




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

@skip_mps
class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__						,							lowerCAmelCase__						,							unittest.TestCase				):
					__UpperCamelCase     	= StableDiffusionModelEditingPipeline
					__UpperCamelCase     	= TEXT_TO_IMAGE_PARAMS
					__UpperCamelCase     	= TEXT_TO_IMAGE_BATCH_PARAMS
					__UpperCamelCase     	= TEXT_TO_IMAGE_IMAGE_PARAMS
					__UpperCamelCase     	= TEXT_TO_IMAGE_IMAGE_PARAMS




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									torch.manual_seed(0)
									__lowerCAmelCase					=  UNetaDConditionModel(
									    block_out_channels=(32, 64),     layers_per_block=2,     sample_size=32,     in_channels=4,     out_channels=4,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D"""),     up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D"""),     cross_attention_dim=32,     )
									__lowerCAmelCase					=  DDIMScheduler()
									torch.manual_seed(0)
									__lowerCAmelCase					=  AutoencoderKL(
									    block_out_channels=[32, 64],     in_channels=3,     out_channels=3,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],     latent_channels=4,     )
									torch.manual_seed(0)
									__lowerCAmelCase					=  CLIPTextConfig(
									    bos_token_id=0,     eos_token_id=2,     hidden_size=32,     intermediate_size=37,     layer_norm_eps=1e-05,     num_attention_heads=4,     num_hidden_layers=5,     pad_token_id=1,     vocab_size=1000,     )
									__lowerCAmelCase					=  CLIPTextModel(_lowercase)
									__lowerCAmelCase					=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

									__lowerCAmelCase					=  {
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components




					def     _lowercase						(   self:		List[str],     _lowercase:		Tuple,     _lowercase:		Union[str, Any]=0):


									'''simple docstring'''


									__lowerCAmelCase					=  torch.manual_seed(_lowercase)
									__lowerCAmelCase					=  {
									    """prompt""": """A field of roses""",
									    """generator""": generator,
									    # Setting height and width to None to prevent OOMs on CPU.
									    """height""": None,
									    """width""": None,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									}
									return inputs




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									__lowerCAmelCase					=  self.get_dummy_components()
									__lowerCAmelCase					=  StableDiffusionModelEditingPipeline(**_lowercase)
									__lowerCAmelCase					=  sd_pipe.to(_lowercase)
									sd_pipe.set_progress_bar_config(disable=_lowercase)

									__lowerCAmelCase					=  self.get_dummy_inputs(_lowercase)
									__lowerCAmelCase					=  sd_pipe(**_lowercase).images
									__lowerCAmelCase					=  image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									__lowerCAmelCase					=  np.array([0.4_755, 0.5_132, 0.4_976, 0.3_904, 0.3_554, 0.4_765, 0.5_139, 0.5_158, 0.4_889])

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




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									__lowerCAmelCase					=  self.get_dummy_components()
									__lowerCAmelCase					=  StableDiffusionModelEditingPipeline(**_lowercase)
									__lowerCAmelCase					=  sd_pipe.to(_lowercase)
									sd_pipe.set_progress_bar_config(disable=_lowercase)

									__lowerCAmelCase					=  self.get_dummy_inputs(_lowercase)
									__lowerCAmelCase					=  """french fries"""
									__lowerCAmelCase					=  sd_pipe(**_lowercase,     negative_prompt=_lowercase)
									__lowerCAmelCase					=  output.images
									__lowerCAmelCase					=  image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									__lowerCAmelCase					=  np.array([0.4_992, 0.5_101, 0.5_004, 0.3_949, 0.3_604, 0.4_735, 0.5_216, 0.5_204, 0.4_913])

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




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									__lowerCAmelCase					=  self.get_dummy_components()
									__lowerCAmelCase					=  EulerAncestralDiscreteScheduler(
									    beta_start=0.00_085,     beta_end=0.012,     beta_schedule="""scaled_linear""")
									__lowerCAmelCase					=  StableDiffusionModelEditingPipeline(**_lowercase)
									__lowerCAmelCase					=  sd_pipe.to(_lowercase)
									sd_pipe.set_progress_bar_config(disable=_lowercase)

