




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			,     snake_case			,     snake_case=None       )						->				Union[str, Any]:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
      _UpperCAmelCase      =			nn.Parameter(snake_case       )
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
            _UpperCAmelCase      =			nn.Parameter(snake_case       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				str:
      # set torch weights for 1-to-1 comparison
      _UpperCAmelCase      =			np.asarray(weights[0]       )
      _UpperCAmelCase      =			np.asarray(weights[1]       )
      _UpperCAmelCase      =			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			,     snake_case			,     snake_case       )						->				Tuple:
      # set torch weights for 1-to-1 comparison
      _UpperCAmelCase      =			np.asarray(weights[0]       )
      _UpperCAmelCase      =			np.asarray(weights[1]       )
      _UpperCAmelCase      =			np.asarray(weights[2]       )
      _UpperCAmelCase      =			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			,     snake_case			,     snake_case       )						->				Tuple:
      # layernorm 1
      _UpperCAmelCase      =			weights[0][0][0]
      _UpperCAmelCase      =			np.asarray(layer_norm_a[0]       )
      _UpperCAmelCase      =			np.asarray(layer_norm_a[1]       )
      set_param(
          torch_block.attention.layer_norm			,     torch.tensor(snake_case       )			,     torch.tensor(snake_case       )			,     )

      # lsh weights + output
      _UpperCAmelCase      =			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
      _UpperCAmelCase      =			weights[2][0][1][2]

      # Chunked Feed Forward
      if len(snake_case       ) == 4:
            _UpperCAmelCase      =			intermediate_weights[2]

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

      # intermediate dense
      _UpperCAmelCase      =			np.asarray(intermediate_weights[1][0]       )
      _UpperCAmelCase      =			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
      _UpperCAmelCase      =			np.asarray(intermediate_weights[4][0]       )
      _UpperCAmelCase      =			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			,     snake_case			,     snake_case       )						->				Dict:
      # reformer model
      _UpperCAmelCase      =			torch_model.reformer

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

      if isinstance(weights[3]			,     snake_case       ):
            _UpperCAmelCase      =			torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights       )       ):
                  _UpperCAmelCase      =			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"
                  _UpperCAmelCase      =			nn.Parameter(torch.tensor(snake_case       )       )

      _UpperCAmelCase      =			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       ):
            _UpperCAmelCase      =			trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(snake_case			,     snake_case			,     snake_case       )

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

      # output embeddings
      _UpperCAmelCase      =			np.asarray(weights[9][0]       )
      _UpperCAmelCase      =			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			,     snake_case			,     snake_case       )						->				Union[str, Any]:
      # Initialise PyTorch model
      _UpperCAmelCase      =			ReformerConfig.from_json_file(snake_case       )
      print(f"Building PyTorch model from configuration: {config}"       )
      _UpperCAmelCase      =			ReformerModelWithLMHead(snake_case       )

      with open(snake_case			,     """rb"""       ) as f:
            _UpperCAmelCase      =			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__":
    a													=			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."
    )
    a													=			parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





import itertools
import string
from collections.abc import Generator, Iterable




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Generator[tuple[str, ...], None, None]:
						_UpperCAmelCase      =			iter(snake_case       )
						while True:
												_UpperCAmelCase      =			tuple(itertools.islice(snake_case			,     snake_case       )       )
												if not chunk:
																		return
												yield chunk




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				str:
						_UpperCAmelCase      =			"""""".join([c.upper() for c in dirty if c in string.ascii_letters]       )
						_UpperCAmelCase      =			""""""

						if len(snake_case       ) < 2:
												return dirty

						for i in range(len(snake_case       ) - 1       ):
												clean += dirty[i]

												if dirty[i] == dirty[i + 1]:
																		clean += "X"

						clean += dirty[-1]

						if len(snake_case       ) & 1:
												clean += "X"

						return clean




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				list[str]:
						# I and J are used interchangeably to allow
						# us to use a 5x5 table (25 letters)
						_UpperCAmelCase      =			"""ABCDEFGHIKLMNOPQRSTUVWXYZ"""
						# we're using a list instead of a '2d' array because it makes the math
						# for setting up the table and doing the actual encoding/decoding simpler
						_UpperCAmelCase      =			[]

						# copy key chars into the table if they are in `alphabet` ignoring duplicates
						for char in key.upper():
												if char not in table and char in alphabet:
																		table.append(snake_case       )

    # fill the rest of the table in with the remaining alphabet chars
						for char in alphabet:
												if char not in table:
																		table.append(snake_case       )

						return table




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				str:
						_UpperCAmelCase      =			generate_table(snake_case       )
						_UpperCAmelCase      =			prepare_input(snake_case       )
						_UpperCAmelCase      =			""""""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(snake_case			,     2       ):
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )

												if rowa == rowa:
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
												elif cola == cola:
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												else:  # rectangle
																		ciphertext += table[rowa * 5 + cola]
																		ciphertext += table[rowa * 5 + cola]

						return ciphertext




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				str:
						_UpperCAmelCase      =			generate_table(snake_case       )
						_UpperCAmelCase      =			""""""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(snake_case			,     2       ):
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )

												if rowa == rowa:
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
												elif cola == cola:
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
												else:  # rectangle
																		plaintext += table[rowa * 5 + cola]
																		plaintext += table[rowa * 5 + cola]

						return plaintext