'''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      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			Dict ,   snake_case_	:			Any=None      )   ->				Dict:


     '''simple docstring'''




     assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
     UpperCAmelCase_	   =       nn.Parameter(snake_case_      )
     if bias is not None:
          assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
          UpperCAmelCase_	   =       nn.Parameter(snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			Optional[int] ,   snake_case_	:			Dict ,   snake_case_	:			List[str]      )   ->				List[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       np.asarray(weights[0]      )
     UpperCAmelCase_	   =       np.asarray(weights[1]      )
     UpperCAmelCase_	   =       np.asarray(weights[2]      )

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





def      lowerCAmelCase_    ( snake_case_	:			Optional[int] ,   snake_case_	:			List[Any] ,   snake_case_	:			Union[str, Any]      )   ->				List[str]:


     '''simple docstring'''




     UpperCAmelCase_	   =       np.asarray(weights[0]      )
     UpperCAmelCase_	   =       np.asarray(weights[1]      )
     UpperCAmelCase_	   =       np.asarray(weights[2]      )
     UpperCAmelCase_	   =       np.asarray(weights[3]      )

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





def      lowerCAmelCase_    ( snake_case_	:			Any ,   snake_case_	:			Tuple ,   snake_case_	:			Union[str, Any]      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       weights[0][0][0]
     UpperCAmelCase_	   =       np.asarray(layer_norm_a[0]      )
     UpperCAmelCase_	   =       np.asarray(layer_norm_a[1]      )
     set_param(
         torch_block.attention.layer_norm ,   torch.tensor(snake_case_      ) ,   torch.tensor(snake_case_      ) ,   )

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

     # intermediate weighs
     UpperCAmelCase_	   =       weights[2][0][1][2]

     # Chunked Feed Forward
     if len(snake_case_      ) == 4:
          UpperCAmelCase_	   =       intermediate_weights[2]

     # layernorm 2
     UpperCAmelCase_	   =       np.asarray(intermediate_weights[0][0]      )
     UpperCAmelCase_	   =       np.asarray(intermediate_weights[0][1]      )
     set_param(
         torch_block.feed_forward.layer_norm ,   torch.tensor(snake_case_      ) ,   torch.tensor(snake_case_      ) ,   )

     # intermediate dense
     UpperCAmelCase_	   =       np.asarray(intermediate_weights[1][0]      )
     UpperCAmelCase_	   =       np.asarray(intermediate_weights[1][1]      )
     set_param(
         torch_block.feed_forward.dense.dense ,   torch.tensor(snake_case_      ).transpose(0 ,   1      ).contiguous() ,   torch.tensor(snake_case_      ) ,   )

     # intermediate out
     UpperCAmelCase_	   =       np.asarray(intermediate_weights[4][0]      )
     UpperCAmelCase_	   =       np.asarray(intermediate_weights[4][1]      )
     set_param(
         torch_block.feed_forward.output.dense ,   torch.tensor(snake_case_      ).transpose(0 ,   1      ).contiguous() ,   torch.tensor(snake_case_      ) ,   )





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			List[Any]      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       torch_model.reformer

     # word embeds
     UpperCAmelCase_	   =       np.asarray(weights[1]      )
     set_param(
         torch_model_reformer.embeddings.word_embeddings ,   torch.tensor(snake_case_      ) ,   )

     if isinstance(weights[3] ,   snake_case_      ):
          UpperCAmelCase_	   =       torch_model_reformer.embeddings.position_embeddings
          for emb_idx in range(len(position_embeddings.weights      )      ):
               UpperCAmelCase_	   =       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_	   =       nn.Parameter(torch.tensor(snake_case_      )      )

     UpperCAmelCase_	   =       weights[5]
     assert len(torch_model_reformer.encoder.layers      ) * 4 == len(
         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_	   =       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
          set_block_weights_in_torch(snake_case_ ,   snake_case_ ,   snake_case_      )

     # output layer norm
     UpperCAmelCase_	   =       np.asarray(weights[7][0]      )
     UpperCAmelCase_	   =       np.asarray(weights[7][1]      )
     set_param(
         torch_model_reformer.encoder.layer_norm ,   torch.tensor(snake_case_      ) ,   torch.tensor(snake_case_      ) ,   )

     # output embeddings
     UpperCAmelCase_	   =       np.asarray(weights[9][0]      )
     UpperCAmelCase_	   =       np.asarray(weights[9][1]      )
     set_param(
         torch_model.lm_head.decoder ,   torch.tensor(snake_case_      ).transpose(0 ,   1      ).contiguous() ,   torch.tensor(snake_case_      ) ,   )





def      lowerCAmelCase_    ( snake_case_	:			Dict ,   snake_case_	:			List[str] ,   snake_case_	:			Any      )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       ReformerConfig.from_json_file(snake_case_      )
     print(f"""Building PyTorch model from configuration: {config}"""      )
     UpperCAmelCase_	   =       ReformerModelWithLMHead(snake_case_      )

     with open(snake_case_ ,   "rb"      ) as f:
          UpperCAmelCase_	   =       pickle.load(snake_case_      )["weights"]

     set_model_weights_in_torch(snake_case_ ,   snake_case_ ,   config.hidden_size      )

