





'''simple docstring'''




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

import yaml


class 					SCREAMING_SNAKE_CASE       (yaml.SafeLoader				):


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					str						=			[self.constructed_objects[key_node] for key_node, _ in node.value]
          __A		:					Dict						=			[tuple(_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else key for key in keys]
          __A		:					str						=			Counter(_UpperCAmelCase)
          __A		:					Optional[int]						=			[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		,      _UpperCAmelCase		,      _UpperCAmelCase=False):

          '''simple docstring'''






          __A		:					Optional[int]						=			super().construct_mapping(_UpperCAmelCase		,      deep=_UpperCAmelCase)
          self._check_no_duplicates_on_constructed_node(_UpperCAmelCase)
          return mapping






def 			_lowerCAmelCase     (    __snake_case    :					str     )	->	Tuple[Optional[str], str]:
     __A		:					Union[str, Any]						=			list(readme_content.splitlines()     )
     if full_content and full_content[0] == "---" and "---" in full_content[1:]:
          __A		:					Optional[int]						=			full_content[1:].index('---'     ) + 1
          __A		:					int						=			'\n'.join(full_content[1:sep_idx]     )
          return yamlblock, "\n".join(full_content[sep_idx + 1 :]     )

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


class 					SCREAMING_SNAKE_CASE       (a__				):
     # class attributes
     lowerCAmelCase						     =			{'''train_eval_index'''}  # train-eval-index in the YAML metadata


     @classmethod
     def 				SCREAMING_SNAKE_CASE    (		cls		,      _UpperCAmelCase):

          '''simple docstring'''






          with open(_UpperCAmelCase		,      encoding='utf-8') as readme_file:
               __A					,__A		:					int						=			_split_yaml_from_readme(readme_file.read())
          if yaml_string is not None:
               return cls.from_yaml_string(_UpperCAmelCase)
          else:
               return cls()


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          if path.exists():
               with open(_UpperCAmelCase		,      encoding='utf-8') as readme_file:
                    __A		:					Dict						=			readme_file.read()
          else:
               __A		:					Optional[Any]						=			None
          __A		:					str						=			self._to_readme(_UpperCAmelCase)
          with open(_UpperCAmelCase		,      'w'		,      encoding='utf-8') as readme_file:
               readme_file.write(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase = None):

          '''simple docstring'''






          if readme_content is not None:
               __A					,__A		:					Any						=			_split_yaml_from_readme(_UpperCAmelCase)
               __A		:					Union[str, Any]						=			'---\n' + self.to_yaml_string() + '---\n' + content
          else:
               __A		:					Optional[int]						=			'---\n' + self.to_yaml_string() + '---\n'
          return full_content


     @classmethod
     def 				SCREAMING_SNAKE_CASE    (		cls		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Dict						=			yaml.load(_UpperCAmelCase		,      Loader=_NoDuplicateSafeLoader) or {}

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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          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')




lowercase__       : List[Any]         =       {
    '''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

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

  lowercase__       : int         =       Path(args.readme_filepath)
  lowercase__       : Any         =       DatasetMetadata.from_readme(readme_filepath)
  print(dataset_metadata)
  dataset_metadata.to_readme(readme_filepath)






'''simple docstring'''




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 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			42




class 					SCREAMING_SNAKE_CASE       (a__   ,						a__				):


     @register_to_config
     def __init__(		self		,      _UpperCAmelCase = 6_5536		,      _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		,      ):

          '''simple docstring'''






          super().__init__()
          __A		:					Dict						=			sample_size

          # time
          if time_embedding_type == "fourier":
               __A		:					int						=			GaussianFourierProjection(
                   embedding_size=8		,      set_W_to_weight=_UpperCAmelCase		,      log=_UpperCAmelCase		,      flip_sin_to_cos=_UpperCAmelCase)
               __A		:					Any						=			2 * block_out_channels[0]
          elif time_embedding_type == "positional":
               __A		:					List[str]						=			Timesteps(
                   block_out_channels[0]		,      flip_sin_to_cos=_UpperCAmelCase		,      downscale_freq_shift=_UpperCAmelCase)
               __A		:					List[str]						=			block_out_channels[0]

          if use_timestep_embedding:
               __A		:					Optional[Any]						=			block_out_channels[0] * 4
               __A		:					Optional[int]						=			TimestepEmbedding(
                   in_channels=_UpperCAmelCase		,      time_embed_dim=_UpperCAmelCase		,      act_fn=_UpperCAmelCase		,      out_dim=block_out_channels[0]		,      )

