



"""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   (		UpperCamelCase		:	Optional[int]    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	Optional[int]=None		):
 assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
 UpperCAmelCase			:      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"
  UpperCAmelCase			:      Any			=			nn.Parameter(_A		)

def    _snake_case   (		UpperCamelCase		:	List[Any]    ,	UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      List[Any]			=			np.asarray(weights[0]		)
 UpperCAmelCase			:      Dict			=			np.asarray(weights[1]		)
 UpperCAmelCase			:      Optional[int]			=			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   (		UpperCamelCase		:	Dict    ,	UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	Optional[int]		):
 UpperCAmelCase			:      Tuple			=			np.asarray(weights[0]		)
 UpperCAmelCase			:      List[Any]			=			np.asarray(weights[1]		)
 UpperCAmelCase			:      str			=			np.asarray(weights[2]		)
 UpperCAmelCase			:      Optional[int]			=			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   (		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	str    ,	UpperCamelCase		:	Dict		):
 UpperCAmelCase			:      List[Any]			=			weights[0][0][0]
 UpperCAmelCase			:      str			=			np.asarray(layer_norm_a[0]		)
 UpperCAmelCase			:      Any			=			np.asarray(layer_norm_a[1]		)
 set_param(
     torch_block.attention.layer_norm    ,	torch.tensor(_A		)    ,	torch.tensor(_A		)    ,	)

 # lsh weights + output
 UpperCAmelCase			:      Union[str, Any]			=			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
 UpperCAmelCase			:      Dict			=			weights[2][0][1][2]

 # Chunked Feed Forward
 if len(_A		) == 4:
  UpperCAmelCase			:      Optional[int]			=			intermediate_weights[2]

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

 # intermediate dense
 UpperCAmelCase			:      Optional[Any]			=			np.asarray(intermediate_weights[1][0]		)
 UpperCAmelCase			:      Optional[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
 UpperCAmelCase			:      str			=			np.asarray(intermediate_weights[4][0]		)
 UpperCAmelCase			:      Tuple			=			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   (		UpperCamelCase		:	List[Any]    ,	UpperCamelCase		:	List[str]    ,	UpperCamelCase		:	List[str]		):
 UpperCAmelCase			:      Optional[int]			=			torch_model.reformer

 # word embeds
 UpperCAmelCase			:      List[Any]			=			np.asarray(weights[1]		)
 set_param(
     torch_model_reformer.embeddings.word_embeddings    ,	torch.tensor(_A		)    ,	)

 if isinstance(weights[3]    ,	_A		):
  UpperCAmelCase			:      Union[str, Any]			=			torch_model_reformer.embeddings.position_embeddings
  for emb_idx in range(len(position_embeddings.weights		)		):
   UpperCAmelCase			:      Optional[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"
   UpperCAmelCase			:      str			=			nn.Parameter(torch.tensor(_A		)		)

 UpperCAmelCase			:      Optional[int]			=			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		):
  UpperCAmelCase			:      Optional[int]			=			trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
  set_block_weights_in_torch(_A    ,	_A    ,	_A		)

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

 # output embeddings
 UpperCAmelCase			:      List[Any]			=			np.asarray(weights[9][0]		)
 UpperCAmelCase			:      Union[str, 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   (		UpperCamelCase		:	Any    ,	UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	Optional[int]		):
 UpperCAmelCase			:      Any			=			ReformerConfig.from_json_file(_A		)
 print(F"Building PyTorch model from configuration: {config}"		)
 UpperCAmelCase			:      List[Any]			=			ReformerModelWithLMHead(_A		)

 with open(_A    ,	"""rb"""		) as f:
  UpperCAmelCase			:      Tuple			=			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__":
      A:     Tuple										=	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:     Union[str, Any]										=	parser.parse_args()
      convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


A:     Optional[int]										=	logging.get_logger(__name__)

A:     Union[str, Any]										=	{
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     List[str]		=		'gpt_neo'
   __lowerCAmelCase						:     Any		=		['past_key_values']
   __lowerCAmelCase						:     int		=		{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE=50257   ,  _SCREAMING_SNAKE_CASE=2048   ,  _SCREAMING_SNAKE_CASE=2048   ,  _SCREAMING_SNAKE_CASE=24   ,  _SCREAMING_SNAKE_CASE=[[["global", "local"], 12]]   ,  _SCREAMING_SNAKE_CASE=16   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=256   ,  _SCREAMING_SNAKE_CASE="gelu_new"   ,  _SCREAMING_SNAKE_CASE=0.0   ,  _SCREAMING_SNAKE_CASE=0.0   ,  _SCREAMING_SNAKE_CASE=0.0   ,  _SCREAMING_SNAKE_CASE=0.1   ,  _SCREAMING_SNAKE_CASE=1E-5   ,  _SCREAMING_SNAKE_CASE=0.02   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=50256   ,  _SCREAMING_SNAKE_CASE=50256   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			vocab_size
    UpperCAmelCase			:      List[Any]			=			max_position_embeddings
    UpperCAmelCase			:      str			=			hidden_size
    UpperCAmelCase			:      Dict			=			num_layers
    UpperCAmelCase			:      List[Any]			=			num_heads
    UpperCAmelCase			:      Optional[Any]			=			intermediate_size
    UpperCAmelCase			:      Dict			=			window_size
    UpperCAmelCase			:      int			=			activation_function
    UpperCAmelCase			:      Tuple			=			resid_dropout
    UpperCAmelCase			:      int			=			embed_dropout
    UpperCAmelCase			:      Optional[int]			=			attention_dropout
    UpperCAmelCase			:      Optional[int]			=			classifier_dropout
    UpperCAmelCase			:      Any			=			layer_norm_epsilon
    UpperCAmelCase			:      Union[str, Any]			=			initializer_range
    UpperCAmelCase			:      List[str]			=			use_cache

