


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      a	(							SCREAMING_SNAKE_CASE_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Union[str, Any]=None       ):






       """simple docstring"""





       assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
       UpperCamelCase    :	Tuple   	=		nn.Parameter(SCREAMING_SNAKE_CASE_       )
       if bias is not None:
              assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
              UpperCamelCase    :	Optional[int]   	=		nn.Parameter(SCREAMING_SNAKE_CASE_       )


def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		List[Any]       ):






       """simple docstring"""





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

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


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[int]       ):






       """simple docstring"""





       UpperCamelCase    :	List[Any]   	=		np.asarray(weights[0]       )
       UpperCamelCase    :	str   	=		np.asarray(weights[1]       )
       UpperCamelCase    :	Dict   	=		np.asarray(weights[2]       )
       UpperCamelCase    :	Any   	=		np.asarray(weights[3]       )

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


def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		Tuple							,			SCREAMING_SNAKE_CASE_      :		List[Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		weights[0][0][0]
       UpperCamelCase    :	List[Any]   	=		np.asarray(layer_norm_a[0]       )
       UpperCamelCase    :	Optional[int]   	=		np.asarray(layer_norm_a[1]       )
       set_param(
           torch_block.attention.layer_norm							,			torch.tensor(SCREAMING_SNAKE_CASE_       )							,			torch.tensor(SCREAMING_SNAKE_CASE_       )							,			)

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

       # intermediate weighs
       UpperCamelCase    :	List[str]   	=		weights[2][0][1][2]

       # Chunked Feed Forward
       if len(SCREAMING_SNAKE_CASE_       ) == 4:
              UpperCamelCase    :	Optional[Any]   	=		intermediate_weights[2]

       # layernorm 2
       UpperCamelCase    :	str   	=		np.asarray(intermediate_weights[0][0]       )
       UpperCamelCase    :	List[str]   	=		np.asarray(intermediate_weights[0][1]       )
       set_param(
           torch_block.feed_forward.layer_norm							,			torch.tensor(SCREAMING_SNAKE_CASE_       )							,			torch.tensor(SCREAMING_SNAKE_CASE_       )							,			)

       # intermediate dense
       UpperCamelCase    :	Optional[int]   	=		np.asarray(intermediate_weights[1][0]       )
       UpperCamelCase    :	Optional[int]   	=		np.asarray(intermediate_weights[1][1]       )
       set_param(
           torch_block.feed_forward.dense.dense							,			torch.tensor(SCREAMING_SNAKE_CASE_       ).transpose(0							,			1       ).contiguous()							,			torch.tensor(SCREAMING_SNAKE_CASE_       )							,			)

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


def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	List[Any]   	=		torch_model.reformer

       # word embeds
       UpperCamelCase    :	str   	=		np.asarray(weights[1]       )
       set_param(
           torch_model_reformer.embeddings.word_embeddings							,			torch.tensor(SCREAMING_SNAKE_CASE_       )							,			)

       if isinstance(weights[3]							,			SCREAMING_SNAKE_CASE_       ):
              UpperCamelCase    :	Any   	=		torch_model_reformer.embeddings.position_embeddings
              for emb_idx in range(len(position_embeddings.weights       )       ):
                     UpperCamelCase    :	Union[str, Any]   	=		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    :	List[Any]   	=		nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE_       )       )

       UpperCamelCase    :	Optional[int]   	=		weights[5]
       assert len(torch_model_reformer.encoder.layers       ) * 4 == len(
           SCREAMING_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    :	List[str]   	=		trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
              set_block_weights_in_torch(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

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

       # output embeddings
       UpperCamelCase    :	int   	=		np.asarray(weights[9][0]       )
       UpperCamelCase    :	Tuple   	=		np.asarray(weights[9][1]       )
       set_param(
           torch_model.lm_head.decoder							,			torch.tensor(SCREAMING_SNAKE_CASE_       ).transpose(0							,			1       ).contiguous()							,			torch.tensor(SCREAMING_SNAKE_CASE_       )							,			)


def      a	(							SCREAMING_SNAKE_CASE_      :		Any							,			SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		str       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		ReformerConfig.from_json_file(SCREAMING_SNAKE_CASE_       )
       print(F"""Building PyTorch model from configuration: {config}"""       )
       UpperCamelCase    :	str   	=		ReformerModelWithLMHead(SCREAMING_SNAKE_CASE_       )

       with open(SCREAMING_SNAKE_CASE_							,			'''rb'''       ) as f:
              UpperCamelCase    :	str   	=		pickle.load(SCREAMING_SNAKE_CASE_       )['''weights''']

       set_model_weights_in_torch(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			config.hidden_size       )

       # Save pytorch-model
       print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
       torch.save(model.state_dict()							,			SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
  __UpperCAmelCase : int 			=				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 : str 			=				parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





import torch

from transformers import AutoModel



class    UpperCAmelCase_	(							torch.nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"						):




              """simple docstring"""




              super(__SCREAMING_SNAKE_CASE ,					self						).__init__()

              UpperCamelCase    :	List[str]   	=		AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE ,					return_dict=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		torch.nn.CosineSimilarity(3 ,					1e-08						)
              UpperCamelCase    :	List[Any]   	=		torch.nn.Softmax(dim=1						)






       def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return self.bert(**__SCREAMING_SNAKE_CASE						).last_hidden_state






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return token_embeddings.sum(2 ,					keepdim=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=1						):




              """simple docstring"""




              return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		W_supports['''sizes'''].tolist()
              UpperCamelCase    :	Optional[int]   	=		W_supports['''start_token_id'''].item()
              UpperCamelCase    :	Any   	=		W_supports['''end_token_id'''].item()

              del W_supports["sizes"]
              del W_supports["start_token_id"]
              del W_supports["end_token_id"]

              UpperCamelCase    :	Union[str, Any]   	=		self.BERT(**__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		self.BERT(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[Any]   	=		None
              UpperCamelCase    :	Any   	=		None

              UpperCamelCase    :	Optional[Any]   	=		W_supports['''input_ids'''] == start_token_id
              UpperCamelCase    :	Any   	=		W_supports['''input_ids'''] == end_token_id

              for i, size in enumerate(__SCREAMING_SNAKE_CASE						):
                     if i == 0:
                            UpperCamelCase    :	Optional[int]   	=		0
                     else:
                            UpperCamelCase    :	Tuple   	=		support_sizes[i - 1]

                     UpperCamelCase    :	Tuple   	=		S[s : s + size][start_token_masks[s : s + size]]
                     UpperCamelCase    :	List[str]   	=		S[s : s + size][end_token_masks[s : s + size]]

                     UpperCamelCase    :	Dict   	=		torch.matmul(q[i] ,					s_start.T						).sum(1						).softmax(0						)
                     UpperCamelCase    :	Tuple   	=		torch.matmul(q[i] ,					s_end.T						).sum(1						).softmax(0						)

                     if p_starts is not None:
                            UpperCamelCase    :	List[str]   	=		torch.vstack((p_starts, p_start)						)
                            UpperCamelCase    :	Union[str, Any]   	=		torch.vstack((p_ends, p_end)						)
                     else:
                            UpperCamelCase    :	str   	=		p_start
                            UpperCamelCase    :	Optional[int]   	=		p_end

              return p_starts, p_ends

