





"""simple docstring"""




from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


lowerCamelCase_	:		Union[str, Any]         =   '\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_    ):

        """simple docstring"""

        super().__init__()

        self.register_modules(
            unet=snake_case_   , scheduler=snake_case_   , )

        A_    :			List[Any]									=    None

      def    lowerCamelCase_     (	self   , snake_case_=0    ):

        """simple docstring"""

        if is_accelerate_available() and is_accelerate_version('>='   , '0.14.0'    ):
          from accelerate import cpu_offload
        else:
          raise ImportError('`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'    )

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

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

        for cpu_offloaded_model in [self.unet]:
          cpu_offload(snake_case_   , snake_case_    )

        if self.safety_checker is not None:
          cpu_offload(self.safety_checker   , execution_device=snake_case_   , offload_buffers=snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_=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.'    )

        A_    :			Any									=    torch.device(F"""cuda:{gpu_id}"""    )

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

        A_    :			Optional[int]									=    None
        for cpu_offloaded_model in [self.unet]:
          A_		,			A_    :			List[Any]									=    cpu_offload_with_hook(snake_case_   , snake_case_   , prev_module_hook=snake_case_    )

        if self.safety_checker is not None:
          A_		,			A_    :			List[str]									=    cpu_offload_with_hook(self.safety_checker   , snake_case_   , prev_module_hook=snake_case_    )

        # We'll offload the last model manually.
        A_    :			Any									=    hook

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

        """simple docstring"""

        if not hasattr(self.unet   , '_hf_hook'    ):
          return self.device
        for module in self.unet.modules():
          if (
              hasattr(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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_=None    ):

        """simple docstring"""

        A_    :			Optional[int]									=    (batch_size, num_channels, height, width)
        if isinstance(snake_case_   , snake_case_    ) and len(snake_case_    ) != batch_size:
          raise ValueError(
              F"""You have passed a list of generators of length {len(snake_case_    )}, but requested an effective batch"""
              F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

        if latents is None:
          A_    :			List[Any]									=    randn_tensor(snake_case_   , generator=snake_case_   , device=snake_case_   , dtype=snake_case_    )
        else:
          A_    :			Optional[int]									=    latents.to(device=snake_case_   , dtype=snake_case_    )

        # scale the initial noise by the standard deviation required by the scheduler
        A_    :			Union[str, Any]									=    latents * self.scheduler.init_noise_sigma
        return latents

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = "pil"    ):

        """simple docstring"""

        if output_type not in ["pt", "np", "pil"]:
          raise ValueError(
              F"""output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"""    )

        # Equivalent to diffusers.VaeImageProcessor.denormalize
        A_    :			Tuple									=    (sample / 2 + 0.5).clamp(0   , 1    )
        if output_type == "pt":
          return sample

        # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
        A_    :			Optional[int]									=    sample.cpu().permute(0   , 2   , 3   , 1    ).numpy()
        if output_type == "np":
          return sample

        # Output_type must be 'pil'
        A_    :			str									=    self.numpy_to_pil(snake_case_    )
        return sample

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_=None    ):

        """simple docstring"""

        if self.unet.config.num_class_embeds is not None:
          if isinstance(snake_case_   , snake_case_    ):
            A_    :			Optional[int]									=    torch.tensor(snake_case_   , dtype=torch.int    )
          elif isinstance(snake_case_   , snake_case_    ):
            assert batch_size == 1, "Batch size must be 1 if classes is an int"
            A_    :			Optional[Any]									=    torch.tensor([class_labels]   , dtype=torch.int    )
          elif class_labels is None:
            # Randomly generate batch_size class labels
            # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
            A_    :			Tuple									=    torch.randint(0   , self.unet.config.num_class_embeds   , size=(batch_size,)    )
          A_    :			Optional[int]									=    class_labels.to(snake_case_    )
        else:
          A_    :			Optional[Any]									=    None
        return class_labels

