
"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__          = logging.get_logger(__name__)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int]		)     ->					Optional[int]:
  _snake_case					     =       DPTConfig(embedding_type='''hybrid'''		)

  if "large" in checkpoint_url:
    _snake_case					     =       10_24
    _snake_case					     =       40_96
    _snake_case					     =       24
    _snake_case					     =       16
    _snake_case					     =       [5, 11, 17, 23]
    _snake_case					     =       [2_56, 5_12, 10_24, 10_24]
    _snake_case					     =       (1, 3_84, 3_84)

  if "nyu" or "midas" in checkpoint_url:
    _snake_case					     =       7_68
    _snake_case					     =       [1, 1, 1, 0.5]
    _snake_case					     =       [2_56, 5_12, 7_68, 7_68]
    _snake_case					     =       1_50
    _snake_case					     =       16
    _snake_case					     =       (1, 3_84, 3_84)
    _snake_case					     =       False
    _snake_case					     =       '''project'''

  if "ade" in checkpoint_url:
    _snake_case					     =       True
    _snake_case					     =       7_68
    _snake_case					     =       [1, 1, 1, 0.5]
    _snake_case					     =       1_50
    _snake_case					     =       16
    _snake_case					     =       '''huggingface/label-files'''
    _snake_case					     =       '''ade20k-id2label.json'''
    _snake_case					     =       json.load(open(cached_download(hf_hub_url(_lowerCAmelCase ,					_lowerCAmelCase ,					repo_type='''dataset'''		)		) ,					'''r'''		)		)
    _snake_case					     =       {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
    _snake_case					     =       idalabel
    _snake_case					     =       {v: k for k, v in idalabel.items()}
    _snake_case					     =       [1, 1_50, 4_80, 4_80]

  return config, expected_shape
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any]		)     ->					int:
  _snake_case					     =       ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
  for k in ignore_keys:
    state_dict.pop(_lowerCAmelCase ,					_lowerCAmelCase		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Any:
  if (
      "pretrained.model" in name
      and "cls_token" not in name
      and "pos_embed" not in name
      and "patch_embed" not in name
  ):
    _snake_case					     =       name.replace('''pretrained.model''' ,					'''dpt.encoder'''		)
  if "pretrained.model" in name:
    _snake_case					     =       name.replace('''pretrained.model''' ,					'''dpt.embeddings'''		)
  if "patch_embed" in name:
    _snake_case					     =       name.replace('''patch_embed''' ,					''''''		)
  if "pos_embed" in name:
    _snake_case					     =       name.replace('''pos_embed''' ,					'''position_embeddings'''		)
  if "attn.proj" in name:
    _snake_case					     =       name.replace('''attn.proj''' ,					'''attention.output.dense'''		)
  if "proj" in name and "project" not in name:
    _snake_case					     =       name.replace('''proj''' ,					'''projection'''		)
  if "blocks" in name:
    _snake_case					     =       name.replace('''blocks''' ,					'''layer'''		)
  if "mlp.fc1" in name:
    _snake_case					     =       name.replace('''mlp.fc1''' ,					'''intermediate.dense'''		)
  if "mlp.fc2" in name:
    _snake_case					     =       name.replace('''mlp.fc2''' ,					'''output.dense'''		)
  if "norm1" in name and "backbone" not in name:
    _snake_case					     =       name.replace('''norm1''' ,					'''layernorm_before'''		)
  if "norm2" in name and "backbone" not in name:
    _snake_case					     =       name.replace('''norm2''' ,					'''layernorm_after'''		)
  if "scratch.output_conv" in name:
    _snake_case					     =       name.replace('''scratch.output_conv''' ,					'''head'''		)
  if "scratch" in name:
    _snake_case					     =       name.replace('''scratch''' ,					'''neck'''		)
  if "layer1_rn" in name:
    _snake_case					     =       name.replace('''layer1_rn''' ,					'''convs.0'''		)
  if "layer2_rn" in name:
    _snake_case					     =       name.replace('''layer2_rn''' ,					'''convs.1'''		)
  if "layer3_rn" in name:
    _snake_case					     =       name.replace('''layer3_rn''' ,					'''convs.2'''		)
  if "layer4_rn" in name:
    _snake_case					     =       name.replace('''layer4_rn''' ,					'''convs.3'''		)
  if "refinenet" in name:
    _snake_case					     =       int(name[len('''neck.refinenet'''		) : len('''neck.refinenet'''		) + 1]		)
    # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
    _snake_case					     =       name.replace(f'''refinenet{layer_idx}''' ,					f'''fusion_stage.layers.{abs(layer_idx-4		)}'''		)
  if "out_conv" in name:
    _snake_case					     =       name.replace('''out_conv''' ,					'''projection'''		)
  if "resConfUnit1" in name:
    _snake_case					     =       name.replace('''resConfUnit1''' ,					'''residual_layer1'''		)
  if "resConfUnit2" in name:
    _snake_case					     =       name.replace('''resConfUnit2''' ,					'''residual_layer2'''		)
  if "conv1" in name:
    _snake_case					     =       name.replace('''conv1''' ,					'''convolution1'''		)
  if "conv2" in name:
    _snake_case					     =       name.replace('''conv2''' ,					'''convolution2'''		)
  # readout blocks
  if "pretrained.act_postprocess1.0.project.0" in name:
    _snake_case					     =       name.replace('''pretrained.act_postprocess1.0.project.0''' ,					'''neck.reassemble_stage.readout_projects.0.0'''		)
  if "pretrained.act_postprocess2.0.project.0" in name:
    _snake_case					     =       name.replace('''pretrained.act_postprocess2.0.project.0''' ,					'''neck.reassemble_stage.readout_projects.1.0'''		)
  if "pretrained.act_postprocess3.0.project.0" in name:
    _snake_case					     =       name.replace('''pretrained.act_postprocess3.0.project.0''' ,					'''neck.reassemble_stage.readout_projects.2.0'''		)
  if "pretrained.act_postprocess4.0.project.0" in name:
    _snake_case					     =       name.replace('''pretrained.act_postprocess4.0.project.0''' ,					'''neck.reassemble_stage.readout_projects.3.0'''		)

