




"""simple docstring"""





import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCamelCase      =		getLogger(__name__)


lowerCamelCase      =		'cuda' if torch.cuda.is_available() else 'cpu'
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__ = 8			,    lowerCAmelCase__ = DEFAULT_DEVICE			,    lowerCAmelCase__=False			,    lowerCAmelCase__="summarization"			,    lowerCAmelCase__=None			,    **lowerCAmelCase__			,    ):
      UpperCAmelCase_    				=							Path(_SCREAMING_SNAKE_CASE   ).open("w"			,    encoding="utf-8"   )
      UpperCAmelCase_    				=							str(_SCREAMING_SNAKE_CASE   )
      UpperCAmelCase_    				=							AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE   ).to(_SCREAMING_SNAKE_CASE   )
      if fpaa:
            UpperCAmelCase_    				=							model.half()

      UpperCAmelCase_    				=							AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
      logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""   )  # if this is wrong, check config.model_type.

      UpperCAmelCase_    				=							time.time()
      # update config with task specific params
      use_task_specific_params(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE   )
      if prefix is None:
            UpperCAmelCase_    				=							prefix or getattr(model.config			,    "prefix"			,    ""   ) or ""
      for examples_chunk in tqdm(list(chunks(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE   )   )   ):
            UpperCAmelCase_    				=							[prefix + text for text in examples_chunk]
            UpperCAmelCase_    				=							tokenizer(_SCREAMING_SNAKE_CASE			,    return_tensors="pt"			,    truncation=_SCREAMING_SNAKE_CASE			,    padding="longest"   ).to(_SCREAMING_SNAKE_CASE   )
            UpperCAmelCase_    				=							model.generate(
                input_ids=batch.input_ids			,    attention_mask=batch.attention_mask			,    **_SCREAMING_SNAKE_CASE			,    )
            UpperCAmelCase_    				=							tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			,    skip_special_tokens=_SCREAMING_SNAKE_CASE			,    clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE   )
            for hypothesis in dec:
                  fout.write(hypothesis + "\n"   )
                  fout.flush()
      fout.close()
      UpperCAmelCase_    				=							int(time.time() - start_time   )  # seconds
      UpperCAmelCase_    				=							len(_SCREAMING_SNAKE_CASE   )
      return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			,    4   )}
def      a__			(   ):
      return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"   )
def      a__			(   lowerCAmelCase__=True   ):
      UpperCAmelCase_    				=							argparse.ArgumentParser()
      parser.add_argument("model_name"			,    type=_SCREAMING_SNAKE_CASE			,    help="like facebook/bart-large-cnn,t5-base, etc."   )
      parser.add_argument("input_path"			,    type=_SCREAMING_SNAKE_CASE			,    help="like cnn_dm/test.source"   )
      parser.add_argument("save_path"			,    type=_SCREAMING_SNAKE_CASE			,    help="where to save summaries"   )
      parser.add_argument("--reference_path"			,    type=_SCREAMING_SNAKE_CASE			,    required=_SCREAMING_SNAKE_CASE			,    help="like cnn_dm/test.target"   )
      parser.add_argument("--score_path"			,    type=_SCREAMING_SNAKE_CASE			,    required=_SCREAMING_SNAKE_CASE			,    default="metrics.json"			,    help="where to save metrics"   )
      parser.add_argument("--device"			,    type=_SCREAMING_SNAKE_CASE			,    required=_SCREAMING_SNAKE_CASE			,    default=_SCREAMING_SNAKE_CASE			,    help="cuda, cuda:1, cpu etc."   )
      parser.add_argument(
          "--prefix"			,    type=_SCREAMING_SNAKE_CASE			,    required=_SCREAMING_SNAKE_CASE			,    default=_SCREAMING_SNAKE_CASE			,    help="will be added to the begininng of src examples"   )
      parser.add_argument("--task"			,    type=_SCREAMING_SNAKE_CASE			,    default="summarization"			,    help="used for task_specific_params + metrics"   )
      parser.add_argument("--bs"			,    type=_SCREAMING_SNAKE_CASE			,    default=8			,    required=_SCREAMING_SNAKE_CASE			,    help="batch size"   )
      parser.add_argument(
          "--n_obs"			,    type=_SCREAMING_SNAKE_CASE			,    default=-1			,    required=_SCREAMING_SNAKE_CASE			,    help="How many observations. Defaults to all."   )
      parser.add_argument("--fp16"			,    action="store_true"   )
      parser.add_argument("--dump-args"			,    action="store_true"			,    help="print the custom hparams with the results"   )
      parser.add_argument(
          "--info"			,    nargs="?"			,    type=_SCREAMING_SNAKE_CASE			,    const=datetime_now()			,    help=(
              "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
              " lang=en-ru. If no value is passed, the current datetime string will be used."
          )			,    )
      # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
      UpperCAmelCase_				,					UpperCAmelCase_    				=							parser.parse_known_args()
      UpperCAmelCase_    				=							parse_numeric_n_bool_cl_kwargs(_SCREAMING_SNAKE_CASE   )
      if parsed_args and verbose:
            print(f"""parsed the following generate kwargs: {parsed_args}"""   )
      UpperCAmelCase_    				=							[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path   ).readlines()]
      if args.n_obs > 0:
            UpperCAmelCase_    				=							examples[: args.n_obs]
      Path(args.save_path   ).parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE   )

