






'''simple docstring'''

import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


UpperCamelCase							=logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm
      import torch_xla.debug.metrics as met




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     *__lowerCAmelCase			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     **__lowerCAmelCase ):
       super().__init__(*__lowerCAmelCase			,     **__lowerCAmelCase )
       UpperCamelCase_				:   List[str]					    =							eval_examples
       UpperCamelCase_				:   Optional[Any]					    =							post_process_function
       UpperCamelCase_				:   Tuple					    =							quant_trainer_args
       UpperCamelCase_				:   Optional[Any]					    =							1_28  # default number of calibration samples
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=None ):
       if calib_dataset is None and self.calib_dataset is None:
             raise ValueError("""Trainer: calibration requires an calib_dataset.""" )
       UpperCamelCase_				:   Dict					    =							calib_dataset if calib_dataset is not None else self.calib_dataset

       UpperCamelCase_				:   Any					    =							self._remove_unused_columns(__lowerCAmelCase			,     description="""Calibration""" )

       return DataLoader(
           __lowerCAmelCase			,     batch_size=self.args.eval_batch_size			,     collate_fn=self.data_collator			,     drop_last=self.args.dataloader_drop_last			,     num_workers=self.args.dataloader_num_workers			,     pin_memory=self.args.dataloader_pin_memory			,     shuffle=__lowerCAmelCase			,     )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=None ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.train_dataset if calib_dataset is None else calib_dataset
       UpperCamelCase_				:   str					    =							self.get_calib_dataloader(__lowerCAmelCase )

       UpperCamelCase_				:   int					    =							self.model
       quant_trainer.configure_model(__lowerCAmelCase			,     self.quant_trainer_args			,     calib=__lowerCAmelCase )
       model.eval()
       quant_trainer.enable_calibration(__lowerCAmelCase )

       logger.info("""***** Running calibration *****""" )
       logger.info(F"  Num examples = {self.calib_num}" )
       logger.info(F"  Batch size = {calib_dataloader.batch_size}" )

       for step, inputs in enumerate(__lowerCAmelCase ):
             # Prediction step
             UpperCamelCase_				:   int					    =							self.prediction_step(__lowerCAmelCase			,     __lowerCAmelCase			,     prediction_loss_only=__lowerCAmelCase )
             if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
                   break

       quant_trainer.finish_calibration(__lowerCAmelCase			,     self.quant_trainer_args )
       UpperCamelCase_				:   List[Any]					    =							model
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase = "eval" ):
       UpperCamelCase_				:   List[str]					    =							self.eval_dataset if eval_dataset is None else eval_dataset
       UpperCamelCase_				:   Tuple					    =							self.get_eval_dataloader(__lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       UpperCamelCase_				:   Tuple					    =							self.compute_metrics
       UpperCamelCase_				:   str					    =							None
       UpperCamelCase_				:   int					    =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
             UpperCamelCase_				:   int					    =							eval_loop(
                 __lowerCAmelCase			,     description="""Evaluation"""			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=__lowerCAmelCase			,     )
       finally:
             UpperCamelCase_				:   Optional[Any]					    =							compute_metrics

       if self.post_process_function is not None and self.compute_metrics is not None:
             UpperCamelCase_				:   Optional[Any]					    =							self.post_process_function(__lowerCAmelCase			,     __lowerCAmelCase			,     output.predictions )
             UpperCamelCase_				:   Tuple					    =							self.compute_metrics(__lowerCAmelCase )

             # Prefix all keys with metric_key_prefix + '_'
             for key in list(metrics.keys() ):
                   if not key.startswith(F"{metric_key_prefix}_" ):
                         UpperCamelCase_				:   Union[str, Any]					    =							metrics.pop(__lowerCAmelCase )

             self.log(__lowerCAmelCase )
       else:
             UpperCamelCase_				:   Optional[int]					    =							{}

       if self.args.tpu_metrics_debug or self.args.debug:
             # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
             xm.master_print(met.metrics_report() )

       UpperCamelCase_				:   int					    =							self.callback_handler.on_evaluate(self.args			,     self.state			,     self.control			,     __lowerCAmelCase )
       return metrics
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase=None			,     __lowerCAmelCase = "test" ):
       UpperCamelCase_				:   int					    =							self.get_test_dataloader(__lowerCAmelCase )

