
'''simple docstring'''

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, 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""",
    """mask_emb""": """masked_spec_embed""",
}


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Tuple					, _lowerCamelCase  :    str					, _lowerCamelCase  :    int					, _lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    int						)      ->  List[str]:
  for attribute in key.split("""."""						):
    _lowerCAmelCase    :					Union[str, Any]							=   getattr(_lowerCamelCase					, _lowerCamelCase						)

  if weight_type is not None:
    _lowerCAmelCase    :					Union[str, Any]							=   getattr(_lowerCamelCase					, _lowerCamelCase						).shape
  else:
    _lowerCAmelCase    :					Union[str, 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":
    _lowerCAmelCase    :					int							=   value
  elif weight_type == "weight_g":
    _lowerCAmelCase    :					Union[str, Any]							=   value
  elif weight_type == "weight_v":
    _lowerCAmelCase    :					int							=   value
  elif weight_type == "bias":
    _lowerCAmelCase    :					int							=   value
  else:
    _lowerCAmelCase    :					Optional[Any]							=   value

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


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    Any						)      ->  Optional[int]:
  _lowerCAmelCase    :					Optional[Any]							=   []
  _lowerCAmelCase    :					Dict							=   fairseq_model.state_dict()

  _lowerCAmelCase    :					List[str]							=   hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    _lowerCAmelCase    :					List[str]							=   False
    if "conv_layers" in name:
      load_conv_layer(
          _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, hf_model.config.feat_extract_norm == """group"""					, )
      _lowerCAmelCase    :					List[str]							=   True
    else:
      for key, mapped_key in MAPPING.items():
        _lowerCAmelCase    :					Optional[Any]							=   mapped_key

        if key in name:
          _lowerCAmelCase    :					Optional[int]							=   True
          if "*" in mapped_key:
            _lowerCAmelCase    :					Optional[Any]							=   name.split(_lowerCamelCase						)[0].split("""."""						)[-2]
            _lowerCAmelCase    :					Tuple							=   mapped_key.replace("""*"""					, _lowerCamelCase						)
          if "weight_g" in name:
            _lowerCAmelCase    :					str							=   """weight_g"""
          elif "weight_v" in name:
            _lowerCAmelCase    :					List[str]							=   """weight_v"""
          elif "weight" in name:
            _lowerCAmelCase    :					str							=   """weight"""
          elif "bias" in name:
            _lowerCAmelCase    :					Any							=   """bias"""
          else:
            _lowerCAmelCase    :					Optional[int]							=   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 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    Optional[Any]					, _lowerCamelCase  :    List[str]					, _lowerCamelCase  :    int					, _lowerCamelCase  :    Union[str, Any]						)      ->  Dict:
  _lowerCAmelCase    :					List[Any]							=   full_name.split("""conv_layers."""						)[-1]
  _lowerCAmelCase    :					List[Any]							=   name.split("""."""						)
  _lowerCAmelCase    :					str							=   int(items[0]						)
  _lowerCAmelCase    :					List[str]							=   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    :					List[str]							=   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    :					Optional[Any]							=   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    :					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.'
      )
      _lowerCAmelCase    :					List[str]							=   value
      logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
  else:
    unused_weights.append(_lowerCamelCase						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  Optional[int]:
  _lowerCAmelCase    :					Dict							=   HubertConfig()
  _lowerCAmelCase    :					Dict							=   model.config

