


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_				(   SCREAMING_SNAKE_CASE__ :				List[str]		,      SCREAMING_SNAKE_CASE__ :				Dict		,      SCREAMING_SNAKE_CASE__ :				List[Any]					):
       '''simple docstring'''





       _lowerCamelCase	:		List[str]          =FNetConfig.from_json_file(SCREAMING_SNAKE_CASE__					)
       print(F'''Building PyTorch model from configuration: {config}'''					)
       _lowerCamelCase	:		Optional[Any]          =FNetForPreTraining(SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		Tuple          =restore_checkpoint(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Optional[int]          =checkpoint_dict['target']

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

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

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

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

              _lowerCamelCase	:		List[str]          =torch.tensor(
                  pretrained_model_params['encoder'][F'''feed_forward_{layer}''']['output']['kernel']					).T
              _lowerCamelCase	:		Tuple          =torch.tensor(
                  pretrained_model_params['encoder'][F'''feed_forward_{layer}''']['output']['bias']					)

              _lowerCamelCase	:		List[str]          =torch.tensor(
                  pretrained_model_params['encoder'][F'''encoder_{layer}''']['output_layer_norm']['scale']					)
              _lowerCamelCase	:		List[str]          =torch.tensor(
                  pretrained_model_params['encoder'][F'''encoder_{layer}''']['output_layer_norm']['bias']					)

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

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

       # Seq Relationship Layers
       _lowerCamelCase	:		Optional[Any]          =torch.tensor(
           pretrained_model_params['classification']['output_kernel']					)
       _lowerCamelCase	:		Tuple          =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__":
 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)






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase						 =	logging.getLogger()




def      a_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		Any          =argparse.ArgumentParser()
       parser.add_argument('-f'					)
       _lowerCamelCase	:		Optional[Any]          =parser.parse_args()
       return args.f




class A  (       UpperCamelCase_					):






      def 	lowerCamelCase	( self						:					List[Any]			) ->						int:
             """simple docstring"""




             _lowerCamelCase	:		Any          =logging.StreamHandler(sys.stdout			)
             logger.addHandler(lowercase_			)

             _lowerCamelCase	:		Dict          =self.get_auto_remove_tmp_dir()
             _lowerCamelCase	:		int          =F'''
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

             with patch.object(lowercase_      ,       'argv'      ,       lowercase_			):
                    _lowerCamelCase	:		Dict          =run_glue_with_pabee.main()
                    for value in result.values():
                           self.assertGreaterEqual(lowercase_      ,       0.75			)


