


import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
      import torch

      from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin



class 							_UpperCamelCase :

   '''simple docstring'''







   def __init__(  self  : Any       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : List[Any]=None       ,  _lowerCamelCase  : Optional[Any]=None       ,  _lowerCamelCase  : Optional[int]=None       ,  _lowerCamelCase  : Tuple="resnet50"       ,  _lowerCamelCase  : List[str]=3       ,  _lowerCamelCase  : Tuple=3_2       ,  _lowerCamelCase  : str=3       ,  _lowerCamelCase  : int=True       ,  _lowerCamelCase  : str=True       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						parent
         __lowerCamelCase     :      Dict       						=						out_indices if out_indices is not None else [4]
         __lowerCamelCase     :      List[str]       						=						stage_names
         __lowerCamelCase     :      str       						=						out_features
         __lowerCamelCase     :      Union[str, Any]       						=						backbone
         __lowerCamelCase     :      int       						=						batch_size
         __lowerCamelCase     :      List[str]       						=						image_size
         __lowerCamelCase     :      Optional[Any]       						=						num_channels
         __lowerCamelCase     :      Any       						=						use_pretrained_backbone
         __lowerCamelCase     :      Tuple       						=						is_training

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
         __lowerCamelCase     :      List[str]       						=						self.get_config()

         return config, pixel_values

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         return TimmBackboneConfig(
             image_size=self.image_size       ,  num_channels=self.num_channels       ,  out_features=self.out_features       ,  out_indices=self.out_indices       ,  stage_names=self.stage_names       ,  use_pretrained_backbone=self.use_pretrained_backbone       ,  backbone=self.backbone       ,  )

   def   _snake_case    (  self  : Dict       ,  _lowerCamelCase  : List[Any]       ,  _lowerCamelCase  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						TimmBackbone(config=_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()
         with torch.no_grad():
               __lowerCamelCase     :      List[str]       						=						model(_lowerCamelCase							)
         self.parent.assertEqual(
             result.feature_map[-1].shape       ,  (self.batch_size, model.channels[-1], 1_4, 1_4)       ,  )


   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						self.prepare_config_and_inputs()
         __lowerCamelCase     :      List[Any]       						=						config_and_inputs
         __lowerCamelCase     :      str       						=						{"pixel_values": pixel_values}
         return config, inputs_dict



@require_torch
@require_timm
class 							_UpperCamelCase (						_A,_A,_A,unittest.TestCase						):

   '''simple docstring'''







   a_				:							List[Any]    = (TimmBackbone,) if is_torch_available() else ()
   a_				:							Optional[int]    = {"feature-extraction": TimmBackbone} if is_torch_available() else {}
   a_				:							List[Any]    = False
   a_				:							List[Any]    = False
   a_				:							Optional[Any]    = False
   a_				:							Union[str, Any]    = False

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						TimmBackboneModelTester(self							)
         __lowerCamelCase     :      Optional[Any]       						=						ConfigTester(self       ,  config_class=_lowerCamelCase       ,  has_text_modality=_lowerCamelCase							)

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						"resnet18"
         __lowerCamelCase     :      int       						=						"microsoft/resnet-18"

         __lowerCamelCase     :      List[Any]       						=						AutoBackbone.from_pretrained(_lowerCamelCase       ,  use_timm_backbone=_lowerCamelCase							)
         __lowerCamelCase     :      List[Any]       						=						AutoBackbone.from_pretrained(_lowerCamelCase							)

         self.assertEqual(len(timm_model.out_features							)       ,  len(transformers_model.out_features							)							)
         self.assertEqual(len(timm_model.stage_names							)       ,  len(transformers_model.stage_names							)							)
         self.assertEqual(timm_model.channels       ,  transformers_model.channels							)
         # Out indices are set to the last layer by default. For timm models, we don't know
         # the number of layers in advance, so we set it to (-1,), whereas for transformers
         # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
         self.assertEqual(timm_model.out_indices       ,  (-1,)							)
         self.assertEqual(transformers_model.out_indices       ,  [len(timm_model.stage_names							) - 1]							)

