
'''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 	_snake_case						(      A_       :    Tuple     ,		A_       :    int     ,		A_       :    int=None   ):
  """simple docstring"""







  assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
  a_      :	List[Any]      =							nn.Parameter(A_   )
  if bias is not None:
    assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
    a_      :	Tuple      =							nn.Parameter(A_   )






def 	_snake_case						(      A_       :    List[Any]     ,		A_       :    str     ,		A_       :    Dict   ):
  """simple docstring"""







  a_      :	Optional[int]      =							np.asarray(weights[0]   )
  a_      :	Union[str, Any]      =							np.asarray(weights[1]   )
  a_      :	str      =							np.asarray(weights[2]   )

  set_param(
      torch_layer.self_attention.query_key     ,		torch.tensor(A_   ).transpose(1     ,		2   ).contiguous().view(-1     ,		A_   )     ,		)
  set_param(
      torch_layer.self_attention.value     ,		torch.tensor(A_   ).transpose(1     ,		2   ).contiguous().view(-1     ,		A_   )     ,		)
  set_param(
      torch_layer.output.dense     ,		torch.tensor(A_   ).view(-1     ,		A_   ).contiguous().transpose(0     ,		1   )     ,		)






def 	_snake_case						(      A_       :    Optional[int]     ,		A_       :    Union[str, Any]     ,		A_       :    str   ):
  """simple docstring"""







  a_      :	Any      =							np.asarray(weights[0]   )
  a_      :	Tuple      =							np.asarray(weights[1]   )
  a_      :	Any      =							np.asarray(weights[2]   )
  a_      :	Any      =							np.asarray(weights[3]   )

  set_param(
      torch_layer.self_attention.query     ,		torch.tensor(A_   ).transpose(1     ,		2   ).contiguous().view(-1     ,		A_   )     ,		)
  set_param(
      torch_layer.self_attention.key     ,		torch.tensor(A_   ).transpose(1     ,		2   ).contiguous().view(-1     ,		A_   )     ,		)
  set_param(
      torch_layer.self_attention.value     ,		torch.tensor(A_   ).transpose(1     ,		2   ).contiguous().view(-1     ,		A_   )     ,		)
  set_param(
      torch_layer.output.dense     ,		torch.tensor(A_   ).view(-1     ,		A_   ).contiguous().transpose(0     ,		1   )     ,		)






def 	_snake_case						(      A_       :    int     ,		A_       :    str     ,		A_       :    Optional[Any]   ):
  """simple docstring"""







  a_      :	Any      =							weights[0][0][0]
  a_      :	str      =							np.asarray(layer_norm_a[0]   )
  a_      :	str      =							np.asarray(layer_norm_a[1]   )
  set_param(
      torch_block.attention.layer_norm     ,		torch.tensor(A_   )     ,		torch.tensor(A_   )     ,		)

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

  # intermediate weighs
  a_      :	Any      =							weights[2][0][1][2]

  # Chunked Feed Forward
  if len(A_   ) == 4:
    a_      :	str      =							intermediate_weights[2]

  # layernorm 2
  a_      :	Optional[int]      =							np.asarray(intermediate_weights[0][0]   )
  a_      :	Optional[int]      =							np.asarray(intermediate_weights[0][1]   )
  set_param(
      torch_block.feed_forward.layer_norm     ,		torch.tensor(A_   )     ,		torch.tensor(A_   )     ,		)

  # intermediate dense
  a_      :	str      =							np.asarray(intermediate_weights[1][0]   )
  a_      :	List[Any]      =							np.asarray(intermediate_weights[1][1]   )
  set_param(
      torch_block.feed_forward.dense.dense     ,		torch.tensor(A_   ).transpose(0     ,		1   ).contiguous()     ,		torch.tensor(A_   )     ,		)

  # intermediate out
  a_      :	str      =							np.asarray(intermediate_weights[4][0]   )
  a_      :	Optional[int]      =							np.asarray(intermediate_weights[4][1]   )
  set_param(
      torch_block.feed_forward.output.dense     ,		torch.tensor(A_   ).transpose(0     ,		1   ).contiguous()     ,		torch.tensor(A_   )     ,		)






def 	_snake_case						(      A_       :    List[str]     ,		A_       :    Dict     ,		A_       :    List[Any]   ):
  """simple docstring"""







  a_      :	Optional[int]      =							torch_model.reformer

  # word embeds
  a_      :	int      =							np.asarray(weights[1]   )
  set_param(
      torch_model_reformer.embeddings.word_embeddings     ,		torch.tensor(A_   )     ,		)

  if isinstance(weights[3]     ,		A_   ):
    a_      :	Optional[Any]      =							torch_model_reformer.embeddings.position_embeddings
    for emb_idx in range(len(position_embeddings.weights   )   ):
      a_      :	int      =							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'''
      a_      :	int      =							nn.Parameter(torch.tensor(A_   )   )

