
'''simple docstring'''


import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowercase							:   Union[str, Any]					=     '''src/diffusers'''
__lowercase							:   Tuple					=     '''.'''


# This is to make sure the diffusers module imported is the one in the repo.
__lowercase							:   str					=     importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
    submodule_search_locations=[DIFFUSERS_PATH],
)
__lowercase							:   str					=     spec.loader.load_module()




def lowercase_	(   _lowercase			,       _lowercase				)     ->							List[str]:
					'''simple docstring'''

					return line.startswith(_lowercase				) or len(_lowercase				) <= 1 or re.search(R'''^\s*\)(\s*->.*:|:)\s*$'''			,       _lowercase				) is not None




def lowercase_	(   _lowercase				)     ->							Tuple:
					'''simple docstring'''

					lowerCamelCase_					:      int     =		object_name.split('''.'''				)
					lowerCamelCase_					:      Union[str, Any]     =		0

					# First let's find the module where our object lives.
					lowerCamelCase_					:      Dict     =		parts[i]
					while i < len(_lowercase				) and not os.path.isfile(os.path.join(_lowercase			,       F"""{module}.py"""				)				):
										i += 1
										if i < len(_lowercase				):
															lowerCamelCase_					:      Optional[int]     =		os.path.join(_lowercase			,       parts[i]				)
					if i >= len(_lowercase				):
										raise ValueError(F"""`object_name` should begin with the name of a module of diffusers but got {object_name}."""				)

					with open(os.path.join(_lowercase			,       F"""{module}.py"""				)			,       '''r'''			,       encoding='''utf-8'''			,       newline='''\n'''				) as f:
										lowerCamelCase_					:      Optional[int]     =		f.readlines()

					# Now let's find the class / func in the code!
					lowerCamelCase_					:      Optional[int]     =		''''''
					lowerCamelCase_					:      Optional[Any]     =		0
					for name in parts[i + 1 :]:
										while (
										    line_index < len(_lowercase				) and re.search(RF"""^{indent}(class|def)\s+{name}(\(|\:)"""			,       lines[line_index]				) is None
										):
															line_index += 1
										indent += "    "
										line_index += 1

					if line_index >= len(_lowercase				):
										raise ValueError(F""" {object_name} does not match any function or class in {module}."""				)

					# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
					lowerCamelCase_					:      Dict     =		line_index
					while line_index < len(_lowercase				) and _should_continue(lines[line_index]			,       _lowercase				):
										line_index += 1
					# Clean up empty lines at the end (if any).
					while len(lines[line_index - 1]				) <= 1:
										line_index -= 1

					lowerCamelCase_					:      str     =		lines[start_index:line_index]
					return "".join(_lowercase				)


__lowercase							:   Any					=     re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
__lowercase							:   Optional[int]					=     re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
__lowercase							:   int					=     re.compile(r'''<FILL\s+[^>]*>''')




def lowercase_	(   _lowercase				)     ->							Optional[int]:
					'''simple docstring'''

					lowerCamelCase_					:      List[Any]     =		code.split('''\n'''				)
					lowerCamelCase_					:      int     =		0
					while idx < len(_lowercase				) and len(lines[idx]				) == 0:
										idx += 1
					if idx < len(_lowercase				):
										return re.search(R'''^(\s*)\S'''			,       lines[idx]				).groups()[0]
					return ""




def lowercase_	(   _lowercase				)     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      List[str]     =		len(get_indent(_lowercase				)				) > 0
					if has_indent:
										lowerCamelCase_					:      Optional[Any]     =		F"""class Bla:\n{code}"""
					lowerCamelCase_					:      Any     =		black.Mode(target_versions={black.TargetVersion.PYaa}			,       line_length=119			,       preview=_lowercase				)
					lowerCamelCase_					:      Any     =		black.format_str(_lowercase			,       mode=_lowercase				)
					lowerCamelCase_,      lowerCamelCase_					:      List[str]     =		style_docstrings_in_code(_lowercase				)
					return result[len('''class Bla:\n'''				) :] if has_indent else result




