




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__SCREAMING_SNAKE_CASE    :   Dict       ={
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}

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


      '''simple docstring'''




      for attribute in key.split('.'						):
            lowerCamelCase__        =				getattr(lowerCamelCase     ,				lowerCamelCase						)

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

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

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[Any]     ,				lowerCamelCase:       Optional[int]     ,				lowerCamelCase:       int						)					->			str:


      '''simple docstring'''




      lowerCamelCase__        =				[]
      lowerCamelCase__        =				fairseq_model.state_dict()

      lowerCamelCase__        =				hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            lowerCamelCase__        =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCamelCase     ,				lowerCamelCase     ,				lowerCamelCase     ,				lowerCamelCase     ,				hf_model.config.feat_extract_norm == 'group'     ,				)
                  lowerCamelCase__        =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        lowerCamelCase__        =				'sew_d.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

                        if key in name or key.split('w2v_model.'						)[-1] == name.split('.'						)[0]:
                              lowerCamelCase__        =				True
                              if "*" in mapped_key:
                                    lowerCamelCase__        =				name.split(lowerCamelCase						)[0].split('.'						)[-2]
                                    if not layer_index.isnumeric():
                                          continue
                                    lowerCamelCase__        =				mapped_key.replace('*'     ,				lowerCamelCase						)
                              if "weight_g" in name:
                                    lowerCamelCase__        =				'weight_g'
                              elif "weight_v" in name:
                                    lowerCamelCase__        =				'weight_v'
                              elif "weight" in name:
                                    lowerCamelCase__        =				'weight'
                              elif "bias" in name:
                                    lowerCamelCase__        =				'bias'
                              else:
                                    lowerCamelCase__        =				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 			lowerCAmelCase_			(  lowerCamelCase:       Optional[Any]     ,				lowerCamelCase:       Dict     ,				lowerCamelCase:       Tuple     ,				lowerCamelCase:       Optional[Any]     ,				lowerCamelCase:       Tuple						)					->			int:


      '''simple docstring'''




      lowerCamelCase__        =				full_name.split('conv_layers.'						)[-1]
      lowerCamelCase__        =				name.split('.'						)
      lowerCamelCase__        =				int(items[0]						)
      lowerCamelCase__        =				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."""
                  )
                  lowerCamelCase__        =				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."""
                  )
                  lowerCamelCase__        =				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."
                  )
                  lowerCamelCase__        =				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."""
                  )
                  lowerCamelCase__        =				value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
      else:
            unused_weights.append(lowerCamelCase						)

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


      '''simple docstring'''




      lowerCamelCase__        =				SEWDConfig()
      if is_finetuned:
            lowerCamelCase__        =				model.wav_encoder.wav_model.cfg
      else:
            lowerCamelCase__        =				model.cfg

      lowerCamelCase__        =				fs_config.conv_bias
      lowerCamelCase__        =				eval(fs_config.conv_feature_layers						)
      lowerCamelCase__        =				[x[0] for x in conv_layers]
      lowerCamelCase__        =				[x[1] for x in conv_layers]
      lowerCamelCase__        =				[x[2] for x in conv_layers]
      lowerCamelCase__        =				'gelu'
      lowerCamelCase__        =				'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
      lowerCamelCase__        =				0.0
      lowerCamelCase__        =				fs_config.activation_fn.name
      lowerCamelCase__        =				fs_config.encoder_embed_dim
      lowerCamelCase__        =				0.02
      lowerCamelCase__        =				fs_config.encoder_ffn_embed_dim
      lowerCamelCase__        =				1e-5
      lowerCamelCase__        =				fs_config.encoder_layerdrop
      lowerCamelCase__        =				fs_config.encoder_attention_heads
      lowerCamelCase__        =				fs_config.conv_pos_groups
      lowerCamelCase__        =				fs_config.conv_pos
      lowerCamelCase__        =				len(lowerCamelCase						)
      lowerCamelCase__        =				fs_config.encoder_layers
      lowerCamelCase__        =				fs_config.squeeze_factor
      # DeBERTa-specific parameters:
      lowerCamelCase__        =				fs_config.max_position_embeddings
      lowerCamelCase__        =				fs_config.position_buckets
      lowerCamelCase__        =				fs_config.share_att_key
      lowerCamelCase__        =				fs_config.relative_attention
      lowerCamelCase__        =				fs_config.position_biased_input
      lowerCamelCase__        =				tuple(fs_config.pos_att_type.split('|'						)						)
      lowerCamelCase__        =				fs_config.norm_rel_ebd

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            lowerCamelCase__        =				model.cfg
            lowerCamelCase__        =				fs_config.final_dropout
            lowerCamelCase__        =				fs_config.layerdrop
      lowerCamelCase__        =				fs_config.activation_dropout
      lowerCamelCase__        =				fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      lowerCamelCase__        =				fs_config.attention_dropout
      lowerCamelCase__        =				fs_config.dropout_input
      lowerCamelCase__        =				fs_config.dropout
      lowerCamelCase__        =				fs_config.mask_channel_length
      lowerCamelCase__        =				fs_config.mask_channel_prob
      lowerCamelCase__        =				fs_config.mask_length
      lowerCamelCase__        =				fs_config.mask_prob

      lowerCamelCase__        =				'Wav2Vec2FeatureExtractor'
      lowerCamelCase__        =				'Wav2Vec2CTCTokenizer'

      return config

@torch.no_grad()
def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[Any]     ,				lowerCamelCase:       Dict     ,				lowerCamelCase:       List[Any]=None     ,				lowerCamelCase:       Any=None     ,				lowerCamelCase:       List[str]=True						)					->			List[str]:


      '''simple docstring'''




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

      if config_path is not None:
            lowerCamelCase__        =				SEWDConfig.from_pretrained(lowerCamelCase						)
      else:
            lowerCamelCase__        =				convert_config(model[0]     ,				lowerCamelCase						)
      lowerCamelCase__        =				model[0].eval()

      lowerCamelCase__        =				True if config.feat_extract_norm == 'layer' else False
      lowerCamelCase__        =				WavaVecaFeatureExtractor(
          feature_size=1     ,				sampling_rate=1_60_00     ,				padding_value=0     ,				do_normalize=lowerCamelCase     ,				return_attention_mask=lowerCamelCase     ,				)

      if is_finetuned:
            if dict_path:
                  lowerCamelCase__        =				Dictionary.load(lowerCamelCase						)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  lowerCamelCase__        =				target_dict.pad_index
                  lowerCamelCase__        =				target_dict.bos_index
                  lowerCamelCase__        =				target_dict.pad_index
                  lowerCamelCase__        =				target_dict.bos_index
                  lowerCamelCase__        =				target_dict.eos_index
                  lowerCamelCase__        =				len(target_dict.symbols						)
                  lowerCamelCase__        =				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						)
                  with open(lowerCamelCase     ,				'w'     ,				encoding='utf-8'						) as vocab_handle:
                        json.dump(target_dict.indices     ,				lowerCamelCase						)
                  lowerCamelCase__        =				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     ,				)
                  lowerCamelCase__        =				WavaVecaProcessor(feature_extractor=lowerCamelCase     ,				tokenizer=lowerCamelCase						)
                  processor.save_pretrained(lowerCamelCase						)

            lowerCamelCase__        =				SEWDForCTC(lowerCamelCase						)
      else:
            lowerCamelCase__        =				SEWDModel(lowerCamelCase						)
            feature_extractor.save_pretrained(lowerCamelCase						)

      recursively_load_weights(lowerCamelCase     ,				lowerCamelCase     ,				lowerCamelCase						)

      hf_model.save_pretrained(lowerCamelCase						)


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE    :   Optional[Any]       =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(
          """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
      )
      __SCREAMING_SNAKE_CASE    :   Optional[int]       =parser.parse_args()
      convert_sew_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
      )





import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


__SCREAMING_SNAKE_CASE    :   int       =importlib.metadata.version("""numpy""")
__SCREAMING_SNAKE_CASE    :   Optional[int]       =""".""".join([str(x) for x in sys.version_info[:3]])






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def  __snake_case   (    self  :   List[Any]			)				->       Optional[int]:
           # lt + different version strings
           require_version_core('numpy<1000.4.5'			)
           require_version_core('numpy<1000.4'			)
           require_version_core('numpy<1000'			)

           # le
           require_version_core('numpy<=1000.4.5'			)
           require_version_core(F"""numpy<={numpy_ver}"""			)

           # eq
           require_version_core(F"""numpy=={numpy_ver}"""			)

           # ne
           require_version_core('numpy!=1000.4.5'			)

           # ge
           require_version_core('numpy>=1.0'			)
           require_version_core('numpy>=1.0.0'			)
           require_version_core(F"""numpy>={numpy_ver}"""			)

           # gt
           require_version_core('numpy>1.0.0'			)

           # mix
           require_version_core('numpy>1.0.0,<1000'			)

           # requirement w/o version
           require_version_core('numpy'			)

           # unmet requirements due to version conflict
           for req in ["numpy==1.0.0", "numpy>=1000.0.0", F"""numpy<{numpy_ver}"""]:
                 try:
                       require_version_core(A__			)
                 except ImportError as e:
                       self.assertIn(F"""{req} is required"""	,  str(A__			)			)
                       self.assertIn('but found'	,  str(A__			)			)

        # unmet requirements due to missing module
           for req in ["numpipypie>1", "numpipypie2"]:
                 try:
                       require_version_core(A__			)
                 except importlib.metadata.PackageNotFoundError as e:
                       self.assertIn(F"""The '{req}' distribution was not found and is required by this application"""	,  str(A__			)			)
                       self.assertIn('Try: pip install transformers -U'	,  str(A__			)			)

        # bogus requirements formats:
        # 1. whole thing
           for req in ["numpy??1.0.0", "numpy1.0.0"]:
                 try:
                       require_version_core(A__			)
                 except ValueError as e:
                       self.assertIn('requirement needs to be in the pip package format'	,  str(A__			)			)
        # 2. only operators
           for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
                 try:
                       require_version_core(A__			)
                 except ValueError as e:
                       self.assertIn('need one of '	,  str(A__			)			)




     def  __snake_case   (    self  :   str			)				->       Optional[int]:
           # matching requirement
           require_version('python>=3.6.0'			)

           # not matching requirements
           for req in ["python>9.9.9", "python<3.0.0"]:
                 try:
                       require_version_core(A__			)
                 except ImportError as e:
                       self.assertIn(F"""{req} is required"""	,  str(A__			)			)
                       self.assertIn(F"""but found python=={python_ver}"""	,  str(A__			)			)