






"""simple docstring"""



import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  Dict		,					__snake_case			:  Optional[int]=None    ):





  '''simple docstring'''





  assert torch_layer.weight.shape == weight.shape, f'{torch_layer} layer.weight does not match'
  lowercase							    =       nn.Parameter(__snake_case    )
  if bias is not None:
    assert torch_layer.bias.shape == bias.shape, f'{torch_layer} layer.bias does not match'
    lowercase							    =       nn.Parameter(__snake_case    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str		,					__snake_case			:  Tuple		,					__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  lowercase							    =       np.asarray(weights[0]    )
  lowercase							    =       np.asarray(weights[1]    )
  lowercase							    =       np.asarray(weights[2]    )

  set_param(
      torch_layer.self_attention.query_key		,					torch.tensor(__snake_case    ).transpose(1		,					2    ).contiguous().view(-1		,					__snake_case    )		,					)
  set_param(
      torch_layer.self_attention.value		,					torch.tensor(__snake_case    ).transpose(1		,					2    ).contiguous().view(-1		,					__snake_case    )		,					)
  set_param(
      torch_layer.output.dense		,					torch.tensor(__snake_case    ).view(-1		,					__snake_case    ).contiguous().transpose(0		,					1    )		,					)
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  Tuple		,					__snake_case			:  int    ):





  '''simple docstring'''





  lowercase							    =       np.asarray(weights[0]    )
  lowercase							    =       np.asarray(weights[1]    )
  lowercase							    =       np.asarray(weights[2]    )
  lowercase							    =       np.asarray(weights[3]    )

  set_param(
      torch_layer.self_attention.query		,					torch.tensor(__snake_case    ).transpose(1		,					2    ).contiguous().view(-1		,					__snake_case    )		,					)
  set_param(
      torch_layer.self_attention.key		,					torch.tensor(__snake_case    ).transpose(1		,					2    ).contiguous().view(-1		,					__snake_case    )		,					)
  set_param(
      torch_layer.self_attention.value		,					torch.tensor(__snake_case    ).transpose(1		,					2    ).contiguous().view(-1		,					__snake_case    )		,					)
  set_param(
      torch_layer.output.dense		,					torch.tensor(__snake_case    ).view(-1		,					__snake_case    ).contiguous().transpose(0		,					1    )		,					)
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict		,					__snake_case			:  List[str]		,					__snake_case			:  Tuple    ):





  '''simple docstring'''





  lowercase							    =       weights[0][0][0]
  lowercase							    =       np.asarray(layer_norm_a[0]    )
  lowercase							    =       np.asarray(layer_norm_a[1]    )
  set_param(
      torch_block.attention.layer_norm		,					torch.tensor(__snake_case    )		,					torch.tensor(__snake_case    )		,					)

  # lsh weights + output
  lowercase							    =       weights[0][1]
  if len(__snake_case    ) < 4:
    set_layer_weights_in_torch_lsh(__snake_case		,					torch_block.attention		,					__snake_case    )
  else:
    set_layer_weights_in_torch_local(__snake_case		,					torch_block.attention		,					__snake_case    )

  # intermediate weighs
  lowercase							    =       weights[2][0][1][2]

  # Chunked Feed Forward
  if len(__snake_case    ) == 4:
    lowercase							    =       intermediate_weights[2]

  # layernorm 2
  lowercase							    =       np.asarray(intermediate_weights[0][0]    )
  lowercase							    =       np.asarray(intermediate_weights[0][1]    )
  set_param(
      torch_block.feed_forward.layer_norm		,					torch.tensor(__snake_case    )		,					torch.tensor(__snake_case    )		,					)

  # intermediate dense
  lowercase							    =       np.asarray(intermediate_weights[1][0]    )
  lowercase							    =       np.asarray(intermediate_weights[1][1]    )
  set_param(
      torch_block.feed_forward.dense.dense		,					torch.tensor(__snake_case    ).transpose(0		,					1    ).contiguous()		,					torch.tensor(__snake_case    )		,					)

  # intermediate out
  lowercase							    =       np.asarray(intermediate_weights[4][0]    )
  lowercase							    =       np.asarray(intermediate_weights[4][1]    )
  set_param(
      torch_block.feed_forward.output.dense		,					torch.tensor(__snake_case    ).transpose(0		,					1    ).contiguous()		,					torch.tensor(__snake_case    )		,					)
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[Any]		,					__snake_case			:  str		,					__snake_case			:  str    ):





