


import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
A      :    Any		=					logging.get_logger(__name__)

A      :    Tuple		=					{
    "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.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "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",
}
A      :    Tuple		=					[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def       lowercase_		(		_A			:    Any    ,					_A			:    Tuple    ,					_A			:    List[Any]    ,					_A			:    List[Any]    ,					_A			:    List[Any]						):

      """simple docstring"""




      for attribute in key.split("."						):
            lowerCamelCase__		:				List[Any]    =  getattr(__lowerCAmelCase    ,					__lowerCAmelCase						)

      if weight_type is not None:
            lowerCamelCase__		:				str    =  getattr(__lowerCAmelCase    ,					__lowerCAmelCase						).shape
      else:
            lowerCamelCase__		:				str    =  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__		:				Tuple    =  value
      elif weight_type == "weight_g":
            lowerCamelCase__		:				List[Any]    =  value
      elif weight_type == "weight_v":
            lowerCamelCase__		:				int    =  value
      elif weight_type == "bias":
            lowerCamelCase__		:				Dict    =  value
      else:
            lowerCamelCase__		:				Any    =  value

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

def       lowercase_		(		_A			:    Dict    ,					_A			:    Union[str, Any]						):

      """simple docstring"""




      lowerCamelCase__		:				Optional[int]    =  []
      lowerCamelCase__		:				List[Any]    =  fairseq_model.state_dict()

      lowerCamelCase__		:				Optional[int]    =  hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            lowerCamelCase__		:				int    =  False
            if "conv_layers" in name:
                  load_conv_layer(
                      __lowerCAmelCase    ,					__lowerCAmelCase    ,					__lowerCAmelCase    ,					__lowerCAmelCase    ,					hf_model.config.feat_extract_norm == "group"    ,					)
                  lowerCamelCase__		:				str    =  True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("w2v_model."						)[-1] == name.split("."						)[0]:
                              lowerCamelCase__		:				Any    =  True
                              if "*" in mapped_key:
                                    lowerCamelCase__		:				Union[str, Any]    =  name.split(__lowerCAmelCase						)[0].split("."						)[-2]
                                    lowerCamelCase__		:				Tuple    =  mapped_key.replace("*"    ,					__lowerCAmelCase						)
                              if "weight_g" in name:
                                    lowerCamelCase__		:				Any    =  "weight_g"
                              elif "weight_v" in name:
                                    lowerCamelCase__		:				List[Any]    =  "weight_v"
                              elif "bias" in name and "relative_attention_bias" not in name:
                                    lowerCamelCase__		:				Union[str, Any]    =  "bias"
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    lowerCamelCase__		:				Dict    =  "weight"
                              else:
                                    lowerCamelCase__		:				str    =  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       lowercase_		(		_A			:    Tuple    ,					_A			:    Any    ,					_A			:    Optional[Any]    ,					_A			:    Optional[int]    ,					_A			:    List[Any]						):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  full_name.split("conv_layers."						)[-1]
      lowerCamelCase__		:				List[Any]    =  name.split("."						)
      lowerCamelCase__		:				Optional[int]    =  int(items[0]						)
      lowerCamelCase__		:				Tuple    =  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__		:				int    =  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__		:				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."
                  )
                  lowerCamelCase__		:				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."
                  )
                  lowerCamelCase__		:				Union[str, Any]    =  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       lowercase_		(		_A			:    Union[str, Any]    ,					_A			:    Any    ,					_A			:    List[str]=None						):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  torch.load(__lowerCAmelCase						)
      lowerCamelCase__		:				List[str]    =  WavLMConfigOrig(checkpoint["cfg"]						)
      lowerCamelCase__		:				Any    =  WavLMOrig(__lowerCAmelCase						)
      model.load_state_dict(checkpoint["model"]						)
      model.eval()

      if config_path is not None:
            lowerCamelCase__		:				int    =  WavLMConfig.from_pretrained(__lowerCAmelCase						)
      else:
            lowerCamelCase__		:				Union[str, Any]    =  WavLMConfig()

      lowerCamelCase__		:				Optional[int]    =  WavLMModel(__lowerCAmelCase						)

      recursively_load_weights(__lowerCAmelCase    ,					__lowerCAmelCase						)

      hf_wavlm.save_pretrained(__lowerCAmelCase						)


if __name__ == "__main__":
    A      :    Optional[int]		=					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")
    A      :    Any		=					parser.parse_args()
    convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ResNetForImageClassification",
					    "ResNetModel",
					    "ResNetPreTrainedModel",
					    "ResNetBackbone",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			List[Any]												=    [
					    "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFResNetForImageClassification",
					    "TFResNetModel",
					    "TFResNetPreTrainedModel",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    [
					    "FlaxResNetForImageClassification",
					    "FlaxResNetModel",
					    "FlaxResNetPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_resnet import (
										    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ResNetBackbone,
										    ResNetForImageClassification,
										    ResNetModel,
										    ResNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_resnet import (
										    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFResNetForImageClassification,
										    TFResNetModel,
										    TFResNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Any												=    _LazyModule(__name__, globals()["__file__"], _import_structure)



