






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

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]						,					__A				:    Tuple						,					__A				:    int=8       )			->	Any:

       """simple docstring"""

       a_      :	List[str]							=						height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       a_      :	Any							=						width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor
class 				SCREAMING_SNAKE_CASE__						(       lowercase_    ):
      def __init__( self       : Any  ,							SCREAMING_SNAKE_CASE__       : UNetaDConditionModel  ,							SCREAMING_SNAKE_CASE__       : DDPMScheduler  ,							SCREAMING_SNAKE_CASE__       : VQModel  ,							)	->    Dict:
             super().__init__()

             self.register_modules(
                 unet=UpperCamelCase__  ,							scheduler=UpperCamelCase__  ,							movq=UpperCamelCase__  ,							)
             a_      :	Dict							=						2 ** (len(self.movq.config.block_out_channels  ) - 1)
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Any  )	->    List[str]:







             if latents is None:
                    a_      :	Tuple							=						randn_tensor(UpperCamelCase__  ,							generator=UpperCamelCase__  ,							device=UpperCamelCase__  ,							dtype=UpperCamelCase__  )
             else:
                    if latents.shape != shape:
                           raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
                    a_      :	int							=						latents.to(UpperCamelCase__  )

             a_      :	List[str]							=						latents * scheduler.init_noise_sigma
             return latents
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=0  )	->    Union[str, Any]:







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

             a_      :	str							=						torch.device(F"""cuda:{gpu_id}"""  )

             a_      :	List[str]							=						[
                 self.unet,
                 self.movq,
             ]
             for cpu_offloaded_model in models:
                    if cpu_offloaded_model is not None:
                           cpu_offload(UpperCamelCase__  ,							UpperCamelCase__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : str=0  )	->    List[Any]:







             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.'  )

             a_      :	Optional[int]							=						torch.device(F"""cuda:{gpu_id}"""  )

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

             a_      :	Optional[Any]							=						None
             for cpu_offloaded_model in [self.unet, self.movq]:
                    a_							,	a_      :	Dict							=						cpu_offload_with_hook(UpperCamelCase__  ,							UpperCamelCase__  ,							prev_module_hook=UpperCamelCase__  )

             # We'll offload the last model manually.
             a_      :	int							=						hook
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Any:







             if not hasattr(self.unet  ,							'_hf_hook'  ):
                    return self.device
             for module in self.unet.modules():
                    if (
                        hasattr(UpperCamelCase__  ,							'_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(UpperCamelCase__  )
      def __call__( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Union[torch.FloatTensor, List[torch.FloatTensor]]  ,							SCREAMING_SNAKE_CASE__       : Union[torch.FloatTensor, List[torch.FloatTensor]]  ,							SCREAMING_SNAKE_CASE__       : int = 5_1_2  ,							SCREAMING_SNAKE_CASE__       : int = 5_1_2  ,							SCREAMING_SNAKE_CASE__       : int = 1_0_0  ,							SCREAMING_SNAKE_CASE__       : float = 4.0  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[torch.FloatTensor] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[str] = "pil"  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							)	->    Optional[int]:
             a_      :	Any							=						self._execution_device

             a_      :	List[str]							=						guidance_scale > 1.0

             if isinstance(UpperCamelCase__  ,							UpperCamelCase__  ):
                    a_      :	Dict							=						torch.cat(UpperCamelCase__  ,							dim=0  )
             a_      :	int							=						image_embeds.shape[0] * num_images_per_prompt
             if isinstance(UpperCamelCase__  ,							UpperCamelCase__  ):
                    a_      :	Optional[int]							=						torch.cat(UpperCamelCase__  ,							dim=0  )

             if do_classifier_free_guidance:
                    a_      :	Optional[Any]							=						image_embeds.repeat_interleave(UpperCamelCase__  ,							dim=0  )
                    a_      :	Union[str, Any]							=						negative_image_embeds.repeat_interleave(UpperCamelCase__  ,							dim=0  )

             a_      :	Optional[Any]							=						torch.cat([negative_image_embeds, image_embeds]  ,							dim=0  ).to(dtype=self.unet.dtype  ,							device=UpperCamelCase__  )

             self.scheduler.set_timesteps(UpperCamelCase__  ,							device=UpperCamelCase__  )
             a_      :	Optional[int]							=						self.scheduler.timesteps

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

             a_							,	a_      :	int							=						downscale_height_and_width(UpperCamelCase__  ,							UpperCamelCase__  ,							self.movq_scale_factor  )

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

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

                    a_      :	Optional[int]							=						{'image_embeds': image_embeds}
                    a_      :	List[Any]							=						self.unet(
                        sample=UpperCamelCase__  ,							timestep=UpperCamelCase__  ,							encoder_hidden_states=UpperCamelCase__  ,							added_cond_kwargs=UpperCamelCase__  ,							return_dict=UpperCamelCase__  ,							)[0]