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

        if num_inference_steps is None and timesteps is None:
          raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'    )

        if num_inference_steps is not None and timesteps is not None:
          logger.warning(
              F"""Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"""
              ' `timesteps` will be used over `num_inference_steps`.'    )

        if latents is not None:
          A_    :			List[Any]									=    (batch_size, 3, img_size, img_size)
          if latents.shape != expected_shape:
            raise ValueError(F"""The shape of latents is {latents.shape} but is expected to be {expected_shape}."""    )

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(snake_case_   , snake_case_    ) or callback_steps <= 0)
        ):
          raise ValueError(
              F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
              F""" {type(snake_case_    )}."""    )

      @torch.no_grad()
      @replace_example_docstring(snake_case_    )
      def __call__(	self   , snake_case_ = 1   , snake_case_ = None   , snake_case_ = 1   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = "pil"   , snake_case_ = True   , snake_case_ = None   , snake_case_ = 1   , ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    self.unet.config.sample_size
        A_    :			Any									=    self._execution_device

        # 1. Check inputs
        self.check_inputs(snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_    )

        # 2. Prepare image latents
        # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
        A_    :			Tuple									=    self.prepare_latents(
            batch_size=snake_case_   , num_channels=self.unet.config.in_channels   , height=snake_case_   , width=snake_case_   , dtype=self.unet.dtype   , device=snake_case_   , generator=snake_case_   , latents=snake_case_   , )

        # 3. Handle class_labels for class-conditional models
        A_    :			List[str]									=    self.prepare_class_labels(snake_case_   , snake_case_   , class_labels=snake_case_    )

        # 4. Prepare timesteps
        if timesteps is not None:
          self.scheduler.set_timesteps(timesteps=snake_case_   , device=snake_case_    )
          A_    :			Tuple									=    self.scheduler.timesteps
          A_    :			List[Any]									=    len(snake_case_    )
        else:
          self.scheduler.set_timesteps(snake_case_    )
          A_    :			str									=    self.scheduler.timesteps

        # 5. Denoising loop
        # Multistep sampling: implements Algorithm 1 in the paper
        with self.progress_bar(total=snake_case_    ) as progress_bar:
          for i, t in enumerate(snake_case_    ):
            A_    :			int									=    self.scheduler.scale_model_input(snake_case_   , snake_case_    )
            A_    :			Tuple									=    self.unet(snake_case_   , snake_case_   , class_labels=snake_case_   , return_dict=snake_case_    )[0]

            A_    :			Any									=    self.scheduler.step(snake_case_   , snake_case_   , snake_case_   , generator=snake_case_    )[0]

            # call the callback, if provided
            progress_bar.update()
            if callback is not None and i % callback_steps == 0:
              callback(snake_case_   , snake_case_   , snake_case_    )

        # 6. Post-process image sample
        A_    :			str									=    self.postprocess_image(snake_case_   , output_type=snake_case_    )

        # Offload last model to CPU
        if hasattr(self   , 'final_offload_hook'    ) and self.final_offload_hook is not None:
          self.final_offload_hook.offload()

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=snake_case_    )



'''simple docstring'''
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


lowerCAmelCase__  =     '1.5.0'

lowerCAmelCase__  =     ['vision-encoder-decoder']



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       if is_optimum_available():
              from optimum.version import __version__ as optimum_version

              snake_case__					:	Dict          =					version.parse(UpperCAmelCase     )
              if parsed_optimum_version < version.parse(UpperCAmelCase     ):
                     raise RuntimeError(
                         f"""transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You """
                         """can upgrade optimum by running: pip install -U optimum[exporters]"""     )
       else:
              raise RuntimeError(
                  """transformers.onnx requires optimum to run, you can install the library by running: pip install """
                  """optimum[exporters]"""     )
       snake_case__					:	Union[str, Any]          =					[
           sys.executable,
           """-m""",
           """optimum.exporters.onnx""",
           f"""--model {args.model}""",
           f"""--task {args.feature}""",
           f"""--framework {args.framework}""" if args.framework is not None else """""",
           f"""{args.output}""",
       ]
       snake_case__					:	Union[str, Any]          =					subprocess.Popen(""" """.join(UpperCAmelCase     )    ,       stdout=subprocess.PIPE    ,       shell=UpperCAmelCase     )
       proc.wait()

       logger.info(
           """The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as """
           """transformers.onnx is deprecated, and will be removed in v5. You can find more information here: """
           """https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""     )



