


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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       (							a_			,							unittest.TestCase  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[Any]			       =		KandinskyVaaControlnetPipeline
		UpperCamelCase     :					Tuple			       =		["image_embeds", "negative_image_embeds", "hint"]
		UpperCamelCase     :					str			       =		["image_embeds", "negative_image_embeds", "hint"]
		UpperCamelCase     :					Tuple			       =		[
		    "generator",
		    "height",
		    "width",
		    "latents",
		    "guidance_scale",
		    "num_inference_steps",
		    "return_dict",
		    "guidance_scale",
		    "num_images_per_prompt",
		    "output_type",
		    "return_dict",
		]
		UpperCamelCase     :					Optional[Any]			       =		False







		@property
		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					return 32







		@property
		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					return 32







		@property
		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					return self.time_input_dim







		@property
		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					return self.time_input_dim * 4







		@property
		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					return 1_00







		@property
		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					torch.manual_seed(0	)

					lowerCamelCase    		=  {
					    "in_channels": 8,
					    # Out channels is double in channels because predicts mean and variance
					    "out_channels": 8,
					    "addition_embed_type": "image_hint",
					    "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    		=  UNetaDConditionModel(**a_	)
					return model







		@property
		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					return {
					    "block_out_channels": [32, 32, 64, 64],
					    "down_block_types": [
					        "DownEncoderBlock2D",
					        "DownEncoderBlock2D",
					        "DownEncoderBlock2D",
					        "AttnDownEncoderBlock2D",
					    ],
					    "in_channels": 3,
					    "latent_channels": 4,
					    "layers_per_block": 1,
					    "norm_num_groups": 8,
					    "norm_type": "spatial",
					    "num_vq_embeddings": 12,
					    "out_channels": 3,
					    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
					    "vq_embed_dim": 4,
					}







		@property
		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					torch.manual_seed(0	)
					lowerCamelCase    		=  VQModel(**self.dummy_movq_kwargs	)
					return model







		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  self.dummy_unet
					lowerCamelCase    		=  self.dummy_movq

					lowerCamelCase    		=  DDIMScheduler(
					    num_train_timesteps=10_00		,      beta_schedule="""linear"""		,      beta_start=0.00085		,      beta_end=0.012		,      clip_sample=a_		,      set_alpha_to_one=a_		,      steps_offset=1		,      prediction_type="""epsilon"""		,      thresholding=a_		,      )

					lowerCamelCase    		=  {
					    "unet": unet,
					    "scheduler": scheduler,
					    "movq": movq,
					}
					return components







		def 					__A   ( self		,      A		,      A=0	)  -> Optional[int]:







					'''simple docstring'''







					lowerCamelCase    		=  floats_tensor((1, self.text_embedder_hidden_size)		,      rng=random.Random(a_	)	).to(a_	)
					lowerCamelCase    		=  floats_tensor((1, self.text_embedder_hidden_size)		,      rng=random.Random(seed + 1	)	).to(
					    a_	)

					# create hint
					lowerCamelCase    		=  floats_tensor((1, 3, 64, 64)		,      rng=random.Random(a_	)	).to(a_	)

					if str(a_	).startswith("""mps"""	):
								lowerCamelCase    		=  torch.manual_seed(a_	)
					else:
								lowerCamelCase    		=  torch.Generator(device=a_	).manual_seed(a_	)
					lowerCamelCase    		=  {
					    "image_embeds": image_embeds,
					    "negative_image_embeds": negative_image_embeds,
					    "hint": hint,
					    "generator": generator,
					    "height": 64,
					    "width": 64,
					    "guidance_scale": 4.0,
					    "num_inference_steps": 2,
					    "output_type": "np",
					}
					return inputs







		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  "cpu"

					lowerCamelCase    		=  self.get_dummy_components()

					lowerCamelCase    		=  self.pipeline_class(**a_	)
					lowerCamelCase    		=  pipe.to(a_	)

					pipe.set_progress_bar_config(disable=a_	)

					lowerCamelCase    		=  pipe(**self.get_dummy_inputs(a_	)	)
					lowerCamelCase    		=  output.images

					lowerCamelCase    		=  pipe(
					    **self.get_dummy_inputs(a_	)		,      return_dict=a_		,      )[0]

					lowerCamelCase    		=  image[0, -3:, -3:, -1]
					lowerCamelCase    		=  image_from_tuple[0, -3:, -3:, -1]

