'''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()





'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:					int  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Dict  ={
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
SCREAMING_SNAKE_CASE_:					List[str]  =[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





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


     '''simple docstring'''




     for attribute in key.split("."      ):
          UpperCAmelCase_	   =       getattr(snake_case_ ,   snake_case_      )

     if weight_type is not None:
          UpperCAmelCase_	   =       getattr(snake_case_ ,   snake_case_      ).shape
     else:
          UpperCAmelCase_	   =       hf_pointer.shape

     assert hf_shape == value.shape, (
         f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
         f""" {value.shape} for {full_name}"""
     )

     if weight_type == "weight":
          UpperCAmelCase_	   =       value
     elif weight_type == "weight_g":
          UpperCAmelCase_	   =       value
     elif weight_type == "weight_v":
          UpperCAmelCase_	   =       value
     elif weight_type == "bias":
          UpperCAmelCase_	   =       value
     else:
          UpperCAmelCase_	   =       value

     logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""      )





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


     '''simple docstring'''




     UpperCAmelCase_	   =       []
     UpperCAmelCase_	   =       fairseq_model.state_dict()

     UpperCAmelCase_	   =       hf_model.feature_extractor

     # if encoder has different dim to decoder -> use proj_weight
     UpperCAmelCase_	   =       None

     for name, value in fairseq_dict.items():
          UpperCAmelCase_	   =       False
          if "conv_layers" in name:
               load_conv_layer(
                   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   hf_model.config.feat_extract_norm == "group" ,   )
               UpperCAmelCase_	   =       True
          elif name.split("."      )[0] == "proj":
               UpperCAmelCase_	   =       fairseq_model.proj
               UpperCAmelCase_	   =       True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
                         UpperCAmelCase_	   =       True
                         if "*" in mapped_key:
                              UpperCAmelCase_	   =       name.split(snake_case_      )[0].split("."      )[-2]
                              UpperCAmelCase_	   =       mapped_key.replace("*" ,   snake_case_      )
                         if "weight_g" in name:
                              UpperCAmelCase_	   =       "weight_g"
                         elif "weight_v" in name:
                              UpperCAmelCase_	   =       "weight_v"
                         elif "bias" in name:
                              UpperCAmelCase_	   =       "bias"
                         elif "weight" in name:
                              UpperCAmelCase_	   =       "weight"
                         else:
                              UpperCAmelCase_	   =       None
                         set_recursively(snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_      )
                    continue
          if not is_used:
               unused_weights.append(snake_case_      )

     logger.warning(f"""Unused weights: {unused_weights}"""      )

     return proj_weight





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


     '''simple docstring'''




     UpperCAmelCase_	   =       full_name.split("conv_layers."      )[-1]
     UpperCAmelCase_	   =       name.split("."      )
     UpperCAmelCase_	   =       int(items[0]      )
     UpperCAmelCase_	   =       int(items[1]      )

     if type_id == 0:
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                   f"""{full_name} has size {value.shape}, but"""
                   f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
               )
               UpperCAmelCase_	   =       value
               logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                   f"""{full_name} has size {value.shape}, but"""
                   f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
               )
               UpperCAmelCase_	   =       value
               logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                   " found."
               )
               UpperCAmelCase_	   =       value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                   f"""{full_name} has size {value.shape}, but"""
                   f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
               )
               UpperCAmelCase_	   =       value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
     else:
          unused_weights.append(snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			Any      )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_   ,			UpperCAmelCase_	   =       emb.weight.shape
     UpperCAmelCase_	   =       nn.Linear(snake_case_ ,   snake_case_ ,   bias=snake_case_      )
     UpperCAmelCase_	   =       emb.weight.data
     return lin_layer





def      lowerCAmelCase_    ( snake_case_	:			int      )   ->				List[str]:


     '''simple docstring'''




     with open(snake_case_ ,   "r" ,   encoding="utf-8"      ) as f:
          UpperCAmelCase_	   =       f.readlines()
          UpperCAmelCase_	   =       [line.split(" "      )[0] for line in lines]

     UpperCAmelCase_	   =       len(snake_case_      )