  # resize blocks
  if "pretrained.act_postprocess1.3" in name:
    _snake_case					     =       name.replace('''pretrained.act_postprocess1.3''' ,					'''neck.reassemble_stage.layers.0.projection'''		)
  if "pretrained.act_postprocess1.4" in name:
    _snake_case					     =       name.replace('''pretrained.act_postprocess1.4''' ,					'''neck.reassemble_stage.layers.0.resize'''		)
  if "pretrained.act_postprocess2.3" in name:
    _snake_case					     =       name.replace('''pretrained.act_postprocess2.3''' ,					'''neck.reassemble_stage.layers.1.projection'''		)
  if "pretrained.act_postprocess2.4" in name:
    _snake_case					     =       name.replace('''pretrained.act_postprocess2.4''' ,					'''neck.reassemble_stage.layers.1.resize'''		)
  if "pretrained.act_postprocess3.3" in name:
    _snake_case					     =       name.replace('''pretrained.act_postprocess3.3''' ,					'''neck.reassemble_stage.layers.2.projection'''		)
  if "pretrained.act_postprocess4.3" in name:
    _snake_case					     =       name.replace('''pretrained.act_postprocess4.3''' ,					'''neck.reassemble_stage.layers.3.projection'''		)
  if "pretrained.act_postprocess4.4" in name:
    _snake_case					     =       name.replace('''pretrained.act_postprocess4.4''' ,					'''neck.reassemble_stage.layers.3.resize'''		)
  if "pretrained" in name:
    _snake_case					     =       name.replace('''pretrained''' ,					'''dpt'''		)
  if "bn" in name:
    _snake_case					     =       name.replace('''bn''' ,					'''batch_norm'''		)
  if "head" in name:
    _snake_case					     =       name.replace('''head''' ,					'''head.head'''		)
  if "encoder.norm" in name:
    _snake_case					     =       name.replace('''encoder.norm''' ,					'''layernorm'''		)
  if "auxlayer" in name:
    _snake_case					     =       name.replace('''auxlayer''' ,					'''auxiliary_head.head'''		)
  if "backbone" in name:
    _snake_case					     =       name.replace('''backbone''' ,					'''backbone.bit.encoder'''		)

  if ".." in name:
    _snake_case					     =       name.replace('''..''' ,					'''.'''		)