      if args.reference_path is None and Path(args.score_path   ).exists():
            warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""   )

      if args.device == "cpu" and args.fpaa:
            # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
            raise ValueError("Can't mix --fp16 and --device cpu"   )

      UpperCAmelCase_    				=							generate_summaries_or_translations(
          _SCREAMING_SNAKE_CASE			,    args.save_path			,    args.model_name			,    batch_size=args.bs			,    device=args.device			,    fpaa=args.fpaa			,    task=args.task			,    prefix=args.prefix			,    **_SCREAMING_SNAKE_CASE			,    )

      if args.reference_path is None:
            return {}

      # Compute scores
      UpperCAmelCase_    				=							calculate_bleu if "translation" in args.task else calculate_rouge
      UpperCAmelCase_    				=							[x.rstrip() for x in open(args.save_path   ).readlines()]
      UpperCAmelCase_    				=							[x.rstrip() for x in open(args.reference_path   ).readlines()][: len(_SCREAMING_SNAKE_CASE   )]
      UpperCAmelCase_    				=							score_fn(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE   )
      scores.update(_SCREAMING_SNAKE_CASE   )

      if args.dump_args:
            scores.update(_SCREAMING_SNAKE_CASE   )
      if args.info:
            UpperCAmelCase_    				=							args.info

      if verbose:
            print(_SCREAMING_SNAKE_CASE   )

      if args.score_path is not None:
            json.dump(_SCREAMING_SNAKE_CASE			,    open(args.score_path			,    "w"   )   )

      return scores


if __name__ == "__main__":
  # Usage for MT:
  # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
  run_generate(verbose=True)









"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """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""",
}
lowerCamelCase      =		[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      for attribute in key.split("."   ):
            UpperCAmelCase_    				=							getattr(lowerCAmelCase__			,    lowerCAmelCase__   )

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

      if hf_shape != value.shape:
            raise ValueError(
                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
      elif weight_type == "running_mean":
            UpperCAmelCase_    				=							value
      elif weight_type == "running_var":
            UpperCAmelCase_    				=							value
      elif weight_type == "num_batches_tracked":
            UpperCAmelCase_    				=							value
      elif weight_type == "inv_freq":
            UpperCAmelCase_    				=							value
      else:
            UpperCAmelCase_    				=							value

      logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_    				=							fairseq_model.state_dict()

      UpperCAmelCase_    				=							hf_model.wavaveca_conformer.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCAmelCase_    				=							False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    hf_model.config.feat_extract_norm == "group"			,    )
                  UpperCAmelCase_    				=							True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCAmelCase_    				=							"wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                        if key in name or key.split("w2v_model."   )[-1] == name.split("."   )[0]:
                              UpperCAmelCase_    				=							True
                              if "*" in mapped_key:
                                    UpperCAmelCase_    				=							name.split(lowerCAmelCase__   )[0].split("."   )[-2]
                                    UpperCAmelCase_    				=							mapped_key.replace("*"			,    lowerCAmelCase__   )
                              if "pos_bias_u" in name:
                                    UpperCAmelCase_    				=							None
                              elif "pos_bias_v" in name:
                                    UpperCAmelCase_    				=							None
                              elif "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:
                                    # TODO: don't match quantizer.weight_proj
                                    UpperCAmelCase_    				=							"weight"
                              elif "running_mean" in name:
                                    UpperCAmelCase_    				=							"running_mean"
                              elif "inv_freq" in name:
                                    UpperCAmelCase_    				=							"inv_freq"
                              elif "running_var" in name:
                                    UpperCAmelCase_    				=							"running_var"
                              elif "num_batches_tracked" in name:
                                    UpperCAmelCase_    				=							"num_batches_tracked"
                              else:
                                    UpperCAmelCase_    				=							None
                              set_recursively(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
                        continue
            if not is_used:
                  unused_weights.append(lowerCAmelCase__   )