         __lowerCamelCase     :      str       						=						AutoBackbone.from_pretrained(_lowerCamelCase       ,  use_timm_backbone=_lowerCamelCase       ,  out_indices=[1, 2, 3]							)
         __lowerCamelCase     :      str       						=						AutoBackbone.from_pretrained(_lowerCamelCase       ,  out_indices=[1, 2, 3]							)

         self.assertEqual(timm_model.out_indices       ,  transformers_model.out_indices							)
         self.assertEqual(len(timm_model.out_features							)       ,  len(transformers_model.out_features							)							)
         self.assertEqual(timm_model.channels       ,  transformers_model.channels							)

   @unittest.skip("""TimmBackbone doesn't support feed forward chunking"""							)
   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         pass

   @unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""							)
   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         pass

   @unittest.skip("""TimmBackbone initialization is managed on the timm side"""							)
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         pass

   @unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""							)
   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         pass

   @unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""							)
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         pass

   @unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint"""							)
   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         pass

   @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""							)
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         pass

   @unittest.skip("""model weights aren't tied in TimmBackbone."""							)
   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         pass

   @unittest.skip("""model weights aren't tied in TimmBackbone."""							)
   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         pass

   @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""							)
   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         pass

   @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""							)
   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         pass

   @unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration."""							)
   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         pass

   @unittest.skip("""TimmBackbone doesn't support output_attentions."""							)
   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         pass

   @unittest.skip("""Safetensors is not supported by timm."""							)
   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         pass

   @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         pass

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               __lowerCamelCase     :      Tuple       						=						model_class(_lowerCamelCase							)
               __lowerCamelCase     :      List[str]       						=						inspect.signature(model.forward							)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __lowerCamelCase     :      List[str]       						=						[*signature.parameters.keys()]

               __lowerCamelCase     :      Optional[Any]       						=						["pixel_values"]
               self.assertListEqual(arg_names[:1]       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						self.model_tester.prepare_config_and_inputs_for_common()
         __lowerCamelCase     :      Any       						=						True
         __lowerCamelCase     :      str       						=						self.has_attentions

         # no need to test all models as different heads yield the same functionality
         __lowerCamelCase     :      Tuple       						=						self.all_model_classes[0]
         __lowerCamelCase     :      str       						=						model_class(_lowerCamelCase							)
         model.to(_lowerCamelCase							)

         __lowerCamelCase     :      Optional[int]       						=						self._prepare_for_class(_lowerCamelCase       ,  _lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						model(**_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						outputs[0][-1]

         # Encoder-/Decoder-only models
         __lowerCamelCase     :      Dict       						=						outputs.hidden_states[0]
         hidden_states.retain_grad()

         if self.has_attentions:
               __lowerCamelCase     :      Optional[Any]       						=						outputs.attentions[0]
               attentions.retain_grad()

         output.flatten()[0].backward(retain_graph=_lowerCamelCase							)

         self.assertIsNotNone(hidden_states.grad							)

         if self.has_attentions:
               self.assertIsNotNone(attentions.grad							)


   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               __lowerCamelCase     :      List[str]       						=						model_class(_lowerCamelCase							)
               model.to(_lowerCamelCase							)
               model.eval()
               __lowerCamelCase     :      Tuple       						=						model(**_lowerCamelCase							)

               self.assertEqual(len(result.feature_maps							)       ,  len(config.out_indices							)							)
               self.assertEqual(len(model.channels							)       ,  len(config.out_indices							)							)

               # Check output of last stage is taken if out_features=None, out_indices=None
               __lowerCamelCase     :      Optional[Any]       						=						copy.deepcopy(_lowerCamelCase							)
               __lowerCamelCase     :      List[str]       						=						None
               __lowerCamelCase     :      List[Any]       						=						model_class(_lowerCamelCase							)
               model.to(_lowerCamelCase							)
               model.eval()
               __lowerCamelCase     :      List[Any]       						=						model(**_lowerCamelCase							)

               self.assertEqual(len(result.feature_maps							)       ,  1							)
               self.assertEqual(len(model.channels							)       ,  1							)

