






"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml







class      UpperCamelCase_			(yaml.SafeLoader	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					Any		)		->     int:
				UpperCAmelCase_	:					int     	=   [self.constructed_objects[key_node] for key_node, _ in node.value]
				UpperCAmelCase_	:					Union[str, Any]     	=   [tuple(lowerCAmelCase_		) if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) else key for key in keys]
				UpperCAmelCase_	:					List[Any]     	=   Counter(lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[Any]     	=   [key for key in counter if counter[key] > 1]
				if duplicate_keys:
							raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Optional[int]=False		)		->     Any:
				UpperCAmelCase_	:					int     	=   super().construct_mapping(lowerCAmelCase_      ,      deep=lowerCAmelCase_		)
				self._check_no_duplicates_on_constructed_node(lowerCAmelCase_		)
				return mapping





def 							snake_case (  A__		):
			UpperCAmelCase_	:					int     	=   list(readme_content.splitlines()		)
			if full_content and full_content[0] == "---" and "---" in full_content[1:]:
						UpperCAmelCase_	:					Dict     	=   full_content[1:].index("---"		) + 1
						UpperCAmelCase_	:					List[str]     	=   "\n".join(full_content[1:sep_idx]		)
						return yamlblock, "\n".join(full_content[sep_idx + 1 :]		)

			return None, "\n".join(A__		)







class      UpperCamelCase_			(__A	):
	# class attributes
	__magic_name__       =							{'''train_eval_index'''}  # train-eval-index in the YAML metadata





	@classmethod
	def 	_SCREAMING_SNAKE_CASE ( cls   :					int      ,      lowerCAmelCase_   :					Path		)		->     "DatasetMetadata":
				with open(lowerCAmelCase_      ,      encoding="utf-8"		) as readme_file:
							UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   _split_yaml_from_readme(readme_file.read()		)
				if yaml_string is not None:
							return cls.from_yaml_string(lowerCAmelCase_		)
				else:
							return cls()





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Path		)		->     List[str]:
				if path.exists():
							with open(lowerCAmelCase_      ,      encoding="utf-8"		) as readme_file:
										UpperCAmelCase_	:					str     	=   readme_file.read()
				else:
							UpperCAmelCase_	:					str     	=   None
				UpperCAmelCase_	:					Optional[int]     	=   self._to_readme(lowerCAmelCase_		)
				with open(lowerCAmelCase_      ,      "w"      ,      encoding="utf-8"		) as readme_file:
							readme_file.write(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					Optional[str] = None		)		->     str:
				if readme_content is not None:
							UpperCAmelCase_	,      UpperCAmelCase_	:					Union[str, Any]     	=   _split_yaml_from_readme(lowerCAmelCase_		)
							UpperCAmelCase_	:					List[str]     	=   "---\n" + self.to_yaml_string() + "---\n" + content
				else:
							UpperCAmelCase_	:					Optional[int]     	=   "---\n" + self.to_yaml_string() + "---\n"
				return full_content





	@classmethod
	def 	_SCREAMING_SNAKE_CASE ( cls   :					Tuple      ,      lowerCAmelCase_   :					str		)		->     "DatasetMetadata":
				UpperCAmelCase_	:					Tuple     	=   yaml.load(lowerCAmelCase_      ,      Loader=_NoDuplicateSafeLoader		) or {}

				# Convert the YAML keys to DatasetMetadata fields
				UpperCAmelCase_	:					Dict     	=   {
				    (key.replace("-"      ,      "_"		) if key.replace("-"      ,      "_"		) in cls._FIELDS_WITH_DASHES else key): value
				    for key, value in metadata_dict.items()
				}
				return cls(**lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     str:
				return yaml.safe_dump(
				    {
				        (key.replace("_"      ,      "-"		) if key in self._FIELDS_WITH_DASHES else key): value
				        for key, value in self.items()
				    }      ,      sort_keys=lowerCAmelCase_      ,      allow_unicode=lowerCAmelCase_      ,      encoding="utf-8"      ,      ).decode("utf-8"		)





