


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

import yaml






class   __lowercase				(  yaml.SafeLoader  ):





      def        _lowercase				(		self						:					str					,       __lowerCamelCase						:					List[str]       )    ->   Tuple:






         """simple docstring"""







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





      def        _lowercase				(		self						:					Tuple					,       __lowerCamelCase						:					Dict					,       __lowerCamelCase						:					str=False       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     super().construct_mapping(__lowerCamelCase					,       deep=__lowerCamelCase       )
         self._check_no_duplicates_on_constructed_node(__lowerCamelCase       )
         return mapping

def   _UpperCamelCase				(						lowerCAmelCase_   )						->Tuple[Optional[str], str]:
   UpperCAmelCase							=     list(readme_content.splitlines()   )
   if full_content and full_content[0] == "---" and "---" in full_content[1:]:
      UpperCAmelCase							=     full_content[1:].index("""---"""   ) + 1
      UpperCAmelCase							=     """\n""".join(full_content[1:sep_idx]   )
      return yamlblock, "\n".join(full_content[sep_idx + 1 :]   )

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






class   __lowercase				(  __snake_case  ):
      # class attributes
      UpperCamelCase								=       {'''train_eval_index'''}  # train-eval-index in the YAML metadata





      @classmethod
      def        _lowercase				(		cls						:					Tuple					,       __lowerCamelCase						:					Path       )    ->   "DatasetMetadata":






         """simple docstring"""







         with open(__lowerCamelCase					,       encoding="""utf-8"""       ) as readme_file:
            UpperCAmelCase				,  UpperCAmelCase							=     _split_yaml_from_readme(readme_file.read()       )
         if yaml_string is not None:
            return cls.from_yaml_string(__lowerCamelCase       )
         else:
            return cls()





      def        _lowercase				(		self						:					Dict					,       __lowerCamelCase						:					Path       )    ->   int:






         """simple docstring"""







         if path.exists():
            with open(__lowerCamelCase					,       encoding="""utf-8"""       ) as readme_file:
               UpperCAmelCase							=     readme_file.read()
         else:
            UpperCAmelCase							=     None
         UpperCAmelCase							=     self._to_readme(__lowerCamelCase       )
         with open(__lowerCamelCase					,       """w"""					,       encoding="""utf-8"""       ) as readme_file:
            readme_file.write(__lowerCamelCase       )





      def        _lowercase				(		self						:					str					,       __lowerCamelCase						:					Optional[str] = None       )    ->   str:






         """simple docstring"""







         if readme_content is not None:
            UpperCAmelCase				,  UpperCAmelCase							=     _split_yaml_from_readme(__lowerCamelCase       )
            UpperCAmelCase							=     """---\n""" + self.to_yaml_string() + """---\n""" + content
         else:
            UpperCAmelCase							=     """---\n""" + self.to_yaml_string() + """---\n"""
         return full_content





      @classmethod
      def        _lowercase				(		cls						:					Dict					,       __lowerCamelCase						:					str       )    ->   "DatasetMetadata":






         """simple docstring"""







         UpperCAmelCase							=     yaml.load(__lowerCamelCase					,       Loader=_NoDuplicateSafeLoader       ) or {}

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





      def        _lowercase				(		self						:					int       )    ->   str:






         """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=__lowerCamelCase					,       allow_unicode=__lowerCamelCase					,       encoding="""utf-8"""					,       ).decode("""utf-8"""       )

__a     =			{
    """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

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

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







from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


__a     =			logging.get_logger(__name__)
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Tuple:
   try:
      with open(lowerCAmelCase_  ,						"""rb"""   ) as flax_state_f:
         UpperCAmelCase							=     from_bytes(lowerCAmelCase_  ,						flax_state_f.read()   )
   except UnpicklingError as e:
      try:
         with open(lowerCAmelCase_   ) as f:
            if f.read().startswith("""version"""   ):
               raise OSError(
                   """You seem to have cloned a repository without having git-lfs installed. Please"""
                   """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
                   """ folder you cloned."""   )
            else:
               raise ValueError from e
      except (UnicodeDecodeError, ValueError):
         raise EnvironmentError(F"""Unable to convert {model_file} to Flax deserializable object. """   )

   return load_flax_weights_in_pytorch_model(lowerCAmelCase_  ,						lowerCAmelCase_   )







def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Dict:

   try:
      import torch  # noqa: F401
   except ImportError:
      logger.error(
          """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
          """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
          """ instructions."""   )
      raise