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	str          =					args.output if args.output.is_file() else args.output.joinpath("""model.onnx"""     )
       if not args.output.parent.exists():
              args.output.parent.mkdir(parents=UpperCAmelCase     )

       # Allocate the model
       snake_case__					:	Dict          =					FeaturesManager.get_model_from_feature(
           args.feature    ,       args.model    ,       framework=args.framework    ,       cache_dir=args.cache_dir     )

       snake_case__       ,   snake_case__					:	List[Any]          =					FeaturesManager.check_supported_model_or_raise(UpperCAmelCase    ,       feature=args.feature     )
       snake_case__					:	Any          =					model_onnx_config(model.config     )

       if model_kind in ENCODER_DECODER_MODELS:
              snake_case__					:	Optional[int]          =					model.get_encoder()
              snake_case__					:	Union[str, Any]          =					model.get_decoder()

              snake_case__					:	Optional[Any]          =					onnx_config.get_encoder_config(encoder_model.config     )
              snake_case__					:	Any          =					onnx_config.get_decoder_config(
                  encoder_model.config    ,       decoder_model.config    ,       feature=args.feature     )

              if args.opset is None:
                     snake_case__					:	List[Any]          =					max(encoder_onnx_config.default_onnx_opset    ,       decoder_onnx_config.default_onnx_opset     )

              if args.opset < min(encoder_onnx_config.default_onnx_opset    ,       decoder_onnx_config.default_onnx_opset     ):
                     raise ValueError(
                         f"""Opset {args.opset} is not sufficient to export {model_kind}. At least """
                         f""" {min(encoder_onnx_config.default_onnx_opset    ,       decoder_onnx_config.default_onnx_opset     )} is required."""     )

              snake_case__					:	Dict          =					AutoFeatureExtractor.from_pretrained(args.model     )

              snake_case__       ,   snake_case__					:	List[str]          =					export(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.opset    ,       args.output.parent.joinpath("""encoder_model.onnx"""     )    ,       )

              validate_model_outputs(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.output.parent.joinpath("""encoder_model.onnx"""     )    ,       UpperCAmelCase    ,       args.atol if args.atol else encoder_onnx_config.atol_for_validation    ,       )

              snake_case__					:	List[Any]          =					AutoTokenizer.from_pretrained(args.model     )

              snake_case__       ,   snake_case__					:	Dict          =					export(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.opset    ,       args.output.parent.joinpath("""decoder_model.onnx"""     )    ,       )

              validate_model_outputs(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.output.parent.joinpath("""decoder_model.onnx"""     )    ,       UpperCAmelCase    ,       args.atol if args.atol else decoder_onnx_config.atol_for_validation    ,       )
              logger.info(
                  f"""All good, model saved at: {args.output.parent.joinpath("encoder_model.onnx"     ).as_posix()},"""
                  f""" {args.output.parent.joinpath("decoder_model.onnx"     ).as_posix()}"""     )

       else:
              # Instantiate the appropriate preprocessor
              if args.preprocessor == "auto":
                     snake_case__					:	int          =					get_preprocessor(args.model     )
              elif args.preprocessor == "tokenizer":
                     snake_case__					:	List[str]          =					AutoTokenizer.from_pretrained(args.model     )
              elif args.preprocessor == "image_processor":
                     snake_case__					:	Optional[int]          =					AutoImageProcessor.from_pretrained(args.model     )
              elif args.preprocessor == "feature_extractor":
                     snake_case__					:	Tuple          =					AutoFeatureExtractor.from_pretrained(args.model     )
              elif args.preprocessor == "processor":
                     snake_case__					:	int          =					AutoProcessor.from_pretrained(args.model     )
              else:
                     raise ValueError(f"""Unknown preprocessor type '{args.preprocessor}'"""     )

