




'''simple docstring'''





import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
   from PIL import Image
else:

   class   _a							:






        @staticmethod
        def 							_lowercase      (					*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     List[Any]:
             pass

@is_pipeline_test
@require_torch
@require_vision
class   _a							(		unittest.TestCase				):
     SCREAMING_SNAKE_CASE_				:       Any       							=		MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          _snake_case				= pipeline("visual-question-answering"   ,model="hf-internal-testing/tiny-vilt-random-vqa"       )
          _snake_case				= [
              {
                  'image': Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       ),
                  'question': 'How many cats are there?',
              },
              {
                  'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                  'question': 'How many cats are there?',
              },
          ]
          return vqa_pipeline, examples






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Dict:
          _snake_case				= vqa_pipeline(_SCREAMING_SNAKE_CASE   ,top_k=1       )
          self.assertEqual(
              _SCREAMING_SNAKE_CASE   ,[
                  [{"score": ANY(_SCREAMING_SNAKE_CASE       ), "answer": ANY(_SCREAMING_SNAKE_CASE       )}],
                  [{"score": ANY(_SCREAMING_SNAKE_CASE       ), "answer": ANY(_SCREAMING_SNAKE_CASE       )}],
              ]   ,)






     @require_torch
     def 							_lowercase      (					self       )				->     Optional[Any]:
          _snake_case				= pipeline("visual-question-answering"   ,model="hf-internal-testing/tiny-vilt-random-vqa"       )
          _snake_case				= './tests/fixtures/tests_samples/COCO/000000039769.png'
          _snake_case				= 'How many cats are there?'

          _snake_case				= vqa_pipeline(image=_SCREAMING_SNAKE_CASE   ,question="How many cats are there?"   ,top_k=2       )
          self.assertEqual(
              _SCREAMING_SNAKE_CASE   ,[{"score": ANY(_SCREAMING_SNAKE_CASE       ), "answer": ANY(_SCREAMING_SNAKE_CASE       )}, {"score": ANY(_SCREAMING_SNAKE_CASE       ), "answer": ANY(_SCREAMING_SNAKE_CASE       )}]       )

          _snake_case				= vqa_pipeline({"image": image, "question": question}   ,top_k=2       )
          self.assertEqual(
              _SCREAMING_SNAKE_CASE   ,[{"score": ANY(_SCREAMING_SNAKE_CASE       ), "answer": ANY(_SCREAMING_SNAKE_CASE       )}, {"score": ANY(_SCREAMING_SNAKE_CASE       ), "answer": ANY(_SCREAMING_SNAKE_CASE       )}]       )






     @slow
     @require_torch
     def 							_lowercase      (					self       )				->     Any:
          _snake_case				= pipeline("visual-question-answering"   ,model="dandelin/vilt-b32-finetuned-vqa"       )
          _snake_case				= './tests/fixtures/tests_samples/COCO/000000039769.png'
          _snake_case				= 'How many cats are there?'

          _snake_case				= vqa_pipeline(image=_SCREAMING_SNAKE_CASE   ,question=_SCREAMING_SNAKE_CASE   ,top_k=2       )
          self.assertEqual(
              nested_simplify(_SCREAMING_SNAKE_CASE   ,decimals=4       )   ,[{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]       )

          _snake_case				= vqa_pipeline({"image": image, "question": question}   ,top_k=2       )
          self.assertEqual(
              nested_simplify(_SCREAMING_SNAKE_CASE   ,decimals=4       )   ,[{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]       )

          _snake_case				= vqa_pipeline(
              [{"image": image, "question": question}, {"image": image, "question": question}]   ,top_k=2       )
          self.assertEqual(
              nested_simplify(_SCREAMING_SNAKE_CASE   ,decimals=4       )   ,[[{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]] * 2   ,)






     @require_tf
     @unittest.skip("Visual question answering not implemented in TF"       )
     def 							_lowercase      (					self       )				->     Optional[int]:
          pass







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCamelCase    :		Any		      =    False


class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						pass


@nightly
@require_torch_gpu
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Dict						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe.dual_guided(
											    prompt='first prompt'       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='numpy'       ,	).images

											with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(snake_case	)
																SCREAMING_SNAKE_CASE							:     Any						=				VersatileDiffusionPipeline.from_pretrained(snake_case       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				generator.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe.dual_guided(
											    prompt='first prompt'       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='numpy'       ,	).images

											assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				'cyberpunk 2077'
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Any						=				pipe.dual_guided(
											    prompt=snake_case       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=50       ,	output_type='numpy'       ,	).images

											SCREAMING_SNAKE_CASE							:     List[Any]						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     Tuple						=				np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]	)

											assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1

											SCREAMING_SNAKE_CASE							:     Any						=				'A painting of a squirrel eating a burger '
											SCREAMING_SNAKE_CASE							:     Any						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				pipe.text_to_image(
											    prompt=snake_case       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=50       ,	output_type='numpy'	).images

											SCREAMING_SNAKE_CASE							:     Dict						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

											assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				pipe.image_variation(snake_case       ,	generator=snake_case       ,	output_type='numpy'	).images

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]	)

											assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1