      logger.warning(f"""Unused weights: {unused_weights}"""   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      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:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            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:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            f"""{full_name} has size {value.shape}, but"""
                            f""" {feature_extractor.conv_layers[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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            f"""{full_name} has size {value.shape}, but"""
                            f""" {feature_extractor.conv_layers[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(lowerCAmelCase__   )
@torch.no_grad()
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=None			,    lowerCAmelCase__=None			,    lowerCAmelCase__=True   ):
      if config_path is not None:
            UpperCAmelCase_    				=							WavaVecaConformerConfig.from_pretrained(lowerCAmelCase__			,    hidden_act="swish"   )
      else:
            UpperCAmelCase_    				=							WavaVecaConformerConfig()

      if "rope" in checkpoint_path:
            UpperCAmelCase_    				=							"rotary"

      if is_finetuned:
            if dict_path:
                  UpperCAmelCase_    				=							Dictionary.load(lowerCAmelCase__   )

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  UpperCAmelCase_    				=							target_dict.pad_index
                  UpperCAmelCase_    				=							target_dict.bos_index
                  UpperCAmelCase_    				=							target_dict.eos_index
                  UpperCAmelCase_    				=							len(target_dict.symbols   )
                  UpperCAmelCase_    				=							os.path.join(lowerCAmelCase__			,    "vocab.json"   )
                  if not os.path.isdir(lowerCAmelCase__   ):
                        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowerCAmelCase__   )   )
                        return
                  os.makedirs(lowerCAmelCase__			,    exist_ok=lowerCAmelCase__   )
                  UpperCAmelCase_    				=							target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  UpperCAmelCase_    				=							0
                  UpperCAmelCase_    				=							1
                  with open(lowerCAmelCase__			,    "w"			,    encoding="utf-8"   ) as vocab_handle:
                        json.dump(lowerCAmelCase__			,    lowerCAmelCase__   )
                  UpperCAmelCase_    				=							WavaVecaCTCTokenizer(
                      lowerCAmelCase__			,    unk_token=target_dict.unk_word			,    pad_token=target_dict.pad_word			,    bos_token=target_dict.bos_word			,    eos_token=target_dict.eos_word			,    word_delimiter_token="|"			,    do_lower_case=lowerCAmelCase__			,    )
                  UpperCAmelCase_    				=							True if config.feat_extract_norm == "layer" else False
                  UpperCAmelCase_    				=							WavaVecaFeatureExtractor(
                      feature_size=1			,    sampling_rate=16000			,    padding_value=0			,    do_normalize=lowerCAmelCase__			,    return_attention_mask=lowerCAmelCase__			,    )
                  UpperCAmelCase_    				=							WavaVecaProcessor(feature_extractor=lowerCAmelCase__			,    tokenizer=lowerCAmelCase__   )
                  processor.save_pretrained(lowerCAmelCase__   )

            UpperCAmelCase_    				=							WavaVecaConformerForCTC(lowerCAmelCase__   )
      else:
            UpperCAmelCase_    				=							WavaVecaConformerForPreTraining(lowerCAmelCase__   )

      if is_finetuned:
            UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]			,    arg_overrides={"data": "/".join(dict_path.split("/"   )[:-1]   )}   )
      else:
            UpperCAmelCase_    				=							argparse.Namespace(task="audio_pretraining"   )
            UpperCAmelCase_    				=							fairseq.tasks.setup_task(lowerCAmelCase__   )

            UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,    task=lowerCAmelCase__   )

      UpperCAmelCase_    				=							model[0].eval()

      recursively_load_weights(lowerCAmelCase__			,    lowerCAmelCase__			,    not is_finetuned   )

      hf_wavavec.save_pretrained(lowerCAmelCase__   )


if __name__ == "__main__":
  lowerCamelCase      =		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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
  parser.add_argument(
      """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
  )
  lowerCamelCase      =		parser.parse_args()
  convert_wavaveca_conformer_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )




