





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
snake_case__					:							Dict													=     logging.get_logger(__name__)

snake_case__					:							List[str]													=     {
    '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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
snake_case__					:							Optional[Any]													=     [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]



def _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   List[str]					,				lowerCamelCase:   str					,				lowerCamelCase:   List[str]					,				lowerCamelCase:   str					,				lowerCamelCase:   Tuple )	->						Dict:







      '''simple docstring'''






      for attribute in key.split('''.''' ):
            if is_finetuned:
                  if attribute in ["quantizer", "project_q", "project_hid"]:
                        # those layers are only relevant for pretraining and should be dropped
                        return

                  if attribute == "ctc_proj":
                        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                        __A          =				'''lm_head'''

            __A          =				getattr(lowerCamelCase					,				lowerCamelCase )

      if weight_type is not None:
            __A          =				getattr(lowerCamelCase					,				lowerCamelCase ).shape
      else:
            __A          =				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":
            __A          =				value
      elif weight_type == "weight_g":
            __A          =				value
      elif weight_type == "weight_v":
            __A          =				value
      elif weight_type == "bias":
            __A          =				value
      else:
            __A          =				value

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



def _a				(	lowerCamelCase:   Dict					,				lowerCamelCase:   int					,				lowerCamelCase:   Dict )	->						Optional[Any]:







      '''simple docstring'''






      __A          =				[]
      __A          =				fairseq_model.state_dict()

      __A          =				hf_model.unispeech.feature_extractor

      for name, value in fairseq_dict.items():
            __A          =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				hf_model.config.feat_extract_norm == '''group'''					,				)
                  __A          =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        __A          =				'''unispeech.''' + 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]:
                              __A          =				True
                              if "*" in mapped_key:
                                    __A          =				name.split(lowerCamelCase )[0].split('''.''' )[-2]
                                    __A          =				mapped_key.replace('''*'''					,				lowerCamelCase )
                              if "weight_g" in name:
                                    __A          =				'''weight_g'''
                              elif "weight_v" in name:
                                    __A          =				'''weight_v'''
                              elif "bias" in name:
                                    __A          =				'''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    __A          =				'''weight'''
                              else:
                                    __A          =				None
                              set_recursively(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
                        continue
            if not is_used:
                  unused_weights.append(lowerCamelCase )

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



def _a				(	lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   Tuple					,				lowerCamelCase:   List[Any]					,				lowerCamelCase:   Any					,				lowerCamelCase:   int )	->						int:







      '''simple docstring'''






      __A          =				full_name.split('''conv_layers.''' )[-1]
      __A          =				name.split('''.''' )
      __A          =				int(items[0] )
      __A          =				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."""
                  )
                  __A          =				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."""
                  )
                  __A          =				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."
                  )
                  __A          =				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."""
                  )
                  __A          =				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:   Dict					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   str=None					,				lowerCamelCase:   Tuple=None					,				lowerCamelCase:   str=True )	->						Any:







      '''simple docstring'''






      if config_path is not None:
            __A          =				UniSpeechConfig.from_pretrained(lowerCamelCase )
      else:
            __A          =				UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  __A          =				Dictionary.load_from_json(lowerCamelCase )

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  __A          =				target_dict.pad_index
                  __A          =				target_dict.bos_index
                  __A          =				target_dict.eos_index
                  __A          =				len(target_dict.symbols )
                  __A          =				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 )
                  __A          =				target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  __A          =				42
                  __A          =				43
                  with open(lowerCamelCase					,				'''w'''					,				encoding='''utf-8''' ) as vocab_handle:
                        json.dump(lowerCamelCase					,				lowerCamelCase )
                  __A          =				WavaVecaPhonemeCTCTokenizer(
                      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					,				)
                  __A          =				True if config.feat_extract_norm == '''layer''' else False
                  __A          =				WavaVecaFeatureExtractor(
                      feature_size=1					,				sampling_rate=1_60_00					,				padding_value=0					,				do_normalize=lowerCamelCase					,				return_attention_mask=lowerCamelCase					,				)
                  __A          =				WavaVecaProcessor(feature_extractor=lowerCamelCase					,				tokenizer=lowerCamelCase )
                  processor.save_pretrained(lowerCamelCase )

            __A          =				UniSpeechForCTC(lowerCamelCase )
      else:
            __A          =				UniSpeechForPreTraining(lowerCamelCase )

      if is_finetuned:
            __A   ,						__A   ,						__A          =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]					,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ), '''w2v_path''': checkpoint_path} )
      else:
            __A   ,						__A   ,						__A          =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )

      __A          =				model[0].eval()

      recursively_load_weights(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )

      hf_unispeech.save_pretrained(lowerCamelCase )


if __name__ == "__main__":
       snake_case__					:							Dict													=     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'
       )
       snake_case__					:							List[Any]													=     parser.parse_args()
       convert_unispeech_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )












import os
from datetime import datetime as dt

from github import Github


snake_case__					:							Union[str, Any]													=     [
    'good first issue',
    'feature request',
    'wip',
]



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				Github(os.environ['''GITHUB_TOKEN'''] )
      __A          =				g.get_repo('''huggingface/accelerate''' )
      __A          =				repo.get_issues(state='''open''' )

      for issue in open_issues:
            __A          =				sorted([comment for comment in issue.get_comments()]					,				key=lambda lowerCamelCase : i.created_at					,				reverse=lowerCamelCase )
            __A          =				comments[0] if len(lowerCamelCase ) > 0 else None
            __A          =				dt.utcnow()
            __A          =				(current_time - issue.updated_at).days
            __A          =				(current_time - issue.created_at).days
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and days_since_updated > 7
                and days_since_creation >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
            ):
                  # Close issue since it has been 7 days of inactivity since bot mention.
                  issue.edit(state='''closed''' )
            elif (
                days_since_updated > 23
                and days_since_creation >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
            ):
                  # Add stale comment
                  issue.create_comment(
                      '''This issue has been automatically marked as stale because it has not had '''
                      '''recent activity. If you think this still needs to be addressed '''
                      '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
                      '''[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '''
                      '''are likely to be ignored.''' )


if __name__ == "__main__":
       main()





