






"""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 						lowercase   (    __snake_case    :			Union[str, Any]		, __snake_case    :			Union[str, Any]		, __snake_case    :			Dict     ):
		# Initialise PyTorch model
		lowercase_			:			List[Any]								=       FNetConfig.from_json_file(__snake_case     )
		print(F'''Building PyTorch model from configuration: {config}'''     )
		lowercase_			:			List[Any]								=       FNetForPreTraining(__snake_case     )

		lowercase_			:			List[str]								=       restore_checkpoint(__snake_case		, __snake_case     )
		lowercase_			:			int								=       checkpoint_dict['''target''']

		# Embeddings
		# Position IDs
		lowercase_			:			List[str]								=       fnet_pretraining_model.state_dict()

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

		# Encoder Layers
		for layer in range(config.num_hidden_layers     ):
				lowercase_			:			Optional[int]								=       torch.tensor(
				    pretrained_model_params['''encoder'''][F'''encoder_{layer}''']['''mixing_layer_norm''']['''scale''']     )
				lowercase_			:			Any								=       torch.tensor(
				    pretrained_model_params['''encoder'''][F'''encoder_{layer}''']['''mixing_layer_norm''']['''bias''']     )

				lowercase_			:			Dict								=       torch.tensor(
				    pretrained_model_params['''encoder'''][F'''feed_forward_{layer}''']['''intermediate''']['''kernel''']     ).T
				lowercase_			:			Tuple								=       torch.tensor(
				    pretrained_model_params['''encoder'''][F'''feed_forward_{layer}''']['''intermediate''']['''bias''']     )

				lowercase_			:			List[Any]								=       torch.tensor(
				    pretrained_model_params['''encoder'''][F'''feed_forward_{layer}''']['''output''']['''kernel''']     ).T
				lowercase_			:			List[str]								=       torch.tensor(
				    pretrained_model_params['''encoder'''][F'''feed_forward_{layer}''']['''output''']['''bias''']     )

				lowercase_			:			Dict								=       torch.tensor(
				    pretrained_model_params['''encoder'''][F'''encoder_{layer}''']['''output_layer_norm''']['''scale''']     )
				lowercase_			:			Any								=       torch.tensor(
				    pretrained_model_params['''encoder'''][F'''encoder_{layer}''']['''output_layer_norm''']['''bias''']     )

		# Pooler Layers
		lowercase_			:			Optional[int]								=       torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']     ).T
		lowercase_			:			int								=       torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']     )

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

		# Seq Relationship Layers
		lowercase_			:			Tuple								=       torch.tensor(
		    pretrained_model_params['''classification''']['''output_kernel''']     )
		lowercase_			:			Optional[int]								=       torch.tensor(
		    pretrained_model_params['''classification''']['''output_bias''']     )

		# Load State Dict
		fnet_pretraining_model.load_state_dict(__snake_case     )

		# Save PreTrained
		print(F'''Saving pretrained model to {save_path}'''     )
		fnet_pretraining_model.save_pretrained(__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		:	Union[str, 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


__A		:	Any      =		{
    '''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:
						__A		:	List[str]      =		['''MarkupLMTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	Optional[int]      =		[
						    '''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

						__A		:	Optional[Any]      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)