lowerCamelCase_							      = {
    '''image-classification''': [],
    '''translation''': [],
    '''image-segmentation''': [],
    '''fill-mask''': [],
    '''automatic-speech-recognition''': [],
    '''token-classification''': [],
    '''sentence-similarity''': [],
    '''audio-classification''': [],
    '''question-answering''': [],
    '''summarization''': [],
    '''zero-shot-classification''': [],
    '''table-to-text''': [],
    '''feature-extraction''': [],
    '''other''': [],
    '''multiple-choice''': [],
    '''text-classification''': [],
    '''text-to-image''': [],
    '''text2text-generation''': [],
    '''zero-shot-image-classification''': [],
    '''tabular-classification''': [],
    '''tabular-regression''': [],
    '''image-to-image''': [],
    '''tabular-to-text''': [],
    '''unconditional-image-generation''': [],
    '''text-retrieval''': [],
    '''text-to-speech''': [],
    '''object-detection''': [],
    '''audio-to-audio''': [],
    '''text-generation''': [],
    '''conversational''': [],
    '''table-question-answering''': [],
    '''visual-question-answering''': [],
    '''image-to-text''': [],
    '''reinforcement-learning''': [],
    '''voice-activity-detection''': [],
    '''time-series-forecasting''': [],
    '''document-question-answering''': [],
}


if __name__ == "__main__":
			from argparse import ArgumentParser

			lowerCamelCase_							      = ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
			ap.add_argument('''readme_filepath''')
			lowerCamelCase_							      = ap.parse_args()

			lowerCamelCase_							      = Path(args.readme_filepath)
			lowerCamelCase_							      = DatasetMetadata.from_readme(readme_filepath)
			print(dataset_metadata)
			dataset_metadata.to_readme(readme_filepath)











"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class      UpperCamelCase_			(__A	):





	def __init__( self   :					List[Any]      ,      lowerCAmelCase_   :					TransformeraDModel      ,      lowerCAmelCase_   :					AutoencoderKL      ,      lowerCAmelCase_   :					KarrasDiffusionSchedulers      ,      lowerCAmelCase_   :					Optional[Dict[int, str]] = None      ,      )		->     List[Any]:
				super().__init__()
				self.register_modules(transformer=lowerCAmelCase_      ,      vae=lowerCAmelCase_      ,      scheduler=lowerCAmelCase_		)

				# create a imagenet -> id dictionary for easier use
				UpperCAmelCase_	:					str     	=   {}
				if idalabel is not None:
							for key, value in idalabel.items():
										for label in value.split(","		):
													UpperCAmelCase_	:					Union[str, Any]     	=   int(lowerCAmelCase_		)
							UpperCAmelCase_	:					Optional[int]     	=   dict(sorted(self.labels.items()		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Union[str, List[str]]		)		->     List[int]:

				if not isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
							UpperCAmelCase_	:					str     	=   list(lowerCAmelCase_		)

				for l in label:
							if l not in self.labels:
										raise ValueError(
										    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""		)

				return [self.labels[l] for l in label]





	@torch.no_grad()
	def __call__( self   :					List[str]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					float = 4.0      ,      lowerCAmelCase_   :					Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,      lowerCAmelCase_   :					int = 50      ,      lowerCAmelCase_   :					Optional[str] = "pil"      ,      lowerCAmelCase_   :					bool = True      ,      )		->     Union[ImagePipelineOutput, Tuple]:
				UpperCAmelCase_	:					Optional[int]     	=   len(lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   self.transformer.config.sample_size
				UpperCAmelCase_	:					Optional[Any]     	=   self.transformer.config.in_channels

				UpperCAmelCase_	:					Tuple     	=   randn_tensor(
				    shape=(batch_size, latent_channels, latent_size, latent_size)      ,      generator=lowerCAmelCase_      ,      device=self.device      ,      dtype=self.transformer.dtype      ,      )
				UpperCAmelCase_	:					str     	=   torch.cat([latents] * 2		) if guidance_scale > 1 else latents

