





"""simple docstring"""


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,
)


__magic_name__       :					List[Any]						=			logging.get_logger(__name__)  # pylint: disable=invalid-name

__magic_name__       :					Any						=			"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=8     ):
						UpperCamelCase				:		Tuple				   =			height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						UpperCamelCase				:		Tuple				   =			width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A						,   _A						,   ):






									'''simple docstring'''







									super().__init__()

									self.register_modules(
									    unet=_A						,   scheduler=_A						,   movq=_A						,   )
									UpperCamelCase				:		str				   =			2 ** (len(self.movq.config.block_out_channels    ) - 1)







			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									if latents is None:
															UpperCamelCase				:		int				   =			randn_tensor(_A						,   generator=_A						,   device=_A						,   dtype=_A    )
									else:
															if latents.shape != shape:
																					raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""    )
															UpperCamelCase				:		List[str]				   =			latents.to(_A    )

									UpperCamelCase				:		Dict				   =			latents * scheduler.init_noise_sigma
									return latents







			def   _a      (      self						,   _A=0    ):






									'''simple docstring'''







									if is_accelerate_available():
															from accelerate import cpu_offload
									else:
															raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

									UpperCamelCase				:		List[Any]				   =			torch.device(f"""cuda:{gpu_id}"""    )

									UpperCamelCase				:		Dict				   =			[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																					cpu_offload(_A						,   _A    )







			def   _a      (      self						,   _A=0    ):






									'''simple docstring'''







									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."""    )

									UpperCamelCase				:		List[str]				   =			torch.device(f"""cuda:{gpu_id}"""    )

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

									UpperCamelCase				:		List[str]				   =			None
									for cpu_offloaded_model in [self.unet, self.movq]:
															UpperCamelCase   ,						UpperCamelCase				:		List[str]				   =			cpu_offload_with_hook(_A						,   _A						,   prev_module_hook=_A    )

									# We'll offload the last model manually.
									UpperCamelCase				:		List[Any]				   =			hook







			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def   _a      (      self    ):






									'''simple docstring'''







									if not hasattr(self.unet						,   """_hf_hook"""    ):
															return self.device
									for module in self.unet.modules():
															if (
															    hasattr(_A						,   """_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(_A    )
			def __call__(      self						,   _A						,   _A						,   _A = 5_1_2						,   _A = 5_1_2						,   _A = 1_0_0						,   _A = 4.0						,   _A = 1						,   _A = None						,   _A = None						,   _A = "pil"						,   _A = True						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			self._execution_device

									UpperCamelCase				:		Optional[Any]				   =			guidance_scale > 1.0

									if isinstance(_A						,   _A    ):
															UpperCamelCase				:		List[str]				   =			torch.cat(_A						,   dim=0    )
									UpperCamelCase				:		List[Any]				   =			image_embeds.shape[0] * num_images_per_prompt
									if isinstance(_A						,   _A    ):
															UpperCamelCase				:		Optional[int]				   =			torch.cat(_A						,   dim=0    )

									if do_classifier_free_guidance:
															UpperCamelCase				:		List[str]				   =			image_embeds.repeat_interleave(_A						,   dim=0    )
															UpperCamelCase				:		str				   =			negative_image_embeds.repeat_interleave(_A						,   dim=0    )

									UpperCamelCase				:		Tuple				   =			torch.cat([negative_image_embeds, image_embeds]						,   dim=0    ).to(dtype=self.unet.dtype						,   device=_A    )

									self.scheduler.set_timesteps(_A						,   device=_A    )
									UpperCamelCase				:		str				   =			self.scheduler.timesteps

									UpperCamelCase				:		Optional[Any]				   =			self.unet.config.in_channels

									UpperCamelCase   ,						UpperCamelCase				:		int				   =			downscale_height_and_width(_A						,   _A						,   self.movq_scale_factor    )

									# create initial latent
									UpperCamelCase				:		Any				   =			self.prepare_latents(
									    (batch_size, num_channels_latents, height, width)						,   image_embeds.dtype						,   _A						,   _A						,   _A						,   self.scheduler						,   )

