




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 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  int:
		# Initialise PyTorch model
		SCREAMING_SNAKE_CASE_							=       FNetConfig.from_json_file(lowerCAmelCase   )
		print(F'''Building PyTorch model from configuration: {config}'''   )
		SCREAMING_SNAKE_CASE_							=       FNetForPreTraining(lowerCAmelCase   )

		SCREAMING_SNAKE_CASE_							=       restore_checkpoint(lowerCAmelCase						,	lowerCAmelCase   )
		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(lowerCAmelCase   )

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


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





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE:    List[str]											=   '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''



class       lowercase_		(unittest.TestCase       ,  SCREAMING_SNAKE_CASE__   ):



						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       load_tool('summarization'     )
								self.tool.setup()
								SCREAMING_SNAKE_CASE_							=       load_tool('summarization'				,       remote=snake_case__     )



						def   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.tool(snake_case__     )
								self.assertEqual(
								    snake_case__				,       'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'				,       )



						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.remote_tool(snake_case__     )
								self.assertEqual(
								    snake_case__				,       'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'				,       )



						def   __a				( self  :      Optional[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.tool(text=snake_case__     )
								self.assertEqual(
								    snake_case__				,       'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'				,       )






						def   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.remote_tool(text=snake_case__     )
								self.assertEqual(
								    snake_case__				,       'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'				,       )