


from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_UpperCAmelCase	:     Optional[int]   =logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCAmelCase	:     Optional[Any]   ="""\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"""



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_=8       )->       Any:
				lowerCAmelCase_       :					List[str]			 = height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				lowerCAmelCase_       :					List[str]			 = width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor







class  snake_case__(							snake_case_							):







						'''simple docstring'''




						def __init__( self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  )    ->			Tuple:
										super().__init__()

										self.register_modules(
										    unet=__lowercase      ,  scheduler=__lowercase      ,  movq=__lowercase      ,  )
										lowerCAmelCase_       :					Optional[int]			 = 2 ** (len(self.movq.config.block_out_channels							) - 1)




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			Any:
										if latents is None:
														lowerCAmelCase_       :					str			 = randn_tensor(__lowercase      ,  generator=__lowercase      ,  device=__lowercase      ,  dtype=__lowercase							)
										else:
														if latents.shape != shape:
																		raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""							)
														lowerCAmelCase_       :					List[Any]			 = latents.to(__lowercase							)

										lowerCAmelCase_       :					Optional[int]			 = latents * scheduler.init_noise_sigma
										return latents




						def   lowercase_       ( self      ,  __lowercase=0							)    ->			str:
										if is_accelerate_available():
														from accelerate import cpu_offload
										else:
														raise ImportError('''Please install accelerate via `pip install accelerate`'''							)

										lowerCAmelCase_       :					str			 = torch.device(f"""cuda:{gpu_id}"""							)

										lowerCAmelCase_       :					List[str]			 = [
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																		cpu_offload(__lowercase      ,  __lowercase							)




						def   lowercase_       ( self      ,  __lowercase=0							)    ->			List[str]:
										if is_accelerate_available() and is_accelerate_version('''>='''      ,  '''0.17.0.dev0'''							):
														from accelerate import cpu_offload_with_hook
										else:
														raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''							)

										lowerCAmelCase_       :					Dict			 = torch.device(f"""cuda:{gpu_id}"""							)

										if self.device.type != "cpu":
														self.to('''cpu'''      ,  silence_dtype_warnings=__lowercase							)
														torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										lowerCAmelCase_       :					List[Any]			 = None
										for cpu_offloaded_model in [self.unet, self.movq]:
														lowerCAmelCase_ ,     lowerCAmelCase_       :					List[str]			 = cpu_offload_with_hook(__lowercase      ,  __lowercase      ,  prev_module_hook=__lowercase							)

										# We'll offload the last model manually.
										lowerCAmelCase_       :					Tuple			 = hook




						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def   lowercase_       ( self							)    ->			Any:
										if not hasattr(self.unet      ,  '''_hf_hook'''							):
														return self.device
										for module in self.unet.modules():
														if (
														    hasattr(__lowercase      ,  '''_hf_hook'''							)
														    and hasattr(module._hf_hook      ,  '''execution_device'''							)
														    and module._hf_hook.execution_device is not None
														):
																		return torch.device(module._hf_hook.execution_device							)
										return self.device




						@torch.no_grad()
						@replace_example_docstring(__lowercase							)
						def __call__( self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase = 5_1_2      ,  __lowercase = 5_1_2      ,  __lowercase = 1_0_0      ,  __lowercase = 4.0      ,  __lowercase = 1      ,  __lowercase = None      ,  __lowercase = None      ,  __lowercase = "pil"      ,  __lowercase = True      ,  )    ->			List[str]:
										lowerCAmelCase_       :					List[Any]			 = self._execution_device

										lowerCAmelCase_       :					Optional[Any]			 = guidance_scale > 1.0

										if isinstance(__lowercase      ,  __lowercase							):
														lowerCAmelCase_       :					int			 = torch.cat(__lowercase      ,  dim=0							)
										if isinstance(__lowercase      ,  __lowercase							):
														lowerCAmelCase_       :					Union[str, Any]			 = torch.cat(__lowercase      ,  dim=0							)
										if isinstance(__lowercase      ,  __lowercase							):
														lowerCAmelCase_       :					Dict			 = torch.cat(__lowercase      ,  dim=0							)

										lowerCAmelCase_       :					Optional[int]			 = image_embeds.shape[0] * num_images_per_prompt