def lowercase_	(   _lowercase			,       _lowercase=False				)     ->							Union[str, Any]:
					'''simple docstring'''

					with open(_lowercase			,       '''r'''			,       encoding='''utf-8'''			,       newline='''\n'''				) as f:
										lowerCamelCase_					:      Optional[Any]     =		f.readlines()
					lowerCamelCase_					:      List[str]     =		[]
					lowerCamelCase_					:      Dict     =		0
					# Not a for loop cause `lines` is going to change (if `overwrite=True`).
					while line_index < len(_lowercase				):
										lowerCamelCase_					:      str     =		_re_copy_warning.search(lines[line_index]				)
										if search is None:
															line_index += 1
															continue

										# There is some copied code here, let's retrieve the original.
										lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      List[str]     =		search.groups()
										lowerCamelCase_					:      Optional[Any]     =		find_code_in_diffusers(_lowercase				)
										lowerCamelCase_					:      List[str]     =		get_indent(_lowercase				)

										lowerCamelCase_					:      List[str]     =		line_index + 1 if indent == theoretical_indent else line_index + 2
										lowerCamelCase_					:      str     =		theoretical_indent
										lowerCamelCase_					:      Dict     =		start_index

										# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
										lowerCamelCase_					:      Any     =		True
										while line_index < len(_lowercase				) and should_continue:
															line_index += 1
															if line_index >= len(_lowercase				):
																				break
															lowerCamelCase_					:      int     =		lines[line_index]
															lowerCamelCase_					:      Any     =		_should_continue(_lowercase			,       _lowercase				) and re.search(F"""^{indent}# End copy"""			,       _lowercase				) is None
										# Clean up empty lines at the end (if any).
										while len(lines[line_index - 1]				) <= 1:
															line_index -= 1

										lowerCamelCase_					:      List[Any]     =		lines[start_index:line_index]
										lowerCamelCase_					:      Tuple     =		''''''.join(_lowercase				)

										# Remove any nested `Copied from` comments to avoid circular copies
										lowerCamelCase_					:      Optional[Any]     =		[line for line in theoretical_code.split('''\n'''				) if _re_copy_warning.search(_lowercase				) is None]
										lowerCamelCase_					:      str     =		'''\n'''.join(_lowercase				)

										# Before comparing, use the `replace_pattern` on the original code.
										if len(_lowercase				) > 0:
															lowerCamelCase_					:      Optional[Any]     =		replace_pattern.replace('''with'''			,       ''''''				).split(''','''				)
															lowerCamelCase_					:      Optional[int]     =		[_re_replace_pattern.search(_lowercase				) for p in patterns]
															for pattern in patterns:
																				if pattern is None:
																									continue
																				lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      Optional[Any]     =		pattern.groups()
																				lowerCamelCase_					:      Optional[int]     =		re.sub(_lowercase			,       _lowercase			,       _lowercase				)
																				if option.strip() == "all-casing":
																									lowerCamelCase_					:      Dict     =		re.sub(obja.lower()			,       obja.lower()			,       _lowercase				)
																									lowerCamelCase_					:      Any     =		re.sub(obja.upper()			,       obja.upper()			,       _lowercase				)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
															lowerCamelCase_					:      Optional[Any]     =		blackify(lines[start_index - 1] + theoretical_code				)
															lowerCamelCase_					:      Any     =		theoretical_code[len(lines[start_index - 1]				) :]

										# Test for a diff and act accordingly.
										if observed_code != theoretical_code:
															diffs.append([object_name, start_index]				)
															if overwrite:
																				lowerCamelCase_					:      Dict     =		lines[:start_index] + [theoretical_code] + lines[line_index:]
																				lowerCamelCase_					:      Optional[int]     =		start_index + 1

					if overwrite and len(_lowercase				) > 0:
										# Warn the user a file has been modified.
										print(F"""Detected changes, rewriting {filename}."""				)
										with open(_lowercase			,       '''w'''			,       encoding='''utf-8'''			,       newline='''\n'''				) as f:
															f.writelines(_lowercase				)
					return diffs




