





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__							(				UpperCamelCase__:							Union[str, Any]    ,      UpperCamelCase__:							str    ,      UpperCamelCase__:							List[str]			):
		# Initialise PyTorch model
		SCREAMING_SNAKE_CASE__								=					FNetConfig.from_json_file(UpperCamelCase__			)
		print(f'''Building PyTorch model from configuration: {config}'''			)
		SCREAMING_SNAKE_CASE__								=					FNetForPreTraining(UpperCamelCase__			)

		SCREAMING_SNAKE_CASE__								=					restore_checkpoint(UpperCamelCase__    ,      UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__								=					checkpoint_dict["""target"""]

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

		SCREAMING_SNAKE_CASE__								=					state_dict["""fnet.embeddings.position_ids"""]
		SCREAMING_SNAKE_CASE__								=					{"""fnet.embeddings.position_ids""": position_ids}
		# Embedding Layers
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]			)
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]			)
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]			).T
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]			)
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]			)
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]			)

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

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

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

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

		# Pooler Layers
		SCREAMING_SNAKE_CASE__								=					torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]			).T
		SCREAMING_SNAKE_CASE__								=					torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]			)

		# Masked LM Layers
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""kernel"""]			).T
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""bias"""]			)
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]			)
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]			)
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""predictions_output"""]["""output_bias"""]			)
		SCREAMING_SNAKE_CASE__								=					torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]			)

		# Seq Relationship Layers
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""classification"""]["""output_kernel"""]			)
		SCREAMING_SNAKE_CASE__								=					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__":
	_lowerCamelCase       =	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.')
	_lowerCamelCase       =	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__)