              # Ensure the requested opset is sufficient
              if args.opset is None:
                     snake_case__					:	Dict          =					onnx_config.default_onnx_opset

              if args.opset < onnx_config.default_onnx_opset:
                     raise ValueError(
                         f"""Opset {args.opset} is not sufficient to export {model_kind}. """
                         f"""At least  {onnx_config.default_onnx_opset} is required."""     )

              snake_case__       ,   snake_case__					:	Dict          =					export(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.opset    ,       args.output    ,       )

              if args.atol is None:
                     snake_case__					:	int          =					onnx_config.atol_for_validation

              validate_model_outputs(UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.output    ,       UpperCAmelCase    ,       args.atol     )
              logger.info(f"""All good, model saved at: {args.output.as_posix()}"""     )
              warnings.warn(
                  """The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"""
                  """ using optimum.exporters.onnx in future. You can find more information here:"""
                  """ https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""    ,       UpperCAmelCase    ,       )



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





       snake_case__					:	List[str]          =					ArgumentParser("""Hugging Face Transformers ONNX exporter"""     )
       parser.add_argument(
           """-m"""    ,       """--model"""    ,       type=UpperCAmelCase    ,       required=UpperCAmelCase    ,       help="""Model ID on huggingface.co or path on disk to load model from."""     )
       parser.add_argument(
           """--feature"""    ,       default="""default"""    ,       help="""The type of features to export the model with."""    ,       )
       parser.add_argument("""--opset"""    ,       type=UpperCAmelCase    ,       default=UpperCAmelCase    ,       help="""ONNX opset version to export the model with."""     )
       parser.add_argument(
           """--atol"""    ,       type=UpperCAmelCase    ,       default=UpperCAmelCase    ,       help="""Absolute difference tolerance when validating the model."""     )
       parser.add_argument(
           """--framework"""    ,       type=UpperCAmelCase    ,       choices=["""pt""", """tf"""]    ,       default=UpperCAmelCase    ,       help=(
               """The framework to use for the ONNX export."""
               """ If not provided, will attempt to use the local checkpoint's original framework"""
               """ or what is available in the environment."""
           )    ,       )
       parser.add_argument("""output"""    ,       type=UpperCAmelCase    ,       help="""Path indicating where to store generated ONNX model."""     )
       parser.add_argument("""--cache_dir"""    ,       type=UpperCAmelCase    ,       default=UpperCAmelCase    ,       help="""Path indicating where to store cache."""     )
       parser.add_argument(
           """--preprocessor"""    ,       type=UpperCAmelCase    ,       choices=["""auto""", """tokenizer""", """feature_extractor""", """image_processor""", """processor"""]    ,       default="""auto"""    ,       help="""Which type of preprocessor to use. 'auto' tries to automatically detect it."""    ,       )
       parser.add_argument(
           """--export_with_transformers"""    ,       action="""store_true"""    ,       help=(
               """Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be """
               """useful when exporting a model supported in transformers but not in optimum, otherwise it is not """
               """recommended."""
           )    ,       )

       snake_case__					:	str          =					parser.parse_args()
       if args.export_with_transformers or not is_optimum_available():
              export_with_transformers(UpperCAmelCase     )
       else:
              export_with_optimum(UpperCAmelCase     )


if __name__ == "__main__":
     lowerCAmelCase__  =     logging.get_logger('transformers.onnx')  # pylint: disable=invalid-name
     logger.setLevel(logging.INFO)
     main()




