
import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class       _snake_case					(     snake_case				,						snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    StableDiffusionDiffEditPipeline
			UpperCamelCase__          =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
			UpperCamelCase__          =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
			UpperCamelCase__          =    frozenset(
			    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			UpperCamelCase__          =    frozenset([]				)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				torch.manual_seed(0		)
				__magic_name__		:		List[str]	   =     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						,   attention_head_dim=(2, 4)						,   use_linear_projection=_a						,   )
				__magic_name__		:		Optional[Any]	   =     DDIMScheduler(
				    beta_start=0.0_00_85						,   beta_end=0.0_12						,   beta_schedule="scaled_linear"						,   clip_sample=_a						,   set_alpha_to_one=_a						,   )
				__magic_name__		:		Tuple	   =     DDIMInverseScheduler(
				    beta_start=0.0_00_85						,   beta_end=0.0_12						,   beta_schedule="scaled_linear"						,   clip_sample=_a						,   set_alpha_to_zero=_a						,   )
				torch.manual_seed(0		)
				__magic_name__		:		List[Any]	   =     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						,   sample_size=128						,   )
				torch.manual_seed(0		)
				__magic_name__		:		List[Any]	   =     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=1_000						,   hidden_act="gelu"						,   projection_dim=512						,   )
				__magic_name__		:		Dict	   =     CLIPTextModel(_a		)
				__magic_name__		:		List[Any]	   =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

				__magic_name__		:		Optional[Any]	   =     {
				    "unet": unet,
				    "scheduler": scheduler,
				    "inverse_scheduler": inverse_scheduler,
				    "vae": vae,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "safety_checker": None,
				    "feature_extractor": None,
				}

				return components







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=0		):
				__magic_name__		:		Optional[int]	   =     floats_tensor((1, 16, 16)						,   rng=random.Random(_a		)		).to(_a		)
				__magic_name__		:		Union[str, Any]	   =     floats_tensor((1, 2, 4, 16, 16)						,   rng=random.Random(_a		)		).to(_a		)
				if str(_a		).startswith("mps"		):
					__magic_name__		:		List[str]	   =     torch.manual_seed(_a		)
				else:
					__magic_name__		:		Optional[int]	   =     torch.Generator(device=_a		).manual_seed(_a		)
				__magic_name__		:		Dict	   =     {
				    "prompt": "a dog and a newt",
				    "mask_image": mask,
				    "image_latents": latents,
				    "generator": generator,
				    "num_inference_steps": 2,
				    "inpaint_strength": 1.0,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				}

				return inputs







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=0		):
				__magic_name__		:		List[str]	   =     floats_tensor((1, 3, 32, 32)						,   rng=random.Random(_a		)		).to(_a		)
				__magic_name__		:		Optional[int]	   =     image.cpu().permute(0						,   2						,   3						,   1		)[0]
				__magic_name__		:		List[Any]	   =     Image.fromarray(np.uinta(_a		)		).convert("RGB"		)
				if str(_a		).startswith("mps"		):
					__magic_name__		:		List[str]	   =     torch.manual_seed(_a		)
				else:
					__magic_name__		:		Tuple	   =     torch.Generator(device=_a		).manual_seed(_a		)
				__magic_name__		:		List[Any]	   =     {
				    "image": image,
				    "source_prompt": "a cat and a frog",
				    "target_prompt": "a dog and a newt",
				    "generator": generator,
				    "num_inference_steps": 2,
				    "num_maps_per_mask": 2,
				    "mask_encode_strength": 1.0,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				}

				return inputs







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=0		):
				__magic_name__		:		str	   =     floats_tensor((1, 3, 32, 32)						,   rng=random.Random(_a		)		).to(_a		)
				__magic_name__		:		Tuple	   =     image.cpu().permute(0						,   2						,   3						,   1		)[0]
				__magic_name__		:		str	   =     Image.fromarray(np.uinta(_a		)		).convert("RGB"		)
				if str(_a		).startswith("mps"		):
					__magic_name__		:		int	   =     torch.manual_seed(_a		)
				else:
					__magic_name__		:		Dict	   =     torch.Generator(device=_a		).manual_seed(_a		)
				__magic_name__		:		Tuple	   =     {
				    "image": image,
				    "prompt": "a cat and a frog",
				    "generator": generator,
				    "num_inference_steps": 2,
				    "inpaint_strength": 1.0,
				    "guidance_scale": 6.0,
				    "decode_latents": True,
				    "output_type": "numpy",
				}
				return inputs







			def 			SCREAMING_SNAKE_CASE    (				self		):
				if not hasattr(self.pipeline_class						,   "_optional_components"		):
					return

				__magic_name__		:		Dict	   =     self.get_dummy_components()
				__magic_name__		:		Any	   =     self.pipeline_class(**_a		)
				pipe.to(_a		)
				pipe.set_progress_bar_config(disable=_a		)

				# set all optional components to None and update pipeline config accordingly
				for optional_component in pipe._optional_components:
					setattr(_a						,   _a						,   _a		)
				pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}		)

				__magic_name__		:		List[Any]	   =     self.get_dummy_inputs(_a		)
				__magic_name__		:		int	   =     pipe(**_a		)[0]

				with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(_a		)
					__magic_name__		:		Optional[int]	   =     self.pipeline_class.from_pretrained(_a		)
					pipe_loaded.to(_a		)
					pipe_loaded.set_progress_bar_config(disable=_a		)

				for optional_component in pipe._optional_components:
					self.assertTrue(
					    getattr(_a						,   _a		) is None						,   f'''`{optional_component}` did not stay set to None after loading.'''						,   )

				__magic_name__		:		str	   =     self.get_dummy_inputs(_a		)
				__magic_name__		:		Optional[int]	   =     pipe_loaded(**_a		)[0]

				__magic_name__		:		Dict	   =     np.abs(output - output_loaded		).max()
				self.assertLess(_a						,   1e-4		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     "cpu"

				__magic_name__		:		List[Any]	   =     self.get_dummy_components()
				__magic_name__		:		Optional[int]	   =     self.pipeline_class(**_a		)
				pipe.to(_a		)
				pipe.set_progress_bar_config(disable=_a		)

				__magic_name__		:		Optional[int]	   =     self.get_dummy_mask_inputs(_a		)
				__magic_name__		:		Optional[Any]	   =     pipe.generate_mask(**_a		)
				__magic_name__		:		List[Any]	   =     mask[0, -3:, -3:]

				self.assertEqual(mask.shape						,   (1, 16, 16)		)
				__magic_name__		:		Tuple	   =     np.array([0] * 9		)
				__magic_name__		:		Union[str, Any]	   =     np.abs(mask_slice.flatten() - expected_slice		).max()
				self.assertLessEqual(_a						,   1e-3		)
				self.assertEqual(mask[0, -3, -4]						,   0		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     "cpu"

				__magic_name__		:		Dict	   =     self.get_dummy_components()
				__magic_name__		:		List[Any]	   =     self.pipeline_class(**_a		)
				pipe.to(_a		)
				pipe.set_progress_bar_config(disable=_a		)

				__magic_name__		:		str	   =     self.get_dummy_inversion_inputs(_a		)
				__magic_name__		:		Tuple	   =     pipe.invert(**_a		).images
				__magic_name__		:		Union[str, Any]	   =     image[0, -1, -3:, -3:]

				self.assertEqual(image.shape						,   (2, 32, 32, 3)		)
				__magic_name__		:		Union[str, Any]	   =     np.array(
				    [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]						,   )
				__magic_name__		:		List[str]	   =     np.abs(image_slice.flatten() - expected_slice		).max()
				self.assertLessEqual(_a						,   1e-3		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				super().test_inference_batch_single_identical(expected_max_diff=5e-3		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     "cpu"

				__magic_name__		:		Any	   =     self.get_dummy_components()

				__magic_name__		:		Any	   =     {"beta_start": 0.0_00_85, "beta_end": 0.0_12, "beta_schedule": "scaled_linear"}
				__magic_name__		:		Optional[int]	   =     DPMSolverMultistepScheduler(**_a		)
				__magic_name__		:		List[str]	   =     DPMSolverMultistepInverseScheduler(**_a		)

				__magic_name__		:		Optional[Any]	   =     self.pipeline_class(**_a		)
				pipe.to(_a		)
				pipe.set_progress_bar_config(disable=_a		)

				__magic_name__		:		Optional[Any]	   =     self.get_dummy_inversion_inputs(_a		)
				__magic_name__		:		Optional[Any]	   =     pipe.invert(**_a		).images
				__magic_name__		:		Union[str, Any]	   =     image[0, -1, -3:, -3:]

				self.assertEqual(image.shape						,   (2, 32, 32, 3)		)
				__magic_name__		:		Dict	   =     np.array(
				    [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]						,   )
				__magic_name__		:		Optional[Any]	   =     np.abs(image_slice.flatten() - expected_slice		).max()
				self.assertLessEqual(_a						,   1e-3		)







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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()







			@classmethod
			def 			SCREAMING_SNAKE_CASE    (				cls		):
				__magic_name__		:		Any	   =     load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"		)

				__magic_name__		:		Any	   =     raw_image.convert("RGB"		).resize((768, 768)		)

				__magic_name__		:		Union[str, Any]	   =     raw_image







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     torch.manual_seed(0		)

				__magic_name__		:		Dict	   =     StableDiffusionDiffEditPipeline.from_pretrained(
				    "stabilityai/stable-diffusion-2-1"						,   safety_checker=_a						,   torch_dtype=torch.floataa		)
				__magic_name__		:		int	   =     DDIMScheduler.from_config(pipe.scheduler.config		)
				__magic_name__		:		Optional[Any]	   =     DDIMInverseScheduler.from_config(pipe.scheduler.config		)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=_a		)

				__magic_name__		:		Dict	   =     "a bowl of fruit"
				__magic_name__		:		Any	   =     "a bowl of pears"

				__magic_name__		:		str	   =     pipe.generate_mask(
				    image=self.raw_image						,   source_prompt=_a						,   target_prompt=_a						,   generator=_a						,   )

				__magic_name__		:		Dict	   =     pipe.invert(
				    prompt=_a						,   image=self.raw_image						,   inpaint_strength=0.7						,   generator=_a		).latents

				__magic_name__		:		Dict	   =     pipe(
				    prompt=_a						,   mask_image=_a						,   image_latents=_a						,   generator=_a						,   negative_prompt=_a						,   inpaint_strength=0.7						,   output_type="numpy"						,   ).images[0]

				__magic_name__		:		Optional[Any]	   =     (
				    np.array(
				        load_image(
				            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				            "/diffedit/pears.png"		).resize((768, 768)		)		)
				    / 255
				)
				assert np.abs((expected_image - image).max()		) < 5e-1







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     torch.manual_seed(0		)

				__magic_name__		:		Optional[Any]	   =     StableDiffusionDiffEditPipeline.from_pretrained(
				    "stabilityai/stable-diffusion-2-1"						,   safety_checker=_a						,   torch_dtype=torch.floataa		)
				__magic_name__		:		List[Any]	   =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
				__magic_name__		:		Optional[int]	   =     DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config		)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=_a		)

				__magic_name__		:		str	   =     "a bowl of fruit"
				__magic_name__		:		Tuple	   =     "a bowl of pears"

				__magic_name__		:		Union[str, Any]	   =     pipe.generate_mask(
				    image=self.raw_image						,   source_prompt=_a						,   target_prompt=_a						,   generator=_a						,   )

				__magic_name__		:		Optional[int]	   =     pipe.invert(
				    prompt=_a						,   image=self.raw_image						,   inpaint_strength=0.7						,   generator=_a						,   num_inference_steps=25						,   ).latents

				__magic_name__		:		Optional[Any]	   =     pipe(
				    prompt=_a						,   mask_image=_a						,   image_latents=_a						,   generator=_a						,   negative_prompt=_a						,   inpaint_strength=0.7						,   num_inference_steps=25						,   output_type="numpy"						,   ).images[0]

				__magic_name__		:		Any	   =     (
				    np.array(
				        load_image(
				            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				            "/diffedit/pears.png"		).resize((768, 768)		)		)
				    / 255
				)
				assert np.abs((expected_image - image).max()		) < 5e-1


import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard






def   lowerCAmelCase_						(					_snake_case      :  List[Any]   )				->		List[Any]:







	'''simple docstring'''


	assert "mock" in _fsspec_registry
	assert "bz2" in _fsspec_registry






def   lowerCAmelCase_						(					)				->		Tuple:







	'''simple docstring'''


	assert "mock" not in _fsspec_registry
	assert "bz2" in _fsspec_registry






def   lowerCAmelCase_						(					)				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		Dict	   =     "mock-s3-bucket"
	__magic_name__		:		Any	   =     F'''s3://{mock_bucket}'''
	__magic_name__		:		str	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path.startswith("s3://"   ) is False

	__magic_name__		:		Tuple	   =     "./local/path"
	__magic_name__		:		Optional[Any]	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path == new_dataset_path






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		Optional[Any]:







	'''simple docstring'''


	__magic_name__		:		str	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is True

	__magic_name__		:		Optional[int]	   =     fsspec.filesystem("file"   )

	__magic_name__		:		int	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is False






@pytest.mark.parametrize("compression_fs_class"      ,   _snake_case   )
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  int      ,   _snake_case      :  Tuple      ,   _snake_case      :  Any      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  Any   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Any	   =     {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
	__magic_name__		:		str	   =     input_paths[compression_fs_class.protocol]
	if input_path is None:
		__magic_name__		:		Dict	   =     F'''for \'{compression_fs_class.protocol}\' compression protocol, '''
		if compression_fs_class.protocol == "lz4":
			reason += require_lza.kwargs["reason"]
		elif compression_fs_class.protocol == "zstd":
			reason += require_zstandard.kwargs["reason"]
		pytest.skip(_snake_case   )
	__magic_name__		:		str	   =     fsspec.filesystem(compression_fs_class.protocol      ,   fo=_snake_case   )
	assert isinstance(_snake_case      ,   _snake_case   )
	__magic_name__		:		int	   =     os.path.basename(_snake_case   )
	__magic_name__		:		Optional[int]	   =     expected_filename[: expected_filename.rindex("."   )]
	assert fs.glob("*"   ) == [expected_filename]
	with fs.open(_snake_case      ,   "r"      ,   encoding="utf-8"   ) as f, open(_snake_case      ,   encoding="utf-8"   ) as expected_file:
		assert f.read() == expected_file.read()






@pytest.mark.parametrize("protocol"      ,   ["zip", "gzip"]   )
def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  Optional[Any]   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
	__magic_name__		:		int	   =     compressed_file_paths[protocol]
	__magic_name__		:		Tuple	   =     "dataset.jsonl"
	__magic_name__		:		List[str]	   =     F'''{protocol}://{member_file_path}::{compressed_file_path}'''
	__magic_name__   ,    *__magic_name__		:		Optional[Any]	   =     fsspec.get_fs_token_paths(_snake_case   )
	assert fs.isfile(_snake_case   )
	assert not fs.isfile("non_existing_" + member_file_path   )






@pytest.mark.integration
def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Dict      ,   _snake_case      :  List[str]      ,   _snake_case      :  Tuple   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     hf_api.dataset_info(_snake_case      ,   token=_snake_case   )
	__magic_name__		:		Optional[Any]	   =     HfFileSystem(repo_info=_snake_case      ,   token=_snake_case   )
	assert sorted(hffs.glob("*"   )   ) == [".gitattributes", "data"]
	assert hffs.isdir("data"   )
	assert hffs.isfile(".gitattributes"   ) and hffs.isfile("data/text_data.txt"   )
	with open(_snake_case   ) as f:
		assert hffs.open("data/text_data.txt"      ,   "r"   ).read() == f.read()






def   lowerCAmelCase_						(					)				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		Optional[Any]	   =     "bz2"

	# Import module
	import datasets.filesystems

	# Overwrite protocol and reload
	register_implementation(_snake_case      ,   _snake_case      ,   clobber=_snake_case   )
	with pytest.warns(_snake_case   ) as warning_info:
		importlib.reload(datasets.filesystems   )

	assert len(_snake_case   ) == 1
	assert (
	    str(warning_info[0].message   )
	    == F'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
	)
