






"""simple docstring"""


import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()




def 			snake_case  (A_      :int	,			A_      :Union[str, Any]	,			A_      :Dict      ):







  '''simple docstring'''

  a     :			Union[str, Any]				=   FNetConfig.from_json_file(A_      )
  print(f'''Building PyTorch model from configuration: {config}'''      )
  a     :			Union[str, Any]				=   FNetForPreTraining(A_      )

  a     :			str				=   restore_checkpoint(A_	,			A_      )
  a     :			Dict				=   checkpoint_dict['target']

  # Embeddings
  # Position IDs
  a     :			Optional[int]				=   fnet_pretraining_model.state_dict()

  a     :			Optional[int]				=   state_dict['fnet.embeddings.position_ids']
  a     :			List[str]				=   {'fnet.embeddings.position_ids': position_ids}
  # Embedding Layers
  a     :			List[Any]				=   torch.tensor(
      pretrained_model_params['encoder']['embedder']['word']['embedding']      )
  a     :			List[str]				=   torch.tensor(
      pretrained_model_params['encoder']['embedder']['position']['embedding'][0]      )
  a     :			Optional[Any]				=   torch.tensor(
      pretrained_model_params['encoder']['embedder']['type']['embedding']      )
  a     :			Any				=   torch.tensor(
      pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']      ).T
  a     :			Any				=   torch.tensor(
      pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']      )
  a     :			Union[str, Any]				=   torch.tensor(
      pretrained_model_params['encoder']['embedder']['layer_norm']['scale']      )
  a     :			Any				=   torch.tensor(
      pretrained_model_params['encoder']['embedder']['layer_norm']['bias']      )

  # Encoder Layers
  for layer in range(config.num_hidden_layers      ):
    a     :			Dict				=   torch.tensor(
        pretrained_model_params['encoder'][f'''encoder_{layer}''']['mixing_layer_norm']['scale']      )
    a     :			Dict				=   torch.tensor(
        pretrained_model_params['encoder'][f'''encoder_{layer}''']['mixing_layer_norm']['bias']      )

    a     :			int				=   torch.tensor(
        pretrained_model_params['encoder'][f'''feed_forward_{layer}''']['intermediate']['kernel']      ).T
    a     :			int				=   torch.tensor(
        pretrained_model_params['encoder'][f'''feed_forward_{layer}''']['intermediate']['bias']      )

    a     :			str				=   torch.tensor(
        pretrained_model_params['encoder'][f'''feed_forward_{layer}''']['output']['kernel']      ).T
    a     :			str				=   torch.tensor(
        pretrained_model_params['encoder'][f'''feed_forward_{layer}''']['output']['bias']      )

    a     :			Optional[Any]				=   torch.tensor(
        pretrained_model_params['encoder'][f'''encoder_{layer}''']['output_layer_norm']['scale']      )
    a     :			Tuple				=   torch.tensor(
        pretrained_model_params['encoder'][f'''encoder_{layer}''']['output_layer_norm']['bias']      )

  # Pooler Layers
  a     :			Union[str, Any]				=   torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']      ).T
  a     :			Dict				=   torch.tensor(pretrained_model_params['encoder']['pooler']['bias']      )

  # Masked LM Layers
  a     :			Union[str, Any]				=   torch.tensor(
      pretrained_model_params['predictions_dense']['kernel']      ).T
  a     :			Dict				=   torch.tensor(
      pretrained_model_params['predictions_dense']['bias']      )
  a     :			int				=   torch.tensor(
      pretrained_model_params['predictions_layer_norm']['scale']      )
  a     :			Union[str, Any]				=   torch.tensor(
      pretrained_model_params['predictions_layer_norm']['bias']      )
  a     :			Dict				=   torch.tensor(
      pretrained_model_params['encoder']['embedder']['word']['embedding']      )
  a     :			List[str]				=   torch.tensor(
      pretrained_model_params['predictions_output']['output_bias']      )
  a     :			Tuple				=   torch.tensor(pretrained_model_params['predictions_output']['output_bias']      )

  # Seq Relationship Layers
  a     :			Any				=   torch.tensor(
      pretrained_model_params['classification']['output_kernel']      )
  a     :			Optional[Any]				=   torch.tensor(
      pretrained_model_params['classification']['output_bias']      )

  # Load State Dict
  fnet_pretraining_model.load_state_dict(A_      )

  # Save PreTrained
  print(f'''Saving pretrained model to {save_path}'''      )
  fnet_pretraining_model.save_pretrained(A_      )


if __name__ == "__main__":
   _UpperCamelCase				:    Optional[int]     =						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--flax_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--fnet_config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained FNet model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument('--save_path', default=None, type=str, required=True, help='Path to the output model.')
   _UpperCamelCase				:    int     =						parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)












"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase				:    List[Any]     =						{
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    Any     =						['OpenAIGPTTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    int     =						[
       'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'OpenAIGPTDoubleHeadsModel',
       'OpenAIGPTForSequenceClassification',
       'OpenAIGPTLMHeadModel',
       'OpenAIGPTModel',
       'OpenAIGPTPreTrainedModel',
       'load_tf_weights_in_openai_gpt',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    List[str]     =						[
       'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFOpenAIGPTDoubleHeadsModel',
       'TFOpenAIGPTForSequenceClassification',
       'TFOpenAIGPTLMHeadModel',
       'TFOpenAIGPTMainLayer',
       'TFOpenAIGPTModel',
       'TFOpenAIGPTPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
   from .tokenization_openai import OpenAIGPTTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_openai_fast import OpenAIGPTTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_openai import (
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OpenAIGPTDoubleHeadsModel,
          OpenAIGPTForSequenceClassification,
          OpenAIGPTLMHeadModel,
          OpenAIGPTModel,
          OpenAIGPTPreTrainedModel,
          load_tf_weights_in_openai_gpt,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTMainLayer,
          TFOpenAIGPTModel,
          TFOpenAIGPTPreTrainedModel,
      )

else:
   import sys

   _UpperCamelCase				:    Union[str, Any]     =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




