


import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


SCREAMING_SNAKE_CASE       :		List[str]         =				logging.get_logger(__name__)
logging.set_verbosity_info()




def     UpperCamelCase    (      _a       ,		_a		)					->							Optional[int]:
					'''simple docstring'''





					if "xprophetnet" in prophetnet_checkpoint_path:
										lowercase_       :List[str]     =       XLMProphetNetForConditionalGenerationOld.from_pretrained(_a		)
										lowercase_       :int     =       XLMProphetNetForConditionalGeneration.from_pretrained(
										    _a       ,		output_loading_info=_a		)
					else:
										lowercase_       :str     =       ProphetNetForConditionalGenerationOld.from_pretrained(_a		)
										lowercase_       :Dict     =       ProphetNetForConditionalGeneration.from_pretrained(
										    _a       ,		output_loading_info=_a		)

					lowercase_       :int     =       ['''key_proj''', '''value_proj''', '''query_proj''']

					lowercase_       :str     =       {
					    '''self_attn''': '''ngram_self_attn''',
					    '''cross_attn''': '''encoder_attn''',
					    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
					    '''feed_forward_layer_norm''': '''final_layer_norm''',
					    '''feed_forward''': '''''',
					    '''intermediate''': '''fc1''',
					    '''output''': '''fc2''',
					    '''key_proj''': '''k_proj''',
					    '''query_proj''': '''q_proj''',
					    '''value_proj''': '''v_proj''',
					    '''word_embeddings''': '''embed_tokens''',
					    '''embeddings_layer_norm''': '''emb_layer_norm''',
					    '''relative_pos_embeddings''': '''relative_linear''',
					    '''ngram_embeddings''': '''ngram_input_embed''',
					    '''position_embeddings''': '''embed_positions''',
					}

					for key in loading_info["missing_keys"]:
										lowercase_       :Dict     =       key.split('''.'''		)

										if attributes[0] == "lm_head":
															lowercase_       :List[Any]     =       prophet
															lowercase_       :str     =       prophet_old
										else:
															lowercase_       :Any     =       prophet.prophetnet
															lowercase_       :Optional[Any]     =       prophet_old.model

										lowercase_       :int     =       False
										for attribute in attributes:
															if attribute in mapping:
																				lowercase_       :List[str]     =       mapping[attribute]
																				if not hasattr(_a       ,		_a		) and len(_a		) > 0:
																									lowercase_       :Any     =       attribute
															elif hasattr(_a       ,		_a		):
																				lowercase_       :Dict     =       attribute

															if attribute == "weight":
																				assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																				lowercase_       :Dict     =       old_model.weight
																				logger.info(f"{attribute} is initialized."		)
																				lowercase_       :Any     =       True
																				break
															elif attribute == "bias":
																				assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																				lowercase_       :Tuple     =       old_model.bias
																				logger.info(f"{attribute} is initialized"		)
																				lowercase_       :int     =       True
																				break
															elif attribute in special_keys and hasattr(_a       ,		'''in_proj_weight'''		):
																				lowercase_       :Optional[Any]     =       old_model.in_proj_weight.shape[0] // 3
																				lowercase_       :Any     =       getattr(_a       ,		_a		)
																				param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																				param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																				if attribute == "query_proj":
																									lowercase_       :List[str]     =       nn.Parameter(old_model.in_proj_weight[:embed_dim, :]		)
																									lowercase_       :str     =       nn.Parameter(old_model.in_proj_bias[:embed_dim]		)

																				elif attribute == "key_proj":
																									lowercase_       :Optional[Any]     =       nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]		)
																									lowercase_       :Union[str, Any]     =       nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]		)
																				elif attribute == "value_proj":
																									lowercase_       :Dict     =       nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]		)
																									lowercase_       :Dict     =       nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]		)
																				lowercase_       :int     =       True
																				break
															elif attribute == "position_embeddings":
																				assert (
																				    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																				), "Hidden size has to match"
																				assert model.position_embeddings.weight.shape[0] == 5_1_2, "We want 512 position_embeddings."
																				lowercase_       :Optional[Any]     =       nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]		)
																				lowercase_       :str     =       True
																				break

