






import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class 		SCREAMING_SNAKE_CASE__	(  nn.Module     ):

  '''simple docstring'''







  _lowerCamelCase    =  42
  _lowerCamelCase    =  42
  _lowerCamelCase    =  0.0
  _lowerCamelCase    =  1
  _lowerCamelCase    =  1
  _lowerCamelCase    =  True
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  jnp.floataa


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    []
         _UpperCAmelCase							=    []

         for i in range(self.num_layers							):
                _UpperCAmelCase							=    self.in_channels if i == 0 else self.out_channels

                _UpperCAmelCase							=    FlaxResnetBlockaD(
                    in_channels=lowerCamelCase       ,				out_channels=self.out_channels       ,				dropout_prob=self.dropout       ,				dtype=self.dtype       ,				)
                resnets.append(lowerCamelCase							)

                _UpperCAmelCase							=    FlaxTransformeraDModel(
                    in_channels=self.out_channels       ,				n_heads=self.num_attention_heads       ,				d_head=self.out_channels // self.num_attention_heads       ,				depth=1       ,				use_linear_projection=self.use_linear_projection       ,				only_cross_attention=self.only_cross_attention       ,				use_memory_efficient_attention=self.use_memory_efficient_attention       ,				dtype=self.dtype       ,				)
                attentions.append(lowerCamelCase							)

         _UpperCAmelCase							=    resnets
         _UpperCAmelCase							=    attentions

         if self.add_downsample:
                _UpperCAmelCase							=    FlaxDownsampleaD(self.out_channels       ,				dtype=self.dtype							)





  def __call__(      self		:			Any       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			str=True							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    ()

         for resnet, attn in zip(self.resnets       ,				self.attentions							):
                _UpperCAmelCase							=    resnet(lowerCamelCase       ,				lowerCamelCase       ,				deterministic=lowerCamelCase							)
                _UpperCAmelCase							=    attn(lowerCamelCase       ,				lowerCamelCase       ,				deterministic=lowerCamelCase							)
                output_states += (hidden_states,)

         if self.add_downsample:
                _UpperCAmelCase							=    self.downsamplers_a(lowerCamelCase							)
                output_states += (hidden_states,)

         return hidden_states, output_states



class 		SCREAMING_SNAKE_CASE__	(  nn.Module     ):

  '''simple docstring'''







  _lowerCamelCase    =  42
  _lowerCamelCase    =  42
  _lowerCamelCase    =  0.0
  _lowerCamelCase    =  1
  _lowerCamelCase    =  True
  _lowerCamelCase    =  jnp.floataa


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    []

         for i in range(self.num_layers							):
                _UpperCAmelCase							=    self.in_channels if i == 0 else self.out_channels

                _UpperCAmelCase							=    FlaxResnetBlockaD(
                    in_channels=lowerCamelCase       ,				out_channels=self.out_channels       ,				dropout_prob=self.dropout       ,				dtype=self.dtype       ,				)
                resnets.append(lowerCamelCase							)
         _UpperCAmelCase							=    resnets

         if self.add_downsample:
                _UpperCAmelCase							=    FlaxDownsampleaD(self.out_channels       ,				dtype=self.dtype							)





  def __call__(      self		:			List[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			str=True							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    ()

         for resnet in self.resnets:
                _UpperCAmelCase							=    resnet(lowerCamelCase       ,				lowerCamelCase       ,				deterministic=lowerCamelCase							)
                output_states += (hidden_states,)

         if self.add_downsample:
                _UpperCAmelCase							=    self.downsamplers_a(lowerCamelCase							)
                output_states += (hidden_states,)

         return hidden_states, output_states



class 		SCREAMING_SNAKE_CASE__	(  nn.Module     ):

  '''simple docstring'''







  _lowerCamelCase    =  42
  _lowerCamelCase    =  42
  _lowerCamelCase    =  42
  _lowerCamelCase    =  0.0
  _lowerCamelCase    =  1
  _lowerCamelCase    =  1
  _lowerCamelCase    =  True
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  jnp.floataa


  def  lowerCamelCase       (      self		:			Any							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    []
         _UpperCAmelCase							=    []

         for i in range(self.num_layers							):
                _UpperCAmelCase							=    self.in_channels if (i == self.num_layers - 1) else self.out_channels
                _UpperCAmelCase							=    self.prev_output_channel if i == 0 else self.out_channels

