






"""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)









from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block







@dataclass
class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			42







class        __lowerCAmelCase					(			__magic_name__     ,							__magic_name__					):

			"""simple docstring"""







			@register_to_config
			def __init__( self     ,		lowerCamelCase__ = 65_536     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = 2     ,		lowerCamelCase__ = 2     ,		lowerCamelCase__ = 0     ,		lowerCamelCase__ = "fourier"     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = False     ,		lowerCamelCase__ = 0.0     ,		lowerCamelCase__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")     ,		lowerCamelCase__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")     ,		lowerCamelCase__ = "UNetMidBlock1D"     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = (32, 32, 64)     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = 8     ,		lowerCamelCase__ = 1     ,		lowerCamelCase__ = False     ,		)       -> Dict:



							'''simple docstring'''
							super().__init__()
							__lowerCamelCase									=       sample_size

							# time
							if time_embedding_type == "fourier":
											__lowerCamelCase									=       GaussianFourierProjection(
											    embedding_size=8     ,		set_W_to_weight=lowerCamelCase__     ,		log=lowerCamelCase__     ,		flip_sin_to_cos=lowerCamelCase__				)
											__lowerCamelCase									=       2 * block_out_channels[0]
							elif time_embedding_type == "positional":
											__lowerCamelCase									=       Timesteps(
											    block_out_channels[0]     ,		flip_sin_to_cos=lowerCamelCase__     ,		downscale_freq_shift=lowerCamelCase__				)
											__lowerCamelCase									=       block_out_channels[0]

							if use_timestep_embedding:
											__lowerCamelCase									=       block_out_channels[0] * 4
											__lowerCamelCase									=       TimestepEmbedding(
											    in_channels=lowerCamelCase__     ,		time_embed_dim=lowerCamelCase__     ,		act_fn=lowerCamelCase__     ,		out_dim=block_out_channels[0]     ,		)

							__lowerCamelCase									=       nn.ModuleList([]				)
							__lowerCamelCase									=       None
							__lowerCamelCase									=       nn.ModuleList([]				)
							__lowerCamelCase									=       None

							# down
							__lowerCamelCase									=       in_channels
							for i, down_block_type in enumerate(lowerCamelCase__				):
											__lowerCamelCase									=       output_channel
											__lowerCamelCase									=       block_out_channels[i]

											if i == 0:
															input_channel += extra_in_channels

											__lowerCamelCase									=       i == len(lowerCamelCase__				) - 1

											__lowerCamelCase									=       get_down_block(
											    lowerCamelCase__     ,		num_layers=lowerCamelCase__     ,		in_channels=lowerCamelCase__     ,		out_channels=lowerCamelCase__     ,		temb_channels=block_out_channels[0]     ,		add_downsample=not is_final_block or downsample_each_block     ,		)
											self.down_blocks.append(lowerCamelCase__				)

							# mid
							__lowerCamelCase									=       get_mid_block(
							    lowerCamelCase__     ,		in_channels=block_out_channels[-1]     ,		mid_channels=block_out_channels[-1]     ,		out_channels=block_out_channels[-1]     ,		embed_dim=block_out_channels[0]     ,		num_layers=lowerCamelCase__     ,		add_downsample=lowerCamelCase__     ,		)

							# up
							__lowerCamelCase									=       list(reversed(lowerCamelCase__				)				)
							__lowerCamelCase									=       reversed_block_out_channels[0]
							if out_block_type is None:
											__lowerCamelCase									=       out_channels
							else:
											__lowerCamelCase									=       block_out_channels[0]

							for i, up_block_type in enumerate(lowerCamelCase__				):
											__lowerCamelCase									=       output_channel
											__lowerCamelCase									=       (
											    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase__				) - 1 else final_upsample_channels
											)

											__lowerCamelCase									=       i == len(lowerCamelCase__				) - 1

											__lowerCamelCase									=       get_up_block(
											    lowerCamelCase__     ,		num_layers=lowerCamelCase__     ,		in_channels=lowerCamelCase__     ,		out_channels=lowerCamelCase__     ,		temb_channels=block_out_channels[0]     ,		add_upsample=not is_final_block     ,		)
											self.up_blocks.append(lowerCamelCase__				)
											__lowerCamelCase									=       output_channel

							# out
							__lowerCamelCase									=       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     ,		32				)
							__lowerCamelCase									=       get_out_block(
							    out_block_type=lowerCamelCase__     ,		num_groups_out=lowerCamelCase__     ,		embed_dim=block_out_channels[0]     ,		out_channels=lowerCamelCase__     ,		act_fn=lowerCamelCase__     ,		fc_dim=block_out_channels[-1] // 4     ,		)






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = True     ,		)       -> Union[UNetaDOutput, Tuple]:



							'''simple docstring'''
							__lowerCamelCase									=       timestep
							if not torch.is_tensor(lowerCamelCase__				):
											__lowerCamelCase									=       torch.tensor([timesteps]     ,		dtype=torch.long     ,		device=sample.device				)
							elif torch.is_tensor(lowerCamelCase__				) and len(timesteps.shape				) == 0:
											__lowerCamelCase									=       timesteps[None].to(sample.device				)

							__lowerCamelCase									=       self.time_proj(lowerCamelCase__				)
							if self.config.use_timestep_embedding:
											__lowerCamelCase									=       self.time_mlp(lowerCamelCase__				)
							else:
											__lowerCamelCase									=       timestep_embed[..., None]
											__lowerCamelCase									=       timestep_embed.repeat([1, 1, sample.shape[2]]				).to(sample.dtype				)
											__lowerCamelCase									=       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])				)

							# 2. down
							__lowerCamelCase									=       ()
							for downsample_block in self.down_blocks:
											__lowerCamelCase			,		__lowerCamelCase									=       downsample_block(hidden_states=lowerCamelCase__     ,		temb=lowerCamelCase__				)
											down_block_res_samples += res_samples

							# 3. mid
							if self.mid_block:
											__lowerCamelCase									=       self.mid_block(lowerCamelCase__     ,		lowerCamelCase__				)

							# 4. up
							for i, upsample_block in enumerate(self.up_blocks				):
											__lowerCamelCase									=       down_block_res_samples[-1:]
											__lowerCamelCase									=       down_block_res_samples[:-1]
											__lowerCamelCase									=       upsample_block(lowerCamelCase__     ,		res_hidden_states_tuple=lowerCamelCase__     ,		temb=lowerCamelCase__				)

							# 5. post-process
							if self.out_block:
											__lowerCamelCase									=       self.out_block(lowerCamelCase__     ,		lowerCamelCase__				)

							if not return_dict:
											return (sample,)

							return UNetaDOutput(sample=lowerCamelCase__				)