															if attribute.isdigit():
																				lowercase_       :Any     =       model[int(_a		)]
																				lowercase_       :int     =       old_model[int(_a		)]
															else:
																				lowercase_       :List[str]     =       getattr(_a       ,		_a		)

																				if old_attribute == "":
																									lowercase_       :int     =       old_model
																				else:
																									if not hasattr(_a       ,		_a		):
																														raise ValueError(f"{old_model} does not have {old_attribute}"		)
																									lowercase_       :Any     =       getattr(_a       ,		_a		)

										if not is_key_init:
															raise ValueError(f"{key} was not correctly initialized!"		)

					print(f"Saving model to {pytorch_dump_folder_path}"		)
					prophet.save_pretrained(_a		)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE       :		int         =				argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--prophetnet_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				parser.parse_args()
			convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





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     UpperCamelCase    (      _a       ,		_a       ,		_a=None		)					->							Dict:
     '''simple docstring'''





     assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
     lowercase_       :str     =       nn.Parameter(_a		)
     if bias is not None:
          assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
          lowercase_       :List[str]     =       nn.Parameter(_a		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							List[str]:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       np.asarray(weights[0]		)
     lowercase_       :str     =       np.asarray(weights[1]		)
     lowercase_       :Union[str, Any]     =       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     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[int]:
     '''simple docstring'''





     lowercase_       :Union[str, Any]     =       np.asarray(weights[0]		)
     lowercase_       :Tuple     =       np.asarray(weights[1]		)
     lowercase_       :List[Any]     =       np.asarray(weights[2]		)
     lowercase_       :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     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :List[str]     =       weights[0][0][0]
     lowercase_       :Optional[Any]     =       np.asarray(layer_norm_a[0]		)
     lowercase_       :Any     =       np.asarray(layer_norm_a[1]		)
     set_param(
         torch_block.attention.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # lsh weights + output
     lowercase_       :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
     lowercase_       :Optional[int]     =       weights[2][0][1][2]

     # Chunked Feed Forward
     if len(_a		) == 4:
          lowercase_       :Tuple     =       intermediate_weights[2]

     # layernorm 2
     lowercase_       :int     =       np.asarray(intermediate_weights[0][0]		)
     lowercase_       :Any     =       np.asarray(intermediate_weights[0][1]		)
     set_param(
         torch_block.feed_forward.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # intermediate dense
     lowercase_       :Any     =       np.asarray(intermediate_weights[1][0]		)
     lowercase_       :int     =       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
     lowercase_       :Optional[int]     =       np.asarray(intermediate_weights[4][0]		)
     lowercase_       :Any     =       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     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :Any     =       torch_model.reformer

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

     if isinstance(weights[3]       ,		_a		):
          lowercase_       :Union[str, Any]     =       torch_model_reformer.embeddings.position_embeddings
          for emb_idx in range(len(position_embeddings.weights		)		):
               lowercase_       :Tuple     =       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_       :Optional[int]     =       nn.Parameter(torch.tensor(_a		)		)

     lowercase_       :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		):
          lowercase_       :int     =       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
          set_block_weights_in_torch(_a       ,		_a       ,		_a		)

     # output layer norm
     lowercase_       :Optional[Any]     =       np.asarray(weights[7][0]		)
     lowercase_       :int     =       np.asarray(weights[7][1]		)
     set_param(
         torch_model_reformer.encoder.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # output embeddings
     lowercase_       :Optional[int]     =       np.asarray(weights[9][0]		)
     lowercase_       :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     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							str:
     '''simple docstring'''





     lowercase_       :List[str]     =       ReformerConfig.from_json_file(_a		)
     print(f"Building PyTorch model from configuration: {config}"		)
     lowercase_       :List[str]     =       ReformerModelWithLMHead(_a		)

     with open(_a       ,		'''rb'''		) as f:
          lowercase_       :Dict     =       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__":
   SCREAMING_SNAKE_CASE       :		Optional[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."
   )
   SCREAMING_SNAKE_CASE       :		Dict         =				parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)

