
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()
lowercase__          =logging.get_logger(__name__)

lowercase__          ={
    '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',
}
lowercase__          =[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Optional[Any]  ):
       for attribute in key.split('''.'''  ):
              __a			:  List[str]		    =  getattr(lowerCAmelCase__  ,       lowerCAmelCase__  )

       if weight_type is not None:
              __a			:  int		    =  getattr(lowerCAmelCase__  ,       lowerCAmelCase__  ).shape
       else:
              __a			:  List[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			:  Union[str, Any]		    =  value
       elif weight_type == "weight_g":
              __a			:  Dict		    =  value
       elif weight_type == "weight_v":
              __a			:  List[str]		    =  value
       elif weight_type == "bias":
              __a			:  Optional[Any]		    =  value
       else:
              __a			:  Tuple		    =  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__							:    Dict  ):
       __a			:  Dict		    =  []
       __a			:  int		    =  fairseq_model.state_dict()

       __a			:  Optional[int]		    =  hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              __a			:  Dict		    =  False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       hf_model.config.feat_extract_norm == '''group'''  ,       )
                     __a			:  List[str]		    =  True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split('''w2v_model.'''  )[-1] == name.split('''.'''  )[0]:
                                   __a			:  Optional[Any]		    =  True
                                   if "*" in mapped_key:
                                          __a			:  Union[str, Any]		    =  name.split(lowerCAmelCase__  )[0].split('''.'''  )[-2]
                                          __a			:  List[str]		    =  mapped_key.replace('''*'''  ,       lowerCAmelCase__  )
                                   if "weight_g" in name:
                                          __a			:  int		    =  '''weight_g'''
                                   elif "weight_v" in name:
                                          __a			:  Optional[int]		    =  '''weight_v'''
                                   elif "bias" in name and "relative_attention_bias" not in name:
                                          __a			:  Any		    =  '''bias'''
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          __a			:  Union[str, Any]		    =  '''weight'''
                                   else:
                                          __a			:  List[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        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Optional[int]  ):
       __a			:  Any		    =  full_name.split('''conv_layers.'''  )[-1]
       __a			:  int		    =  name.split('''.'''  )
       __a			:  Optional[int]		    =  int(items[0]  )
       __a			:  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."
                     )
                     __a			:  Union[str, Any]		    =  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			:  List[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."
                     )
                     __a			:  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			:  List[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        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    Tuple=None  ):
       # load the pre-trained checkpoints
       __a			:  Any		    =  torch.load(lowerCAmelCase__  )
       __a			:  List[Any]		    =  WavLMConfigOrig(checkpoint['''cfg''']  )
       __a			:  List[str]		    =  WavLMOrig(lowerCAmelCase__  )
       model.load_state_dict(checkpoint['''model''']  )
       model.eval()

       if config_path is not None:
              __a			:  int		    =  WavLMConfig.from_pretrained(lowerCAmelCase__  )
       else:
              __a			:  Optional[int]		    =  WavLMConfig()

       __a			:  Union[str, Any]		    =  WavLMModel(lowerCAmelCase__  )

       recursively_load_weights(lowerCAmelCase__  ,       lowerCAmelCase__  )

       hf_wavlm.save_pretrained(lowerCAmelCase__  )


if __name__ == "__main__":
      lowercase__          =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')
      lowercase__          =parser.parse_args()
      convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__          ={
    'configuration_efficientnet': [
        'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientNetConfig',
        'EfficientNetOnnxConfig',
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['EfficientNetImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
          'EfficientNetForImageClassification',
          'EfficientNetModel',
          'EfficientNetPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_efficientnet import (
          EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EfficientNetConfig,
          EfficientNetOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientnet import EfficientNetImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientnet import (
                EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientNetForImageClassification,
                EfficientNetModel,
                EfficientNetPreTrainedModel,
            )

else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure)
