import gc
import tempfile
import unittest

import numpy as np
import torch

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


UpperCamelCase      			=     False


@nightly
@require_torch_gpu
class _a       (	unittest.TestCase ):

	'''simple docstring'''



	def 					__UpperCAmelCase(       self   ):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

	def 					__UpperCAmelCase(       self   ):
			__A							:   Tuple	  =				VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
			# remove text_unet
			pipe.remove_unused_weights()
			pipe.to(__UpperCAmelCase   )
			pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A							:   int	  =				load_image(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )

			__A							:   List[str]	  =				torch.manual_seed(0   )
			__A							:   List[str]	  =				pipe(
			    prompt="first prompt"							,			image=__UpperCAmelCase							,			text_to_image_strength=0.75							,			generator=__UpperCAmelCase							,			guidance_scale=7.5							,			num_inference_steps=2							,			output_type="numpy"							,			).images

			with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(__UpperCAmelCase   )
					__A							:   Dict	  =				VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCAmelCase   )

			pipe.to(__UpperCAmelCase   )
			pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A							:   List[str]	  =				generator.manual_seed(0   )
			__A							:   str	  =				pipe(
			    prompt="first prompt"							,			image=__UpperCAmelCase							,			text_to_image_strength=0.75							,			generator=__UpperCAmelCase							,			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 					__UpperCAmelCase(       self   ):
			__A							:   List[str]	  =				VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
			pipe.remove_unused_weights()
			pipe.to(__UpperCAmelCase   )
			pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A							:   Tuple	  =				"cyberpunk 2077"
			__A							:   Dict	  =				load_image(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
			__A							:   Dict	  =				torch.manual_seed(0   )
			__A							:   Tuple	  =				pipe(
			    prompt=__UpperCAmelCase							,			image=__UpperCAmelCase							,			text_to_image_strength=0.75							,			generator=__UpperCAmelCase							,			guidance_scale=7.5							,			num_inference_steps=50							,			output_type="numpy"							,			).images

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

			assert image.shape == (1, 512, 512, 3)
			__A							:   Any	  =				np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]   )

			assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata



def    lowerCamelCase_   (  _lowercase    )							->	str:
		__A							:   Optional[int]	  =				min(re.search("^ +" ,     _lowercase    ).end() if t.startswith(" "    ) else 0 for t in string.splitlines()    )
		return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_lowercase    )]    )


UpperCamelCase      			=     '\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

UpperCamelCase      			=     '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


UpperCamelCase      			=     '\\n# Begin of markdown\n\nSome cool dataset card\n'





class _a       (	unittest.TestCase ):

	'''simple docstring'''



	def 					__UpperCAmelCase(       self   ):
			with tempfile.TemporaryDirectory() as tmp_dir:
					__A							:   List[Any]	  =				Path(__UpperCAmelCase   ) / "README.md"
					with open(__UpperCAmelCase							,			"w+"   ) as readme_file:
							readme_file.write(__UpperCAmelCase   )
					__A							:   Tuple	  =				DatasetMetadata.from_readme(__UpperCAmelCase   )
					self.assertDictEqual(__UpperCAmelCase							,			{"language": ["zh", "en"], "task_ids": ["sentiment-classification"]}   )

					with open(__UpperCAmelCase							,			"w+"   ) as readme_file:
							readme_file.write(__UpperCAmelCase   )
					__A							:   Any	  =				DatasetMetadata.from_readme(__UpperCAmelCase   )
					self.assertDictEqual(__UpperCAmelCase							,			{}   )

					with open(__UpperCAmelCase							,			"w+"   ) as readme_file:
							readme_file.write(__UpperCAmelCase   )
					__A							:   Union[str, Any]	  =				DatasetMetadata.from_readme(__UpperCAmelCase   )
					self.assertEqual(__UpperCAmelCase							,			{}   )







	def 					__UpperCAmelCase(       self   ):
			__A							:   List[str]	  =				_dedent(
			    "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            "   )
			assert DatasetMetadata.from_yaml_string(__UpperCAmelCase   )

			__A							:   List[Any]	  =				_dedent(
			    "\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            "   )
			with self.assertRaises(__UpperCAmelCase   ):
					DatasetMetadata.from_yaml_string(__UpperCAmelCase   )

			__A							:   Optional[int]	  =				_dedent(
			    "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            "   )
			assert DatasetMetadata.from_yaml_string(__UpperCAmelCase   )





