


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=1						)     ->			Any:







 """simple docstring"""


 if n_shave_prefix_segments >= 0:
  return ".".join(path.split('''.'''						)[n_shave_prefix_segments:]						)
 else:
  return ".".join(path.split('''.'''						)[:n_shave_prefix_segments]						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=0						)     ->			str:







 """simple docstring"""


 _lowercase							      =[]
 for old_item in old_list:
  _lowercase							      =old_item.replace('''in_layers.0'''     ,						'''norm1'''						)
  _lowercase							      =new_item.replace('''in_layers.2'''     ,						'''conv1'''						)

  _lowercase							      =new_item.replace('''out_layers.0'''     ,						'''norm2'''						)
  _lowercase							      =new_item.replace('''out_layers.3'''     ,						'''conv2'''						)

  _lowercase							      =new_item.replace('''emb_layers.1'''     ,						'''time_emb_proj'''						)
  _lowercase							      =new_item.replace('''skip_connection'''     ,						'''conv_shortcut'''						)

  _lowercase							      =shave_segments(__snake_case     ,						n_shave_prefix_segments=__snake_case						)

  mapping.append({'''old''': old_item, '''new''': new_item}						)

 return mapping
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=0						)     ->			Tuple:







 """simple docstring"""


 _lowercase							      =[]
 for old_item in old_list:
  _lowercase							      =old_item

  _lowercase							      =new_item.replace('''norm.weight'''     ,						'''group_norm.weight'''						)
  _lowercase							      =new_item.replace('''norm.bias'''     ,						'''group_norm.bias'''						)

  _lowercase							      =new_item.replace('''proj_out.weight'''     ,						'''proj_attn.weight'''						)
  _lowercase							      =new_item.replace('''proj_out.bias'''     ,						'''proj_attn.bias'''						)

  _lowercase							      =shave_segments(__snake_case     ,						n_shave_prefix_segments=__snake_case						)

  mapping.append({'''old''': old_item, '''new''': new_item}						)

 return mapping
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=None     ,						__snake_case=None     ,						__snake_case=None						)     ->			Any:







 """simple docstring"""


 assert isinstance(__snake_case     ,						__snake_case						), "Paths should be a list of dicts containing 'old' and 'new' keys."

 # Splits the attention layers into three variables.
 if attention_paths_to_split is not None:
  for path, path_map in attention_paths_to_split.items():
   _lowercase							      =old_checkpoint[path]
   _lowercase							      =old_tensor.shape[0] // 3

   _lowercase							      =(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

   _lowercase							      =old_tensor.shape[0] // config['''num_head_channels'''] // 3

   _lowercase							      =old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
   _lowercase      ,    _lowercase      ,    _lowercase							      =old_tensor.split(channels // num_heads     ,						dim=1						)

   _lowercase							      =query.reshape(__snake_case						)
   _lowercase							      =key.reshape(__snake_case						)
   _lowercase							      =value.reshape(__snake_case						)

 for path in paths:
  _lowercase							      =path['''new''']

  # These have already been assigned
  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  # Global renaming happens here
  _lowercase							      =new_path.replace('''middle_block.0'''     ,						'''mid_block.resnets.0'''						)
  _lowercase							      =new_path.replace('''middle_block.1'''     ,						'''mid_block.attentions.0'''						)
  _lowercase							      =new_path.replace('''middle_block.2'''     ,						'''mid_block.resnets.1'''						)

  if additional_replacements is not None:
   for replacement in additional_replacements:
    _lowercase							      =new_path.replace(replacement['''old''']     ,						replacement['''new''']						)

        # proj_attn.weight has to be converted from conv 1D to linear
  if "proj_attn.weight" in new_path:
   _lowercase							      =old_checkpoint[path['''old''']][:, :, 0]
  else:
   _lowercase							      =old_checkpoint[path['''old''']]
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			str:







 """simple docstring"""


 _lowercase							      ={}

 _lowercase							      =checkpoint['''time_embed.0.weight''']
 _lowercase							      =checkpoint['''time_embed.0.bias''']
 _lowercase							      =checkpoint['''time_embed.2.weight''']
 _lowercase							      =checkpoint['''time_embed.2.bias''']

