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 					__lowerCAmelCase			(  _A ,_A ,_A					):



							"""simple docstring"""


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

							_lowercase				=	restore_checkpoint(_A ,_A					)
							_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(_A					)

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


if __name__ == "__main__":
				A_:      Tuple        =			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 ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore


class _lowercase					(       _UpperCAmelCase						):





							"""simple docstring"""




							lowerCAmelCase__       =					c_float



							@property
							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														return sizeof(self.value  )






def 					__lowerCAmelCase			(  _A ,_A ,_A = 0					):



							"""simple docstring"""


							if dimension <= 0:
														_lowercase				=	fixed_dimension

							dimension -= num_token_to_add
							return dimension


def 					__lowerCAmelCase			(  _A ,_A					):



							"""simple docstring"""


							return num_parameters * dtype.size







def 					__lowerCAmelCase			(  _A					):



							"""simple docstring"""


							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

							try:
														return AutoProcessor.from_pretrained(_A					)
							except (ValueError, OSError, KeyError):
														_lowercase				,       _lowercase				=	None, None
														try:
																					_lowercase				=	AutoTokenizer.from_pretrained(_A					)
														except (OSError, KeyError):
																					pass
														try:
																					_lowercase				=	AutoFeatureExtractor.from_pretrained(_A					)
														except (OSError, KeyError):
																					pass

														if tokenizer is not None and feature_extractor is not None:
																					raise ValueError(
																					    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''					)
														elif tokenizer is None and feature_extractor is None:
																					return None
														elif tokenizer is not None:
																					return tokenizer
														else:
																					return feature_extractor