def lowercase_	(   _lowercase = False				)     ->							List[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      int     =		glob.glob(os.path.join(_lowercase			,       '''**/*.py'''				)			,       recursive=_lowercase				)
					lowerCamelCase_					:      Tuple     =		[]
					for filename in all_files:
										lowerCamelCase_					:      List[str]     =		is_copy_consistent(_lowercase			,       _lowercase				)
										diffs += [F"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
					if not overwrite and len(_lowercase				) > 0:
										lowerCamelCase_					:      List[Any]     =		'''\n'''.join(_lowercase				)
										raise Exception(
										    '''Found the following copy inconsistencies:\n'''
										    + diff
										    + '''\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.'''				)


if __name__ == "__main__":
					__lowercase							:   Dict					=     argparse.ArgumentParser()
					parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
					__lowercase							:   int					=     parser.parse_args()

					check_copies(args.fix_and_overwrite)




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 	__lowercase				(  _lowercase      ,     unittest.TestCase    ):
			lowerCamelCase			:		Optional[Any]	      =			KandinskyVaaInpaintPipeline
			lowerCamelCase			:		Tuple	      =			["image_embeds", "negative_image_embeds", "image", "mask_image"]
			lowerCamelCase			:		List[str]	      =			[
			    "image_embeds",
			    "negative_image_embeds",
			    "image",
			    "mask_image",
			]
			lowerCamelCase			:		Optional[int]	      =			[
			    "generator",
			    "height",
			    "width",
			    "latents",
			    "guidance_scale",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			lowerCamelCase			:		List[Any]	      =			False

			@property
			def        UpperCAmelCase__ (self      ):
								return 3_2

			@property
			def        UpperCAmelCase__ (self      ):
								return 3_2

			@property
			def        UpperCAmelCase__ (self      ):
								return self.time_input_dim

			@property
			def        UpperCAmelCase__ (self      ):
								return self.time_input_dim * 4

			@property
			def        UpperCAmelCase__ (self      ):
								return 1_0_0

			@property
			def        UpperCAmelCase__ (self      ):
								torch.manual_seed(0      )

								lowerCamelCase_					:      Optional[Any]     =		{
								    '''in_channels''': 9,
								    # Out channels is double in channels because predicts mean and variance
								    '''out_channels''': 8,
								    '''addition_embed_type''': '''image''',
								    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
								    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
								    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
								    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
								    '''layers_per_block''': 1,
								    '''encoder_hid_dim''': self.text_embedder_hidden_size,
								    '''encoder_hid_dim_type''': '''image_proj''',
								    '''cross_attention_dim''': self.cross_attention_dim,
								    '''attention_head_dim''': 4,
								    '''resnet_time_scale_shift''': '''scale_shift''',
								    '''class_embed_type''': None,
								}

								lowerCamelCase_					:      str     =		UNetaDConditionModel(**A      )
								return model

			@property
			def        UpperCAmelCase__ (self      ):
								return {
								    "block_out_channels": [3_2, 6_4],
								    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 1_2,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}

			@property
			def        UpperCAmelCase__ (self      ):
								torch.manual_seed(0      )
								lowerCamelCase_					:      Optional[Any]     =		VQModel(**self.dummy_movq_kwargs      )
								return model

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		self.dummy_unet
								lowerCamelCase_					:      Optional[int]     =		self.dummy_movq

								lowerCamelCase_					:      Union[str, Any]     =		DDIMScheduler(
								    num_train_timesteps=1_0_0_0			,   beta_schedule='''linear'''			,   beta_start=0.0_00_85			,   beta_end=0.0_12			,   clip_sample=A			,   set_alpha_to_one=A			,   steps_offset=1			,   prediction_type='''epsilon'''			,   thresholding=A			,   )

								lowerCamelCase_					:      Dict     =		{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}

