






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

__UpperCAmelCase								=						'\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      lowercase__      (     __snake_case    :							int     ,       __snake_case    :							List[str]     ,       __snake_case    :							str=8  ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[Any]            =						height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					UpperCAmelCase_    :      List[Any]            =						width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor






class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							Any:
							super().__init__()

							self.register_modules(
							    unet=_UpperCamelCase				,		scheduler=_UpperCamelCase				,		movq=_UpperCamelCase				,		)
							UpperCAmelCase_    :      List[str]            =						2 ** (len(self.movq.config.block_out_channels   ) - 1)



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							int:
							if latents is None:
												UpperCAmelCase_    :      List[Any]            =						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}"   )
												UpperCAmelCase_    :      Optional[int]            =						latents.to(_UpperCamelCase   )

							UpperCAmelCase_    :      str            =						latents * scheduler.init_noise_sigma
							return latents



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

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

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=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.'   )

							UpperCAmelCase_    :      Any            =						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)

							UpperCAmelCase_    :      Dict            =						None
							for cpu_offloaded_model in [self.unet, self.movq]:
												UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						cpu_offload_with_hook(_UpperCamelCase				,		_UpperCamelCase				,		prev_module_hook=_UpperCamelCase   )

							# We'll offload the last model manually.
							UpperCAmelCase_    :      Dict            =						hook



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 					__UpperCAmelCase			(							self   )	->							int:
							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				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = 5_1_2				,		_UpperCamelCase = 5_1_2				,		_UpperCamelCase = 1_0_0				,		_UpperCamelCase = 4.0				,		_UpperCamelCase = 1				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		)	->							Optional[int]:
							UpperCAmelCase_    :      str            =						self._execution_device

							UpperCAmelCase_    :      Union[str, Any]            =						guidance_scale > 1.0

							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Optional[Any]            =						torch.cat(_UpperCamelCase				,		dim=0   )
							UpperCAmelCase_    :      Optional[int]            =						image_embeds.shape[0] * num_images_per_prompt
							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Tuple            =						torch.cat(_UpperCamelCase				,		dim=0   )

							if do_classifier_free_guidance:
												UpperCAmelCase_    :      Optional[Any]            =						image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )
												UpperCAmelCase_    :      Dict            =						negative_image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )

							UpperCAmelCase_    :      Union[str, Any]            =						torch.cat([negative_image_embeds, image_embeds]				,		dim=0   ).to(dtype=self.unet.dtype				,		device=_UpperCamelCase   )

							self.scheduler.set_timesteps(_UpperCamelCase				,		device=_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						self.scheduler.timesteps

							UpperCAmelCase_    :      Any            =						self.unet.config.in_channels

							UpperCAmelCase_       ,  UpperCAmelCase_    :      Dict            =						downscale_height_and_width(_UpperCamelCase				,		_UpperCamelCase				,		self.movq_scale_factor   )

							# create initial latent
							UpperCAmelCase_    :      List[str]            =						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
												UpperCAmelCase_    :      Optional[int]            =						torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

												UpperCAmelCase_    :      Optional[int]            =						{'image_embeds': image_embeds}
												UpperCAmelCase_    :      Union[str, Any]            =						self.unet(
												    sample=_UpperCamelCase				,		timestep=_UpperCamelCase				,		encoder_hidden_states=_UpperCamelCase				,		added_cond_kwargs=_UpperCamelCase				,		return_dict=_UpperCamelCase				,		)[0]

												if do_classifier_free_guidance:
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						noise_pred.split(latents.shape[1]				,		dim=1   )
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						noise_pred.chunk(2   )
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      List[Any]            =						variance_pred.chunk(2   )
																	UpperCAmelCase_    :      Dict            =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																	UpperCAmelCase_    :      Tuple            =						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_    :      str            =						noise_pred.split(latents.shape[1]				,		dim=1   )