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

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

             if output_type == "pil":
                    a_      :	Any							=						self.numpy_to_pil(UpperCamelCase__  )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=UpperCamelCase__  )






import argparse
import os
import re


UpperCAmelCase_   :				List[Any]						      = 'src/transformers'

# Pattern that looks at the indentation in a line.
UpperCAmelCase_   :				Any						      = re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCAmelCase_   :				str						      = re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
UpperCAmelCase_   :				Dict						      = re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
UpperCAmelCase_   :				int						      = re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
UpperCAmelCase_   :				List[str]						      = re.compile(R'\[([^\]]+)\]')







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

       """simple docstring"""

       a_      :	List[Any]							=						_re_indent.search(__A       )
       return "" if search is None else search.groups()[0]







def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    Union[str, Any]=""						,					__A				:    Dict=None						,					__A				:    Dict=None       )			->	int:

       """simple docstring"""

       a_      :	Tuple							=						0
       a_      :	Dict							=						code.split('\n'       )
       if start_prompt is not None:
              while not lines[index].startswith(__A       ):
                     index += 1
              a_      :	List[Any]							=						['\n'.join(lines[:index]       )]
       else:
              a_      :	List[str]							=						[]

       # We split into blocks until we get to the `end_prompt` (or the end of the block).
       a_      :	Tuple							=						[lines[index]]
       index += 1
       while index < len(__A       ) and (end_prompt is None or not lines[index].startswith(__A       )):
              if len(lines[index]       ) > 0 and get_indent(lines[index]       ) == indent_level:
                     if len(__A       ) > 0 and get_indent(current_block[-1]       ).startswith(indent_level + ' '       ):
                            current_block.append(lines[index]       )
                            blocks.append('\n'.join(__A       )       )
                            if index < len(__A       ) - 1:
                                   a_      :	Dict							=						[lines[index + 1]]
                                   index += 1
                            else:
                                   a_      :	Dict							=						[]
                     else:
                            blocks.append('\n'.join(__A       )       )
                            a_      :	int							=						[lines[index]]
              else:
                     current_block.append(lines[index]       )
              index += 1

       # Adds current block if it's nonempty.
       if len(__A       ) > 0:
              blocks.append('\n'.join(__A       )       )

       # Add final block after end_prompt if provided.
       if end_prompt is not None and index < len(__A       ):
              blocks.append('\n'.join(lines[index:]       )       )

       return blocks







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict       )			->	Any:

       """simple docstring"""

       def _inner(__A				:    Tuple       ):
              return key(__A       ).lower().replace('_'						,					''       )

       return _inner







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    List[Any]=None       )			->	List[str]:

       """simple docstring"""

       def noop(__A				:    Tuple       ):
              return x

       if key is None:
              a_      :	Optional[Any]							=						noop
       # Constants are all uppercase, they go first.
       a_      :	List[Any]							=						[obj for obj in objects if key(__A       ).isupper()]
       # Classes are not all uppercase but start with a capital, they go second.
       a_      :	Dict							=						[obj for obj in objects if key(__A       )[0].isupper() and not key(__A       ).isupper()]
       # Functions begin with a lowercase, they go last.
       a_      :	Optional[int]							=						[obj for obj in objects if not key(__A       )[0].isupper()]

       a_      :	Optional[Any]							=						ignore_underscore(__A       )
       return sorted(__A						,					key=__A       ) + sorted(__A						,					key=__A       ) + sorted(__A						,					key=__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple       )			->	Optional[int]:

       """simple docstring"""

       def _replace(__A				:    List[Any]       ):
              a_      :	Tuple							=						match.groups()[0]
              if "," not in imports:
                     return F"""[{imports}]"""
              a_      :	int							=						[part.strip().replace('"'						,					''       ) for part in imports.split(','       )]
              # We will have a final empty element if the line finished with a comma.
              if len(keys[-1]       ) == 0:
                     a_      :	str							=						keys[:-1]
              return "[" + ", ".join([F"""\"{k}\"""" for k in sort_objects(__A       )]       ) + "]"

       a_      :	Optional[int]							=						import_statement.split('\n'       )
       if len(__A       ) > 3:
              # Here we have to sort internal imports that are on several lines (one per name):
              # key: [
              #     "object1",
              #     "object2",
              #     ...
              # ]

