


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


snake_case__				:	Any					       =					logging.get_logger(__name__)  # pylint: disable=invalid-name

snake_case__				:	Tuple					       =					'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'


def    __lowerCamelCase   (      A__						: Dict						,						A__						: Dict						,						A__						: List[str]=8					)     ->    List[Any]:
   lowerCamelCase_						:   Union[str, Any]		= height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   lowerCamelCase_						:   Optional[int]		= width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor


def    __lowerCamelCase   (      A__						: Any						,						A__						: Union[str, Any]=512						,						A__						: int=512					)     ->    Union[str, Any]:
   lowerCamelCase_						:   Tuple		= pil_image.resize((w, h)						,						resample=Image.BICUBIC						,						reducing_gap=1					)
   lowerCamelCase_						:   List[Any]		= np.array(pil_image.convert("""RGB"""					)					)
   lowerCamelCase_						:   List[str]		= arr.astype(np.floataa					) / 127.5 - 1
   lowerCamelCase_						:   Dict		= np.transpose(__A						,						[2, 0, 1]					)
   lowerCamelCase_						:   Dict		= torch.from_numpy(__A					).unsqueeze(0					)
   return image





class   SCREAMING_SNAKE_CASE_			(_lowerCamelCase						):




       '''simple docstring'''



       def __init__(						self				:				int		,				__a				:				UNetaDConditionModel		,				__a				:				DDPMScheduler		,				__a				:				VQModel		,				)					->Dict:
          super().__init__()

          self.register_modules(
              unet=_SCREAMING_SNAKE_CASE		,				scheduler=_SCREAMING_SNAKE_CASE		,				movq=_SCREAMING_SNAKE_CASE		,				)
          lowerCamelCase_						:   Any		= 2 ** (len(self.movq.config.block_out_channels							) - 1)



       def 				_lowerCAmelCase							(						self				:				Optional[int]		,				__a				:				List[str]		,				__a				:				Optional[int]		,				__a				:				List[Any]							)					->List[Any]:
          lowerCamelCase_						:   Optional[int]		= min(int(num_inference_steps * strength							)		,				_SCREAMING_SNAKE_CASE							)

          lowerCamelCase_						:   List[str]		= max(num_inference_steps - init_timestep		,				0							)
          lowerCamelCase_						:   str		= self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start



       def 				_lowerCAmelCase							(						self				:				str		,				__a				:				Any		,				__a				:				List[str]		,				__a				:				int		,				__a				:				Optional[Any]		,				__a				:				Any		,				__a				:				List[Any]		,				__a				:				Union[str, Any]=None							)					->List[str]:


          if not isinstance(_SCREAMING_SNAKE_CASE		,				(torch.Tensor, PIL.Image.Image, list)							):
             raise ValueError(
                 F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_SCREAMING_SNAKE_CASE							)}'''							)

          lowerCamelCase_						:   Union[str, Any]		= image.to(device=_SCREAMING_SNAKE_CASE		,				dtype=_SCREAMING_SNAKE_CASE							)

          lowerCamelCase_						:   Dict		= batch_size * num_images_per_prompt

          if image.shape[1] == 4:
             lowerCamelCase_						:   int		= image

          else:
             if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE							) and len(_SCREAMING_SNAKE_CASE							) != batch_size:
                raise ValueError(
                    F'''You have passed a list of generators of length {len(_SCREAMING_SNAKE_CASE							)}, but requested an effective batch'''
                    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''							)

             elif isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE							):
                lowerCamelCase_						:   Union[str, Any]		= [
                    self.movq.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(_SCREAMING_SNAKE_CASE							)
                ]
                lowerCamelCase_						:   Union[str, Any]		= torch.cat(_SCREAMING_SNAKE_CASE		,				dim=0							)
             else:
                lowerCamelCase_						:   List[Any]		= self.movq.encode(_SCREAMING_SNAKE_CASE							).latent_dist.sample(_SCREAMING_SNAKE_CASE							)

             lowerCamelCase_						:   Optional[int]		= self.movq.config.scaling_factor * init_latents

          lowerCamelCase_						:   Union[str, Any]		= torch.cat([init_latents]		,				dim=0							)

          lowerCamelCase_						:   Union[str, Any]		= init_latents.shape
          lowerCamelCase_						:   List[Any]		= randn_tensor(_SCREAMING_SNAKE_CASE		,				generator=_SCREAMING_SNAKE_CASE		,				device=_SCREAMING_SNAKE_CASE		,				dtype=_SCREAMING_SNAKE_CASE							)

          # get latents
          lowerCamelCase_						:   Optional[Any]		= self.scheduler.add_noise(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE							)

          lowerCamelCase_						:   int		= init_latents

          return latents



       def 				_lowerCAmelCase							(						self				:				str		,				__a				:				Dict=0							)					->int:


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

          lowerCamelCase_						:   Dict		= 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(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE							)



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				Optional[Any]=0							)					->Dict:


          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_						:   Any		= torch.device(F'''cuda:{gpu_id}'''							)

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

          lowerCamelCase_						:   Optional[Any]		= None
          for cpu_offloaded_model in [self.unet, self.movq]:
             lowerCamelCase_						:   List[Any]		= cpu_offload_with_hook(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				prev_module_hook=_SCREAMING_SNAKE_CASE							)

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



       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 				_lowerCAmelCase							(						self				:				int							)					->str:


          if not hasattr(self.unet		,				"""_hf_hook"""							):
             return self.device
          for module in self.unet.modules():
             if (
                 hasattr(_SCREAMING_SNAKE_CASE		,				"""_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(_SCREAMING_SNAKE_CASE							)
       def __call__(						self				:				Any		,				__a				:				Union[torch.FloatTensor, List[torch.FloatTensor]]		,				__a				:				Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]		,				__a				:				Union[torch.FloatTensor, List[torch.FloatTensor]]		,				__a				:				int = 512		,				__a				:				int = 512		,				__a				:				int = 100		,				__a				:				float = 4.0		,				__a				:				float = 0.3		,				__a				:				int = 1		,				__a				:				Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				__a				:				Optional[str] = "pil"		,				__a				:				bool = True		,				)					->Tuple:
          lowerCamelCase_						:   str		= self._execution_device

          lowerCamelCase_						:   Optional[int]		= guidance_scale > 1.0

          if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE							):
             lowerCamelCase_						:   str		= torch.cat(_SCREAMING_SNAKE_CASE		,				dim=0							)
          lowerCamelCase_						:   Dict		= image_embeds.shape[0]
          if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE							):
             lowerCamelCase_						:   List[Any]		= torch.cat(_SCREAMING_SNAKE_CASE		,				dim=0							)

          if do_classifier_free_guidance:
             lowerCamelCase_						:   str		= image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE		,				dim=0							)
             lowerCamelCase_						:   Any		= negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE		,				dim=0							)

          lowerCamelCase_						:   List[Any]		= torch.cat([negative_image_embeds, image_embeds]		,				dim=0							).to(dtype=self.unet.dtype		,				device=_SCREAMING_SNAKE_CASE							)

          if not isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE							):
             lowerCamelCase_						:   Optional[Any]		= [image]
          if not all(isinstance(_SCREAMING_SNAKE_CASE		,				(PIL.Image.Image, torch.Tensor)							) for i in image							):
             raise ValueError(
                 F'''Input is in incorrect format: {[type(_SCREAMING_SNAKE_CASE							) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''							)

          lowerCamelCase_						:   List[Any]		= torch.cat([prepare_image(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE							) for i in image]		,				dim=0							)
          lowerCamelCase_						:   Any		= image.to(dtype=image_embeds.dtype		,				device=_SCREAMING_SNAKE_CASE							)

          lowerCamelCase_						:   int		= self.movq.encode(_SCREAMING_SNAKE_CASE							)['latents']
          lowerCamelCase_						:   str		= latents.repeat_interleave(_SCREAMING_SNAKE_CASE		,				dim=0							)
          self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		,				device=_SCREAMING_SNAKE_CASE							)
          lowerCamelCase_						:   Tuple		= self.get_timesteps(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE							)
          lowerCamelCase_						:   Dict		= timesteps[:1].repeat(batch_size * num_images_per_prompt							)
          lowerCamelCase_						:   int		= downscale_height_and_width(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				self.movq_scale_factor							)
          lowerCamelCase_						:   Optional[Any]		= self.prepare_latents(
              _SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				image_embeds.dtype		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE							)
          for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE							)							):
             # expand the latents if we are doing classifier free guidance
             lowerCamelCase_						:   List[str]		= torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

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

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

             # compute the previous noisy sample x_t -> x_t-1
             lowerCamelCase_						:   Any		= self.scheduler.step(
                 _SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				generator=_SCREAMING_SNAKE_CASE		,				)[0]

          # post-processing
          lowerCamelCase_						:   Dict		= self.movq.decode(_SCREAMING_SNAKE_CASE		,				force_not_quantize=_SCREAMING_SNAKE_CASE							)['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_						:   Tuple		= image * 0.5 + 0.5
             lowerCamelCase_						:   Any		= image.clamp(0		,				1							)
             lowerCamelCase_						:   str		= image.cpu().permute(0		,				2		,				3		,				1							).float().numpy()

          if output_type == "pil":
             lowerCamelCase_						:   Dict		= self.numpy_to_pil(_SCREAMING_SNAKE_CASE							)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE							)










"""simple docstring"""

import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard
def  __snake_case							(	__A      :						str     )     ->       Dict:






				'''simple docstring'''
				assert "mock" in _fsspec_registry
				assert "bz2" in _fsspec_registry
def  __snake_case							(	)     ->       Union[str, Any]:






				'''simple docstring'''
				assert "mock" not in _fsspec_registry
				assert "bz2" in _fsspec_registry
def  __snake_case							(	)     ->       Tuple:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Tuple							=     'mock-s3-bucket'
				SCREAMING_SNAKE_CASE						:							Tuple							=     F"""s3://{mock_bucket}"""
				SCREAMING_SNAKE_CASE						:							str							=     extract_path_from_uri(__A     )
				assert dataset_path.startswith('s3://'     ) is False

				SCREAMING_SNAKE_CASE						:							Optional[int]							=     './local/path'
				SCREAMING_SNAKE_CASE						:							str							=     extract_path_from_uri(__A     )
				assert dataset_path == new_dataset_path
def  __snake_case							(	__A      :						Tuple     )     ->       Optional[int]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Tuple							=     is_remote_filesystem(__A     )
				assert is_remote is True

				SCREAMING_SNAKE_CASE						:							Dict							=     fsspec.filesystem('file'     )

				SCREAMING_SNAKE_CASE						:							Dict							=     is_remote_filesystem(__A     )
				assert is_remote is False
@pytest.mark.parametrize('compression_fs_class'							,		__A     )
def  __snake_case							(	__A      :						Any							,		__A      :						Optional[int]							,		__A      :						List[Any]							,		__A      :						Optional[Any]							,		__A      :						str							,		__A      :						Tuple							,		__A      :						Dict     )     ->       Tuple:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_file, 'bz2': bza_file, 'lz4': lza_file}
				SCREAMING_SNAKE_CASE						:							List[Any]							=     input_paths[compression_fs_class.protocol]
				if input_path is None:
								SCREAMING_SNAKE_CASE						:							str							=     F"""for '{compression_fs_class.protocol}' compression protocol, """
								if compression_fs_class.protocol == "lz4":
												reason += require_lza.kwargs["reason"]
								elif compression_fs_class.protocol == "zstd":
												reason += require_zstandard.kwargs["reason"]
								pytest.skip(__A     )
				SCREAMING_SNAKE_CASE						:							Any							=     fsspec.filesystem(compression_fs_class.protocol							,		fo=__A     )
				assert isinstance(__A							,		__A     )
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     os.path.basename(__A     )
				SCREAMING_SNAKE_CASE						:							Dict							=     expected_filename[: expected_filename.rindex('.'     )]
				assert fs.glob('*'     ) == [expected_filename]
				with fs.open(__A							,		'r'							,		encoding='utf-8'     ) as f, open(__A							,		encoding='utf-8'     ) as expected_file:
								assert f.read() == expected_file.read()
@pytest.mark.parametrize('protocol'							,		['zip', 'gzip']     )
def  __snake_case							(	__A      :						Any							,		__A      :						int							,		__A      :						Union[str, Any]     )     ->       Union[str, Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							List[Any]							=     {'zip': zip_jsonl_path, 'gzip': jsonl_gz_path}
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     compressed_file_paths[protocol]
				SCREAMING_SNAKE_CASE						:							str							=     'dataset.jsonl'
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     F"""{protocol}://{member_file_path}::{compressed_file_path}"""
				SCREAMING_SNAKE_CASE				,						*SCREAMING_SNAKE_CASE						:							Any							=     fsspec.get_fs_token_paths(__A     )
				assert fs.isfile(__A     )
				assert not fs.isfile('non_existing_' + member_file_path     )
@pytest.mark.integration
def  __snake_case							(	__A      :						List[str]							,		__A      :						int							,		__A      :						int							,		__A      :						Optional[int]     )     ->       Union[str, Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							List[str]							=     hf_api.dataset_info(__A							,		token=__A     )
				SCREAMING_SNAKE_CASE						:							str							=     HfFileSystem(repo_info=__A							,		token=__A     )
				assert sorted(hffs.glob('*'     )     ) == [".gitattributes", "data"]
				assert hffs.isdir('data'     )
				assert hffs.isfile('.gitattributes'     ) and hffs.isfile('data/text_data.txt'     )
				with open(__A     ) as f:
								assert hffs.open('data/text_data.txt'							,		'r'     ).read() == f.read()







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






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     'bz2'

				# Import module
				import datasets.filesystems

				# Overwrite protocol and reload
				register_implementation(__A							,		__A							,		clobber=__A     )
				with pytest.warns(__A     ) as warning_info:
								importlib.reload(datasets.filesystems     )

				assert len(__A     ) == 1
				assert (
				    str(warning_info[0].message     )
				    == F"""A filesystem protocol was already set for {protocol} and will be overwritten."""
				)