       # Temporarily disable metric computation, we will do it in the loop here.
       UpperCamelCase_				:   Dict					    =							self.compute_metrics
       UpperCamelCase_				:   Optional[int]					    =							None
       UpperCamelCase_				:   Optional[Any]					    =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
             UpperCamelCase_				:   Dict					    =							eval_loop(
                 __lowerCAmelCase			,     description="""Prediction"""			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=__lowerCAmelCase			,     )
       finally:
             UpperCamelCase_				:   Optional[int]					    =							compute_metrics

       if self.post_process_function is None or self.compute_metrics is None:
             return output

       UpperCamelCase_				:   List[Any]					    =							self.post_process_function(__lowerCAmelCase			,     __lowerCAmelCase			,     output.predictions			,     """predict""" )
       UpperCamelCase_				:   Optional[int]					    =							self.compute_metrics(__lowerCAmelCase )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys() ):
             if not key.startswith(F"{metric_key_prefix}_" ):
                   UpperCamelCase_				:   Dict					    =							metrics.pop(__lowerCAmelCase )

       return PredictionOutput(predictions=predictions.predictions			,     label_ids=predictions.label_ids			,     metrics=__lowerCAmelCase )





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase="./" ):
       UpperCamelCase_				:   Any					    =							self.eval_dataset
       UpperCamelCase_				:   Union[str, Any]					    =							self.get_eval_dataloader(__lowerCAmelCase )

       UpperCamelCase_				:   Optional[int]					    =							next(iter(__lowerCAmelCase ) )

       # saving device - to make it consistent
       UpperCamelCase_				:   List[str]					    =							torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )

       # convert to tuple
       UpperCamelCase_				:   Dict					    =							tuple(v.to(__lowerCAmelCase ) for k, v in batch.items() )

       logger.info("""Converting model to be onnx compatible""" )
       from pytorch_quantization.nn import TensorQuantizer

       UpperCamelCase_				:   Optional[int]					    =							True

       UpperCamelCase_				:   Tuple					    =							self.model.to(__lowerCAmelCase )

       model.eval()
       model.float()

       UpperCamelCase_				:   Optional[int]					    =							model.module if hasattr(__lowerCAmelCase			,     """module""" ) else model
       quant_trainer.configure_model(__lowerCAmelCase			,     self.quant_trainer_args )

       UpperCamelCase_				:   List[str]					    =							os.path.join(__lowerCAmelCase			,     """model.onnx""" )
       logger.info(F"exporting model to {output_model_file}" )

       UpperCamelCase_				:   Optional[Any]					    =							{0: """batch_size""", 1: """seq_len"""}

       torch.onnx.export(
           __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     export_params=__lowerCAmelCase			,     opset_version=13			,     do_constant_folding=__lowerCAmelCase			,     input_names=["""input_ids""", """attention_mask""", """token_type_ids"""]			,     output_names=["""output_start_logits""", """output_end_logits"""]			,     dynamic_axes={
               """input_ids""": axes,
               """attention_mask""": axes,
               """token_type_ids""": axes,
               """output_start_logits""": axes,
               """output_end_logits""": axes,
           }			,     verbose=__lowerCAmelCase			,     )
       logger.info("""onnx export finished""" )














'''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()
UpperCamelCase							=logging.get_logger(__name__)

UpperCamelCase							={
    "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",
}
UpperCamelCase							=[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def 							snake_case					(     a_							: str    ,   a_							: Any    ,   a_							: Dict    ,   a_							: Optional[int]    ,   a_							: Optional[int]	)       ->				int:





      """simple docstring"""

      for attribute in key.split("""."""	):
            UpperCamelCase_				:   Union[str, Any]					    =							getattr(a_    ,   a_	)

      if weight_type is not None:
            UpperCamelCase_				:   Dict					    =							getattr(a_    ,   a_	).shape
      else:
            UpperCamelCase_				:   int					    =							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_				:   str					    =							value
      elif weight_type == "weight_g":
            UpperCamelCase_				:   str					    =							value
      elif weight_type == "weight_v":
            UpperCamelCase_				:   Optional[int]					    =							value
      elif weight_type == "bias":
            UpperCamelCase_				:   Any					    =							value
      else:
            UpperCamelCase_				:   Dict					    =							value

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



def 							snake_case					(     a_							: Optional[int]    ,   a_							: int	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   int					    =							[]
      UpperCamelCase_				:   Dict					    =							fairseq_model.state_dict()

      UpperCamelCase_				:   Union[str, Any]					    =							hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      UpperCamelCase_				:   Optional[int]					    =							None