									__lowerCAmelCase					=  self.get_dummy_inputs(_lowercase)
									__lowerCAmelCase					=  sd_pipe(**_lowercase).images
									__lowerCAmelCase					=  image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									__lowerCAmelCase					=  np.array([0.4_747, 0.5_372, 0.4_779, 0.4_982, 0.5_543, 0.4_816, 0.5_238, 0.4_904, 0.5_027])

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




					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									__lowerCAmelCase					=  self.get_dummy_components()
									__lowerCAmelCase					=  PNDMScheduler()
									__lowerCAmelCase					=  StableDiffusionModelEditingPipeline(**_lowercase)
									__lowerCAmelCase					=  sd_pipe.to(_lowercase)
									sd_pipe.set_progress_bar_config(disable=_lowercase)

									__lowerCAmelCase					=  self.get_dummy_inputs(_lowercase)
									# the pipeline does not expect pndm so test if it raises error.
									with self.assertRaises(_lowercase):
													__lowerCAmelCase					=  sd_pipe(**_lowercase).images




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									super().test_inference_batch_single_identical(expected_max_diff=5e-3)







					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									super().test_attention_slicing_forward_pass(expected_max_diff=5e-3)





@slow
@require_torch_gpu
class 		lowercase_       (	unittest.TestCase				):




					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


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




					def     _lowercase						(   self:		str,     _lowercase:		Tuple=0):


									'''simple docstring'''


									__lowerCAmelCase					=  torch.manual_seed(_lowercase)
									__lowerCAmelCase					=  {
									    """prompt""": """A field of roses""",
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 7.5,
									    """output_type""": """numpy""",
									}
									return inputs




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  """CompVis/stable-diffusion-v1-4"""
									__lowerCAmelCase					=  StableDiffusionModelEditingPipeline.from_pretrained(_lowercase,     safety_checker=_lowercase)
									pipe.to(_lowercase)
									pipe.set_progress_bar_config(disable=_lowercase)
									pipe.enable_attention_slicing()

									__lowerCAmelCase					=  self.get_inputs()
									__lowerCAmelCase					=  pipe(**_lowercase).images
									__lowerCAmelCase					=  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)

									__lowerCAmelCase					=  np.array(
									    [0.6_749_496, 0.6_386_453, 0.51_443_267, 0.66_094_905, 0.61_921_215, 0.5_491_332, 0.5_744_417, 0.58_075_106, 0.5_174_658])

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

									# make sure image changes after editing
									pipe.edit_model("""A pack of roses""",     """A pack of blue roses""")

									__lowerCAmelCase					=  pipe(**_lowercase).images
									__lowerCAmelCase					=  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)

									assert np.abs(expected_slice - image_slice).max() > 1e-1







					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									__lowerCAmelCase					=  """CompVis/stable-diffusion-v1-4"""
									__lowerCAmelCase					=  DDIMScheduler.from_pretrained(_lowercase,     subfolder="""scheduler""")
									__lowerCAmelCase					=  StableDiffusionModelEditingPipeline.from_pretrained(
									    _lowercase,     scheduler=_lowercase,     safety_checker=_lowercase)
									__lowerCAmelCase					=  pipe.to(_lowercase)
									pipe.set_progress_bar_config(disable=_lowercase)
									pipe.enable_attention_slicing(1)
									pipe.enable_sequential_cpu_offload()

									__lowerCAmelCase					=  self.get_inputs()
									__lowerCAmelCase					=  pipe(**_lowercase)

									__lowerCAmelCase					=  torch.cuda.max_memory_allocated()
									# make sure that less than 4.4 GB is allocated
									assert mem_bytes < 4.4 * 10**9











import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     str:







				'''simple docstring'''


				__lowerCAmelCase					=  min(re.search("""^ +"""						,			UpperCamelCase__   ).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(UpperCamelCase__   )]   )


__A						:		str          =						"\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

__A						:		int          =						"\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


__A						:		Any          =						"\\n# Begin of markdown\n\nSome cool dataset card\n"

class 		lowercase_       (	unittest.TestCase				):




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


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

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

													with open(_lowercase,     """w+""") as readme_file:
																	readme_file.write(_lowercase)
													__lowerCAmelCase					=  DatasetMetadata.from_readme(_lowercase)
													self.assertEqual(_lowercase,     {})







					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


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

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

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





