'''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


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


_A      :				Dict 				=    '''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''







class     _lowercase					(    UpperCAmelCase__      ):


 '''simple docstring'''






 def __init__(				self       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     UNetaDModel				,	SCREAMING_SNAKE_CASE__       :     CMStochasticIterativeScheduler							)  ->   None:
  super().__init__()

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

  __lowerCAmelCase    							=						None
 def a (				self       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     List[Any]=0							)  ->   List[Any]:
  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"""							)

  __lowerCAmelCase    							=						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)

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

  if self.safety_checker is not None:
   cpu_offload(self.safety_checker				,	execution_device=SCREAMING_SNAKE_CASE__				,	offload_buffers=SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Dict				,	SCREAMING_SNAKE_CASE__       :     List[Any]=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."""							)

  __lowerCAmelCase    							=						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    							=						None
  for cpu_offloaded_model in [self.unet]:
   __lowerCAmelCase							,	__lowerCAmelCase    							=						cpu_offload_with_hook(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	prev_module_hook=SCREAMING_SNAKE_CASE__							)

  if self.safety_checker is not None:
   __lowerCAmelCase							,	__lowerCAmelCase    							=						cpu_offload_with_hook(self.safety_checker				,	SCREAMING_SNAKE_CASE__				,	prev_module_hook=SCREAMING_SNAKE_CASE__							)

  # We'll offload the last model manually.
  __lowerCAmelCase    							=						hook
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def a (				self       :     List[str]							)  ->   Optional[Any]:
  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
 def a (				self       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     Dict				,	SCREAMING_SNAKE_CASE__       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     Dict				,	SCREAMING_SNAKE_CASE__       :     int=None							)  ->   str:
  __lowerCAmelCase    							=						(batch_size, num_channels, height, width)
  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."""							)

  if latents is None:
   __lowerCAmelCase    							=						randn_tensor(SCREAMING_SNAKE_CASE__				,	generator=SCREAMING_SNAKE_CASE__				,	device=SCREAMING_SNAKE_CASE__				,	dtype=SCREAMING_SNAKE_CASE__							)
  else:
   __lowerCAmelCase    							=						latents.to(device=SCREAMING_SNAKE_CASE__				,	dtype=SCREAMING_SNAKE_CASE__							)

  # scale the initial noise by the standard deviation required by the scheduler
  __lowerCAmelCase    							=						latents * self.scheduler.init_noise_sigma
  return latents
 def a (				self       :     Dict				,	SCREAMING_SNAKE_CASE__       :     torch.FloatTensor				,	SCREAMING_SNAKE_CASE__       :     str = "pil"							)  ->   int:
  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
  __lowerCAmelCase    							=						(sample / 2 + 0.5).clamp(0				,	1							)
  if output_type == "pt":
   return sample

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

  # Output_type must be 'pil'
  __lowerCAmelCase    							=						self.numpy_to_pil(SCREAMING_SNAKE_CASE__							)
  return sample
 def a (				self       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]=None							)  ->   List[Any]:
  if self.unet.config.num_class_embeds is not None:
   if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase    							=						torch.tensor(SCREAMING_SNAKE_CASE__				,	dtype=torch.int							)
   elif isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							):
    assert batch_size == 1, "Batch size must be 1 if classes is an int"
    __lowerCAmelCase    							=						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
    __lowerCAmelCase    							=						torch.randint(0				,	self.unet.config.num_class_embeds				,	size=(batch_size,)							)
   __lowerCAmelCase    							=						class_labels.to(SCREAMING_SNAKE_CASE__							)
  else:
   __lowerCAmelCase    							=						None
  return class_labels
 def a (				self       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     Any				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   str:
  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:
   __lowerCAmelCase    							=						(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(SCREAMING_SNAKE_CASE__				,	SCREAMING_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(SCREAMING_SNAKE_CASE__							)}."""							)





 @torch.no_grad()
 @replace_example_docstring(SCREAMING_SNAKE_CASE__							)
 def __call__(				self       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     int = 1				,	SCREAMING_SNAKE_CASE__       :     Optional[Union[torch.Tensor, List[int], int]] = None				,	SCREAMING_SNAKE_CASE__       :     int = 1				,	SCREAMING_SNAKE_CASE__       :     List[int] = None				,	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				,	SCREAMING_SNAKE_CASE__       :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,	SCREAMING_SNAKE_CASE__       :     int = 1				,	)  ->   List[str]:
  __lowerCAmelCase    							=						self.unet.config.sample_size
  __lowerCAmelCase    							=						self._execution_device

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

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

  # 3. Handle class_labels for class-conditional models
  __lowerCAmelCase    							=						self.prepare_class_labels(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	class_labels=SCREAMING_SNAKE_CASE__							)

  # 4. Prepare timesteps
  if timesteps is not None:
   self.scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__				,	device=SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						self.scheduler.timesteps
   __lowerCAmelCase    							=						len(SCREAMING_SNAKE_CASE__							)
  else:
   self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						self.scheduler.timesteps

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

    __lowerCAmelCase    							=						self.scheduler.step(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	generator=SCREAMING_SNAKE_CASE__							)[0]

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

        # 6. Post-process image sample
  __lowerCAmelCase    							=						self.postprocess_image(SCREAMING_SNAKE_CASE__				,	output_type=SCREAMING_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=SCREAMING_SNAKE_CASE__							)







'''simple docstring'''

import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()







def 					UpperCamelCase_						( snake_case_	:	List[str]						,		snake_case_	:	Optional[Any]				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						CanineConfig()
 __lowerCAmelCase    							=						CanineModel(snake_case_				)
 model.eval()

 print(f"""Building PyTorch model from configuration: {config}"""				)

 # Load weights from tf checkpoint
 load_tf_weights_in_canine(snake_case_						,		snake_case_						,		snake_case_				)

 # Save pytorch-model (weights and configuration)
 print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
 model.save_pretrained(snake_case_				)

 # Save tokenizer files
 __lowerCAmelCase    							=						CanineTokenizer()
 print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
 tokenizer.save_pretrained(snake_case_				)


if __name__ == "__main__":
 _A      :				int 				=    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to a folder where the PyTorch model will be placed.''',
 )
 _A      :				Dict 				=    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