                _UpperCAmelCase							=    FlaxResnetBlockaD(
                    in_channels=resnet_in_channels + res_skip_channels       ,				out_channels=self.out_channels       ,				dropout_prob=self.dropout       ,				dtype=self.dtype       ,				)
                resnets.append(lowerCamelCase							)

                _UpperCAmelCase							=    FlaxTransformeraDModel(
                    in_channels=self.out_channels       ,				n_heads=self.num_attention_heads       ,				d_head=self.out_channels // self.num_attention_heads       ,				depth=1       ,				use_linear_projection=self.use_linear_projection       ,				only_cross_attention=self.only_cross_attention       ,				use_memory_efficient_attention=self.use_memory_efficient_attention       ,				dtype=self.dtype       ,				)
                attentions.append(lowerCamelCase							)

         _UpperCAmelCase							=    resnets
         _UpperCAmelCase							=    attentions

         if self.add_upsample:
                _UpperCAmelCase							=    FlaxUpsampleaD(self.out_channels       ,				dtype=self.dtype							)





  def __call__(      self		:			Any       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Union[str, Any]=True							)						->	str:


         """simple docstring"""
         for resnet, attn in zip(self.resnets       ,				self.attentions							):
                # pop res hidden states
                _UpperCAmelCase							=    res_hidden_states_tuple[-1]
                _UpperCAmelCase							=    res_hidden_states_tuple[:-1]
                _UpperCAmelCase							=    jnp.concatenate((hidden_states, res_hidden_states)       ,				axis=-1							)

                _UpperCAmelCase							=    resnet(lowerCamelCase       ,				lowerCamelCase       ,				deterministic=lowerCamelCase							)
                _UpperCAmelCase							=    attn(lowerCamelCase       ,				lowerCamelCase       ,				deterministic=lowerCamelCase							)

         if self.add_upsample:
                _UpperCAmelCase							=    self.upsamplers_a(lowerCamelCase							)

         return hidden_states



class 		SCREAMING_SNAKE_CASE__	(  nn.Module     ):

  '''simple docstring'''







  _lowerCamelCase    =  42
  _lowerCamelCase    =  42
  _lowerCamelCase    =  42
  _lowerCamelCase    =  0.0
  _lowerCamelCase    =  1
  _lowerCamelCase    =  True
  _lowerCamelCase    =  jnp.floataa


  def  lowerCamelCase       (      self		:			Any							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    []

         for i in range(self.num_layers							):
                _UpperCAmelCase							=    self.in_channels if (i == self.num_layers - 1) else self.out_channels
                _UpperCAmelCase							=    self.prev_output_channel if i == 0 else self.out_channels

                _UpperCAmelCase							=    FlaxResnetBlockaD(
                    in_channels=resnet_in_channels + res_skip_channels       ,				out_channels=self.out_channels       ,				dropout_prob=self.dropout       ,				dtype=self.dtype       ,				)
                resnets.append(lowerCamelCase							)

         _UpperCAmelCase							=    resnets

         if self.add_upsample:
                _UpperCAmelCase							=    FlaxUpsampleaD(self.out_channels       ,				dtype=self.dtype							)





  def __call__(      self		:			int       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			str=True							)						->	Optional[Any]:


         """simple docstring"""
         for resnet in self.resnets:
                # pop res hidden states
                _UpperCAmelCase							=    res_hidden_states_tuple[-1]
                _UpperCAmelCase							=    res_hidden_states_tuple[:-1]
                _UpperCAmelCase							=    jnp.concatenate((hidden_states, res_hidden_states)       ,				axis=-1							)

                _UpperCAmelCase							=    resnet(lowerCamelCase       ,				lowerCamelCase       ,				deterministic=lowerCamelCase							)

         if self.add_upsample:
                _UpperCAmelCase							=    self.upsamplers_a(lowerCamelCase							)

         return hidden_states



class 		SCREAMING_SNAKE_CASE__	(  nn.Module     ):

  '''simple docstring'''