               # Check backbone can be initialized with fresh weights
               __lowerCamelCase     :      Tuple       						=						copy.deepcopy(_lowerCamelCase							)
               __lowerCamelCase     :      Tuple       						=						False
               __lowerCamelCase     :      Tuple       						=						model_class(_lowerCamelCase							)
               model.to(_lowerCamelCase							)
               model.eval()
               __lowerCamelCase     :      Optional[int]       						=						model(**_lowerCamelCase							)







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()
__UpperCamelCase    :						Any     =							logging.get_logger(__name__)

__UpperCamelCase    :						str     =							{
    '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',
}
__UpperCamelCase    :						Optional[int]     =							[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Tuple      ):
      """simple docstring"""




      for attribute in key.split("""."""      ):
            __lowerCamelCase     :      Union[str, Any]       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      )

      if weight_type is not None:
            __lowerCamelCase     :      Union[str, Any]       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      ).shape
      else:
            __lowerCamelCase     :      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     :      List[str]       						=						value
      elif weight_type == "weight_g":
            __lowerCamelCase     :      Dict       						=						value
      elif weight_type == "weight_v":
            __lowerCamelCase     :      str       						=						value
      elif weight_type == "bias":
            __lowerCamelCase     :      Optional[Any]       						=						value
      else:
            __lowerCamelCase     :      str       						=						value

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						[]
      __lowerCamelCase     :      int       						=						fairseq_model.state_dict()

      __lowerCamelCase     :      List[str]       						=						hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            __lowerCamelCase     :      str       						=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	hf_model.config.feat_extract_norm == """group"""						,	)
                  __lowerCamelCase     :      Optional[int]       						=						True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
                              __lowerCamelCase     :      int       						=						True
                              if "*" in mapped_key:
                                    __lowerCamelCase     :      Optional[int]       						=						name.split(UpperCAmelCase      )[0].split("""."""      )[-2]
                                    __lowerCamelCase     :      Optional[int]       						=						mapped_key.replace("""*"""						,	UpperCAmelCase      )
                              if "weight_g" in name:
                                    __lowerCamelCase     :      Optional[Any]       						=						"""weight_g"""
                              elif "weight_v" in name:
                                    __lowerCamelCase     :      Dict       						=						"""weight_v"""
                              elif "bias" in name and "relative_attention_bias" not in name:
                                    __lowerCamelCase     :      List[Any]       						=						"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    __lowerCamelCase     :      List[str]       						=						"""weight"""
                              else:
                                    __lowerCamelCase     :      List[str]       						=						None

                              set_recursively(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
                        continue
            if not is_used:
                  unused_weights.append(UpperCAmelCase      )

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						full_name.split("""conv_layers."""      )[-1]
      __lowerCamelCase     :      List[Any]       						=						name.split("""."""      )
      __lowerCamelCase     :      Tuple       						=						int(items[0]      )
      __lowerCamelCase     :      List[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."""
                  )
                  __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     :      str       						=						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     :      Any       						=						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     :      Tuple       						=						value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
      else:
            unused_weights.append(UpperCAmelCase      )





@torch.no_grad()
def   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[int]=None      ):
      """simple docstring"""




      __lowerCamelCase     :      Any       						=						torch.load(UpperCAmelCase      )
      __lowerCamelCase     :      Optional[int]       						=						WavLMConfigOrig(checkpoint["""cfg"""]      )
      __lowerCamelCase     :      Any       						=						WavLMOrig(UpperCAmelCase      )
      model.load_state_dict(checkpoint["""model"""]      )
      model.eval()

      if config_path is not None:
            __lowerCamelCase     :      Union[str, Any]       						=						WavLMConfig.from_pretrained(UpperCAmelCase      )
      else:
            __lowerCamelCase     :      str       						=						WavLMConfig()

      __lowerCamelCase     :      Dict       						=						WavLMModel(UpperCAmelCase      )

      recursively_load_weights(UpperCAmelCase						,	UpperCAmelCase      )

      hf_wavlm.save_pretrained(UpperCAmelCase      )


if __name__ == "__main__":
    __UpperCamelCase    :						List[str]     =							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    :						Tuple     =							parser.parse_args()
    convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