              # We may have to ignore one or two lines on each side.
              a_      :	int							=						2 if lines[1].strip() == '[' else 1
              a_      :	int							=						[(i, _re_strip_line.search(__A       ).groups()[0]) for i, line in enumerate(lines[idx:-idx]       )]
              a_      :	str							=						sort_objects(__A						,					key=lambda __A       : x[1]       )
              a_      :	int							=						[lines[x[0] + idx] for x in sorted_indices]
              return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]       )
       elif len(__A       ) == 3:
              # Here we have to sort internal imports that are on one separate line:
              # key: [
              #     "object1", "object2", ...
              # ]
              if _re_bracket_content.search(lines[1]       ) is not None:
                     a_      :	str							=						_re_bracket_content.sub(_replace						,					lines[1]       )
              else:
                     a_      :	List[str]							=						[part.strip().replace('"'						,					''       ) for part in lines[1].split(','       )]
                     # We will have a final empty element if the line finished with a comma.
                     if len(keys[-1]       ) == 0:
                            a_      :	Optional[int]							=						keys[:-1]
                     a_      :	Any							=						get_indent(lines[1]       ) + ', '.join([F"""\"{k}\"""" for k in sort_objects(__A       )]       )
              return "\n".join(__A       )
       else:
              # Finally we have to deal with imports fitting on one line
              a_      :	Union[str, Any]							=						_re_bracket_content.sub(_replace						,					__A       )
              return import_statement







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    Any=True       )			->	Union[str, Any]:

       """simple docstring"""

       with open(__A						,					encoding='utf-8'       ) as f:
              a_      :	Union[str, Any]							=						f.read()

       if "_import_structure" not in code:
              return

       # Blocks of indent level 0
       a_      :	Optional[Any]							=						split_code_in_indented_blocks(
           __A						,					start_prompt='_import_structure = {'						,					end_prompt='if TYPE_CHECKING:'       )

       # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
       for block_idx in range(1						,					len(__A       ) - 1       ):
              # Check if the block contains some `_import_structure`s thingy to sort.
              a_      :	Any							=						main_blocks[block_idx]
              a_      :	Any							=						block.split('\n'       )

              # Get to the start of the imports.
              a_      :	Any							=						0
              while line_idx < len(__A       ) and "_import_structure" not in block_lines[line_idx]:
                     # Skip dummy import blocks
                     if "import dummy" in block_lines[line_idx]:
                            a_      :	Tuple							=						len(__A       )
                     else:
                            line_idx += 1
              if line_idx >= len(__A       ):
                     continue

              # Ignore beginning and last line: they don't contain anything.
              a_      :	List[str]							=						'\n'.join(block_lines[line_idx:-1]       )
              a_      :	List[Any]							=						get_indent(block_lines[1]       )
              # Slit the internal block into blocks of indent level 1.
              a_      :	Optional[int]							=						split_code_in_indented_blocks(__A						,					indent_level=__A       )
              # We have two categories of import key: list or _import_structure[key].append/extend
              a_      :	Tuple							=						_re_direct_key if '_import_structure = {' in block_lines[0] else _re_indirect_key
              # Grab the keys, but there is a trap: some lines are empty or just comments.
              a_      :	Union[str, Any]							=						[(pattern.search(__A       ).groups()[0] if pattern.search(__A       ) is not None else None) for b in internal_blocks]
              # We only sort the lines with a key.
              a_      :	str							=						[(i, key) for i, key in enumerate(__A       ) if key is not None]
              a_      :	Optional[int]							=						[x[0] for x in sorted(__A						,					key=lambda __A       : x[1]       )]

              # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
              a_      :	int							=						0
              a_      :	int							=						[]
              for i in range(len(__A       )       ):
                     if keys[i] is None:
                            reorderded_blocks.append(internal_blocks[i]       )
                     else:
                            a_      :	List[str]							=						sort_objects_in_import(internal_blocks[sorted_indices[count]]       )
                            reorderded_blocks.append(__A       )
                            count += 1

        # And we put our main block back together with its first and last line.
              a_      :	Any							=						'\n'.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]       )

       if code != "\n".join(__A       ):
              if check_only:
                     return True
              else:
                     print(F"""Overwriting {file}."""       )
                     with open(__A						,					'w'						,					encoding='utf-8'       ) as f:
                            f.write('\n'.join(__A       )       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]=True       )			->	List[Any]:

       """simple docstring"""

       a_      :	Dict							=						[]
       for root, _, files in os.walk(__A       ):
              if "__init__.py" in files:
                     a_      :	Dict							=						sort_imports(os.path.join(__A						,					'__init__.py'       )						,					check_only=__A       )
                     if result:
                            a_      :	Tuple							=						[os.path.join(__A						,					'__init__.py'       )]
       if len(__A       ) > 0:
              raise ValueError(F"""Would overwrite {len(__A       )} files, run `make style`."""       )


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

   sort_imports_in_all_inits(check_only=args.check_only)