								return components

			def        UpperCAmelCase__ (self			,   A			,   A=0      ):
								lowerCamelCase_					:      List[Any]     =		floats_tensor((1, self.text_embedder_hidden_size)			,   rng=random.Random(A      )      ).to(A      )
								lowerCamelCase_					:      Tuple     =		floats_tensor((1, self.text_embedder_hidden_size)			,   rng=random.Random(seed + 1      )      ).to(
								    A      )
								# create init_image
								lowerCamelCase_					:      Dict     =		floats_tensor((1, 3, 6_4, 6_4)			,   rng=random.Random(A      )      ).to(A      )
								lowerCamelCase_					:      List[str]     =		image.cpu().permute(0			,   2			,   3			,   1      )[0]
								lowerCamelCase_					:      Any     =		Image.fromarray(np.uinta(A      )      ).convert('''RGB'''      ).resize((2_5_6, 2_5_6)      )
								# create mask
								lowerCamelCase_					:      List[str]     =		np.ones((6_4, 6_4)			,   dtype=np.floataa      )
								lowerCamelCase_					:      Union[str, Any]     =		0

								if str(A      ).startswith('''mps'''      ):
													lowerCamelCase_					:      int     =		torch.manual_seed(A      )
								else:
													lowerCamelCase_					:      Union[str, Any]     =		torch.Generator(device=A      ).manual_seed(A      )
								lowerCamelCase_					:      Optional[Any]     =		{
								    '''image''': init_image,
								    '''mask_image''': mask,
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 6_4,
								    '''width''': 6_4,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 4.0,
								    '''output_type''': '''np''',
								}
								return inputs

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		'''cpu'''

								lowerCamelCase_					:      str     =		self.get_dummy_components()

								lowerCamelCase_					:      Tuple     =		self.pipeline_class(**A      )
								lowerCamelCase_					:      Union[str, Any]     =		pipe.to(A      )

								pipe.set_progress_bar_config(disable=A      )

								lowerCamelCase_					:      str     =		pipe(**self.get_dummy_inputs(A      )      )
								lowerCamelCase_					:      Any     =		output.images

								lowerCamelCase_					:      Union[str, Any]     =		pipe(
								    **self.get_dummy_inputs(A      )			,   return_dict=A			,   )[0]

								lowerCamelCase_					:      List[Any]     =		image[0, -3:, -3:, -1]
								lowerCamelCase_					:      Tuple     =		image_from_tuple[0, -3:, -3:, -1]

								print(F"""image.shape {image.shape}"""      )

								assert image.shape == (1, 6_4, 6_4, 3)

								lowerCamelCase_					:      Any     =		np.array(
								    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]      )

								assert (
								    np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
								), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2
								), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






			def        UpperCAmelCase__ (self      ):
								super().test_inference_batch_single_identical(expected_max_diff=3E-3      )

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

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






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''      )

								lowerCamelCase_					:      Optional[Any]     =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
								lowerCamelCase_					:      List[str]     =		np.ones((7_6_8, 7_6_8)			,   dtype=np.floataa      )
								lowerCamelCase_					:      int     =		0

								lowerCamelCase_					:      Union[str, Any]     =		'''a hat'''

								lowerCamelCase_					:      Optional[int]     =		KandinskyVaaPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-prior'''			,   torch_dtype=torch.floataa      )
								pipe_prior.to(A      )

								lowerCamelCase_					:      Any     =		KandinskyVaaInpaintPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''			,   torch_dtype=torch.floataa      )
								lowerCamelCase_					:      Any     =		pipeline.to(A      )
								pipeline.set_progress_bar_config(disable=A      )

								lowerCamelCase_					:      Any     =		torch.Generator(device='''cpu'''      ).manual_seed(0      )
								lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		pipe_prior(
								    A			,   generator=A			,   num_inference_steps=5			,   negative_prompt=''''''			,   ).to_tuple()

								lowerCamelCase_					:      Union[str, Any]     =		pipeline(
								    image=A			,   mask_image=A			,   image_embeds=A			,   negative_image_embeds=A			,   generator=A			,   num_inference_steps=1_0_0			,   height=7_6_8			,   width=7_6_8			,   output_type='''np'''			,   )

								lowerCamelCase_					:      List[str]     =		output.images[0]

								assert image.shape == (7_6_8, 7_6_8, 3)

								assert_mean_pixel_difference(A			,   A      )