										if do_classifier_free_guidance:
														lowerCAmelCase_       :					List[Any]			 = image_embeds.repeat_interleave(__lowercase      ,  dim=0							)
														lowerCAmelCase_       :					Any			 = negative_image_embeds.repeat_interleave(__lowercase      ,  dim=0							)
														lowerCAmelCase_       :					Optional[Any]			 = hint.repeat_interleave(__lowercase      ,  dim=0							)

										lowerCAmelCase_       :					List[Any]			 = torch.cat([negative_image_embeds, image_embeds]      ,  dim=0							).to(dtype=self.unet.dtype      ,  device=__lowercase							)
										lowerCAmelCase_       :					Any			 = torch.cat([hint, hint]      ,  dim=0							).to(dtype=self.unet.dtype      ,  device=__lowercase							)
										self.scheduler.set_timesteps(__lowercase      ,  device=__lowercase							)
										lowerCAmelCase_       :					Dict			 = self.scheduler.timesteps

										lowerCAmelCase_       :					List[str]			 = self.movq.config.latent_channels

										lowerCAmelCase_ ,     lowerCAmelCase_       :					Union[str, Any]			 = downscale_height_and_width(__lowercase      ,  __lowercase      ,  self.movq_scale_factor							)

										# create initial latent
										lowerCAmelCase_       :					Optional[int]			 = self.prepare_latents(
										    (batch_size, num_channels_latents, height, width)      ,  image_embeds.dtype      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  self.scheduler      ,  )

										for i, t in enumerate(self.progress_bar(__lowercase							)							):
														# expand the latents if we are doing classifier free guidance
														lowerCAmelCase_       :					List[Any]			 = torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

														lowerCAmelCase_       :					Dict			 = {'''image_embeds''': image_embeds, '''hint''': hint}
														lowerCAmelCase_       :					List[str]			 = self.unet(
														    sample=__lowercase      ,  timestep=__lowercase      ,  encoder_hidden_states=__lowercase      ,  added_cond_kwargs=__lowercase      ,  return_dict=__lowercase      ,  )[0]

														if do_classifier_free_guidance:
																		lowerCAmelCase_ ,     lowerCAmelCase_       :					Any			 = noise_pred.split(latents.shape[1]      ,  dim=1							)
																		lowerCAmelCase_ ,     lowerCAmelCase_       :					Dict			 = noise_pred.chunk(2							)
																		lowerCAmelCase_ ,     lowerCAmelCase_       :					Optional[Any]			 = variance_pred.chunk(2							)
																		lowerCAmelCase_       :					Optional[int]			 = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																		lowerCAmelCase_       :					Any			 = torch.cat([noise_pred, variance_pred_text]      ,  dim=1							)

														if not (
														    hasattr(self.scheduler.config      ,  '''variance_type'''							)
														    and self.scheduler.config.variance_type in ["learned", "learned_range"]
														):
																		lowerCAmelCase_ ,     lowerCAmelCase_       :					List[Any]			 = noise_pred.split(latents.shape[1]      ,  dim=1							)

														# compute the previous noisy sample x_t -> x_t-1
														lowerCAmelCase_       :					str			 = self.scheduler.step(
														    __lowercase      ,  __lowercase      ,  __lowercase      ,  generator=__lowercase      ,  )[0]
										# post-processing
										lowerCAmelCase_       :					int			 = self.movq.decode(__lowercase      ,  force_not_quantize=__lowercase							)['''sample''']

										if output_type not in ["pt", "np", "pil"]:
														raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""							)

										if output_type in ["np", "pil"]:
														lowerCAmelCase_       :					int			 = image * 0.5 + 0.5
														lowerCAmelCase_       :					Any			 = image.clamp(0      ,  1							)
														lowerCAmelCase_       :					Union[str, Any]			 = image.cpu().permute(0      ,  2      ,  3      ,  1							).float().numpy()

										if output_type == "pil":
														lowerCAmelCase_       :					Optional[int]			 = self.numpy_to_pil(__lowercase							)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__lowercase							)







import collections
import importlib.util
import os
import re
from pathlib import Path


lowercase  							=					"src/transformers"