  if "stem.conv" in name:
    _snake_case					     =       name.replace('''stem.conv''' ,					'''bit.embedder.convolution'''		)
  if "blocks" in name:
    _snake_case					     =       name.replace('''blocks''' ,					'''layers'''		)
  if "convolution" in name and "backbone" in name:
    _snake_case					     =       name.replace('''convolution''' ,					'''conv'''		)
  if "layer" in name and "backbone" in name:
    _snake_case					     =       name.replace('''layer''' ,					'''layers'''		)
  if "backbone.bit.encoder.bit" in name:
    _snake_case					     =       name.replace('''backbone.bit.encoder.bit''' ,					'''backbone.bit'''		)
  if "embedder.conv" in name:
    _snake_case					     =       name.replace('''embedder.conv''' ,					'''embedder.convolution'''		)
  if "backbone.bit.encoder.stem.norm" in name:
    _snake_case					     =       name.replace('''backbone.bit.encoder.stem.norm''' ,					'''backbone.bit.embedder.norm'''		)
  return name
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       List[Any]		)     ->					Optional[Any]:
  for i in range(config.num_hidden_layers		):
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _snake_case					     =       state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''		)
    _snake_case					     =       state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''		)
    # next, add query, keys and values (in that order) to the state dict
    _snake_case					     =       in_proj_weight[: config.hidden_size, :]
    _snake_case					     =       in_proj_bias[: config.hidden_size]
    _snake_case					     =       in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case					     =       in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case					     =       in_proj_weight[
        -config.hidden_size :, :
    ]
    _snake_case					     =       in_proj_bias[-config.hidden_size :]
def 					_UpperCAmelCase      ( )     ->					List[str]:
  _snake_case					     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case					     =       Image.open(requests.get(_lowerCAmelCase ,					stream=_lowerCAmelCase		).raw		)
  return im
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       str ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       List[str]		)     ->					Any:
  _snake_case					     =       get_dpt_config(_lowerCAmelCase		)
  # load original state_dict from URL
  # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
  _snake_case					     =       torch.load(_lowerCAmelCase ,					map_location='''cpu'''		)
  # remove certain keys
  remove_ignore_keys_(_lowerCAmelCase		)
  # rename keys
  for key in state_dict.copy().keys():
    _snake_case					     =       state_dict.pop(_lowerCAmelCase		)
    _snake_case					     =       val
  # read in qkv matrices
  read_in_q_k_v(_lowerCAmelCase ,					_lowerCAmelCase		)

  # load HuggingFace model
  _snake_case					     =       DPTForSemanticSegmentation(_lowerCAmelCase		) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase		)
  model.load_state_dict(_lowerCAmelCase		)
  model.eval()

  # Check outputs on an image
  _snake_case					     =       4_80 if '''ade''' in checkpoint_url else 3_84
  _snake_case					     =       DPTImageProcessor(size=_lowerCAmelCase		)

  _snake_case					     =       prepare_img()
  _snake_case					     =       image_processor(_lowerCAmelCase ,					return_tensors='''pt'''		)

  # forward pass
  _snake_case					     =       model(**_lowerCAmelCase		).logits if '''ade''' in checkpoint_url else model(**_lowerCAmelCase		).predicted_depth

  if show_prediction:
    _snake_case					     =       (
        torch.nn.functional.interpolate(
            outputs.unsqueeze(1		) ,					size=(image.size[1], image.size[0]) ,					mode='''bicubic''' ,					align_corners=_lowerCAmelCase ,					)
        .squeeze()
        .cpu()
        .numpy()
    )

    Image.fromarray((prediction / prediction.max()) * 2_55		).show()

  if pytorch_dump_folder_path is not None:
    Path(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
    print(f'''Saving model to {pytorch_dump_folder_path}'''		)
    model.save_pretrained(_lowerCAmelCase		)
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''		)
    image_processor.save_pretrained(_lowerCAmelCase		)

  if push_to_hub:
    model.push_to_hub('''ybelkada/dpt-hybrid-midas'''		)
    image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''		)


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--checkpoint_url',
     default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
     type=str,
     help='URL of the original DPT checkpoint you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path',
     default=None,
     type=str,
     required=False,
     help='Path to the output PyTorch model directory.',
 )
 parser.add_argument(
     '--push_to_hub',
     action='store_true',
 )
 parser.add_argument(
     '--model_name',
     default='dpt-large',
     type=str,
     help='Name of the model, in case you\'re pushing to the hub.',
 )
 parser.add_argument(
     '--show_prediction',
     action='store_true',
 )

 UpperCAmelCase__          = parser.parse_args()
 convert_dpt_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
 )








'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

UpperCamelCase__:   Dict								=			{
    "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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
UpperCamelCase__:   Optional[Any]								=			[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]


def  snake_case_     (					_lowerCAmelCase  :						str  )      ->   Union[str, Any]:
							UpperCAmelCase :       Optional[int] 							=		{}
							with open(_lowerCAmelCase   ,      '''r'''  ) as file:
														for line_number, line in enumerate(_lowerCAmelCase  ):
																					UpperCAmelCase :       List[str] 							=		line.strip()
																					if line:
																												UpperCAmelCase :       str 							=		line.split()
																												UpperCAmelCase :       Union[str, Any] 							=		line_number
																												UpperCAmelCase :       List[Any] 							=		words[0]
																												UpperCAmelCase :       Union[str, Any] 							=		value
							return result


