


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()
UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """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""",
}
UpperCamelCase								=	[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  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_      :				Union[str, Any]		     =					'''lm_head'''

    A_      :				Union[str, Any]		     =					getattr(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

  if weight_type is not None:
    A_      :				Optional[Any]		     =					getattr(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ).shape
  else:
    A_      :				Any		     =					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_      :				List[str]		     =					value
  elif weight_type == "weight_g":
    A_      :				int		     =					value
  elif weight_type == "weight_v":
    A_      :				int		     =					value
  elif weight_type == "bias":
    A_      :				Any		     =					value
  else:
    A_      :				Any		     =					value

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


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[str]		     =					[]
  A_      :				Dict		     =					fairseq_model.state_dict()

  A_      :				Union[str, Any]		     =					hf_model.unispeech.feature_extractor

  for name, value in fairseq_dict.items():
    A_      :				Dict		     =					False
    if "conv_layers" in name:
      load_conv_layer(
          SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			hf_model.config.feat_extract_norm == '''group''' ,			)
      A_      :				Optional[int]		     =					True
    else:
      for key, mapped_key in MAPPING.items():
        A_      :				Tuple		     =					'''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_      :				Optional[int]		     =					True
          if "*" in mapped_key:
            A_      :				Union[str, Any]		     =					name.split(SCREAMING_SNAKE_CASE       )[0].split('''.'''       )[-2]
            A_      :				Any		     =					mapped_key.replace('''*''' ,			SCREAMING_SNAKE_CASE       )
          if "weight_g" in name:
            A_      :				str		     =					'''weight_g'''
          elif "weight_v" in name:
            A_      :				Any		     =					'''weight_v'''
          elif "bias" in name:
            A_      :				List[str]		     =					'''bias'''
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            A_      :				Tuple		     =					'''weight'''
          else:
            A_      :				Dict		     =					None
          set_recursively(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
        continue
    if not is_used:
      unused_weights.append(SCREAMING_SNAKE_CASE       )

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


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				str		     =					full_name.split('''conv_layers.'''       )[-1]
  A_      :				Optional[Any]		     =					name.split('''.'''       )
  A_      :				Optional[int]		     =					int(items[0]       )
  A_      :				Union[str, Any]		     =					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_      :				Dict		     =					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_      :				Tuple		     =					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_      :				List[str]		     =					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_      :				str		     =					value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
  else:
    unused_weights.append(SCREAMING_SNAKE_CASE       )


@torch.no_grad()
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=None ,			SCREAMING_SNAKE_CASE=None ,			SCREAMING_SNAKE_CASE=True       ):
  if config_path is not None:
    A_      :				Any		     =					UniSpeechConfig.from_pretrained(SCREAMING_SNAKE_CASE       )
  else:
    A_      :				Union[str, Any]		     =					UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      A_      :				Tuple		     =					Dictionary.load_from_json(SCREAMING_SNAKE_CASE       )

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

      # fairseq has the <pad> and <s> switched
      A_      :				Optional[int]		     =					42
      A_      :				Optional[Any]		     =					43
      with open(SCREAMING_SNAKE_CASE ,			'''w''' ,			encoding='''utf-8'''       ) as vocab_handle:
        json.dump(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
      A_      :				List[str]		     =					WavaVecaPhonemeCTCTokenizer(
          SCREAMING_SNAKE_CASE ,			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=SCREAMING_SNAKE_CASE ,			)
      A_      :				List[Any]		     =					True if config.feat_extract_norm == '''layer''' else False
      A_      :				List[Any]		     =					WavaVecaFeatureExtractor(
          feature_size=1 ,			sampling_rate=16_000 ,			padding_value=0 ,			do_normalize=SCREAMING_SNAKE_CASE ,			return_attention_mask=SCREAMING_SNAKE_CASE ,			)
      A_      :				Tuple		     =					WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE ,			tokenizer=SCREAMING_SNAKE_CASE       )
      processor.save_pretrained(SCREAMING_SNAKE_CASE       )

    A_      :				Dict		     =					UniSpeechForCTC(SCREAMING_SNAKE_CASE       )
  else:
    A_      :				Union[str, Any]		     =					UniSpeechForPreTraining(SCREAMING_SNAKE_CASE       )

  if is_finetuned:
    A_     ,       A_     ,       A_      :				Union[str, Any]		     =					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_      :				Dict		     =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

  A_      :				List[str]		     =					model[0].eval()

  recursively_load_weights(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

  hf_unispeech.save_pretrained(SCREAMING_SNAKE_CASE       )


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_unispeech_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )








from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "glpn"



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=[2, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[8, 4, 2, 1]				,       _SCREAMING_SNAKE_CASE=[32, 64, 160, 256]				,       _SCREAMING_SNAKE_CASE=[7, 3, 3, 3]				,       _SCREAMING_SNAKE_CASE=[4, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[1, 2, 5, 8]				,       _SCREAMING_SNAKE_CASE=[4, 4, 4, 4]				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=-1				,       **_SCREAMING_SNAKE_CASE				,       )->Any:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					num_channels
         A_      :				Union[str, Any]		     =					num_encoder_blocks
         A_      :				int		     =					depths
         A_      :				Dict		     =					sr_ratios
         A_      :				Any		     =					hidden_sizes
         A_      :				int		     =					patch_sizes
         A_      :				Optional[int]		     =					strides
         A_      :				str		     =					mlp_ratios
         A_      :				List[str]		     =					num_attention_heads
         A_      :				str		     =					hidden_act
         A_      :				int		     =					hidden_dropout_prob
         A_      :				List[Any]		     =					attention_probs_dropout_prob
         A_      :				Optional[Any]		     =					initializer_range
         A_      :				Tuple		     =					drop_path_rate
         A_      :				Optional[int]		     =					layer_norm_eps
         A_      :				List[str]		     =					decoder_hidden_size
         A_      :				List[Any]		     =					max_depth
         A_      :				List[Any]		     =					head_in_index