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

					lowerCamelCase    		=  np.array(
					    [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]	)

					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()}'







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



		"""simple docstring"""

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







					'''simple docstring'''







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







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







					'''simple docstring'''







					lowerCamelCase    		=  load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""	)

					lowerCamelCase    		=  load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/kandinskyv22/hint_image_cat.png"""	)
					lowerCamelCase    		=  torch.from_numpy(np.array(a_	)	).float() / 2_55.0
					lowerCamelCase    		=  hint.permute(2		,      0		,      1	).unsqueeze(0	)

					lowerCamelCase    		=  KandinskyVaaPriorPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-2-prior"""		,      torch_dtype=torch.floataa	)
					pipe_prior.to(a_	)

					lowerCamelCase    		=  KandinskyVaaControlnetPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-2-controlnet-depth"""		,      torch_dtype=torch.floataa	)
					lowerCamelCase    		=  pipeline.to(a_	)
					pipeline.set_progress_bar_config(disable=a_	)

					lowerCamelCase    		=  "A robot, 4k photo"

					lowerCamelCase    		=  torch.Generator(device="""cuda"""	).manual_seed(0	)
					lowerCamelCase    		=  pipe_prior(
					    a_		,      generator=a_		,      num_inference_steps=5		,      negative_prompt=""""""		,      ).to_tuple()

					lowerCamelCase    		=  torch.Generator(device="""cuda"""	).manual_seed(0	)
					lowerCamelCase    		=  pipeline(
					    image_embeds=a_		,      negative_image_embeds=a_		,      hint=a_		,      generator=a_		,      num_inference_steps=1_00		,      output_type="""np"""		,      )

					lowerCamelCase    		=  output.images[0]

					assert image.shape == (5_12, 5_12, 3)

					assert_mean_pixel_difference(a_		,      a_	)





'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path


lowerCAmelCase   : List[str]             =				"""src/transformers"""


# Matches is_xxx_available()
lowerCAmelCase   : List[str]             =				re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCAmelCase   : str             =				re.compile(R"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCAmelCase   : Optional[int]             =				re.compile(R"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
lowerCAmelCase   : Optional[int]             =				re.compile(R"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
lowerCAmelCase   : str             =				re.compile(R"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCAmelCase   : List[Any]             =				re.compile(R"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma:     "MyModel",
lowerCAmelCase   : Dict             =				re.compile("""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowerCAmelCase   : Tuple             =				re.compile("""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
lowerCAmelCase   : Optional[int]             =				re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
lowerCAmelCase   : Optional[int]             =				re.compile(R"""^\s*try:""")
# Catches a line with else:
lowerCAmelCase   : Optional[int]             =				re.compile(R"""^\s*else:""")




def 	_A  (       A							)  ->				List[Any]:
	if _re_test_backend.search(A							) is None:
		return None
	lowercase						:		Union[str, Any]   					=		[b[0] for b in _re_backend.findall(A							)]
	backends.sort()
	return "_and_".join(A							)




def 	_A  (       A							)  ->				Optional[Any]:
	with open(A							,"r"							,encoding="utf-8"							,newline="\n"							) as f:
		lowercase						:		Tuple   					=		f.readlines()

	lowercase						:		List[str]   					=		0
	while line_index < len(A							) and not lines[line_index].startswith("_import_structure = {"							):
		line_index += 1

