"""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()
UpperCAmelCase__			=    logging.get_logger(__name__)

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






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  for attribute in key.split("""."""		):
    _UpperCAmelCase    			=   getattr(lowercase ,lowercase		)

  if weight_type is not None:
    _UpperCAmelCase    			=   getattr(lowercase ,lowercase		).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      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _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(
          lowercase ,lowercase ,lowercase ,lowercase ,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(lowercase		)[0].split("""."""		)[-2]
            _UpperCAmelCase    			=   mapped_key.replace("""*""" ,lowercase		)
          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(lowercase ,lowercase ,lowercase ,lowercase ,lowercase		)
        continue
    if not is_used:
      unused_weights.append(lowercase		)

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






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _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(lowercase		)






@torch.no_grad()
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase=None ,lowercase=None ,lowercase=True		):







  """simple docstring"""
  if config_path is not None:
    _UpperCAmelCase    			=   WavaVecaConformerConfig.from_pretrained(lowercase ,hidden_act="""swish"""		)
  else:
    _UpperCAmelCase    			=   WavaVecaConformerConfig()

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

  if is_finetuned:
    if dict_path:
      _UpperCAmelCase    			=   Dictionary.load(lowercase		)

      # 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(lowercase ,"""vocab.json"""		)
      if not os.path.isdir(lowercase		):
        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowercase		)		)
        return
      os.makedirs(lowercase ,exist_ok=lowercase		)
      _UpperCAmelCase    			=   target_dict.indices

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

    _UpperCAmelCase    			=   WavaVecaConformerForCTC(lowercase		)
  else:
    _UpperCAmelCase    			=   WavaVecaConformerForPreTraining(lowercase		)

  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(lowercase		)

    _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,task=lowercase		)

  _UpperCAmelCase    			=   model[0].eval()

  recursively_load_weights(lowercase ,lowercase ,not is_finetuned		)

  hf_wavavec.save_pretrained(lowercase		)


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("""--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"""
    )
    UpperCAmelCase__			=    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
    )

"""simple docstring"""


import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs






@pytest.mark.parametrize(
    """kwargs, expected""" ,[
        ({"""num_shards""": 0, """max_num_jobs""": 1}, []),
        ({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10		)]),
        ({"""num_shards""": 10, """max_num_jobs""": 10}, [range(lowercase ,i + 1		) for i in range(10		)]),
        ({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1		)]),
        ({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0 ,4		), range(4 ,7		), range(7 ,10		)]),
        ({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0 ,1		), range(1 ,2		), range(2 ,3		)]),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   _distribute_shards(**lowercase		)
  assert out == expected






@pytest.mark.parametrize(
    """gen_kwargs, max_num_jobs, expected""" ,[
        ({"""foo""": 0}, 10, [{"""foo""": 0}]),
        ({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
        ({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
        ({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
        ({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   _split_gen_kwargs(lowercase ,lowercase		)
  assert out == expected






@pytest.mark.parametrize(
    """gen_kwargs, expected""" ,[
        ({"""foo""": 0}, 1),
        ({"""shards""": [0]}, 1),
        ({"""shards""": [0, 1, 2, 3]}, 4),
        ({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
        ({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
        ({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  if expected is RuntimeError:
    with pytest.raises(lowercase		):
      _number_of_shards_in_gen_kwargs(lowercase		)
  else:
    _UpperCAmelCase    			=   _number_of_shards_in_gen_kwargs(lowercase		)
    assert out == expected
