






"""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 			snake_case  (A_      :int	,			A_      :Union[str, Any]	,			A_      :Dict      ):







  '''simple docstring'''

  a     :			Union[str, Any]				=   FNetConfig.from_json_file(A_      )
  print(f'''Building PyTorch model from configuration: {config}'''      )
  a     :			Union[str, Any]				=   FNetForPreTraining(A_      )

  a     :			str				=   restore_checkpoint(A_	,			A_      )
  a     :			Dict				=   checkpoint_dict['target']

  # Embeddings
  # Position IDs
  a     :			Optional[int]				=   fnet_pretraining_model.state_dict()

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

  # Encoder Layers
  for layer in range(config.num_hidden_layers      ):
    a     :			Dict				=   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     :			int				=   torch.tensor(
        pretrained_model_params['encoder'][f'''feed_forward_{layer}''']['intermediate']['kernel']      ).T
    a     :			int				=   torch.tensor(
        pretrained_model_params['encoder'][f'''feed_forward_{layer}''']['intermediate']['bias']      )

    a     :			str				=   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[Any]				=   torch.tensor(
        pretrained_model_params['encoder'][f'''encoder_{layer}''']['output_layer_norm']['scale']      )
    a     :			Tuple				=   torch.tensor(
        pretrained_model_params['encoder'][f'''encoder_{layer}''']['output_layer_norm']['bias']      )

  # Pooler Layers
  a     :			Union[str, Any]				=   torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']      ).T
  a     :			Dict				=   torch.tensor(pretrained_model_params['encoder']['pooler']['bias']      )

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

  # Seq Relationship Layers
  a     :			Any				=   torch.tensor(
      pretrained_model_params['classification']['output_kernel']      )
  a     :			Optional[Any]				=   torch.tensor(
      pretrained_model_params['classification']['output_bias']      )

  # Load State Dict
  fnet_pretraining_model.load_state_dict(A_      )

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


if __name__ == "__main__":
   _UpperCamelCase				:    Optional[int]     =						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.')
   _UpperCamelCase				:    int     =						parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def 					snake_case						()       ->       int:

						'''simple docstring'''



						_snake_case						:     List[Any]				=    Dataset.from_dict({"i": range(17						)}						)
						_snake_case						:     Tuple				=    len(__lowercase						)
						_snake_case						:     Any				=    3
						_snake_case						:     List[str]				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert sum(len(__lowercase						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def 					snake_case						()       ->       List[Any]:

						'''simple docstring'''



						def gen():
												return ({"i": i} for i in range(17						))

						_snake_case						:     List[Any]				=    3
						_snake_case						:     Dict				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     List[Any]				=    len(list(__lowercase						)						)
						_snake_case						:     str				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert sum(len(list(__lowercase						)						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
@pytest.mark.parametrize("shards_per_node"					,			[1, 2, 3]						)
def 					snake_case						(__lowercase						)       ->       List[Any]:

						'''simple docstring'''



						def gen(__lowercase						):
												for shard in shards:
																		yield from ({"i": i, "shard": shard} for i in range(17						))

						_snake_case						:     str				=    3
						_snake_case						:     List[str]				=    shards_per_node * world_size
						_snake_case						:     List[Any]				=    {"shards": [F"""shard_{shard_idx}.txt""" for shard_idx in range(__lowercase						)]}
						_snake_case						:     Optional[int]				=    IterableDataset.from_generator(__lowercase					,			gen_kwargs=__lowercase						)
						_snake_case						:     Tuple				=    len(list(__lowercase						)						)
						assert full_ds.n_shards == world_size * shards_per_node
						_snake_case						:     List[Any]				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
						assert sum(len(list(__lowercase						)						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def 					snake_case						()       ->       Dict:

						'''simple docstring'''



						def gen():
												return ({"i": i} for i in range(17						))

						_snake_case						:     Dict				=    2
						_snake_case						:     str				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     Dict				=    len(list(__lowercase						)						)

						_snake_case						:     Optional[int]				=    split_dataset_by_node(__lowercase					,			rank=0					,			world_size=__lowercase						).shuffle(seed=42						)
						assert len(list(__lowercase						)						) == 1 + full_size // world_size
						with pytest.raises(__lowercase						):
												split_dataset_by_node(__lowercase					,			rank=0					,			world_size=__lowercase						).shuffle()

						_snake_case						:     Optional[int]				=    split_dataset_by_node(full_ds.shuffle(seed=42						)					,			rank=0					,			world_size=__lowercase						)
						assert len(list(__lowercase						)						) == 1 + full_size // world_size
						with pytest.raises(__lowercase						):
												split_dataset_by_node(full_ds.shuffle()					,			rank=0					,			world_size=__lowercase						)
@pytest.mark.parametrize("streaming"					,			[False, True]						)
@require_torch
@pytest.mark.skipif(os.name == "nt"					,			reason="execute_subprocess_async doesn't support windows"						)
@pytest.mark.integration
def 					snake_case						(__lowercase						)       ->       Any:

						'''simple docstring'''



						_snake_case						:     Union[str, Any]				=    2
						_snake_case						:     Optional[Any]				=    get_torch_dist_unique_port()
						_snake_case						:     Optional[int]				=    Path(__lowercase						).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
						_snake_case						:     Any				=    F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
						_snake_case						:     str				=    F"""
        --streaming={streaming}
    """.split()
						_snake_case						:     Optional[Any]				=    [sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase					,			env=os.environ.copy()						)

@pytest.mark.parametrize(
    "nproc_per_node, num_workers"					,			[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]					,			)
@require_torch
@pytest.mark.skipif(os.name == "nt"					,			reason="execute_subprocess_async doesn't support windows"						)
@pytest.mark.integration
def 					snake_case						(__lowercase					,			__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						_snake_case						:     Dict				=    True
						_snake_case						:     Optional[int]				=    get_torch_dist_unique_port()
						_snake_case						:     List[str]				=    Path(__lowercase						).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
						_snake_case						:     List[str]				=    F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
						_snake_case						:     Tuple				=    F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
						_snake_case						:     Any				=    [sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase					,			env=os.environ.copy()						)