	# If this is a traditional init, just return.
	if line_index >= len(A							):
		return None

	# First grab the objects without a specific backend in _import_structure
	lowercase						:		Union[str, Any]   					=		[]
	while not lines[line_index].startswith("if TYPE_CHECKING"							) and find_backend(lines[line_index]							) is None:
		lowercase						:		Any   					=		lines[line_index]
		# If we have everything on a single line, let's deal with it.
		if _re_one_line_import_struct.search(A							):
			lowercase						:		int   					=		_re_one_line_import_struct.search(A							).groups()[0]
			lowercase						:		Union[str, Any]   					=		re.findall("\[([^\]]+)\]"							,A							)
			for imp in imports:
				objects.extend([obj[1:-1] for obj in imp.split(", "							)]							)
			line_index += 1
			continue
		lowercase						:		Optional[Any]   					=		_re_import_struct_key_value.search(A							)
		if single_line_import_search is not None:
			lowercase						:		List[Any]   					=		[obj[1:-1] for obj in single_line_import_search.groups()[0].split(", "							) if len(A							) > 0]
			objects.extend(A							)
		elif line.startswith(" " * 8 + "\""							):
			objects.append(line[9:-3]							)
		line_index += 1

	lowercase						:		Tuple   					=		{"none": objects}
	# Let's continue with backend-specific objects in _import_structure
	while not lines[line_index].startswith("if TYPE_CHECKING"							):
		# If the line is an if not is_backend_available, we grab all objects associated.
		lowercase						:		int   					=		find_backend(lines[line_index]							)
		# Check if the backend declaration is inside a try block:
		if _re_try.search(lines[line_index - 1]							) is None:
			lowercase						:		str   					=		None

		if backend is not None:
			line_index += 1

			# Scroll until we hit the else block of try-except-else
			while _re_else.search(lines[line_index]							) is None:
				line_index += 1

			line_index += 1

			lowercase						:		Any   					=		[]
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]							) <= 1 or lines[line_index].startswith(" " * 4							):
				lowercase						:		List[Any]   					=		lines[line_index]
				if _re_import_struct_add_one.search(A							) is not None:
					objects.append(_re_import_struct_add_one.search(A							).groups()[0]							)
				elif _re_import_struct_add_many.search(A							) is not None:
					lowercase						:		Dict   					=		_re_import_struct_add_many.search(A							).groups()[0].split(", "							)
					lowercase						:		Any   					=		[obj[1:-1] for obj in imports if len(A							) > 0]
					objects.extend(A							)
				elif _re_between_brackets.search(A							) is not None:
					lowercase						:		Optional[Any]   					=		_re_between_brackets.search(A							).groups()[0].split(", "							)
					lowercase						:		Tuple   					=		[obj[1:-1] for obj in imports if len(A							) > 0]
					objects.extend(A							)
				elif _re_quote_object.search(A							) is not None:
					objects.append(_re_quote_object.search(A							).groups()[0]							)
				elif line.startswith(" " * 8 + "\""							):
					objects.append(line[9:-3]							)
				elif line.startswith(" " * 1_2 + "\""							):
					objects.append(line[1_3:-3]							)
				line_index += 1

			lowercase						:		Optional[int]   					=		objects
		else:
			line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
	lowercase						:		str   					=		[]
	while (
	    line_index < len(A							)
	    and find_backend(lines[line_index]							) is None
	    and not lines[line_index].startswith("else"							)
	):
		lowercase						:		int   					=		lines[line_index]
		lowercase						:		Any   					=		_re_import.search(A							)
		if single_line_import_search is not None:
			objects.extend(single_line_import_search.groups()[0].split(", "							)							)
		elif line.startswith(" " * 8							):
			objects.append(line[8:-2]							)
		line_index += 1

	lowercase						:		Optional[Any]   					=		{"none": objects}
	# Let's continue with backend-specific objects
	while line_index < len(A							):
		# If the line is an if is_backend_available, we grab all objects associated.
		lowercase						:		List[Any]   					=		find_backend(lines[line_index]							)
		# Check if the backend declaration is inside a try block:
		if _re_try.search(lines[line_index - 1]							) is None:
			lowercase						:		int   					=		None

		if backend is not None:
			line_index += 1

			# Scroll until we hit the else block of try-except-else
			while _re_else.search(lines[line_index]							) is None:
				line_index += 1

			line_index += 1

			lowercase						:		Optional[Any]   					=		[]
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]							) <= 1 or lines[line_index].startswith(" " * 8							):
				lowercase						:		List[Any]   					=		lines[line_index]
				lowercase						:		Tuple   					=		_re_import.search(A							)
				if single_line_import_search is not None:
					objects.extend(single_line_import_search.groups()[0].split(", "							)							)
				elif line.startswith(" " * 1_2							):
					objects.append(line[1_2:-2]							)
				line_index += 1

			lowercase						:		Dict   					=		objects
		else:
			line_index += 1

	return import_dict_objects, type_hint_objects




def 	_A  (       A							,A							)  ->				List[str]:

	def find_duplicates(A							):
		return [k for k, v in collections.Counter(A							).items() if v > 1]

	if list(import_dict_objects.keys()							) != list(type_hint_objects.keys()							):
		return ["Both sides of the init do not have the same backends!"]