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						str  )      ->   int:
							for attribute in key.split('''.'''  ):
														UpperCAmelCase :       Dict 							=		getattr(_lowerCAmelCase   ,      _lowerCAmelCase  )

							UpperCAmelCase :       Dict 							=		None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(_lowerCAmelCase  ):
																					UpperCAmelCase :       Any 							=		PARAM_MAPPING[full_name.split('''.'''  )[-1]]
																					UpperCAmelCase :       Dict 							=		'''param'''

							if weight_type is not None and weight_type != "param":
														UpperCAmelCase :       Optional[int] 							=		getattr(_lowerCAmelCase   ,      _lowerCAmelCase  ).shape
							elif weight_type is not None and weight_type == "param":
														UpperCAmelCase :       List[Any] 							=		hf_pointer
														for attribute in hf_param_name.split('''.'''  ):
																					UpperCAmelCase :       Optional[Any] 							=		getattr(_lowerCAmelCase   ,      _lowerCAmelCase  )
														UpperCAmelCase :       int 							=		shape_pointer.shape

														# let's reduce dimension
														UpperCAmelCase :       Union[str, Any] 							=		value[0]
							else:
														UpperCAmelCase :       List[Any] 							=		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 :       int 							=		value
							elif weight_type == "weight_g":
														UpperCAmelCase :       str 							=		value
							elif weight_type == "weight_v":
														UpperCAmelCase :       Dict 							=		value
							elif weight_type == "bias":
														UpperCAmelCase :       str 							=		value
							elif weight_type == "param":
														for attribute in hf_param_name.split('''.'''  ):
																					UpperCAmelCase :       int 							=		getattr(_lowerCAmelCase   ,      _lowerCAmelCase  )
														UpperCAmelCase :       Optional[int] 							=		value
							else:
														UpperCAmelCase :       Tuple 							=		value

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


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						List[Any]  )      ->   List[Any]:
							UpperCAmelCase :       List[str] 							=		None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(_lowerCAmelCase  ):
																					UpperCAmelCase :       List[str] 							=		PARAM_MAPPING[full_name.split('''.'''  )[-1]]
																					UpperCAmelCase :       Any 							=		'''param'''

							if weight_type is not None and weight_type != "param":
														UpperCAmelCase :       Optional[int] 							=		'''.'''.join([key, weight_type]  )
							elif weight_type is not None and weight_type == "param":
														UpperCAmelCase :       Optional[int] 							=		'''.'''.join([key, hf_param_name]  )
							else:
														UpperCAmelCase :       List[Any] 							=		key

							UpperCAmelCase :       Tuple 							=		value if '''lm_head''' in full_key else value[0]


UpperCamelCase__:   Tuple								=			{
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Any=None   ,      _lowerCAmelCase  :						Optional[Any]=None  )      ->   int:
							UpperCAmelCase :       List[Any] 							=		False
							for key, mapped_key in MAPPING.items():
														UpperCAmelCase :       int 							=		'''wav2vec2.''' + 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 :       Optional[Any] 							=		True
																					if "*" in mapped_key:
																												UpperCAmelCase :       Tuple 							=		name.split(_lowerCAmelCase  )[0].split('''.'''  )[-2]
																												UpperCAmelCase :       List[Any] 							=		mapped_key.replace('''*'''   ,      _lowerCAmelCase  )
																					if "weight_g" in name:
																												UpperCAmelCase :       str 							=		'''weight_g'''
																					elif "weight_v" in name:
																												UpperCAmelCase :       int 							=		'''weight_v'''
																					elif "bias" in name:
																												UpperCAmelCase :       int 							=		'''bias'''
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												UpperCAmelCase :       List[str] 							=		'''weight'''
																					else:
																												UpperCAmelCase :       Dict 							=		None
																					if hf_dict is not None:
																												rename_dict(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					else:
																												set_recursively(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					return is_used
							return is_used


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Any  )      ->   Any:
							UpperCAmelCase :       Dict 							=		[]
							UpperCAmelCase :       Dict 							=		fairseq_model.state_dict()

							UpperCAmelCase :       Union[str, Any] 							=		hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														UpperCAmelCase :       Dict 							=		False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      hf_model.config.feat_extract_norm == '''group'''   ,      )
																					UpperCAmelCase :       Any 							=		True
														else:
																					UpperCAmelCase :       Optional[Any] 							=		load_wavaveca_layer(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														if not is_used:
																					unused_weights.append(_lowerCAmelCase  )