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

															UpperCamelCase				:		Union[str, Any]				   =			{"""image_embeds""": image_embeds}
															UpperCamelCase				:		Optional[Any]				   =			self.unet(
															    sample=_A						,   timestep=_A						,   encoder_hidden_states=_A						,   added_cond_kwargs=_A						,   return_dict=_A						,   )[0]

															if do_classifier_free_guidance:
																					UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			noise_pred.split(latents.shape[1]						,   dim=1    )
																					UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			noise_pred.chunk(2    )
																					UpperCamelCase   ,						UpperCamelCase				:		Union[str, Any]				   =			variance_pred.chunk(2    )
																					UpperCamelCase				:		Dict				   =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																					UpperCamelCase				:		int				   =			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"]
															):
																					UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			noise_pred.split(latents.shape[1]						,   dim=1    )

															# compute the previous noisy sample x_t -> x_t-1
															UpperCamelCase				:		Optional[Any]				   =			self.scheduler.step(
															    _A						,   _A						,   _A						,   generator=_A						,   )[0]
									# post-processing
									UpperCamelCase				:		int				   =			self.movq.decode(_A						,   force_not_quantize=_A    )["""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"]:
															UpperCamelCase				:		Any				   =			image * 0.5 + 0.5
															UpperCamelCase				:		Optional[Any]				   =			image.clamp(0						,   1    )
															UpperCamelCase				:		int				   =			image.cpu().permute(0						,   2						,   3						,   1    ).float().numpy()

									if output_type == "pil":
															UpperCamelCase				:		Any				   =			self.numpy_to_pil(_A    )

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=_A    )




import argparse
import os
import re


_lowercase:	Optional[Any]       			=		'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_lowercase:	Union[str, Any]       			=		re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
_lowercase:	Optional[Any]       			=		re.compile(R'''\s*\(\s*"(\S[^"]+)"''')






def _lowerCamelCase  (		snake_case     , snake_case = False  ):
							with open(snake_case     , 'r'     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							f.read()

							_lowerCAmelCase							=							content.split('\n'  )
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							0
							while line_idx < len(snake_case  ):
														if _re_intro_mapping.search(lines[line_idx]  ) is not None:
																					_lowerCAmelCase							=							len(re.search(R'^(\s*)\S'     , lines[line_idx]  ).groups()[0]  ) + 8
																					# Start of a new mapping!
																					while not lines[line_idx].startswith(' ' * indent + '('  ):
																												new_lines.append(lines[line_idx]  )
																												line_idx += 1

																					_lowerCAmelCase							=							[]
																					while lines[line_idx].strip() != "]":
																												# Blocks either fit in one line or not
																												if lines[line_idx].strip() == "(":
																																			_lowerCAmelCase							=							line_idx
																																			while not lines[line_idx].startswith(' ' * indent + ')'  ):
																																										line_idx += 1
																																			blocks.append('\n'.join(lines[start_idx : line_idx + 1]  )  )
																												else:
																																			blocks.append(lines[line_idx]  )
																												line_idx += 1

																					# Sort blocks by their identifiers
																					_lowerCAmelCase							=							sorted(snake_case     , key=lambda snake_case  : _re_identifier.search(snake_case  ).groups()[0]  )
																					new_lines += blocks
														else:
																					new_lines.append(lines[line_idx]  )
																					line_idx += 1

							if overwrite:
														with open(snake_case     , 'w'     , encoding='utf-8'  ) as f:
																					f.write('\n'.join(snake_case  )  )
							elif "\n".join(snake_case  ) != content:
														return True






def _lowerCamelCase  (		snake_case = False  ):
							_lowerCAmelCase							=							[os.path.join(snake_case     , snake_case  ) for f in os.listdir(snake_case  ) if f.endswith('.py'  )]
							_lowerCAmelCase							=							[sort_auto_mapping(snake_case     , overwrite=snake_case  ) for fname in fnames]

							if not overwrite and any(snake_case  ):
														_lowerCAmelCase							=							[f for f, d in zip(snake_case     , snake_case  ) if d]
														raise ValueError(
														    F'The following files have auto mappings that need sorting: {", ".join(snake_case  )}. Run `make style` to fix'
														    ' this.'  )


if __name__ == "__main__":
				_lowercase:	Any       			=		argparse.ArgumentParser()
				parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
				_lowercase:	Tuple       			=		parser.parse_args()

				sort_all_auto_mappings(not args.check_only)