  _lowerCAmelCase    :					str							=   fs_config.activation_dropout
  _lowerCAmelCase    :					List[str]							=   False
  _lowerCAmelCase    :					Union[str, Any]							=   fs_config.attention_dropout
  _lowerCAmelCase    :					List[str]							=   False
  _lowerCAmelCase    :					str							=   eval(fs_config.extractor_conv_feature_layers						)
  _lowerCAmelCase    :					Optional[int]							=   [x[0] for x in conv_layers]
  _lowerCAmelCase    :					Tuple							=   [x[1] for x in conv_layers]
  _lowerCAmelCase    :					Optional[Any]							=   [x[2] for x in conv_layers]
  _lowerCAmelCase    :					Optional[Any]							=   """gelu"""
  _lowerCAmelCase    :					List[str]							=   """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
  _lowerCAmelCase    :					Optional[int]							=   False
  _lowerCAmelCase    :					Any							=   0.0
  _lowerCAmelCase    :					Dict							=   0.0
  _lowerCAmelCase    :					Optional[Any]							=   fs_config.activation_fn
  _lowerCAmelCase    :					Dict							=   fs_config.dropout
  _lowerCAmelCase    :					Union[str, Any]							=   fs_config.encoder_embed_dim
  _lowerCAmelCase    :					str							=   0.02
  _lowerCAmelCase    :					Dict							=   fs_config.encoder_ffn_embed_dim
  _lowerCAmelCase    :					Union[str, Any]							=   1e-5
  _lowerCAmelCase    :					Union[str, Any]							=   0.0
  _lowerCAmelCase    :					Dict							=   fs_config.encoder_attention_heads
  _lowerCAmelCase    :					str							=   fs_config.conv_pos_groups
  _lowerCAmelCase    :					Optional[Any]							=   fs_config.conv_pos
  _lowerCAmelCase    :					Dict							=   len(_lowerCamelCase						)
  _lowerCAmelCase    :					str							=   fs_config.encoder_layers

  return config


@torch.no_grad()
def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    int=None						)      ->  Dict:
  _lowerCAmelCase    :					Any							=   distilhubert().model.model

  if config_path is not None:
    _lowerCAmelCase    :					int							=   HubertConfig.from_pretrained(_lowerCamelCase						)
  else:
    _lowerCAmelCase    :					List[Any]							=   convert_config(_lowerCamelCase						)
  _lowerCAmelCase    :					List[Any]							=   model.eval()

  _lowerCAmelCase    :					Dict							=   WavaVecaFeatureExtractor(
      feature_size=1					, sampling_rate=1_60_00					, padding_value=0					, do_normalize=_lowerCamelCase					, return_attention_mask=_lowerCamelCase					, )
  _lowerCAmelCase    :					int							=   HubertModel(_lowerCamelCase						)

  recursively_load_weights(_lowerCamelCase					, _lowerCamelCase						)

  feature_extractor.save_pretrained(_lowerCamelCase						)
  hf_model.save_pretrained(_lowerCamelCase						)


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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     UpperCamelCase_            =    parser.parse_args()
     convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)




'''simple docstring'''

import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def 		_UpperCAmelCase			(					_lowerCamelCase  :    str					, _lowerCamelCase  :    Dict						)      ->  List[Any]:
  _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase    :					List[Any]							=   pt_attn_layer.in_proj_weight.chunk(3					, dim=0						)
  _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase    :					List[str]							=   pt_attn_layer.in_proj_bias.chunk(3					, dim=0						)

  _lowerCAmelCase    :					Optional[Any]							=   pt_attn_layer.out_proj.weight
  _lowerCAmelCase    :					List[Any]							=   pt_attn_layer.out_proj.bias

  _lowerCAmelCase    :					Union[str, Any]							=   q_proj
  _lowerCAmelCase    :					Optional[Any]							=   q_proj_bias

  _lowerCAmelCase    :					List[Any]							=   k_proj
  _lowerCAmelCase    :					Optional[int]							=   k_proj_bias

  _lowerCAmelCase    :					int							=   v_proj
  _lowerCAmelCase    :					Dict							=   v_proj_bias

  _lowerCAmelCase    :					str							=   out_proj_weights
  _lowerCAmelCase    :					Union[str, Any]							=   out_proj_bias


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    List[Any]						)      ->  Dict:
  copy_linear(hf_mlp.fca					, pt_mlp.c_fc						)
  copy_linear(hf_mlp.fca					, pt_mlp.c_proj						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    List[str]						)      ->  Optional[Any]:
  _lowerCAmelCase    :					List[str]							=   pt_linear.weight
  _lowerCAmelCase    :					Union[str, Any]							=   pt_linear.bias


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[Any]					, _lowerCamelCase  :    int						)      ->  Optional[Any]:
  # copy layer norms
  copy_linear(hf_layer.layer_norma					, pt_layer.ln_a						)
  copy_linear(hf_layer.layer_norma					, pt_layer.ln_a						)