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


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Optional[int]  )      ->   Union[str, Any]:
							UpperCAmelCase :       Any 							=		full_name.split('''conv_layers.'''  )[-1]
							UpperCAmelCase :       Optional[int] 							=		name.split('''.'''  )
							UpperCAmelCase :       Tuple 							=		int(items[0]  )
							UpperCAmelCase :       Tuple 							=		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 :       Tuple 							=		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 :       Union[str, 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:
																					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 :       Union[str, Any] 							=		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 :       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  )


@torch.no_grad()
def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						Dict=None   ,      _lowerCAmelCase  :						List[Any]=None   ,      _lowerCAmelCase  :						int=True   ,      _lowerCAmelCase  :						Optional[int]=False  )      ->   Dict:
							if config_path is not None:
														UpperCAmelCase :       List[str] 							=		WavaVecaConfig.from_pretrained(_lowerCAmelCase  )
							else:
														UpperCAmelCase :       List[Any] 							=		WavaVecaConfig()

							if is_seq_class:
														UpperCAmelCase :       Optional[Any] 							=		read_txt_into_dict(_lowerCAmelCase  )
														UpperCAmelCase :       Optional[int] 							=		idalabel
														UpperCAmelCase :       Optional[Any] 							=		WavaVecaForSequenceClassification(_lowerCAmelCase  )
														UpperCAmelCase :       Dict 							=		WavaVecaFeatureExtractor(
														    feature_size=1   ,      sampling_rate=16000   ,      padding_value=0   ,      do_normalize=_lowerCAmelCase   ,      return_attention_mask=_lowerCAmelCase   ,      )
														feature_extractor.save_pretrained(_lowerCAmelCase  )

							elif is_finetuned:
														if dict_path:
																					UpperCAmelCase :       Dict 							=		Dictionary.load(_lowerCAmelCase  )

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					UpperCAmelCase :       Any 							=		target_dict.pad_index
																					UpperCAmelCase :       Tuple 							=		target_dict.bos_index
																					UpperCAmelCase :       Optional[int] 							=		target_dict.eos_index
																					UpperCAmelCase :       Union[str, Any] 							=		len(target_dict.symbols  )
																					UpperCAmelCase :       Dict 							=		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  )
																					UpperCAmelCase :       List[Any] 							=		target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					UpperCAmelCase :       List[str] 							=		0
																					UpperCAmelCase :       List[str] 							=		1
																					with open(_lowerCAmelCase   ,      '''w'''   ,      encoding='''utf-8'''  ) as vocab_handle:
																												json.dump(_lowerCAmelCase   ,      _lowerCAmelCase  )
																					UpperCAmelCase :       Optional[int] 							=		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   ,      )
																					UpperCAmelCase :       int 							=		True if config.feat_extract_norm == '''layer''' else False
																					UpperCAmelCase :       int 							=		WavaVecaFeatureExtractor(
																					    feature_size=1   ,      sampling_rate=16000   ,      padding_value=0   ,      do_normalize=_lowerCAmelCase   ,      return_attention_mask=_lowerCAmelCase   ,      )
																					UpperCAmelCase :       str 							=		WavaVecaProcessor(feature_extractor=_lowerCAmelCase   ,      tokenizer=_lowerCAmelCase  )
																					processor.save_pretrained(_lowerCAmelCase  )

														UpperCAmelCase :       Union[str, Any] 							=		WavaVecaForCTC(_lowerCAmelCase  )
							else:
														UpperCAmelCase :       Dict 							=		WavaVecaForPreTraining(_lowerCAmelCase  )

							if is_finetuned or is_seq_class:
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]   ,      arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''  )[:-1]  )}  )
							else:
														UpperCAmelCase :       Optional[Any] 							=		argparse.Namespace(task='''audio_pretraining'''  )
														UpperCAmelCase :       List[Any] 							=		fairseq.tasks.setup_task(_lowerCAmelCase  )

														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       int 							=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   ,      task=_lowerCAmelCase  )

							UpperCAmelCase :       Optional[int] 							=		model[0].eval()

							recursively_load_weights(_lowerCAmelCase   ,      _lowerCAmelCase   ,      not is_finetuned  )

							hf_wavavec.save_pretrained(_lowerCAmelCase  )


if __name__ == "__main__":
						UpperCamelCase__:   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"
						)
						parser.add_argument(
						    "--is_seq_class",
						    action="store_true",
						    help="Whether the model to convert is a fine-tuned sequence classification model or not",
						)
						UpperCamelCase__:   Any								=			parser.parse_args()

						UpperCamelCase__:   int								=			not args.not_finetuned and not args.is_seq_class
						convert_wavaveca_checkpoint(
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.dict_path,
						    is_finetuned,
						    args.is_seq_class,
						)





