
'''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


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


UpperCAmelCase            = "\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     a (					__magic_name__     ):




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					UNetaDModel,     SCREAMING_SNAKE_CASE_						:					CMStochasticIterativeScheduler   ):
										super().__init__()

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

										snake_case							:  Optional[int]			=     None




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Optional[int]=0   ):
										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'''   )

										snake_case							:  List[str]			=     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 						__snake_case				(				self						:					int,     SCREAMING_SNAKE_CASE_						:					int=0   ):
										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.'''   )

										snake_case							:  Union[str, 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)

										snake_case							:  Any			=     None
										for cpu_offloaded_model in [self.unet]:
																snake_case,       snake_case							:  List[str]			=     cpu_offload_with_hook(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     prev_module_hook=SCREAMING_SNAKE_CASE_   )

										if self.safety_checker is not None:
																snake_case,       snake_case							:  Optional[int]			=     cpu_offload_with_hook(self.safety_checker,     SCREAMING_SNAKE_CASE_,     prev_module_hook=SCREAMING_SNAKE_CASE_   )

										# We'll offload the last model manually.
										snake_case							:  str			=     hook




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 						__snake_case				(				self						:					List[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




				def 						__snake_case				(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					List[Any]=None   ):
										snake_case							:  Optional[Any]			=     (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:
																snake_case							:  Any			=     randn_tensor(SCREAMING_SNAKE_CASE_,     generator=SCREAMING_SNAKE_CASE_,     device=SCREAMING_SNAKE_CASE_,     dtype=SCREAMING_SNAKE_CASE_   )
										else:
																snake_case							:  Union[str, Any]			=     latents.to(device=SCREAMING_SNAKE_CASE_,     dtype=SCREAMING_SNAKE_CASE_   )

										# scale the initial noise by the standard deviation required by the scheduler
										snake_case							:  Optional[Any]			=     latents * self.scheduler.init_noise_sigma
										return latents




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					torch.FloatTensor,     SCREAMING_SNAKE_CASE_						:					str = "pil"   ):
										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
										snake_case							:  List[Any]			=     (sample / 2 + 0.5).clamp(0,     1   )
										if output_type == "pt":
																return sample

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

										# Output_type must be 'pil'
										snake_case							:  Any			=     self.numpy_to_pil(SCREAMING_SNAKE_CASE_   )
										return sample




				def 						__snake_case				(				self						:					int,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					List[str]=None   ):
										if self.unet.config.num_class_embeds is not None:
																if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																						snake_case							:  str			=     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"
																						snake_case							:  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
																						snake_case							:  str			=     torch.randint(0,     self.unet.config.num_class_embeds,     size=(batch_size,)   )
																snake_case							:  str			=     class_labels.to(SCREAMING_SNAKE_CASE_   )
										else:
																snake_case							:  List[Any]			=     None
										return class_labels




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										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:
																snake_case							:  Dict			=     (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						:					str,     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,     ):
										snake_case							:  List[str]			=     self.unet.config.sample_size
										snake_case							:  Optional[Any]			=     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)
										snake_case							:  int			=     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
										snake_case							:  Any			=     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_   )
																snake_case							:  Union[str, Any]			=     self.scheduler.timesteps
																snake_case							:  List[Any]			=     len(SCREAMING_SNAKE_CASE_   )
										else:
																self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_   )
																snake_case							:  List[Any]			=     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_   ):
																						snake_case							:  Union[str, Any]			=     self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
																						snake_case							:  List[str]			=     self.unet(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     class_labels=SCREAMING_SNAKE_CASE_,     return_dict=SCREAMING_SNAKE_CASE_   )[0]

																						snake_case							:  List[Any]			=     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
										snake_case							:  List[Any]			=     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"""






from __future__ import annotations

import sys


class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					letter
								SCREAMING_SNAKE_CASE__  				=					freq
								SCREAMING_SNAKE_CASE__  				=					{}






				def __repr__(    self  ):
								return f'''{self.letter}:{self.freq}'''


class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					freq
								SCREAMING_SNAKE_CASE__  				=					left
								SCREAMING_SNAKE_CASE__  				=					right


def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					{}
				with open(lowerCamelCase_	) as f:
								while True:
												SCREAMING_SNAKE_CASE__  				=					f.read(1	)
												if not c:
																break
												SCREAMING_SNAKE_CASE__  				=					chars[c] + 1 if c in chars else 1
				return sorted((Letter(lowerCamelCase_    ,       lowerCamelCase_	) for c, f in chars.items())    ,       key=lambda lowerCamelCase_	: x.freq	)


def __lowercase  (   lowerCamelCase_	:   list[Letter]	):
				SCREAMING_SNAKE_CASE__  				=					letters  # type: ignore
				while len(lowerCamelCase_	) > 1:
								SCREAMING_SNAKE_CASE__  				=					response.pop(0	)
								SCREAMING_SNAKE_CASE__  				=					response.pop(0	)
								SCREAMING_SNAKE_CASE__  				=					left.freq + right.freq
								SCREAMING_SNAKE_CASE__  				=					TreeNode(lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_	)
								response.append(lowerCamelCase_	)
								response.sort(key=lambda lowerCamelCase_	: x.freq	)
				return response[0]


def __lowercase  (   lowerCamelCase_	:   Letter | TreeNode    ,       lowerCamelCase_	:   str	):
				if isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								SCREAMING_SNAKE_CASE__  				=					bitstring
								return [root]
				SCREAMING_SNAKE_CASE__  				=					root  # type: ignore
				SCREAMING_SNAKE_CASE__  				=					[]
				letters += traverse_tree(treenode.left    ,       bitstring + "0"	)
				letters += traverse_tree(treenode.right    ,       bitstring + "1"	)
				return letters


def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					parse_file(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					build_tree(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					{
				    k: v for letter in traverse_tree(lowerCamelCase_    ,       ""	) for k, v in letter.bitstring.items()
				}
				print(F'''Huffman Coding  of {file_path}: '''	)
				with open(lowerCamelCase_	) as f:
								while True:
												SCREAMING_SNAKE_CASE__  				=					f.read(1	)
												if not c:
																break
												print(letters[c]    ,       end=" "	)
				print()


if __name__ == "__main__":
	# pass the file path to the huffman function
	huffman(sys.argv[1])