  # copy MLP
  copy_mlp(hf_layer.mlp					, pt_layer.mlp						)

  # copy attn
  copy_attn_layer(hf_layer.self_attn					, pt_layer.attn						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    Union[str, Any]						)      ->  Optional[Any]:
  for hf_layer, pt_layer in zip(_lowerCamelCase					, _lowerCamelCase						):
    copy_layer(_lowerCamelCase					, _lowerCamelCase						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    Tuple						)      ->  Optional[int]:
  # copy  embeds
  _lowerCAmelCase    :					Optional[int]							=   pt_model.token_embedding.weight
  _lowerCAmelCase    :					List[str]							=   pt_model.positional_embedding

  # copy layer norm
  copy_linear(hf_encoder.final_layer_norm					, pt_model.ln_final						)

  # copy hidden layers
  copy_layers(hf_encoder.encoder.layers					, pt_model.transformer.resblocks						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Dict					, _lowerCamelCase  :    Tuple						)      ->  Tuple:
  # copy projection
  _lowerCAmelCase    :					Any							=   pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model					, _lowerCamelCase						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Dict					, _lowerCamelCase  :    Union[str, Any]						)      ->  List[Any]:
  # copy projection
  _lowerCAmelCase    :					List[str]							=   pt_model.visual.proj.data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layrnorm					, pt_model.visual.ln_pre						)
  copy_linear(hf_model.vision_model.post_layernorm					, pt_model.visual.ln_post						)

  # copy embeds
  _lowerCAmelCase    :					List[Any]							=   pt_model.visual.conva.weight.data
  _lowerCAmelCase    :					List[str]							=   pt_model.visual.class_embedding
  _lowerCAmelCase    :					int							=   pt_model.visual.positional_embedding.data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers					, pt_model.visual.transformer.resblocks						)


@torch.no_grad()
def 		_UpperCAmelCase			(					_lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    List[str]					, _lowerCamelCase  :    Any=None						)      ->  int:
  if config_path is not None:
    _lowerCAmelCase    :					Optional[int]							=   CLIPConfig.from_pretrained(_lowerCamelCase						)
  else:
    _lowerCAmelCase    :					int							=   CLIPConfig(projection_dim=5_12					, text_config={}					, vision_config={}						)

  _lowerCAmelCase    :					Optional[int]							=   CLIPModel(_lowerCamelCase						).eval()

  _lowerCAmelCase			,   _lowerCAmelCase    :					List[str]							=   load(_lowerCamelCase					, device="""cpu"""					, jit=_lowerCamelCase						)
  _lowerCAmelCase    :					List[Any]							=   pt_model.eval()

  copy_text_model_and_projection(_lowerCamelCase					, _lowerCamelCase						)
  copy_vison_model_and_projection(_lowerCamelCase					, _lowerCamelCase						)
  _lowerCAmelCase    :					Tuple							=   pt_model.logit_scale

  _lowerCAmelCase    :					List[str]							=   torch.arange(0					, 77						).unsqueeze(0						)
  _lowerCAmelCase    :					int							=   torch.randn(1					, 3					, 2_24					, 2_24						)

  _lowerCAmelCase    :					List[str]							=   hf_model(input_ids=_lowerCamelCase					, pixel_values=_lowerCamelCase					, return_dict=_lowerCamelCase						)
  _lowerCAmelCase    :					Union[str, Any]							=   hf_outputs.logits_per_image
  _lowerCAmelCase    :					List[str]							=   hf_outputs.logits_per_text
  _lowerCAmelCase			,   _lowerCAmelCase    :					Tuple							=   pt_model(_lowerCamelCase					, _lowerCamelCase						)

  assert torch.allclose(_lowerCamelCase					, _lowerCamelCase					, atol=1e-3						)
  assert torch.allclose(_lowerCamelCase					, _lowerCamelCase					, atol=1e-3						)

  hf_model.save_pretrained(_lowerCamelCase						)


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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     UpperCamelCase_            =    parser.parse_args()

     convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)