    UpperCAmelCase			:      Union[str, Any]			=			bos_token_id
    UpperCAmelCase			:      str			=			eos_token_id

    UpperCAmelCase			:      Union[str, Any]			=			attention_types
    UpperCAmelCase			:      int			=			self.expand_attention_types_params(_SCREAMING_SNAKE_CASE		)

    if len(self.attention_layers		) != self.num_layers:
     raise ValueError(
         """Configuration for convolutional module is incorrect. """
         """It is required that `len(config.attention_layers)` == `config.num_layers` """
         F"but is `len(config.attention_layers) = {len(self.attention_layers		)}`, "
         F"`config.num_layers = {self.num_layers}`. "
         """`config.attention_layers` is prepared using `config.attention_types`. """
         """Please verify the value of `config.attention_types` argument."""		)

    super().__init__(bos_token_id=_SCREAMING_SNAKE_CASE   ,  eos_token_id=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)




   @staticmethod
   def 			SCREAMING_SNAKE_CASE  (     _SCREAMING_SNAKE_CASE		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			[]
    for item in attention_types:
     for _ in range(item[1]		):
      attentions.extend(item[0]		)
    return attentions

def    _snake_case		(		UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	Optional[Any]    ,	UpperCamelCase		:	Dict		):
 import torch

 UpperCAmelCase			:      Optional[Any]			=			input.size()
 UpperCAmelCase			:      int			=			len(UpperCamelCase		)
 UpperCAmelCase			:      Optional[Any]			=			shape[dimension]

 UpperCAmelCase			:      Union[str, Any]			=			torch.arange(0    ,	UpperCamelCase    ,	UpperCamelCase		)
 UpperCAmelCase			:      List[Any]			=			torch.div(sizedim - size    ,	UpperCamelCase    ,	rounding_mode="""floor"""		) + 1
 UpperCAmelCase			:      Optional[Any]			=			torch.arange(UpperCamelCase		) + low_indices[:min_length][:, None]

 UpperCAmelCase			:      List[Any]			=			[slice(UpperCamelCase		)] * rank
 UpperCAmelCase			:      Optional[Any]			=			indices
 UpperCAmelCase			:      Optional[Any]			=			input[s]

 UpperCAmelCase			:      List[Any]			=			list(range(0    ,	rank + 1		)		)
 perm.append(perm.pop(dimension + 1		)		)

 return sliced.permute(UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	Dict		):
 import torch

 UpperCAmelCase			:      List[str]			=			torch.arange(1    ,	UpperCamelCase		)
 UpperCAmelCase			:      Any			=			torch.remainder(UpperCamelCase    ,	UpperCamelCase		)
 UpperCAmelCase			:      Union[str, Any]			=			remainders == 0
 UpperCAmelCase			:      str			=			candidates[divisor_indices]
 UpperCAmelCase			:      Optional[int]			=			torch.max(UpperCamelCase		)
 return largest_divisor, torch.div(UpperCamelCase    ,	UpperCamelCase    ,	rounding_mode="""floor"""		)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Mapping[str, Mapping[int, str]]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}		)
    if self.use_past:
     self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE   ,  direction="""inputs"""		)
     UpperCAmelCase			:      str			=			{0: """batch""", 1: """past_sequence + sequence"""}
    else:
     UpperCAmelCase			:      List[str]			=			{0: """batch""", 1: """sequence"""}

    return common_inputs

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    return self._config.num_heads

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = -1   ,  _SCREAMING_SNAKE_CASE = -1   ,  _SCREAMING_SNAKE_CASE = False   ,  _SCREAMING_SNAKE_CASE = None   ,  )						->   Mapping[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			super(_SCREAMING_SNAKE_CASE   ,  self		).generate_dummy_inputs(
        _SCREAMING_SNAKE_CASE   ,  batch_size=_SCREAMING_SNAKE_CASE   ,  seq_length=_SCREAMING_SNAKE_CASE   ,  is_pair=_SCREAMING_SNAKE_CASE   ,  framework=_SCREAMING_SNAKE_CASE		)

    # We need to order the input in the way they appears in the forward()
    UpperCAmelCase			:      Dict			=			OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}		)

    # Need to add the past_keys
    if self.use_past:
     if not is_torch_available():
      raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""		)
     else:
      import torch

      UpperCAmelCase			,	UpperCAmelCase			:      List[Any]			=			common_inputs["""input_ids"""].shape
      # Not using the same length for past_key_values
      UpperCAmelCase			:      Any			=			seqlen + 2
      UpperCAmelCase			:      str			=			(
          batch,
          self.num_attention_heads,
          past_key_values_length,
          self._config.hidden_size // self.num_attention_heads,
      )
      UpperCAmelCase			:      Optional[int]			=			[
          (torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		)) for _ in range(self.num_layers		)
      ]

    UpperCAmelCase			:      List[str]			=			common_inputs["""attention_mask"""]
    if self.use_past:
     UpperCAmelCase			:      str			=			ordered_inputs["""attention_mask"""].dtype
     UpperCAmelCase			:      int			=			torch.cat(
         [ordered_inputs["""attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  dtype=_SCREAMING_SNAKE_CASE		)]   ,  dim=1		)

    return ordered_inputs




   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    return 13