     UpperCAmelCase_	   =       {
         "<s>": 0,
         "<pad>": 1,
         "</s>": 2,
         "<unk>": 3,
     }

     vocab_dict.update(dict(zip(snake_case_ ,   range(4 ,   num_words + 4      )      )      )      )
     return vocab_dict





@torch.no_grad()
def      lowerCAmelCase_    ( snake_case_	:			Dict ,   snake_case_	:			Tuple ,   snake_case_	:			List[str] ,   snake_case_	:			Dict ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			Tuple ,   snake_case_	:			Any ,   )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       WavaVecaConfig.from_pretrained(snake_case_      )
     UpperCAmelCase_	   =       SpeechaTextaConfig.from_pretrained(
         snake_case_ ,   vocab_size=snake_case_ ,   decoder_layers=snake_case_ ,   do_stable_layer_norm=snake_case_      )

     UpperCAmelCase_	   =       WavaVecaFeatureExtractor(
         feature_size=1 ,   sampling_rate=1_60_00 ,   padding_value=0 ,   do_normalize=snake_case_ ,   return_attention_mask=snake_case_ ,   )

     UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path] ,   arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
     UpperCAmelCase_	   =       model[0].eval()

     # set weights for wav2vec2 encoder
     UpperCAmelCase_	   =       WavaVecaModel(snake_case_      )
     UpperCAmelCase_	   =       recursively_load_weights_wavaveca(model.encoder ,   snake_case_      )

     UpperCAmelCase_	   =       SpeechaTextaForCausalLM(snake_case_      )
     UpperCAmelCase_   ,			UpperCAmelCase_	   =       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,   strict=snake_case_      )

     # set output linear layer
     unexpected_keys.remove("embed_out"      )
     UpperCAmelCase_	   =       nn.Parameter(model.decoder.embed_out.detach()      )

     # layer norm is init to identity matrix so leaving it is fine
     logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""      )
     logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""      )

     UpperCAmelCase_	   =       SpeechEncoderDecoderModel(encoder=snake_case_ ,   decoder=snake_case_      )
     UpperCAmelCase_	   =       False

     # add projection layer
     UpperCAmelCase_	   =       nn.Parameter(projection_layer.weight      )
     UpperCAmelCase_	   =       nn.Parameter(projection_layer.bias      )

     UpperCAmelCase_	   =       create_vocab_dict(snake_case_      )

     with open(os.path.join(snake_case_ ,   "vocab.json"      ) ,   "w"      ) as fp:
          json.dump(snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       SpeechaTextaTokenizer(os.path.join(snake_case_ ,   "vocab.json"      )      )
     tokenizer.save_pretrained(snake_case_      )

     UpperCAmelCase_	   =       hf_wavavec.config.to_dict()
     UpperCAmelCase_	   =       tokenizer.pad_token_id
     UpperCAmelCase_	   =       tokenizer.bos_token_id
     UpperCAmelCase_	   =       tokenizer.eos_token_id
     UpperCAmelCase_	   =       "speech_to_text_2"
     UpperCAmelCase_	   =       "wav2vec2"

     UpperCAmelCase_	   =       SpeechEncoderDecoderConfig.from_dict(snake_case_      )

     hf_wavavec.save_pretrained(snake_case_      )
     feature_extractor.save_pretrained(snake_case_      )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					Optional[int]  =argparse.ArgumentParser()
    parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
    parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
    parser.add_argument(
        '--encoder_config_path',
        default='facebook/wav2vec2-large-lv60',
        type=str,
        help='Path to hf encoder wav2vec2 checkpoint config',
    )
    parser.add_argument(
        '--decoder_config_path',
        default='facebook/s2t-small-mustc-en-fr-st',
        type=str,
        help='Path to hf decoder s2t checkpoint config',
    )
    parser.add_argument('--vocab_size', default=1_02_24, type=int, help='Vocab size of decoder')
    parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

    SCREAMING_SNAKE_CASE_:					Dict  =parser.parse_args()
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.dict_path,
        encoder_config_path=args.encoder_config_path,
        decoder_config_path=args.decoder_config_path,
        vocab_size=args.vocab_size,
        num_decoder_layers=args.num_decoder_layers,
    )




