
'''simple docstring'''

import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

UpperCAmelCase__      :       Any 		=						[[12, 10], [3, 9]]
UpperCAmelCase__      :       int 		=						[[3, 4], [7, 4]]
UpperCAmelCase__      :       List[Any] 		=						[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
UpperCAmelCase__      :       Any 		=						[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
UpperCAmelCase__      :       List[Any] 		=						[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
UpperCAmelCase__      :       List[str] 		=						[1]
UpperCAmelCase__      :       Dict 		=						[2]

UpperCAmelCase__      :       Tuple 		=						logging.getLogger()
UpperCAmelCase__      :       Optional[int] 		=						logging.DEBUG
UpperCAmelCase__      :       Dict 		=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")  ,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]					)
def      A   (					UpperCamelCase_    :   Tuple  ,		UpperCamelCase_    :   Any					)    ->				str:







			'''simple docstring'''



			if (np.array(lowerCAmelCase__					)).shape < (2, 2) or (np.array(lowerCAmelCase__					)).shape < (2, 2):
						with pytest.raises(lowerCAmelCase__					):
									logger.info(F"""\n\t{test_addition.__name__} returned integer"""					)
									matop.add(lowerCAmelCase__  ,		lowerCAmelCase__					)
			elif (np.array(lowerCAmelCase__					)).shape == (np.array(lowerCAmelCase__					)).shape:
						logger.info(F"""\n\t{test_addition.__name__} with same matrix dims"""					)
						lowerCAmelCase__									=							(np.array(lowerCAmelCase__					) + np.array(lowerCAmelCase__					)).tolist()
						lowerCAmelCase__									=							matop.add(lowerCAmelCase__  ,		lowerCAmelCase__					)
						assert theo == act
			else:
						with pytest.raises(lowerCAmelCase__					):
									logger.info(F"""\n\t{test_addition.__name__} with different matrix dims"""					)
									matop.add(lowerCAmelCase__  ,		lowerCAmelCase__					)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")  ,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]					)
def      A   (					UpperCamelCase_    :   List[str]  ,		UpperCamelCase_    :   str					)    ->				Optional[Any]:







			'''simple docstring'''



			if (np.array(lowerCAmelCase__					)).shape < (2, 2) or (np.array(lowerCAmelCase__					)).shape < (2, 2):
						with pytest.raises(lowerCAmelCase__					):
									logger.info(F"""\n\t{test_subtraction.__name__} returned integer"""					)
									matop.subtract(lowerCAmelCase__  ,		lowerCAmelCase__					)
			elif (np.array(lowerCAmelCase__					)).shape == (np.array(lowerCAmelCase__					)).shape:
						logger.info(F"""\n\t{test_subtraction.__name__} with same matrix dims"""					)
						lowerCAmelCase__									=							(np.array(lowerCAmelCase__					) - np.array(lowerCAmelCase__					)).tolist()
						lowerCAmelCase__									=							matop.subtract(lowerCAmelCase__  ,		lowerCAmelCase__					)
						assert theo == act
			else:
						with pytest.raises(lowerCAmelCase__					):
									logger.info(F"""\n\t{test_subtraction.__name__} with different matrix dims"""					)
									assert matop.subtract(lowerCAmelCase__  ,		lowerCAmelCase__					)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")  ,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]					)
def      A   (					UpperCamelCase_    :   Union[str, Any]  ,		UpperCamelCase_    :   List[Any]					)    ->				Optional[int]:







			'''simple docstring'''



			if (np.array(lowerCAmelCase__					)).shape < (2, 2) or (np.array(lowerCAmelCase__					)).shape < (2, 2):
						logger.info(F"""\n\t{test_multiplication.__name__} returned integer"""					)
						with pytest.raises(lowerCAmelCase__					):
									matop.add(lowerCAmelCase__  ,		lowerCAmelCase__					)
			elif (np.array(lowerCAmelCase__					)).shape == (np.array(lowerCAmelCase__					)).shape:
						logger.info(F"""\n\t{test_multiplication.__name__} meets dim requirements"""					)
						lowerCAmelCase__									=							(np.matmul(lowerCAmelCase__  ,		lowerCAmelCase__					)).tolist()
						lowerCAmelCase__									=							matop.multiply(lowerCAmelCase__  ,		lowerCAmelCase__					)
						assert theo == act
			else:
						with pytest.raises(lowerCAmelCase__					):
									logger.info(
									    F"""\n\t{test_multiplication.__name__} does not meet dim requirements"""					)
									assert matop.subtract(lowerCAmelCase__  ,		lowerCAmelCase__					)







@pytest.mark.mat_ops
def      A   (					)    ->				Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__									=							(3.5 * np.array(lowerCAmelCase__					)).tolist()
			lowerCAmelCase__									=							matop.scalar_multiply(lowerCAmelCase__  ,		3.5					)
			assert theo == act







@pytest.mark.mat_ops
def      A   (					)    ->				str:







			'''simple docstring'''



			lowerCAmelCase__									=							(np.identity(5					)).tolist()
			lowerCAmelCase__									=							matop.identity(5					)
			assert theo == act







@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"  ,		[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]					)
def      A   (					UpperCamelCase_    :   Optional[Any]					)    ->				int:







			'''simple docstring'''



			if (np.array(lowerCAmelCase__					)).shape < (2, 2):
						with pytest.raises(lowerCAmelCase__					):
									logger.info(F"""\n\t{test_transpose.__name__} returned integer"""					)
									matop.transpose(lowerCAmelCase__					)
			else:
						lowerCAmelCase__									=							(np.transpose(lowerCAmelCase__					)).tolist()
						lowerCAmelCase__									=							matop.transpose(lowerCAmelCase__  ,		return_map=lowerCAmelCase__					)
						assert theo == act






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		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       # Initialise PyTorch model
       lowerCAmelCase__     	=					FNetConfig.from_json_file(lowerCAmelCase__	)
       print(F"""Building PyTorch model from configuration: {config}"""	)
       lowerCAmelCase__     	=					FNetForPreTraining(lowerCAmelCase__	)

       lowerCAmelCase__     	=					restore_checkpoint(lowerCAmelCase__	,  lowerCAmelCase__	)
       lowerCAmelCase__     	=					checkpoint_dict['target']

       # Embeddings
       # Position IDs
       lowerCAmelCase__     	=					fnet_pretraining_model.state_dict()

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

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

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

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

              lowerCAmelCase__     	=					torch.tensor(
                  pretrained_model_params['encoder'][F"""encoder_{layer}"""]['output_layer_norm']['scale']	)
              lowerCAmelCase__     	=					torch.tensor(
                  pretrained_model_params['encoder'][F"""encoder_{layer}"""]['output_layer_norm']['bias']	)

       # Pooler Layers
       lowerCAmelCase__     	=					torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']	).T
       lowerCAmelCase__     	=					torch.tensor(pretrained_model_params['encoder']['pooler']['bias']	)

       # Masked LM Layers
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_dense']['kernel']	).T
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_dense']['bias']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_layer_norm']['scale']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_layer_norm']['bias']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['word']['embedding']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_output']['output_bias']	)
       lowerCAmelCase__     	=					torch.tensor(pretrained_model_params['predictions_output']['output_bias']	)

       # Seq Relationship Layers
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['classification']['output_kernel']	)
       lowerCAmelCase__     	=					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__":
    lowerCAmelCase__							= 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__							= parser.parse_args()
    convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