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





def 						__UpperCAmelCase			(     a_):
							if _re_test_backend.search(a_) is None:
														return None
							snake_case_				=							[b[0] for b in _re_backend.findall(a_)]
							backends.sort()
							return "_and_".join(a_)





def 						__UpperCAmelCase			(     a_):
							with open(a_							,     'r'							,     encoding='utf-8'							,     newline='\n') as f:
														snake_case_				=							f.readlines()

							snake_case_				=							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
							snake_case_				=							[]
							while not lines[line_index].startswith('if TYPE_CHECKING') and find_backend(lines[line_index]) is None:
														snake_case_				=							lines[line_index]
														# If we have everything on a single line, let's deal with it.
														if _re_one_line_import_struct.search(a_):
																					snake_case_				=							_re_one_line_import_struct.search(a_).groups()[0]
																					snake_case_				=							re.findall('\[([^\]]+)\]'							,     a_)
																					for imp in imports:
																												objects.extend([obj[1:-1] for obj in imp.split(', ')])
																					line_index += 1
																					continue
														snake_case_				=							_re_import_struct_key_value.search(a_)
														if single_line_import_search is not None:
																					snake_case_				=							[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

							snake_case_				=							{'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.
														snake_case_				=							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:
																					snake_case_				=							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

																					snake_case_				=							[]
																					# Until we unindent, add backend objects to the list
																					while len(lines[line_index]) <= 1 or lines[line_index].startswith(' ' * 4):
																												snake_case_				=							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:
																																			snake_case_				=							_re_import_struct_add_many.search(a_).groups()[0].split(', ')
																																			snake_case_				=							[obj[1:-1] for obj in imports if len(a_) > 0]
																																			objects.extend(a_)
																												elif _re_between_brackets.search(a_) is not None:
																																			snake_case_				=							_re_between_brackets.search(a_).groups()[0].split(', ')
																																			snake_case_				=							[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(' ' * 12 + '"'):
																																			objects.append(line[13:-3])
																												line_index += 1

																					snake_case_				=							objects
														else:
																					line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
							snake_case_				=							[]
							while (
							    line_index < len(a_)
							    and find_backend(lines[line_index]) is None
							    and not lines[line_index].startswith('else')
							):
														snake_case_				=							lines[line_index]
														snake_case_				=							_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

							snake_case_				=							{'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.
														snake_case_				=							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:
																					snake_case_				=							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

																					snake_case_				=							[]
																					# Until we unindent, add backend objects to the list
																					while len(lines[line_index]) <= 1 or lines[line_index].startswith(' ' * 8):
																												snake_case_				=							lines[line_index]
																												snake_case_				=							_re_import.search(a_)
																												if single_line_import_search is not None:
																																			objects.extend(single_line_import_search.groups()[0].split(', '))
																												elif line.startswith(' ' * 12):
																																			objects.append(line[12:-2])
																												line_index += 1

																					snake_case_				=							objects
														else:
																					line_index += 1

							return import_dict_objects, type_hint_objects





def 						__UpperCAmelCase			(     a_							,     a_):

							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!"]

							snake_case_				=							[]
							for key in import_dict_objects.keys():
														snake_case_				=							find_duplicates(import_dict_objects[key])
														if duplicate_imports:
																					errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}''')
														snake_case_				=							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])):
																					snake_case_				=							'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 						__UpperCAmelCase			(     ):
							snake_case_				=							[]
							for root, _, files in os.walk(a_):
														if "__init__.py" in files:
																					snake_case_				=							os.path.join(a_							,     '__init__.py')
																					snake_case_				=							parse_init(a_)
																					if objects is not None:
																												snake_case_				=							analyze_results(*a_)
																												if len(a_) > 0:
																																			snake_case_				=							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 						__UpperCAmelCase			(     ):
							snake_case_				=							[]
							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
																					snake_case_				=							str((Path(a_) / folder).relative_to(a_))
																					snake_case_				=							short_path.replace(os.path.sep							,     '.')
																					submodules.append(a_)
														for fname in files:
																					if fname == "__init__.py":
																												continue
																					snake_case_				=							str((Path(a_) / fname).relative_to(a_))
																					snake_case_				=							short_path.replace('.py'							,     '').replace(os.path.sep							,     '.')
																					if len(submodule.split('.')) == 1:
																												submodules.append(a_)
							return submodules


lowercase  							=					[
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
]





def 						__UpperCAmelCase			(     ):
							# This is to make sure the transformers module imported is the one in the repo.
							snake_case_				=							importlib.util.spec_from_file_location(
							    'transformers'							,     os.path.join(a_							,     '__init__.py')							,     submodule_search_locations=[PATH_TO_TRANSFORMERS]							,     )
							snake_case_				=							spec.loader.load_module()

							snake_case_				=							[
							    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:
														snake_case_				=							'\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()