												# compute the previous noisy sample x_t -> x_t-1
												UpperCAmelCase_    :      Dict            =						self.scheduler.step(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		generator=_UpperCamelCase				,		)[0]
							# post-processing
							UpperCAmelCase_    :      Dict            =						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"]:
												UpperCAmelCase_    :      Any            =						image * 0.5 + 0.5
												UpperCAmelCase_    :      Optional[Any]            =						image.clamp(0				,		1   )
												UpperCAmelCase_    :      Tuple            =						image.cpu().permute(0				,		2				,		3				,		1   ).float().numpy()

							if output_type == "pil":
												UpperCAmelCase_    :      Tuple            =						self.numpy_to_pil(_UpperCamelCase   )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase   )













"""simple docstring"""





from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(SCREAMING_SNAKE_CASE			):
                  return ext

      raise Exception(
          F'Unable to determine file format from file extension {path}. '
          F'Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[Any]			):







      '''simple docstring'''




      lowerCAmelCase  						=       pipeline(
          task=args.task							,						model=args.model if args.model else None							,						config=args.config							,						tokenizer=args.tokenizer							,						device=args.device							,						)
      lowerCAmelCase  						=       try_infer_format_from_ext(args.input			) if args.format == """infer""" else args.format
      lowerCAmelCase  						=       PipelineDataFormat.from_str(
          format=SCREAMING_SNAKE_CASE							,						output_path=args.output							,						input_path=args.input							,						column=args.column if args.column else nlp.default_input_names							,						overwrite=args.overwrite							,						)
      return RunCommand(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)






class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						lowercase      ,						lowercase )		->     Union[str, Any]:
            lowerCAmelCase  						=       nlp
            lowerCAmelCase  						=       reader





      @staticmethod
      def     _snake_case			(       lowercase )		->     Optional[int]:
            lowerCAmelCase  						=       parser.add_parser("""run"""      ,						help="""Run a pipeline through the CLI""" )
            run_parser.add_argument("""--task"""      ,						choices=get_supported_tasks()      ,						help="""Task to run""" )
            run_parser.add_argument("""--input"""      ,						type=lowercase      ,						help="""Path to the file to use for inference""" )
            run_parser.add_argument("""--output"""      ,						type=lowercase      ,						help="""Path to the file that will be used post to write results.""" )
            run_parser.add_argument("""--model"""      ,						type=lowercase      ,						help="""Name or path to the model to instantiate.""" )
            run_parser.add_argument("""--config"""      ,						type=lowercase      ,						help="""Name or path to the model's config to instantiate.""" )
            run_parser.add_argument(
                """--tokenizer"""      ,						type=lowercase      ,						help="""Name of the tokenizer to use. (default: same as the model name)""" )
            run_parser.add_argument(
                """--column"""      ,						type=lowercase      ,						help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""      ,						)
            run_parser.add_argument(
                """--format"""      ,						type=lowercase      ,						default="""infer"""      ,						choices=PipelineDataFormat.SUPPORTED_FORMATS      ,						help="""Input format to read from"""      ,						)
            run_parser.add_argument(
                """--device"""      ,						type=lowercase      ,						default=-1      ,						help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""      ,						)
            run_parser.add_argument("""--overwrite"""      ,						action="""store_true"""      ,						help="""Allow overwriting the output file.""" )
            run_parser.set_defaults(func=lowercase )





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase						,		lowerCAmelCase  						=       self._nlp, []

            for entry in self._reader:
                  lowerCAmelCase  						=       nlp(**lowercase ) if self._reader.is_multi_columns else nlp(lowercase )
                  if isinstance(lowercase      ,						lowercase ):
                        outputs.append(lowercase )
                  else:
                        outputs += output

        # Saving data
            if self._nlp.binary_output:
                  lowerCAmelCase  						=       self._reader.save_binary(lowercase )
                  logger.warning(f'Current pipeline requires output to be in binary format, saving at {binary_path}' )
            else:
                  self._reader.save(lowercase )






