


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_  (				__lowerCamelCase:		Any  ,			__lowerCamelCase:		Tuple  ,			__lowerCamelCase:		Tuple  ):







	'''simple docstring'''



	lowercase_			 = FNetConfig.from_json_file(_UpperCAmelCase  )
	print(F'Building PyTorch model from configuration: {config}'  )
	lowercase_			 = FNetForPreTraining(_UpperCAmelCase  )

	lowercase_			 = restore_checkpoint(_UpperCAmelCase  ,			_UpperCAmelCase  )
	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(_UpperCAmelCase  )

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				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__       			=				parser.parse_args()
							convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)








"""simple docstring"""




def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			List[str]									=    (boundary[1] - boundary[0]) / steps
  A_    :			Optional[int]									=    boundary[0]
  A_    :			Optional[int]									=    boundary[1]
  A_    :			Any									=    make_points(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   )
  A_    :			Dict									=    0.0
  y += (h / 3.0) * f(_UpperCAmelCase   )
  A_    :			Optional[int]									=    2
  for i in x_i:
    y += (h / 3) * (4 - 2 * (cnt % 2)) * f(_UpperCAmelCase   )
    cnt += 1
  y += (h / 3.0) * f(_UpperCAmelCase   )
  return y





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Optional[Any]									=    a + h
  while x < (b - h):
    yield x
    A_    :			Dict									=    x + h





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):  # enter your function here






  """simple docstring"""





  A_    :			List[Any]									=    (x - 0) * (x - 0)
  return y





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			int									=    0.0  # Lower bound of integration
  A_    :			int									=    1.0  # Upper bound of integration
  A_    :			Tuple									=    10.0  # define number of steps or resolution
  A_    :			List[str]									=    [a, b]  # define boundary of integration
  A_    :			Optional[Any]									=    method_a(_UpperCAmelCase					,   _UpperCAmelCase   )
  print(f"""y = {y}"""   )


if __name__ == "__main__":
   main()