  _lowerCamelCase    =  42
  _lowerCamelCase    =  0.0
  _lowerCamelCase    =  1
  _lowerCamelCase    =  1
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  jnp.floataa


  def  lowerCamelCase       (      self		:			int							)						->	Dict:


         """simple docstring"""
         # there is always at least one resnet
         _UpperCAmelCase							=    [
             FlaxResnetBlockaD(
                 in_channels=self.in_channels       ,				out_channels=self.in_channels       ,				dropout_prob=self.dropout       ,				dtype=self.dtype       ,				)
         ]

         _UpperCAmelCase							=    []

         for _ in range(self.num_layers							):
                _UpperCAmelCase							=    FlaxTransformeraDModel(
                    in_channels=self.in_channels       ,				n_heads=self.num_attention_heads       ,				d_head=self.in_channels // self.num_attention_heads       ,				depth=1       ,				use_linear_projection=self.use_linear_projection       ,				use_memory_efficient_attention=self.use_memory_efficient_attention       ,				dtype=self.dtype       ,				)
                attentions.append(lowerCamelCase							)

                _UpperCAmelCase							=    FlaxResnetBlockaD(
                    in_channels=self.in_channels       ,				out_channels=self.in_channels       ,				dropout_prob=self.dropout       ,				dtype=self.dtype       ,				)
                resnets.append(lowerCamelCase							)

         _UpperCAmelCase							=    resnets
         _UpperCAmelCase							=    attentions





  def __call__(      self		:			Dict       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			List[Any]=True							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.resnets[0](lowerCamelCase       ,				lowerCamelCase							)
         for attn, resnet in zip(self.attentions       ,				self.resnets[1:]							):
                _UpperCAmelCase							=    attn(lowerCamelCase       ,				lowerCamelCase       ,				deterministic=lowerCamelCase							)
                _UpperCAmelCase							=    resnet(lowerCamelCase       ,				lowerCamelCase       ,				deterministic=lowerCamelCase							)

         return hidden_states



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

import yaml



class 						_UpperCAmelCase      (     yaml.SafeLoader							):






		def _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[self.constructed_objects[key_node] for key_node, _ in node.value]
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[tuple(UpperCAmelCase) if isinstance(UpperCAmelCase   ,  UpperCAmelCase) else key for key in keys]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						Counter(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:str				     =						[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				:     int   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     Union[str, Any]=False):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						super().construct_mapping(UpperCAmelCase   ,  deep=UpperCAmelCase)
				self._check_no_duplicates_on_constructed_node(UpperCAmelCase)
				return mapping




def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Any				     =						list(readme_content.splitlines() )
		if full_content and full_content[0] == "---" and "---" in full_content[1:]:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						full_content[1:].index("---" ) + 1
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						"\n".join(full_content[1:sep_idx] )
				return yamlblock, "\n".join(full_content[sep_idx + 1 :] )

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



class 						_UpperCAmelCase      (     lowercase							):
		# class attributes
		lowerCamelCase_	:			List[Any]       			=  {"""train_eval_index"""}  # train-eval-index in the YAML metadata






		@classmethod
		def _snake_case		(						cls				:     Optional[int]   ,  UpperCAmelCase				:     Path):
				with open(UpperCAmelCase   ,  encoding="utf-8") as readme_file:
						SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						_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				:     Dict   ,  UpperCAmelCase				:     Path):
				if path.exists():
						with open(UpperCAmelCase   ,  encoding="utf-8") as readme_file:
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						readme_file.read()
				else:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						None
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self._to_readme(UpperCAmelCase)
				with open(UpperCAmelCase   ,  "w"   ,  encoding="utf-8") as readme_file:
						readme_file.write(UpperCAmelCase)






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






		@classmethod
		def _snake_case		(						cls				:     List[str]   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						yaml.load(UpperCAmelCase   ,  Loader=_NoDuplicateSafeLoader) or {}

				# Convert the YAML keys to DatasetMetadata fields
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{
				    (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				:     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=UpperCAmelCase   ,  allow_unicode=UpperCAmelCase   ,  encoding="utf-8"   ,  ).decode("utf-8")




SCREAMING_SNAKE_CASE__     					=			{
    "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

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

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