	lowercase						:		str   					=		[]
	for key in import_dict_objects.keys():
		lowercase						:		List[str]   					=		find_duplicates(import_dict_objects[key]							)
		if duplicate_imports:
			errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}'''							)
		lowercase						:		List[Any]   					=		find_duplicates(type_hint_objects[key]							)
		if duplicate_type_hints:
			errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''							)

		if sorted(set(import_dict_objects[key]							)							) != sorted(set(type_hint_objects[key]							)							):
			lowercase						:		Optional[int]   					=		"base imports" if key == "none" else F'''{key} backend'''
			errors.append(F'''Differences for {name}:'''							)
			for a in type_hint_objects[key]:
				if a not in import_dict_objects[key]:
					errors.append(F'''  {a} in TYPE_HINT but not in _import_structure.'''							)
			for a in import_dict_objects[key]:
				if a not in type_hint_objects[key]:
					errors.append(F'''  {a} in _import_structure but not in TYPE_HINT.'''							)
	return errors




def 	_A  (       )  ->				List[Any]:
	lowercase						:		Any   					=		[]
	for root, _, files in os.walk(A							):
		if "__init__.py" in files:
			lowercase						:		Optional[Any]   					=		os.path.join(A							,"__init__.py"							)
			lowercase						:		Tuple   					=		parse_init(A							)
			if objects is not None:
				lowercase						:		Tuple   					=		analyze_results(*A							)
				if len(A							) > 0:
					lowercase						:		Tuple   					=		F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
					failures.append("\n".join(A							)							)
	if len(A							) > 0:
		raise ValueError("\n\n".join(A							)							)




def 	_A  (       )  ->				Union[str, Any]:
	lowercase						:		Dict   					=		[]
	for path, directories, files in os.walk(A							):
		for folder in directories:
			# Ignore private modules
			if folder.startswith("_"							):
				directories.remove(A							)
				continue
			# Ignore leftovers from branches (empty folders apart from pycache)
			if len(list((Path(A							) / folder).glob("*.py"							)							)							) == 0:
				continue
			lowercase						:		str   					=		str((Path(A							) / folder).relative_to(A							)							)
			lowercase						:		Optional[Any]   					=		short_path.replace(os.path.sep							,"."							)
			submodules.append(A							)
		for fname in files:
			if fname == "__init__.py":
				continue
			lowercase						:		Tuple   					=		str((Path(A							) / fname).relative_to(A							)							)
			lowercase						:		int   					=		short_path.replace(".py"							,""							).replace(os.path.sep							,"."							)
			if len(submodule.split("."							)							) == 1:
				submodules.append(A							)
	return submodules


lowerCAmelCase   : Dict             =				[
    """convert_pytorch_checkpoint_to_tf2""",
    """modeling_flax_pytorch_utils""",
]




def 	_A  (       )  ->				Optional[int]:
	# This is to make sure the transformers module imported is the one in the repo.
	lowercase						:		int   					=		importlib.util.spec_from_file_location(
	    "transformers"							,os.path.join(A							,"__init__.py"							)							,submodule_search_locations=[PATH_TO_TRANSFORMERS]							,)
	lowercase						:		Optional[int]   					=		spec.loader.load_module()

	lowercase						:		Dict   					=		[
	    module
	    for module in get_transformers_submodules()
	    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
	]
	if len(A							) > 0:
		lowercase						:		Optional[int]   					=		"\n".join(F'''- {module}''' for module in module_not_registered							)
		raise ValueError(
		    "The following submodules are not properly registered in the main init of Transformers:\n"
		    F'''{list_of_modules}\n'''
		    "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."							)


if __name__ == "__main__":
					check_all_inits()
					check_submodules()


