_sample
									snake_case :       Optional[Any]				=    list(snake_case_					)

									snake_case :       Optional[Any]				=    image_processor.postprocess(
									    image_processor.preprocess(snake_case_					)				,			output_type="np"				,			)

									snake_case :       Tuple				=    image_processor.postprocess(
									    image_processor.preprocess(snake_case_					)				,			output_type="np"				,			)

									snake_case :       Any				=    self.to_np(self.dummy_sample					)
									list(snake_case_					)

									snake_case :       List[str]				=    image_processor.postprocess(
									    image_processor.preprocess(snake_case_					)				,			output_type="np"				,			)

									snake_case :       Tuple				=    image_processor.postprocess(
									    image_processor.preprocess(snake_case_					)				,			output_type="np"				,			)

									assert np.abs(out_pt_ad - out_pt_list					).max() < 1e-6
									assert np.abs(out_np_ad - out_np_list					).max() < 1e-6






'''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 "}],
									    ]				,			)