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

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

      return proj_weight



def 							snake_case					(     a_							: Any    ,   a_							: Any    ,   a_							: Union[str, Any]    ,   a_							: Dict    ,   a_							: Tuple	)       ->				List[str]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							full_name.split("""conv_layers."""	)[-1]
      UpperCamelCase_				:   Optional[Any]					    =							name.split("""."""	)
      UpperCamelCase_				:   List[Any]					    =							int(items[0]	)
      UpperCamelCase_				:   Optional[Any]					    =							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_				:   List[Any]					    =							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_				:   Dict					    =							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_				:   List[str]					    =							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_				:   str					    =							value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."	)
      else:
            unused_weights.append(a_	)



def 							snake_case					(     a_							: str	)       ->				Union[str, Any]:





      """simple docstring"""

      UpperCamelCase_    ,			UpperCamelCase_				:   List[str]					    =							emb.weight.shape
      UpperCamelCase_				:   str					    =							nn.Linear(a_    ,   a_    ,   bias=a_	)
      UpperCamelCase_				:   Optional[int]					    =							emb.weight.data
      return lin_layer



def 							snake_case					(     a_							: Any	)       ->				Tuple:





      """simple docstring"""

      with open(a_    ,   """r"""    ,   encoding="""utf-8"""	) as f:
            UpperCamelCase_				:   Optional[int]					    =							f.readlines()
            UpperCamelCase_				:   Union[str, Any]					    =							[line.split(""" """	)[0] for line in lines]

      UpperCamelCase_				:   List[Any]					    =							len(a_	)

      UpperCamelCase_				:   Union[str, Any]					    =							{
          """<s>""": 0,
          """<pad>""": 1,
          """</s>""": 2,
          """<unk>""": 3,
      }

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



@torch.no_grad()
def 							snake_case					(     a_							: List[Any]    ,   a_							: str    ,   a_							: int    ,   a_							: int    ,   a_							: Optional[int]    ,   a_							: Any    ,   a_							: Tuple    ,   )       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							WavaVecaConfig.from_pretrained(a_	)
      UpperCamelCase_				:   Any					    =							SpeechaTextaConfig.from_pretrained(
          a_    ,   vocab_size=a_    ,   decoder_layers=a_    ,   do_stable_layer_norm=a_	)

      UpperCamelCase_				:   Optional[Any]					    =							WavaVecaFeatureExtractor(
          feature_size=1    ,   sampling_rate=16_000    ,   padding_value=0    ,   do_normalize=a_    ,   return_attention_mask=a_    ,   )

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

      # set weights for wav2vec2 encoder
      UpperCamelCase_				:   Tuple					    =							WavaVecaModel(a_	)
      UpperCamelCase_				:   Any					    =							recursively_load_weights_wavaveca(model.encoder    ,   a_	)

      UpperCamelCase_				:   Any					    =							SpeechaTextaForCausalLM(a_	)
      UpperCamelCase_    ,			UpperCamelCase_				:   Optional[Any]					    =							hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()    ,   strict=a_	)

      # set output linear layer
      unexpected_keys.remove("""embed_out"""	)
      UpperCamelCase_				:   Tuple					    =							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_				:   Optional[Any]					    =							SpeechEncoderDecoderModel(encoder=a_    ,   decoder=a_	)
      UpperCamelCase_				:   Dict					    =							False

      # add projection layer
      UpperCamelCase_				:   Any					    =							nn.Parameter(projection_layer.weight	)
      UpperCamelCase_				:   Optional[Any]					    =							nn.Parameter(projection_layer.bias	)

      UpperCamelCase_				:   Dict					    =							create_vocab_dict(a_	)

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

      UpperCamelCase_				:   Optional[int]					    =							SpeechaTextaTokenizer(os.path.join(a_    ,   """vocab.json"""	)	)
      tokenizer.save_pretrained(a_	)

      UpperCamelCase_				:   Optional[int]					    =							hf_wavavec.config.to_dict()
      UpperCamelCase_				:   Union[str, Any]					    =							tokenizer.pad_token_id
      UpperCamelCase_				:   List[Any]					    =							tokenizer.bos_token_id
      UpperCamelCase_				:   str					    =							tokenizer.eos_token_id
      UpperCamelCase_				:   Dict					    =							"""speech_to_text_2"""
      UpperCamelCase_				:   Optional[Any]					    =							"""wav2vec2"""

      UpperCamelCase_				:   Dict					    =							SpeechEncoderDecoderConfig.from_dict(a_	)

      hf_wavavec.save_pretrained(a_	)
      feature_extractor.save_pretrained(a_	)


if __name__ == "__main__":
      UpperCamelCase							=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_0224, type=int, help="Vocab size of decoder")
      parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

      UpperCamelCase							=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,
      )






