
'''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 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[Any]      ,  __SCREAMING_SNAKE_CASE    :	Any      ,  __SCREAMING_SNAKE_CASE    :	List[str]			)					->							List[str]:





  """simple docstring"""

  __A		:  str       				=		FNetConfig.from_json_file(SCREAMING_SNAKE_CASE__			)
  print(F"Building PyTorch model from configuration: {config}"			)
  __A		:  Optional[int]       				=		FNetForPreTraining(SCREAMING_SNAKE_CASE__			)

  __A		:  Optional[Any]       				=		restore_checkpoint(SCREAMING_SNAKE_CASE__      ,  SCREAMING_SNAKE_CASE__			)
  __A		:  Tuple       				=		checkpoint_dict["""target"""]

  # Embeddings
  # Position IDs
  __A		:  Union[str, Any]       				=		fnet_pretraining_model.state_dict()

  __A		:  Union[str, Any]       				=		state_dict["""fnet.embeddings.position_ids"""]
  __A		:  List[Any]       				=		{"""fnet.embeddings.position_ids""": position_ids}
  # Embedding Layers
  __A		:  List[str]       				=		torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
  __A		:  Any       				=		torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]			)
  __A		:  List[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		:  List[str]       				=		torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]			)
  __A		:  Optional[Any]       				=		torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]			)

  # Encoder Layers
  for layer in range(config.num_hidden_layers			):
    __A		:  List[str]       				=		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		:  Tuple       				=		torch.tensor(
        pretrained_model_params["""encoder"""][F"feed_forward_{layer}"]["""intermediate"""]["""kernel"""]			).T
    __A		:  Dict       				=		torch.tensor(
        pretrained_model_params["""encoder"""][F"feed_forward_{layer}"]["""intermediate"""]["""bias"""]			)

    __A		:  Dict       				=		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		:  Optional[Any]       				=		torch.tensor(
        pretrained_model_params["""encoder"""][F"encoder_{layer}"]["""output_layer_norm"""]["""bias"""]			)

  # Pooler Layers
  __A		:  int       				=		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]			).T
  __A		:  Any       				=		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]			)

  # Masked LM Layers
  __A		:  Dict       				=		torch.tensor(
      pretrained_model_params["""predictions_dense"""]["""kernel"""]			).T
  __A		:  Dict       				=		torch.tensor(
      pretrained_model_params["""predictions_dense"""]["""bias"""]			)
  __A		:  Union[str, Any]       				=		torch.tensor(
      pretrained_model_params["""predictions_layer_norm"""]["""scale"""]			)
  __A		:  int       				=		torch.tensor(
      pretrained_model_params["""predictions_layer_norm"""]["""bias"""]			)
  __A		:  Dict       				=		torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
  __A		:  int       				=		torch.tensor(
      pretrained_model_params["""predictions_output"""]["""output_bias"""]			)
  __A		:  Union[str, Any]       				=		torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]			)

  # Seq Relationship Layers
  __A		:  Tuple       				=		torch.tensor(
      pretrained_model_params["""classification"""]["""output_kernel"""]			)
  __A		:  List[str]       				=		torch.tensor(
      pretrained_model_params["""classification"""]["""output_bias"""]			)

  # Load State Dict
  fnet_pretraining_model.load_state_dict(SCREAMING_SNAKE_CASE__			)

  # Save PreTrained
  print(F"Saving pretrained model to {save_path}"			)
  fnet_pretraining_model.save_pretrained(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
     A__ :		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.')
     A__ :		Dict					=parser.parse_args()
     convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCAmelCase					:				Optional[int]   	={
    'configuration_speech_to_text_2': ['SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2Text2Config'],
    'processing_speech_to_text_2': ['Speech2Text2Processor'],
    'tokenization_speech_to_text_2': ['Speech2Text2Tokenizer'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase					:				int   	=[
		    'SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Speech2Text2ForCausalLM',
		    'Speech2Text2PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
		from .processing_speech_to_text_a import SpeechaTextaProcessor
		from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_to_text_a import (
				    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechaTextaForCausalLM,
				    SpeechaTextaPreTrainedModel,
				)

else:
		import sys

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

