

import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[Any]			,							snake_case__  :Tuple			,							snake_case__  :Tuple			,							snake_case__  :Tuple="attention"     )      ->						List[str]:
  _lowercase							= params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
  _lowercase							= params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
  _lowercase							= params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
  _lowercase							= params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
  return k, o, q, v

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple			,							snake_case__  :List[Any]			,							snake_case__  :Optional[Any]			,							snake_case__  :Optional[Any]=False     )      ->						Tuple:
  if split_mlp_wi:
    _lowercase							= params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
    _lowercase							= params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
    _lowercase							= (wi_a, wi_a)
  else:
    _lowercase							= params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

  _lowercase							= params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
  return wi, wo

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple			,							snake_case__  :Any			,							snake_case__  :Optional[Any]			,							snake_case__  :Dict     )      ->						str:
  return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :dict			,							*, snake_case__  :int			,							snake_case__  :bool     )      ->						List[str]:
  _lowercase							= traverse_util.flatten_dict(variables['target']     )
  _lowercase							= {'/'.join(snake_case__     ): v for k, v in old.items()}

  # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
  _lowercase							= 'encoder/layers_0/mlp/wi_0/kernel' in old
  print('Split MLP:'			,							snake_case__     )

  _lowercase							= collections.OrderedDict()

  # Shared embeddings.
  _lowercase							= old['token_embedder/embedding']

  # Encoder.
  for i in range(snake_case__     ):
    # Block i, layer 0 (Self Attention).
    _lowercase							= tax_layer_norm_lookup(snake_case__			,							snake_case__			,							'encoder'			,							'pre_attention_layer_norm'     )
    _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= tax_attention_lookup(snake_case__			,							snake_case__			,							'encoder'			,							'attention'     )
    _lowercase							= layer_norm
    _lowercase							= k.T
    _lowercase							= o.T
    _lowercase							= q.T
    _lowercase							= v.T

    # Block i, layer 1 (MLP).
    _lowercase							= tax_layer_norm_lookup(snake_case__			,							snake_case__			,							'encoder'			,							'pre_mlp_layer_norm'     )
    _lowercase						,		_lowercase							= tax_mlp_lookup(snake_case__			,							snake_case__			,							'encoder'			,							snake_case__     )
    _lowercase							= layer_norm
    if split_mlp_wi:
      _lowercase							= wi[0].T
      _lowercase							= wi[1].T
    else:
      _lowercase							= wi.T
    _lowercase							= wo.T

  _lowercase							= old[
      'encoder/relpos_bias/rel_embedding'
  ].T
  _lowercase							= old['encoder/encoder_norm/scale']

  if not is_encoder_only:
    # Decoder.
    for i in range(snake_case__     ):
      # Block i, layer 0 (Self Attention).
      _lowercase							= tax_layer_norm_lookup(snake_case__			,							snake_case__			,							'decoder'			,							'pre_self_attention_layer_norm'     )
      _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= tax_attention_lookup(snake_case__			,							snake_case__			,							'decoder'			,							'self_attention'     )
      _lowercase							= layer_norm
      _lowercase							= k.T
      _lowercase							= o.T
      _lowercase							= q.T
      _lowercase							= v.T

      # Block i, layer 1 (Cross Attention).
      _lowercase							= tax_layer_norm_lookup(snake_case__			,							snake_case__			,							'decoder'			,							'pre_cross_attention_layer_norm'     )
      _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= tax_attention_lookup(snake_case__			,							snake_case__			,							'decoder'			,							'encoder_decoder_attention'     )
      _lowercase							= layer_norm
      _lowercase							= k.T
      _lowercase							= o.T
      _lowercase							= q.T
      _lowercase							= v.T

      # Block i, layer 2 (MLP).
      _lowercase							= tax_layer_norm_lookup(snake_case__			,							snake_case__			,							'decoder'			,							'pre_mlp_layer_norm'     )
      _lowercase						,		_lowercase							= tax_mlp_lookup(snake_case__			,							snake_case__			,							'decoder'			,							snake_case__     )
      _lowercase							= layer_norm
      if split_mlp_wi:
        _lowercase							= wi[0].T
        _lowercase							= wi[1].T
      else:
        _lowercase							= wi.T
      _lowercase							= wo.T

    _lowercase							= old['decoder/decoder_norm/scale']
    _lowercase							= old[
        'decoder/relpos_bias/rel_embedding'
    ].T

    # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
    if "decoder/logits_dense/kernel" in old:
      _lowercase							= old['decoder/logits_dense/kernel'].T

  return new

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict			,							snake_case__  :bool     )      ->						Union[str, Any]:
  _lowercase							= collections.OrderedDict([(k, torch.from_numpy(v.copy()     )) for (k, v) in converted_params.items()]     )

  # Add what is missing.
  if "encoder.embed_tokens.weight" not in state_dict:
    _lowercase							= state_dict['shared.weight']

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      _lowercase							= state_dict['shared.weight']

    if "lm_head.weight" not in state_dict:  # For old 1.0 models.
      print('Using shared word embeddings as lm_head.'     )
      _lowercase							= state_dict['shared.weight']

  return state_dict

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]			,							snake_case__  :Tuple			,							snake_case__  :Tuple			,							snake_case__  :Optional[int]     )      ->						List[Any]:
  _lowercase							= checkpoints.load_tax_checkpoint(snake_case__     )
  _lowercase							= convert_tax_to_pytorch(snake_case__			,							num_layers=config.num_layers			,							is_encoder_only=snake_case__     )
  _lowercase							= make_state_dict(snake_case__			,							snake_case__     )
  model.load_state_dict(snake_case__			,							strict=snake_case__     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple			,							snake_case__  :Any			,							snake_case__  :int			,							snake_case__  :bool = False     )      ->						Optional[int]:
  _lowercase							= TaConfig.from_json_file(snake_case__     )
  print(F"""Building PyTorch model from configuration: {config}"""     )
  # Non-v1.1 checkpoints could also use T5Model, but this works for all.
  # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
  if is_encoder_only:
    _lowercase							= TaEncoderModel(snake_case__     )
  else:
    _lowercase							= TaForConditionalGeneration(snake_case__     )

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(snake_case__			,							snake_case__			,							snake_case__			,							snake_case__     )

  # Save pytorch-model
  print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
  model.save_pretrained(snake_case__     )

  # Verify that we can load the checkpoint.
  model.from_pretrained(snake_case__     )
  print('Done'     )


if __name__ == "__main__":
      snake_case 				=       argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
      # Required parameters
      parser.add_argument(
          """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      parser.add_argument(
          """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
      )
      snake_case 				=       parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )






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

import yaml





class 				A_				(      yaml.SafeLoader	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	Dict						,__A	:	List[Any]  )						->					Any:
         _lowercase							= [self.constructed_objects[key_node] for key_node, _ in node.value]
         _lowercase							= [tuple(__A  ) if isinstance(__A						,__A  ) else key for key in keys]
         _lowercase							= Counter(__A  )
         _lowercase							= [key for key in counter if counter[key] > 1]
         if duplicate_keys:
           raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""  )







       def    __UpperCAmelCase (    self	:	Any						,__A	:	int						,__A	:	Dict=False  )						->					Union[str, Any]:
         _lowercase							= super().construct_mapping(__A						,deep=__A  )
         self._check_no_duplicates_on_constructed_node(__A  )
         return mapping

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

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





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Any      =  {'''train_eval_index'''}  # train-eval-index in the YAML metadata







       @classmethod
       def    __UpperCAmelCase (    cls	:	List[Any]						,__A	:	Path  )						->					"DatasetMetadata":
         with open(__A						,encoding='utf-8'  ) as readme_file:
           _lowercase						,		_lowercase							= _split_yaml_from_readme(readme_file.read()  )
         if yaml_string is not None:
           return cls.from_yaml_string(__A  )
         else:
           return cls()







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	Path  )						->					Optional[Any]:
         if path.exists():
           with open(__A						,encoding='utf-8'  ) as readme_file:
             _lowercase							= readme_file.read()
         else:
           _lowercase							= None
         _lowercase							= self._to_readme(__A  )
         with open(__A						,'w'						,encoding='utf-8'  ) as readme_file:
           readme_file.write(__A  )







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Optional[str] = None  )						->					str:
         if readme_content is not None:
           _lowercase						,		_lowercase							= _split_yaml_from_readme(__A  )
           _lowercase							= '---\n' + self.to_yaml_string() + '---\n' + content
         else:
           _lowercase							= '---\n' + self.to_yaml_string() + '---\n'
         return full_content







       @classmethod
       def    __UpperCAmelCase (    cls	:	List[Any]						,__A	:	str  )						->					"DatasetMetadata":
         _lowercase							= yaml.load(__A						,Loader=_NoDuplicateSafeLoader  ) or {}

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







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					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=__A						,allow_unicode=__A						,encoding='utf-8'						,).decode('utf-8'  )
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

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

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