

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 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: torch.FloatTensor




class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				@register_to_config
				def __init__( self       , UpperCAmelCase_ = 6_55_36       , UpperCAmelCase_ = None       , UpperCAmelCase_ = 2       , UpperCAmelCase_ = 2       , UpperCAmelCase_ = 0       , UpperCAmelCase_ = "fourier"       , UpperCAmelCase_ = True       , UpperCAmelCase_ = False       , UpperCAmelCase_ = 0.0       , UpperCAmelCase_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")       , UpperCAmelCase_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")       , UpperCAmelCase_ = "UNetMidBlock1D"       , UpperCAmelCase_ = None       , UpperCAmelCase_ = (32, 32, 64)       , UpperCAmelCase_ = None       , UpperCAmelCase_ = 8       , UpperCAmelCase_ = 1       , UpperCAmelCase_ = False       , ):
											super().__init__()
											lowerCAmelCase   =		sample_size

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

											if use_timestep_embedding:
																		lowerCAmelCase   =		block_out_channels[0] * 4
																		lowerCAmelCase   =		TimestepEmbedding(
																		    in_channels=A__       , time_embed_dim=A__       , act_fn=A__       , 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(A__						):
																		lowerCAmelCase   =		output_channel
																		lowerCAmelCase   =		block_out_channels[i]

																		if i == 0:
																									input_channel += extra_in_channels

																		lowerCAmelCase   =		i == len(A__						) - 1

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

											# mid
											lowerCAmelCase   =		get_mid_block(
											    A__       , 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=A__       , add_downsample=A__       , )

											# up
											lowerCAmelCase   =		list(reversed(A__						)						)
											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(A__						):
																		lowerCAmelCase   =		output_channel
																		lowerCAmelCase   =		(
																		    reversed_block_out_channels[i + 1] if i < len(A__						) - 1 else final_upsample_channels
																		)

																		lowerCAmelCase   =		i == len(A__						) - 1

																		lowerCAmelCase   =		get_up_block(
																		    A__       , num_layers=A__       , in_channels=A__       , out_channels=A__       , temb_channels=block_out_channels[0]       , add_upsample=not is_final_block       , )
																		self.up_blocks.append(A__						)
																		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=A__       , num_groups_out=A__       , embed_dim=block_out_channels[0]       , out_channels=A__       , act_fn=A__       , fc_dim=block_out_channels[-1] // 4       , )






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_ = True       , ):
											lowerCAmelCase   =		timestep
											if not torch.is_tensor(A__						):
																		lowerCAmelCase   =		torch.tensor([timesteps]       , dtype=torch.long       , device=sample.device						)
											elif torch.is_tensor(A__						) and len(timesteps.shape						) == 0:
																		lowerCAmelCase   =		timesteps[None].to(sample.device						)

											lowerCAmelCase   =		self.time_proj(A__						)
											if self.config.use_timestep_embedding:
																		lowerCAmelCase   =		self.time_mlp(A__						)
											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   =		downsample_block(hidden_states=A__       , temb=A__						)
																		down_block_res_samples += res_samples

											# 3. mid
											if self.mid_block:
																		lowerCAmelCase   =		self.mid_block(A__       , A__						)

											# 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(A__       , res_hidden_states_tuple=A__       , temb=A__						)

											# 5. post-process
											if self.out_block:
																		lowerCAmelCase   =		self.out_block(A__       , A__						)

											if not return_dict:
																		return (sample,)

											return UNetaDOutput(sample=A__						)









from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class 							__UpperCamelCase			( yaml.SafeLoader  ):





				'''simple docstring'''



				def       __snake_case		( self       , UpperCAmelCase_						):
											lowerCAmelCase   =		[self.constructed_objects[key_node] for key_node, _ in node.value]
											lowerCAmelCase   =		[tuple(UpperCAmelCase_						) if isinstance(UpperCAmelCase_       , UpperCAmelCase_						) else key for key in keys]
											lowerCAmelCase   =		Counter(UpperCAmelCase_						)
											lowerCAmelCase   =		[key for key in counter if counter[key] > 1]
											if duplicate_keys:
																		raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""						)






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=False						):
											lowerCAmelCase   =		super().construct_mapping(UpperCAmelCase_       , deep=UpperCAmelCase_						)
											self._check_no_duplicates_on_constructed_node(UpperCAmelCase_						)
											return mapping




def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		list(readme_content.splitlines()							)
							if full_content and full_content[0] == "---" and "---" in full_content[1:]:
														lowerCAmelCase   =		full_content[1:].index('''---'''							) + 1
														lowerCAmelCase   =		'''\n'''.join(full_content[1:sep_idx]							)
														return yamlblock, "\n".join(full_content[sep_idx + 1 :]							)

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


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Any										={"""train_eval_index"""}  # train-eval-index in the YAML metadata



				@classmethod
				def       __snake_case		( cls       , UpperCAmelCase_						):
											with open(UpperCAmelCase_       , encoding='''utf-8'''						) as readme_file:
																		lowerCAmelCase   ,     lowerCAmelCase   =		_split_yaml_from_readme(readme_file.read()						)
											if yaml_string is not None:
																		return cls.from_yaml_string(UpperCAmelCase_						)
											else:
																		return cls()



				def       __snake_case		( self       , UpperCAmelCase_						):
											if path.exists():
																		with open(UpperCAmelCase_       , encoding='''utf-8'''						) as readme_file:
																									lowerCAmelCase   =		readme_file.read()
											else:
																		lowerCAmelCase   =		None
											lowerCAmelCase   =		self._to_readme(UpperCAmelCase_						)
											with open(UpperCAmelCase_       , '''w'''       , encoding='''utf-8'''						) as readme_file:
																		readme_file.write(UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_ = None						):
											if readme_content is not None:
																		lowerCAmelCase   ,     lowerCAmelCase   =		_split_yaml_from_readme(UpperCAmelCase_						)
																		lowerCAmelCase   =		'''---\n''' + self.to_yaml_string() + '''---\n''' + content
											else:
																		lowerCAmelCase   =		'''---\n''' + self.to_yaml_string() + '''---\n'''
											return full_content



				@classmethod
				def       __snake_case		( cls       , UpperCAmelCase_						):
											lowerCAmelCase   =		yaml.load(UpperCAmelCase_       , Loader=_NoDuplicateSafeLoader						) or {}

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






				def       __snake_case		( self						):
											return yaml.safe_dump(
											    {
											        (key.replace('''_'''       , '''-'''						) if key in self._FIELDS_WITH_DASHES else key): value
											        for key, value in self.items()
											    }       , sort_keys=UpperCAmelCase_       , allow_unicode=UpperCAmelCase_       , encoding='''utf-8'''       , ).decode('''utf-8'''						)




UpperCAmelCase_						={
    """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

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

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