				UpperCAmelCase_	:					List[Any]     	=   torch.tensor(lowerCAmelCase_      ,      device=self.device		).reshape(-1		)
				UpperCAmelCase_	:					Optional[Any]     	=   torch.tensor([1_000] * batch_size      ,      device=self.device		)
				UpperCAmelCase_	:					int     	=   torch.cat([class_labels, class_null]      ,      0		) if guidance_scale > 1 else class_labels

				# set step values
				self.scheduler.set_timesteps(lowerCAmelCase_		)

				for t in self.progress_bar(self.scheduler.timesteps		):
							if guidance_scale > 1:
										UpperCAmelCase_	:					Optional[Any]     	=   latent_model_input[: len(lowerCAmelCase_		) // 2]
										UpperCAmelCase_	:					int     	=   torch.cat([half, half]      ,      dim=0		)
							UpperCAmelCase_	:					Optional[int]     	=   self.scheduler.scale_model_input(lowerCAmelCase_      ,      lowerCAmelCase_		)

							UpperCAmelCase_	:					Union[str, Any]     	=   t
							if not torch.is_tensor(lowerCAmelCase_		):
										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
										# This would be a good case for the `match` statement (Python 3.10+)
										UpperCAmelCase_	:					List[Any]     	=   latent_model_input.device.type == "mps"
										if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
													UpperCAmelCase_	:					Optional[int]     	=   torch.floataa if is_mps else torch.floataa
										else:
													UpperCAmelCase_	:					int     	=   torch.intaa if is_mps else torch.intaa
										UpperCAmelCase_	:					Optional[int]     	=   torch.tensor([timesteps]      ,      dtype=lowerCAmelCase_      ,      device=latent_model_input.device		)
							elif len(timesteps.shape		) == 0:
										UpperCAmelCase_	:					List[Any]     	=   timesteps[None].to(latent_model_input.device		)
							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							UpperCAmelCase_	:					List[Any]     	=   timesteps.expand(latent_model_input.shape[0]		)
							# predict noise model_output
							UpperCAmelCase_	:					List[Any]     	=   self.transformer(
							    lowerCAmelCase_      ,      timestep=lowerCAmelCase_      ,      class_labels=lowerCAmelCase_		).sample

							# perform guidance
							if guidance_scale > 1:
										UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										UpperCAmelCase_	,      UpperCAmelCase_	:					Union[str, Any]     	=   torch.split(lowerCAmelCase_      ,      len(lowerCAmelCase_		) // 2      ,      dim=0		)

										UpperCAmelCase_	:					Optional[Any]     	=   uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										UpperCAmelCase_	:					Tuple     	=   torch.cat([half_eps, half_eps]      ,      dim=0		)

										UpperCAmelCase_	:					Optional[Any]     	=   torch.cat([eps, rest]      ,      dim=1		)

							# learned sigma
							if self.transformer.config.out_channels // 2 == latent_channels:
										UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   torch.split(lowerCAmelCase_      ,      lowerCAmelCase_      ,      dim=1		)
							else:
										UpperCAmelCase_	:					Optional[Any]     	=   noise_pred

							# compute previous image: x_t -> x_t-1
							UpperCAmelCase_	:					Union[str, Any]     	=   self.scheduler.step(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		).prev_sample

				if guidance_scale > 1:
							UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   latent_model_input.chunk(2      ,      dim=0		)
				else:
							UpperCAmelCase_	:					Optional[int]     	=   latent_model_input

				UpperCAmelCase_	:					Dict     	=   1 / self.vae.config.scaling_factor * latents
				UpperCAmelCase_	:					Dict     	=   self.vae.decode(lowerCAmelCase_		).sample

				UpperCAmelCase_	:					Tuple     	=   (samples / 2 + 0.5).clamp(0      ,      1		)

				# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
				UpperCAmelCase_	:					Optional[Any]     	=   samples.cpu().permute(0      ,      2      ,      3      ,      1		).float().numpy()

				if output_type == "pil":
							UpperCAmelCase_	:					Optional[int]     	=   self.numpy_to_pil(lowerCAmelCase_		)

				if not return_dict:
							return (samples,)

				return ImagePipelineOutput(images=lowerCAmelCase_		)



