






"""simple docstring"""



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		(				UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Tuple   )		->int:
						A__    :	Any			=	FNetConfig.from_json_file(__lowercase   )
						print(f'Building PyTorch model from configuration: {config}'   )
						A__    :	str			=	FNetForPreTraining(__lowercase   )

						A__    :	Dict			=	restore_checkpoint(__lowercase,    __lowercase   )
						A__    :	Any			=	checkpoint_dict["""target"""]

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

						A__    :	Tuple			=	state_dict["""fnet.embeddings.position_ids"""]
						A__    :	Any			=	{"""fnet.embeddings.position_ids""": position_ids}
						# Embedding Layers
						A__    :	str			=	torch.tensor(
						    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]   )
						A__    :	Optional[int]			=	torch.tensor(
						    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]   )
						A__    :	int			=	torch.tensor(
						    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]   )
						A__    :	List[str]			=	torch.tensor(
						    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]   ).T
						A__    :	Optional[int]			=	torch.tensor(
						    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]   )
						A__    :	Union[str, Any]			=	torch.tensor(
						    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]   )
						A__    :	Optional[int]			=	torch.tensor(
						    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]   )

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

												A__    :	Optional[int]			=	torch.tensor(
												    pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""intermediate"""]["""kernel"""]   ).T
												A__    :	List[str]			=	torch.tensor(
												    pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""intermediate"""]["""bias"""]   )

												A__    :	Tuple			=	torch.tensor(
												    pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""output"""]["""kernel"""]   ).T
												A__    :	str			=	torch.tensor(
												    pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""output"""]["""bias"""]   )

												A__    :	Optional[int]			=	torch.tensor(
												    pretrained_model_params["""encoder"""][f'encoder_{layer}']["""output_layer_norm"""]["""scale"""]   )
												A__    :	List[Any]			=	torch.tensor(
												    pretrained_model_params["""encoder"""][f'encoder_{layer}']["""output_layer_norm"""]["""bias"""]   )

						# Pooler Layers
						A__    :	List[str]			=	torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]   ).T
						A__    :	Optional[int]			=	torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]   )

						# Masked LM Layers
						A__    :	Any			=	torch.tensor(
						    pretrained_model_params["""predictions_dense"""]["""kernel"""]   ).T
						A__    :	Any			=	torch.tensor(
						    pretrained_model_params["""predictions_dense"""]["""bias"""]   )
						A__    :	Tuple			=	torch.tensor(
						    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]   )
						A__    :	List[str]			=	torch.tensor(
						    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]   )
						A__    :	int			=	torch.tensor(
						    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]   )
						A__    :	str			=	torch.tensor(
						    pretrained_model_params["""predictions_output"""]["""output_bias"""]   )
						A__    :	str			=	torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]   )

						# Seq Relationship Layers
						A__    :	List[str]			=	torch.tensor(
						    pretrained_model_params["""classification"""]["""output_kernel"""]   )
						A__    :	List[str]			=	torch.tensor(
						    pretrained_model_params["""classification"""]["""output_bias"""]   )

						# Load State Dict
						fnet_pretraining_model.load_state_dict(__lowercase   )

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


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













import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__lowercase				    =      (30, 487)
__lowercase				    =      (36, 1_024)
__lowercase				    =      (100, 100)
__lowercase				    =      100

__lowercase				    =      datasets.Features(
    {"""text""": ArrayaD(SHAPE_TEST_1, dtype="""float32"""), """image""": ArrayaD(SHAPE_TEST_2, dtype="""float32""")}
)

__lowercase    ,							__lowercase				    =      os.path.split(__file__)
__lowercase				    =      os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any  ,				__lowercase:		Optional[Any]  ,				__lowercase:		List[Any]):



  '''simple docstring'''






  with ArrowWriter(features=__lowercase  ,				path=os.path.join(__lowercase  ,				'''beta.arrow''')) as writer:
    for key, record in dummy_data:
      __lowerCAmelCase								=					my_features.encode_example(__lowercase)
      writer.write(__lowercase)
    __lowerCAmelCase						,    __lowerCAmelCase								=					writer.finalize()


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		str  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for _ in dataset:
    pass


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for _ in dataset:
    pass


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		str  ,				__lowercase:		Dict):



  '''simple docstring'''






  __lowerCAmelCase								=					10
  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for i in range(0  ,				len(__lowercase)  ,				__lowercase):
    __lowerCAmelCase								=					dataset[i : i + batch_size]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[Any]  ,				__lowercase:		Union[str, Any]):



  '''simple docstring'''






  __lowerCAmelCase								=					10
  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for i in range(0  ,				len(__lowercase)  ,				__lowercase):
    __lowerCAmelCase								=					dataset[i : i + batch_size]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Union[str, Any]  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for col in feats:
    __lowerCAmelCase								=					dataset[col]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Optional[int]  ,				__lowercase:		Optional[Any]):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for col in feats:
    __lowerCAmelCase								=					dataset[col]


def        __SCREAMING_SNAKE_CASE	(				):



  '''simple docstring'''






  __lowerCAmelCase								=					{}
  __lowerCAmelCase								=					(
      read_unformated,
      read_formatted_as_numpy,
      read_batch_unformated,
      read_batch_formatted_as_numpy,
      read_col_unformated,
      read_col_formatted_as_numpy,
  )
  with tempfile.TemporaryDirectory() as tmp_dir:
    __lowerCAmelCase								=					datasets.Features({'''image''': ArrayaD(__lowercase  ,				dtype='''float32''')})
    __lowerCAmelCase								=					generate_examples(features=__lowercase  ,				num_examples=__lowercase)
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

  with tempfile.TemporaryDirectory() as tmp_dir:
    # don't use fixed length for fair comparison
    # feats = datasets.Features(
    #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
    # )
    __lowerCAmelCase								=					datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32''')))})
    __lowerCAmelCase								=					generate_examples(
        features=__lowercase  ,				num_examples=__lowercase  ,				seq_shapes={'''image''': SPEED_TEST_SHAPE})
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

  with tempfile.TemporaryDirectory() as tmp_dir:
    # don't use fixed length for fair comparison
    # feats = datasets.Features(
    #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
    # )
    __lowerCAmelCase								=					datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''))})
    __lowerCAmelCase								=					generate_examples(
        features=__lowercase  ,				num_examples=__lowercase  ,				seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}  ,				)
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

  with open(__lowercase  ,				'''wb''') as f:
    f.write(json.dumps(__lowercase).encode('''utf-8'''))


if __name__ == "__main__":  # useful to run the profiler
      benchmark_array_xd()