  '''simple docstring'''





  lowercase							    =       torch_model.reformer

  # word embeds
  lowercase							    =       np.asarray(weights[1]    )
  set_param(
      torch_model_reformer.embeddings.word_embeddings		,					torch.tensor(__snake_case    )		,					)

  if isinstance(weights[3]		,					__snake_case    ):
    lowercase							    =       torch_model_reformer.embeddings.position_embeddings
    for emb_idx in range(len(position_embeddings.weights    )    ):
      lowercase							    =       np.asarray(weights[3][emb_idx][0]    )
      assert (
          position_embeddings.weights[emb_idx].shape == emb_weights.shape
      ), f'{position_embeddings[emb_idx]} emb does not match'
      lowercase							    =       nn.Parameter(torch.tensor(__snake_case    )    )

  lowercase							    =       weights[5]
  assert len(torch_model_reformer.encoder.layers    ) * 4 == len(
      __snake_case    ), "HF and trax model do not have the same number of layers"
  for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers    ):
    lowercase							    =       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
    set_block_weights_in_torch(__snake_case		,					__snake_case		,					__snake_case    )

  # output layer norm
  lowercase							    =       np.asarray(weights[7][0]    )
  lowercase							    =       np.asarray(weights[7][1]    )
  set_param(
      torch_model_reformer.encoder.layer_norm		,					torch.tensor(__snake_case    )		,					torch.tensor(__snake_case    )		,					)

  # output embeddings
  lowercase							    =       np.asarray(weights[9][0]    )
  lowercase							    =       np.asarray(weights[9][1]    )
  set_param(
      torch_model.lm_head.decoder		,					torch.tensor(__snake_case    ).transpose(0		,					1    ).contiguous()		,					torch.tensor(__snake_case    )		,					)
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict		,					__snake_case			:  Optional[Any]		,					__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  lowercase							    =       ReformerConfig.from_json_file(__snake_case    )
  print(f'Building PyTorch model from configuration: {config}'    )
  lowercase							    =       ReformerModelWithLMHead(__snake_case    )

  with open(__snake_case		,					'rb'    ) as f:
    lowercase							    =       pickle.load(__snake_case    )['weights']

  set_model_weights_in_torch(__snake_case		,					__snake_case		,					config.hidden_size    )

  # Save pytorch-model
  print(f'Save PyTorch model to {pytorch_dump_path}'    )
  torch.save(model.state_dict()		,					__snake_case    )


if __name__ == "__main__":
    _UpperCamelCase		:  List[Any]						=		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained Reformer model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    _UpperCamelCase		:  Tuple						=		parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)










"""simple docstring"""



import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

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


_UpperCamelCase		:  List[Any]						=		4
_UpperCamelCase		:  Optional[Any]						=		3

class        a     (   a_	):
    pass


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  for shard in shards:
    for i in range(__snake_case    ):
      yield {"i": i, "shard": shard}
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       int(os.environ['RANK']    )
  lowercase							    =       int(os.environ['WORLD_SIZE']    )

  lowercase							    =       ArgumentParser()
  parser.add_argument('--streaming'		,					type=__snake_case    )
  parser.add_argument('--local_rank'		,					type=__snake_case    )
  parser.add_argument('--num_workers'		,					type=__snake_case		,					default=0    )
  lowercase							    =       parser.parse_args()
  lowercase							    =       args.streaming
  lowercase							    =       args.num_workers

  lowercase							    =       {'shards': [f'shard_{shard_idx}' for shard_idx in range(__snake_case    )]}
  lowercase							    =       IterableDataset.from_generator(__snake_case		,					gen_kwargs=__snake_case    )
  if not streaming:
    lowercase							    =       Dataset.from_list(list(__snake_case    )    )

  lowercase							    =       split_dataset_by_node(__snake_case		,					rank=__snake_case		,					world_size=__snake_case    )
  lowercase							    =       torch.utils.data.DataLoader(__snake_case		,					num_workers=__snake_case    )

  lowercase							    =       NUM_SHARDS * NUM_ITEMS_PER_SHARD
  lowercase							    =       full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)    )

  lowercase							    =       sum(1 for _ in dataloader    )
  if local_size != expected_local_size:
    raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'    )


if __name__ == "__main__":
    main()