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


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					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.'
    )
    SCREAMING_SNAKE_CASE_:					Optional[int]  =parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





'''simple docstring'''





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_:					Tuple  =logging.getLogger(__name__)

SCREAMING_SNAKE_CASE_:					Any  ={'facebook/bart-base': BartForConditionalGeneration}
SCREAMING_SNAKE_CASE_:					int  ={'facebook/bart-base': BartTokenizer}





def      lowerCAmelCase_    ( )   ->				str:


     '''simple docstring'''




     UpperCAmelCase_	   =       argparse.ArgumentParser(description="Export Bart model + Beam Search to ONNX graph."      )
     parser.add_argument(
         "--validation_file" ,   type=snake_case_ ,   default=snake_case_ ,   help="A csv or a json file containing the validation data."      )
     parser.add_argument(
         "--max_length" ,   type=snake_case_ ,   default=5 ,   help="The maximum total input sequence length after tokenization." ,   )
     parser.add_argument(
         "--num_beams" ,   type=snake_case_ ,   default=snake_case_ ,   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=snake_case_ ,   help="Path to pretrained model or model identifier from huggingface.co/models." ,   required=snake_case_ ,   )
     parser.add_argument(
         "--config_name" ,   type=snake_case_ ,   default=snake_case_ ,   help="Pretrained config name or path if not the same as model_name" ,   )
     parser.add_argument(
         "--device" ,   type=snake_case_ ,   default="cpu" ,   help="Device where the model will be run" ,   )
     parser.add_argument("--output_file_path" ,   type=snake_case_ ,   default=snake_case_ ,   help="Where to store the final ONNX file."      )

     UpperCAmelCase_	   =       parser.parse_args()

     return args





def      lowerCAmelCase_    ( snake_case_	:			Optional[Any] ,   snake_case_	:			int="cpu"      )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       model_dict[model_name].from_pretrained(snake_case_      ).to(snake_case_      )
     UpperCAmelCase_	   =       tokenizer_dict[model_name].from_pretrained(snake_case_      )

     if model_name in ["facebook/bart-base"]:
          UpperCAmelCase_	   =       0
          UpperCAmelCase_	   =       None
          UpperCAmelCase_	   =       0

     return huggingface_model, tokenizer





def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			List[Any] ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			int ,   snake_case_	:			Dict      )   ->				Dict:


     '''simple docstring'''




     model.eval()

     UpperCAmelCase_	   =       None
     UpperCAmelCase_	   =       torch.jit.script(BARTBeamSearchGenerator(snake_case_      )      )

     with torch.no_grad():
          UpperCAmelCase_	   =       "My friends are cool but they eat too many carbs."
          UpperCAmelCase_	   =       tokenizer([ARTICLE_TO_SUMMARIZE] ,   max_length=10_24 ,   return_tensors="pt"      ).to(model.device      )

          UpperCAmelCase_	   =       model.generate(
              inputs["input_ids"] ,   attention_mask=inputs["attention_mask"] ,   num_beams=snake_case_ ,   max_length=snake_case_ ,   early_stopping=snake_case_ ,   decoder_start_token_id=model.config.decoder_start_token_id ,   )

          torch.onnx.export(
              snake_case_ ,   (
                  inputs["input_ids"],
                  inputs["attention_mask"],
                  num_beams,
                  max_length,
                  model.config.decoder_start_token_id,
              ) ,   snake_case_ ,   opset_version=14 ,   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=snake_case_ ,   )

          logger.info("Model exported to {}".format(snake_case_      )      )

          UpperCAmelCase_	   =       remove_dup_initializers(os.path.abspath(snake_case_      )      )

          logger.info("Deduplicated and optimized model written to {}".format(snake_case_      )      )

          UpperCAmelCase_	   =       onnxruntime.InferenceSession(snake_case_      )
          UpperCAmelCase_	   =       ort_sess.run(
              snake_case_ ,   {
                  "input_ids": inputs["input_ids"].cpu().numpy(),
                  "attention_mask": inputs["attention_mask"].cpu().numpy(),
                  "num_beams": np.array(snake_case_      ),
                  "max_length": np.array(snake_case_      ),
                  "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      lowerCAmelCase_    ( )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       parse_args()
     UpperCAmelCase_	   =       5
     UpperCAmelCase_	   =       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()

     UpperCAmelCase_	   =       torch.device(args.device      )

     UpperCAmelCase_   ,			UpperCAmelCase_	   =       load_model_tokenizer(args.model_name_or_path ,   snake_case_      )

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

     model.to(snake_case_      )

     if args.max_length:
          UpperCAmelCase_	   =       args.max_length

     if args.num_beams:
          UpperCAmelCase_	   =       args.num_beams

     if args.output_file_path:
          UpperCAmelCase_	   =       args.output_file_path
     else:
          UpperCAmelCase_	   =       "BART.onnx"

     logger.info("Exporting model to ONNX"      )
     export_and_validate_model(snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_      )


if __name__ == "__main__":
    main()




