


import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format="%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    level=os.environ.get("LOGLEVEL", "INFO").upper(),
    stream=sys.stdout,
)

SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				logging.getLogger(__name__)

SCREAMING_SNAKE_CASE       :		Any         =				{"facebook/bart-base": BartForConditionalGeneration}
SCREAMING_SNAKE_CASE       :		Tuple         =				{"facebook/bart-base": BartTokenizer}




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





     lowercase_       :Tuple     =       argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''		)
     parser.add_argument(
         '''--validation_file'''       ,		type=_a       ,		default=_a       ,		help='''A csv or a json file containing the validation data.'''		)
     parser.add_argument(
         '''--max_length'''       ,		type=_a       ,		default=5       ,		help='''The maximum total input sequence length after tokenization.'''       ,		)
     parser.add_argument(
         '''--num_beams'''       ,		type=_a       ,		default=_a       ,		help=(
             '''Number of beams to use for evaluation. This argument will be '''
             '''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
         )       ,		)
     parser.add_argument(
         '''--model_name_or_path'''       ,		type=_a       ,		help='''Path to pretrained model or model identifier from huggingface.co/models.'''       ,		required=_a       ,		)
     parser.add_argument(
         '''--config_name'''       ,		type=_a       ,		default=_a       ,		help='''Pretrained config name or path if not the same as model_name'''       ,		)
     parser.add_argument(
         '''--device'''       ,		type=_a       ,		default='''cpu'''       ,		help='''Device where the model will be run'''       ,		)
     parser.add_argument('''--output_file_path'''       ,		type=_a       ,		default=_a       ,		help='''Where to store the final ONNX file.'''		)

     lowercase_       :int     =       parser.parse_args()

     return args




def     UpperCamelCase    (      _a       ,		_a="cpu"		)					->							int:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       model_dict[model_name].from_pretrained(_a		).to(_a		)
     lowercase_       :Optional[Any]     =       tokenizer_dict[model_name].from_pretrained(_a		)

     if model_name in ["facebook/bart-base"]:
          lowercase_       :List[str]     =       0
          lowercase_       :List[Any]     =       None
          lowercase_       :str     =       0

     return huggingface_model, tokenizer




def     UpperCamelCase    (      _a       ,		_a       ,		_a       ,		_a       ,		_a		)					->							Any:
     '''simple docstring'''





     model.eval()

     lowercase_       :Any     =       None
     lowercase_       :List[str]     =       torch.jit.script(BARTBeamSearchGenerator(_a		)		)

     with torch.no_grad():
          lowercase_       :List[str]     =       '''My friends are cool but they eat too many carbs.'''
          lowercase_       :List[Any]     =       tokenizer([ARTICLE_TO_SUMMARIZE]       ,		max_length=1_0_2_4       ,		return_tensors='''pt'''		).to(model.device		)

          lowercase_       :int     =       model.generate(
              inputs['''input_ids''']       ,		attention_mask=inputs['''attention_mask''']       ,		num_beams=_a       ,		max_length=_a       ,		early_stopping=_a       ,		decoder_start_token_id=model.config.decoder_start_token_id       ,		)

          torch.onnx.export(
              _a       ,		(
                  inputs['''input_ids'''],
                  inputs['''attention_mask'''],
                  num_beams,
                  max_length,
                  model.config.decoder_start_token_id,
              )       ,		_a       ,		opset_version=1_4       ,		input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id''']       ,		output_names=['''output_ids''']       ,		dynamic_axes={
                  '''input_ids''': {0: '''batch''', 1: '''seq'''},
                  '''output_ids''': {0: '''batch''', 1: '''seq_out'''},
              }       ,		example_outputs=_a       ,		)

          logger.info('''Model exported to {}'''.format(_a		)		)

          lowercase_       :List[str]     =       remove_dup_initializers(os.path.abspath(_a		)		)

          logger.info('''Deduplicated and optimized model written to {}'''.format(_a		)		)

          lowercase_       :Tuple     =       onnxruntime.InferenceSession(_a		)
          lowercase_       :Optional[int]     =       ort_sess.run(
              _a       ,		{
                  '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
                  '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
                  '''num_beams''': np.array(_a		),
                  '''max_length''': np.array(_a		),
                  '''decoder_start_token_id''': np.array(model.config.decoder_start_token_id		),
              }       ,		)

          np.testing.assert_allclose(summary_ids.cpu().numpy()       ,		ort_out[0]       ,		rtol=1E-3       ,		atol=1E-3		)

          logger.info('''Model outputs from torch and ONNX Runtime are similar.'''		)
          logger.info('''Success.'''		)




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





     lowercase_       :int     =       parse_args()
     lowercase_       :Any     =       5
     lowercase_       :Optional[Any]     =       4

     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,		datefmt='''%m/%d/%Y %H:%M:%S'''       ,		level=logging.INFO       ,		)

     logger.setLevel(logging.INFO		)
     transformers.utils.logging.set_verbosity_error()

     lowercase_       :int     =       torch.device(args.device		)

     lowercase_    ,      lowercase_       :str     =       load_model_tokenizer(args.model_name_or_path       ,		_a		)

     if model.config.decoder_start_token_id is None:
          raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined'''		)

     model.to(_a		)

     if args.max_length:
          lowercase_       :str     =       args.max_length

     if args.num_beams:
          lowercase_       :List[Any]     =       args.num_beams

     if args.output_file_path:
          lowercase_       :Dict     =       args.output_file_path
     else:
          lowercase_       :Union[str, Any]     =       '''BART.onnx'''

     logger.info('''Exporting model to ONNX'''		)
     export_and_validate_model(_a       ,		_a       ,		_a       ,		_a       ,		_a		)


if __name__ == "__main__":
   main()





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)

