






"""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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple						,   __snake_case     :    Dict						,   __snake_case     :    Dict       ):





  '''simple docstring'''





  lowercase				       =			FNetConfig.from_json_file(UpperCAmelCase__       )
  print(f'Building PyTorch model from configuration: {config}'       )
  lowercase				       =			FNetForPreTraining(UpperCAmelCase__       )

  lowercase				       =			restore_checkpoint(UpperCAmelCase__						,   UpperCAmelCase__       )
  lowercase				       =			checkpoint_dict["""target"""]

  # Embeddings
  # Position IDs
  lowercase				       =			fnet_pretraining_model.state_dict()

  lowercase				       =			state_dict["""fnet.embeddings.position_ids"""]
  lowercase				       =			{"""fnet.embeddings.position_ids""": position_ids}
  # Embedding Layers
  lowercase				       =			torch.tensor(
      pretrained_model_params['encoder']['embedder']['word']['embedding']       )
  lowercase				       =			torch.tensor(
      pretrained_model_params['encoder']['embedder']['position']['embedding'][0]       )
  lowercase				       =			torch.tensor(
      pretrained_model_params['encoder']['embedder']['type']['embedding']       )
  lowercase				       =			torch.tensor(
      pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']       ).T
  lowercase				       =			torch.tensor(
      pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']       )
  lowercase				       =			torch.tensor(
      pretrained_model_params['encoder']['embedder']['layer_norm']['scale']       )
  lowercase				       =			torch.tensor(
      pretrained_model_params['encoder']['embedder']['layer_norm']['bias']       )

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

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

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

    lowercase				       =			torch.tensor(
        pretrained_model_params['encoder'][f'encoder_{layer}']['output_layer_norm']['scale']       )
    lowercase				       =			torch.tensor(
        pretrained_model_params['encoder'][f'encoder_{layer}']['output_layer_norm']['bias']       )

  # Pooler Layers
  lowercase				       =			torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']       ).T
  lowercase				       =			torch.tensor(pretrained_model_params['encoder']['pooler']['bias']       )

  # Masked LM Layers
  lowercase				       =			torch.tensor(
      pretrained_model_params['predictions_dense']['kernel']       ).T
  lowercase				       =			torch.tensor(
      pretrained_model_params['predictions_dense']['bias']       )
  lowercase				       =			torch.tensor(
      pretrained_model_params['predictions_layer_norm']['scale']       )
  lowercase				       =			torch.tensor(
      pretrained_model_params['predictions_layer_norm']['bias']       )
  lowercase				       =			torch.tensor(
      pretrained_model_params['encoder']['embedder']['word']['embedding']       )
  lowercase				       =			torch.tensor(
      pretrained_model_params['predictions_output']['output_bias']       )
  lowercase				       =			torch.tensor(pretrained_model_params['predictions_output']['output_bias']       )

  # Seq Relationship Layers
  lowercase				       =			torch.tensor(
      pretrained_model_params['classification']['output_kernel']       )
  lowercase				       =			torch.tensor(
      pretrained_model_params['classification']['output_bias']       )

  # Load State Dict
  fnet_pretraining_model.load_state_dict(UpperCAmelCase__       )

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


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						:    Any		=						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_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE						=      {
    "configuration_markuplm": ["MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarkupLMConfig"],
    "feature_extraction_markuplm": ["MarkupLMFeatureExtractor"],
    "processing_markuplm": ["MarkupLMProcessor"],
    "tokenization_markuplm": ["MarkupLMTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__SCREAMING_SNAKE_CASE						=      ["MarkupLMTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__SCREAMING_SNAKE_CASE						=      [
	    "MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "MarkupLMForQuestionAnswering",
	    "MarkupLMForSequenceClassification",
	    "MarkupLMForTokenClassification",
	    "MarkupLMModel",
	    "MarkupLMPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
	from .feature_extraction_markuplm import MarkupLMFeatureExtractor
	from .processing_markuplm import MarkupLMProcessor
	from .tokenization_markuplm import MarkupLMTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_markuplm_fast import MarkupLMTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_markuplm import (
		    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MarkupLMForQuestionAnswering,
		    MarkupLMForSequenceClassification,
		    MarkupLMForTokenClassification,
		    MarkupLMModel,
		    MarkupLMPreTrainedModel,
		)


else:
	import sys

	__SCREAMING_SNAKE_CASE						=      _LazyModule(__name__, globals()["__file__"], _import_structure)