          __A		:					Dict						=			nn.ModuleList([])
          __A		:					Dict						=			None
          __A		:					Tuple						=			nn.ModuleList([])
          __A		:					Tuple						=			None

          # down
          __A		:					Any						=			in_channels
          for i, down_block_type in enumerate(_UpperCAmelCase):
               __A		:					Tuple						=			output_channel
               __A		:					Optional[Any]						=			block_out_channels[i]

               if i == 0:
                    input_channel += extra_in_channels

               __A		:					List[str]						=			i == len(_UpperCAmelCase) - 1

               __A		:					int						=			get_down_block(
                   _UpperCAmelCase		,      num_layers=_UpperCAmelCase		,      in_channels=_UpperCAmelCase		,      out_channels=_UpperCAmelCase		,      temb_channels=block_out_channels[0]		,      add_downsample=not is_final_block or downsample_each_block		,      )
               self.down_blocks.append(_UpperCAmelCase)

          # mid
          __A		:					str						=			get_mid_block(
              _UpperCAmelCase		,      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=_UpperCAmelCase		,      add_downsample=_UpperCAmelCase		,      )

          # up
          __A		:					Optional[int]						=			list(reversed(_UpperCAmelCase))
          __A		:					Optional[int]						=			reversed_block_out_channels[0]
          if out_block_type is None:
               __A		:					str						=			out_channels
          else:
               __A		:					List[Any]						=			block_out_channels[0]

          for i, up_block_type in enumerate(_UpperCAmelCase):
               __A		:					Optional[Any]						=			output_channel
               __A		:					Optional[Any]						=			(
                   reversed_block_out_channels[i + 1] if i < len(_UpperCAmelCase) - 1 else final_upsample_channels
               )

               __A		:					Dict						=			i == len(_UpperCAmelCase) - 1

               __A		:					str						=			get_up_block(
                   _UpperCAmelCase		,      num_layers=_UpperCAmelCase		,      in_channels=_UpperCAmelCase		,      out_channels=_UpperCAmelCase		,      temb_channels=block_out_channels[0]		,      add_upsample=not is_final_block		,      )
               self.up_blocks.append(_UpperCAmelCase)
               __A		:					Optional[int]						=			output_channel

          # out
          __A		:					str						=			norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,      32)
          __A		:					Optional[Any]						=			get_out_block(
              out_block_type=_UpperCAmelCase		,      num_groups_out=_UpperCAmelCase		,      embed_dim=block_out_channels[0]		,      out_channels=_UpperCAmelCase		,      act_fn=_UpperCAmelCase		,      fc_dim=block_out_channels[-1] // 4		,      )


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = True		,      ):

          '''simple docstring'''






          __A		:					Any						=			timestep
          if not torch.is_tensor(_UpperCAmelCase):
               __A		:					Any						=			torch.tensor([timesteps]		,      dtype=torch.long		,      device=sample.device)
          elif torch.is_tensor(_UpperCAmelCase) and len(timesteps.shape) == 0:
               __A		:					Any						=			timesteps[None].to(sample.device)

          __A		:					List[Any]						=			self.time_proj(_UpperCAmelCase)
          if self.config.use_timestep_embedding:
               __A		:					Dict						=			self.time_mlp(_UpperCAmelCase)
          else:
               __A		:					Dict						=			timestep_embed[..., None]
               __A		:					Tuple						=			timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
               __A		:					List[Any]						=			timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

          # 2. down
          __A		:					int						=			()
          for downsample_block in self.down_blocks:
               __A					,__A		:					int						=			downsample_block(hidden_states=_UpperCAmelCase		,      temb=_UpperCAmelCase)
               down_block_res_samples += res_samples

          # 3. mid
          if self.mid_block:
               __A		:					Optional[int]						=			self.mid_block(_UpperCAmelCase		,      _UpperCAmelCase)

          # 4. up
          for i, upsample_block in enumerate(self.up_blocks):
               __A		:					Any						=			down_block_res_samples[-1:]
               __A		:					Optional[int]						=			down_block_res_samples[:-1]
               __A		:					Any						=			upsample_block(_UpperCAmelCase		,      res_hidden_states_tuple=_UpperCAmelCase		,      temb=_UpperCAmelCase)

          # 5. post-process
          if self.out_block:
               __A		:					Dict						=			self.out_block(_UpperCAmelCase		,      _UpperCAmelCase)

          if not return_dict:
               return (sample,)

          return UNetaDOutput(sample=_UpperCAmelCase)