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

			UpperCamelCase__    :		List[str]     =				restore_checkpoint(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		int     =				checkpoint_dict['target']

			# Embeddings
			# Position IDs
			UpperCamelCase__    :		Any     =				fnet_pretraining_model.state_dict()

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

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

						UpperCamelCase__    :		Optional[Any]     =				torch.tensor(
						    pretrained_model_params['encoder'][F"""feed_forward_{layer}"""]['intermediate']['kernel'] ).T
						UpperCamelCase__    :		Dict     =				torch.tensor(
						    pretrained_model_params['encoder'][F"""feed_forward_{layer}"""]['intermediate']['bias'] )

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

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

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

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

			# Seq Relationship Layers
			UpperCamelCase__    :		Dict     =				torch.tensor(
			    pretrained_model_params['classification']['output_kernel'] )
			UpperCamelCase__    :		List[Any]     =				torch.tensor(
			    pretrained_model_params['classification']['output_bias'] )

			# Load State Dict
			fnet_pretraining_model.load_state_dict(__SCREAMING_SNAKE_CASE )

			# Save PreTrained
			print(F"""Saving pretrained model to {save_path}""" )
			fnet_pretraining_model.save_pretrained(__SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							UpperCAmelCase__				:  List[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.''')
							UpperCAmelCase__				:  Any       					=				parser.parse_args()
							convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)





from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCAmelCase__				:  Tuple       					=				[
    '''AutomaticSpeechRecognition''',
    '''AudioClassification''',
    '''ImageClassification''',
    '''LanguageModeling''',
    '''QuestionAnsweringExtractive''',
    '''Summarization''',
    '''TaskTemplate''',
    '''TextClassification''',
]

UpperCAmelCase__				:  Dict       					=				get_logger(__name__)


UpperCAmelCase__				:  Dict       					=				{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     Optional[TaskTemplate]:
			UpperCamelCase__    :		Any     =				task_template_dict.get('task' )
			if task_name is None:
						logger.warning(F"""Couldn't find template for task '{task_name}'. Available templates: {list(__SCREAMING_SNAKE_CASE )}""" )
						return None
			UpperCamelCase__    :		Any     =				NAMEaTEMPLATE.get(__SCREAMING_SNAKE_CASE )
			return template.from_dict(__SCREAMING_SNAKE_CASE )