   # check if we have bf16 weights
   UpperCAmelCase							=     flatten_dict(jax.tree_util.tree_map(lambda lowerCAmelCase_   : x.dtype == jnp.bfloataa  ,						lowerCAmelCase_   )   ).values()
   if any(lowerCAmelCase_   ):
      # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

      # and bf16 is not fully supported in PT yet.
      logger.warning(
          """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
          """before loading those in PyTorch model."""   )
      UpperCAmelCase							=     jax.tree_util.tree_map(
          lambda lowerCAmelCase_   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params  ,						lowerCAmelCase_   )

   UpperCAmelCase							=     """"""

   UpperCAmelCase							=     flatten_dict(lowerCAmelCase_  ,						sep="""."""   )
   UpperCAmelCase							=     pt_model.state_dict()

   # keep track of unexpected & missing keys
   UpperCAmelCase							=     []
   UpperCAmelCase							=     set(pt_model_dict.keys()   )

   for flax_key_tuple, flax_tensor in flax_state_dict.items():
      UpperCAmelCase							=     flax_key_tuple.split("""."""   )

      if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]
         UpperCAmelCase							=     jnp.transpose(lowerCAmelCase_  ,						(3, 2, 0, 1)   )
      elif flax_key_tuple_array[-1] == "kernel":
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]
         UpperCAmelCase							=     flax_tensor.T
      elif flax_key_tuple_array[-1] == "scale":
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]

      if "time_embedding" not in flax_key_tuple_array:
         for i, flax_key_tuple_string in enumerate(lowerCAmelCase_   ):
            UpperCAmelCase							=     (
                flax_key_tuple_string.replace("""_0"""  ,						""".0"""   )
                .replace("""_1"""  ,						""".1"""   )
                .replace("""_2"""  ,						""".2"""   )
                .replace("""_3"""  ,						""".3"""   )
                .replace("""_4"""  ,						""".4"""   )
                .replace("""_5"""  ,						""".5"""   )
                .replace("""_6"""  ,						""".6"""   )
                .replace("""_7"""  ,						""".7"""   )
                .replace("""_8"""  ,						""".8"""   )
                .replace("""_9"""  ,						""".9"""   )
            )

      UpperCAmelCase							=     """.""".join(lowerCAmelCase_   )

      if flax_key in pt_model_dict:
         if flax_tensor.shape != pt_model_dict[flax_key].shape:
            raise ValueError(
                F"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
                F"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )
         else:
            # add weight to pytorch dict
            UpperCAmelCase							=     np.asarray(lowerCAmelCase_   ) if not isinstance(lowerCAmelCase_  ,						np.ndarray   ) else flax_tensor
            UpperCAmelCase							=     torch.from_numpy(lowerCAmelCase_   )
            # remove from missing keys
            missing_keys.remove(lowerCAmelCase_   )
      else:
         # weight is not expected by PyTorch model
         unexpected_keys.append(lowerCAmelCase_   )

   pt_model.load_state_dict(lowerCAmelCase_   )

   # re-transform missing_keys to list
   UpperCAmelCase							=     list(lowerCAmelCase_   )

   if len(lowerCAmelCase_   ) > 0:
      logger.warning(
          """Some weights of the Flax model were not used when initializing the PyTorch model"""
          F""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
          F""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
          """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
          F""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
          """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
          """ FlaxBertForSequenceClassification model)."""   )
   if len(lowerCAmelCase_   ) > 0:
      logger.warning(
          F"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
          F""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
          """ use it for predictions and inference."""   )

   return pt_model



