



"""simple docstring"""





import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=7   ):
      UpperCAmelCase_    				=							None
      if token is not None:
            UpperCAmelCase_    				=							{"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}

      # The id of a workflow (not of a workflow run)
      UpperCAmelCase_    				=							"636036"

      UpperCAmelCase_    				=							f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
      # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
      url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

      UpperCAmelCase_    				=							requests.get(lowerCAmelCase__			,    headers=lowerCAmelCase__   ).json()

      return result["workflow_runs"]
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							get_daily_ci_runs(lowerCAmelCase__   )
      UpperCAmelCase_    				=							None
      for workflow_run in workflow_runs:
            if workflow_run["status"] == "completed":
                  UpperCAmelCase_    				=							workflow_run["id"]
                  break

      return workflow_run_id
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							get_last_daily_ci_runs(lowerCAmelCase__   )
      if workflow_run_id is not None:
            UpperCAmelCase_    				=							get_artifacts_links(worflow_run_id=lowerCAmelCase__			,    token=lowerCAmelCase__   )
            for artifact_name in artifact_names:
                  if artifact_name in artifacts_links:
                        UpperCAmelCase_    				=							artifacts_links[artifact_name]
                        download_artifact(
                            artifact_name=lowerCAmelCase__			,    artifact_url=lowerCAmelCase__			,    output_dir=lowerCAmelCase__			,    token=lowerCAmelCase__   )


def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      get_last_daily_ci_artifacts(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )

      UpperCAmelCase_    				=							{}
      for artifact_name in artifact_names:
            UpperCAmelCase_    				=							os.path.join(lowerCAmelCase__			,    f"""{artifact_name}.zip"""   )
            if os.path.isfile(lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							{}
                  with zipfile.ZipFile(lowerCAmelCase__   ) as z:
                        for filename in z.namelist():
                              if not os.path.isdir(lowerCAmelCase__   ):
                                    # read the file
                                    with z.open(lowerCAmelCase__   ) as f:
                                          UpperCAmelCase_    				=							f.read().decode("UTF-8"   )

      return results









"""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
  )




