

import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			[]
	for line in lines:
		snake_case_   		=			re.sub(R'#.*'      ,			''      ,			lowercase__						)  # remove comments
		if line:
			filtered_lines.append(lowercase__						)
	snake_case_   		=			'\n'.join(lowercase__						)

	# Make a hash from all this code
	snake_case_   		=			full_str.encode('utf-8'						)
	return shaaaa(lowercase__						).hexdigest()


# get importable module names and hash for caching
A									=  {
    'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
A									=  {
    '.csv': ('csv', {}),
    '.tsv': ('csv', {'sep': '\t'}),
    '.json': ('json', {}),
    '.jsonl': ('json', {}),
    '.parquet': ('parquet', {}),
    '.arrow': ('arrow', {}),
    '.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
A									=  {'imagefolder', 'audiofolder'}

# Used to filter data files based on extensions given a module name
A									=  {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
		_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')






_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')



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 SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	StableDiffusionDiffEditPipeline
			__A    	=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
			__A    	=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
			__A    	=	frozenset(
			    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			__A    	=	frozenset([]				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				torch.manual_seed(0				)
				snake_case_   		=			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=__UpperCamelCase			,					)
				snake_case_   		=			DDIMScheduler(
				    beta_start=0.0_0085			,					beta_end=0.012			,					beta_schedule='scaled_linear'			,					clip_sample=__UpperCamelCase			,					set_alpha_to_one=__UpperCamelCase			,					)
				snake_case_   		=			DDIMInverseScheduler(
				    beta_start=0.0_0085			,					beta_end=0.012			,					beta_schedule='scaled_linear'			,					clip_sample=__UpperCamelCase			,					set_alpha_to_zero=__UpperCamelCase			,					)
				torch.manual_seed(0				)
				snake_case_   		=			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=1_28			,					)
				torch.manual_seed(0				)
				snake_case_   		=			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=10_00			,					hidden_act='gelu'			,					projection_dim=5_12			,					)
				snake_case_   		=			CLIPTextModel(__UpperCamelCase				)
				snake_case_   		=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

				snake_case_   		=			{
				    '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       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				snake_case_   		=			floats_tensor((1, 16, 16)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			floats_tensor((1, 2, 4, 16, 16)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    '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       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				snake_case_   		=			floats_tensor((1, 3, 32, 32)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			image.cpu().permute(0			,					2			,					3			,					1				)[0]
				snake_case_   		=			Image.fromarray(np.uinta(__UpperCamelCase				)				).convert('RGB'				)
				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    '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       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				snake_case_   		=			floats_tensor((1, 3, 32, 32)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			image.cpu().permute(0			,					2			,					3			,					1				)[0]
				snake_case_   		=			Image.fromarray(np.uinta(__UpperCamelCase				)				).convert('RGB'				)
				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    '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       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				if not hasattr(self.pipeline_class			,					'_optional_components'				):
					return

				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			self.pipeline_class(**__UpperCamelCase				)
				pipe.to(__UpperCamelCase				)
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

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

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			pipe(**__UpperCamelCase				)[0]

				with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(__UpperCamelCase				)
					snake_case_   		=			self.pipeline_class.from_pretrained(__UpperCamelCase				)
					pipe_loaded.to(__UpperCamelCase				)
					pipe_loaded.set_progress_bar_config(disable=__UpperCamelCase				)

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

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			pipe_loaded(**__UpperCamelCase				)[0]

				snake_case_   		=			np.abs(output - output_loaded				).max()
				self.assertLess(__UpperCamelCase			,					1E-4				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'

				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			self.pipeline_class(**__UpperCamelCase				)
				pipe.to(__UpperCamelCase				)
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_dummy_mask_inputs(__UpperCamelCase				)
				snake_case_   		=			pipe.generate_mask(**__UpperCamelCase				)
				snake_case_   		=			mask[0, -3:, -3:]

				self.assertEqual(mask.shape			,					(1, 16, 16)				)
				snake_case_   		=			np.array([0] * 9				)
				snake_case_   		=			np.abs(mask_slice.flatten() - expected_slice				).max()
				self.assertLessEqual(__UpperCamelCase			,					1E-3				)
				self.assertEqual(mask[0, -3, -4]			,					0				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'