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

  # output layer norm
  a_      :	List[Any]      =							np.asarray(weights[7][0]   )
  a_      :	Dict      =							np.asarray(weights[7][1]   )
  set_param(
      torch_model_reformer.encoder.layer_norm     ,		torch.tensor(A_   )     ,		torch.tensor(A_   )     ,		)

  # output embeddings
  a_      :	Tuple      =							np.asarray(weights[9][0]   )
  a_      :	Optional[Any]      =							np.asarray(weights[9][1]   )
  set_param(
      torch_model.lm_head.decoder     ,		torch.tensor(A_   ).transpose(0     ,		1   ).contiguous()     ,		torch.tensor(A_   )     ,		)






def 	_snake_case						(      A_       :    Union[str, Any]     ,		A_       :    Optional[int]     ,		A_       :    Union[str, Any]   ):
  """simple docstring"""







  a_      :	List[str]      =							ReformerConfig.from_json_file(A_   )
  print(f'''Building PyTorch model from configuration: {config}'''   )
  a_      :	Dict      =							ReformerModelWithLMHead(A_   )

  with open(A_     ,		"""rb"""   ) as f:
    a_      :	List[str]      =							pickle.load(A_   )["""weights"""]

  set_model_weights_in_torch(A_     ,		A_     ,		config.hidden_size   )

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


if __name__ == "__main__":
    __snake_case:  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."
    )
    __snake_case:  Dict								=			parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




'''simple docstring'''

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__snake_case:  Tuple								=			logging.get_logger(__name__)

__snake_case:  Tuple								=			{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					"t5"
     a_    	=					["past_key_values"]
     a_    	=					{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


     def __init__(      self							,		lowerCAmelCase_=3_21_28							,		lowerCAmelCase_=5_12							,		lowerCAmelCase_=64							,		lowerCAmelCase_=20_48							,		lowerCAmelCase_=6							,		lowerCAmelCase_=None							,		lowerCAmelCase_=8							,		lowerCAmelCase_=32							,		lowerCAmelCase_=1_28							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=1E-6							,		lowerCAmelCase_=1.0							,		lowerCAmelCase_="relu"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=0							,		lowerCAmelCase_=1							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	List[Any]      =							vocab_size
       a_      :	List[str]      =							d_model
       a_      :	Union[str, Any]      =							d_kv
       a_      :	List[Any]      =							d_ff
       a_      :	int      =							num_layers
       a_      :	Any      =							(
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       a_      :	Union[str, Any]      =							num_heads
       a_      :	int      =							relative_attention_num_buckets
       a_      :	Dict      =							relative_attention_max_distance
       a_      :	Union[str, Any]      =							dropout_rate
       a_      :	Union[str, Any]      =							layer_norm_epsilon
       a_      :	List[Any]      =							initializer_factor
       a_      :	Any      =							feed_forward_proj
       a_      :	List[Any]      =							use_cache

       a_      :	Dict      =							self.feed_forward_proj.split("""-""" )
       a_      :	Union[str, Any]      =							act_info[-1]
       a_      :	str      =							act_info[0] == """gated"""

       if len(lowerCAmelCase_ ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_ ) > 2:
         raise ValueError(
             f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
             """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
             """'gated-gelu' or 'relu'""" )

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
         a_      :	List[str]      =							"""gelu_new"""

       super().__init__(
           pad_token_id=lowerCAmelCase_							,		eos_token_id=lowerCAmelCase_							,		is_encoder_decoder=lowerCAmelCase_							,		**lowerCAmelCase_							,		)







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							{
           """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
           """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
       }
       if self.use_past:
         a_      :	Tuple      =							"""past_encoder_sequence + sequence"""
         a_      :	str      =							{0: """batch"""}
         a_      :	Any      =							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
       else:
         a_      :	List[Any]      =							{0: """batch""", 1: """decoder_sequence"""}
         a_      :	Optional[int]      =							{0: """batch""", 1: """decoder_sequence"""}

       if self.use_past:
         self.fill_with_past_key_values_(lowerCAmelCase_							,		direction="""inputs""" )

       return common_inputs






     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return 13


