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 					UpperCamelCase__		(							A__     ,					A__     ,					A__       )  ->   Any:
				# Initialise PyTorch model
				snake_case__   :      Union[str, Any]   				= FNetConfig.from_json_file(A__       )
				print(F"""Building PyTorch model from configuration: {config}"""       )
				snake_case__   :      List[str]   				= FNetForPreTraining(A__       )

				snake_case__   :      Optional[Any]   				= restore_checkpoint(A__     ,					A__       )
				snake_case__   :      Any   				= checkpoint_dict['target']

				# Embeddings
				# Position IDs
				snake_case__   :      List[Any]   				= fnet_pretraining_model.state_dict()

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

				# Encoder Layers
				for layer in range(config.num_hidden_layers       ):
								snake_case__   :      List[Any]   				= torch.tensor(
								    pretrained_model_params['encoder'][F"""encoder_{layer}"""]['mixing_layer_norm']['scale']       )
								snake_case__   :      Union[str, Any]   				= torch.tensor(
								    pretrained_model_params['encoder'][F"""encoder_{layer}"""]['mixing_layer_norm']['bias']       )

								snake_case__   :      List[str]   				= torch.tensor(
								    pretrained_model_params['encoder'][F"""feed_forward_{layer}"""]['intermediate']['kernel']       ).T
								snake_case__   :      List[str]   				= torch.tensor(
								    pretrained_model_params['encoder'][F"""feed_forward_{layer}"""]['intermediate']['bias']       )

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

								snake_case__   :      Dict   				= torch.tensor(
								    pretrained_model_params['encoder'][F"""encoder_{layer}"""]['output_layer_norm']['scale']       )
								snake_case__   :      Union[str, Any]   				= torch.tensor(
								    pretrained_model_params['encoder'][F"""encoder_{layer}"""]['output_layer_norm']['bias']       )

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

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

				# Seq Relationship Layers
				snake_case__   :      Any   				= torch.tensor(
				    pretrained_model_params['classification']['output_kernel']       )
				snake_case__   :      Dict   				= 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__":
				lowerCAmelCase__	:  Union[str, Any]						=  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__	:  str						=  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_torch_available, is_vision_available


lowerCAmelCase__	:  int						=  {
    '''configuration_deta''': ['''DETA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetaConfig'''],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  Union[str, Any]						=  ['''DetaImageProcessor''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  Optional[int]						=  [
				    '''DETA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''DetaForObjectDetection''',
				    '''DetaModel''',
				    '''DetaPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

				lowerCAmelCase__	:  int						=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