				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			self.pipeline_class(**__UpperCamelCase				)
				pipe.to(__UpperCamelCase				)
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_dummy_inversion_inputs(__UpperCamelCase				)
				snake_case_   		=			pipe.invert(**__UpperCamelCase				).images
				snake_case_   		=			image[0, -1, -3:, -3:]

				self.assertEqual(image.shape			,					(2, 32, 32, 3)				)
				snake_case_   		=			np.array(
				    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]			,					)
				snake_case_   		=			np.abs(image_slice.flatten() - expected_slice				).max()
				self.assertLessEqual(__UpperCamelCase			,					1E-3				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'

				snake_case_   		=			self.get_dummy_components()

				snake_case_   		=			{'beta_start': 0.0_0085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
				snake_case_   		=			DPMSolverMultistepScheduler(**__UpperCamelCase				)
				snake_case_   		=			DPMSolverMultistepInverseScheduler(**__UpperCamelCase				)

				snake_case_   		=			self.pipeline_class(**__UpperCamelCase				)
				pipe.to(__UpperCamelCase				)
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_dummy_inversion_inputs(__UpperCamelCase				)
				snake_case_   		=			pipe.invert(**__UpperCamelCase				).images
				snake_case_   		=			image[0, -1, -3:, -3:]

				self.assertEqual(image.shape			,					(2, 32, 32, 3)				)
				snake_case_   		=			np.array(
				    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]			,					)
				snake_case_   		=			np.abs(image_slice.flatten() - expected_slice				).max()
				self.assertLessEqual(__UpperCamelCase			,					1E-3				)







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






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			@classmethod
			def       __lowerCAmelCase			(     cls				):

				"""simple docstring"""


				snake_case_   		=			load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'				)

				snake_case_   		=			raw_image.convert('RGB'				).resize((7_68, 7_68)				)

				snake_case_   		=			raw_image


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			torch.manual_seed(0				)

				snake_case_   		=			StableDiffusionDiffEditPipeline.from_pretrained(
				    'stabilityai/stable-diffusion-2-1'			,					safety_checker=__UpperCamelCase			,					torch_dtype=torch.floataa				)
				snake_case_   		=			DDIMScheduler.from_config(pipe.scheduler.config				)
				snake_case_   		=			DDIMInverseScheduler.from_config(pipe.scheduler.config				)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			'a bowl of fruit'
				snake_case_   		=			'a bowl of pears'

				snake_case_   		=			pipe.generate_mask(
				    image=self.raw_image			,					source_prompt=__UpperCamelCase			,					target_prompt=__UpperCamelCase			,					generator=__UpperCamelCase			,					)

				snake_case_   		=			pipe.invert(
				    prompt=__UpperCamelCase			,					image=self.raw_image			,					inpaint_strength=0.7			,					generator=__UpperCamelCase				).latents

				snake_case_   		=			pipe(
				    prompt=__UpperCamelCase			,					mask_image=__UpperCamelCase			,					image_latents=__UpperCamelCase			,					generator=__UpperCamelCase			,					negative_prompt=__UpperCamelCase			,					inpaint_strength=0.7			,					output_type='numpy'			,					).images[0]

				snake_case_   		=			(
				    np.array(
				        load_image(
				            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				            '/diffedit/pears.png'				).resize((7_68, 7_68)				)				)
				    / 2_55
				)
				assert np.abs((expected_image - image).max()				) < 5E-1







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			torch.manual_seed(0				)

				snake_case_   		=			StableDiffusionDiffEditPipeline.from_pretrained(
				    'stabilityai/stable-diffusion-2-1'			,					safety_checker=__UpperCamelCase			,					torch_dtype=torch.floataa				)
				snake_case_   		=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
				snake_case_   		=			DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config				)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			'a bowl of fruit'
				snake_case_   		=			'a bowl of pears'

				snake_case_   		=			pipe.generate_mask(
				    image=self.raw_image			,					source_prompt=__UpperCamelCase			,					target_prompt=__UpperCamelCase			,					generator=__UpperCamelCase			,					)

				snake_case_   		=			pipe.invert(
				    prompt=__UpperCamelCase			,					image=self.raw_image			,					inpaint_strength=0.7			,					generator=__UpperCamelCase			,					num_inference_steps=25			,					).latents

				snake_case_   		=			pipe(
				    prompt=__UpperCamelCase			,					mask_image=__UpperCamelCase			,					image_latents=__UpperCamelCase			,					generator=__UpperCamelCase			,					negative_prompt=__UpperCamelCase			,					inpaint_strength=0.7			,					num_inference_steps=25			,					output_type='numpy'			,					).images[0]

				snake_case_   		=			(
				    np.array(
				        load_image(
				            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				            '/diffedit/pears.png'				).resize((7_68, 7_68)				)				)
				    / 2_55
				)
				assert np.abs((expected_image - image).max()				) < 5E-1