 _lowercase							      =checkpoint['''input_blocks.0.0.weight''']
 _lowercase							      =checkpoint['''input_blocks.0.0.bias''']

 _lowercase							      =checkpoint['''out.0.weight''']
 _lowercase							      =checkpoint['''out.0.bias''']
 _lowercase							      =checkpoint['''out.2.weight''']
 _lowercase							      =checkpoint['''out.2.bias''']

 # Retrieves the keys for the input blocks only
 _lowercase							      =len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''input_blocks''' in layer}						)
 _lowercase							      ={
     layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
     for layer_id in range(__snake_case						)
 }

 # Retrieves the keys for the middle blocks only
 _lowercase							      =len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''middle_block''' in layer}						)
 _lowercase							      ={
     layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
     for layer_id in range(__snake_case						)
 }

 # Retrieves the keys for the output blocks only
 _lowercase							      =len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''output_blocks''' in layer}						)
 _lowercase							      ={
     layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
     for layer_id in range(__snake_case						)
 }

 for i in range(1     ,						__snake_case						):
  _lowercase							      =(i - 1) // (config['''num_res_blocks'''] + 1)
  _lowercase							      =(i - 1) % (config['''num_res_blocks'''] + 1)

  _lowercase							      =[key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
  _lowercase							      =[key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]

  if F"input_blocks.{i}.0.op.weight" in checkpoint:
   _lowercase							      =checkpoint[
       F"input_blocks.{i}.0.op.weight"
   ]
   _lowercase							      =checkpoint[
       F"input_blocks.{i}.0.op.bias"
   ]
   continue

  _lowercase							      =renew_resnet_paths(__snake_case						)
  _lowercase							      ={'''old''': F"input_blocks.{i}.0", '''new''': F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
  _lowercase							      ={'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
  assign_to_checkpoint(
      __snake_case     ,						__snake_case     ,						__snake_case     ,						additional_replacements=[meta_path, resnet_op]     ,						config=__snake_case						)

  if len(__snake_case						):
   _lowercase							      =renew_attention_paths(__snake_case						)
   _lowercase							      ={
       '''old''': F"input_blocks.{i}.1",
       '''new''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
   }
   _lowercase							      ={
       F"input_blocks.{i}.1.qkv.bias": {
           '''key''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
           '''query''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
           '''value''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
       },
       F"input_blocks.{i}.1.qkv.weight": {
           '''key''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
           '''query''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
           '''value''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
       },
   }
   assign_to_checkpoint(
       __snake_case     ,						__snake_case     ,						__snake_case     ,						additional_replacements=[meta_path]     ,						attention_paths_to_split=__snake_case     ,						config=__snake_case     ,						)

 _lowercase							      =middle_blocks[0]
 _lowercase							      =middle_blocks[1]
 _lowercase							      =middle_blocks[2]

 _lowercase							      =renew_resnet_paths(__snake_case						)
 assign_to_checkpoint(__snake_case     ,						__snake_case     ,						__snake_case     ,						config=__snake_case						)

 _lowercase							      =renew_resnet_paths(__snake_case						)
 assign_to_checkpoint(__snake_case     ,						__snake_case     ,						__snake_case     ,						config=__snake_case						)

 _lowercase							      =renew_attention_paths(__snake_case						)
 _lowercase							      ={
     '''middle_block.1.qkv.bias''': {
         '''key''': '''mid_block.attentions.0.key.bias''',
         '''query''': '''mid_block.attentions.0.query.bias''',
         '''value''': '''mid_block.attentions.0.value.bias''',
     },
     '''middle_block.1.qkv.weight''': {
         '''key''': '''mid_block.attentions.0.key.weight''',
         '''query''': '''mid_block.attentions.0.query.weight''',
         '''value''': '''mid_block.attentions.0.value.weight''',
     },
 }
 assign_to_checkpoint(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						attention_paths_to_split=__snake_case     ,						config=__snake_case						)

 for i in range(__snake_case						):
  _lowercase							      =i // (config['''num_res_blocks'''] + 1)
  _lowercase							      =i % (config['''num_res_blocks'''] + 1)
  _lowercase							      =[shave_segments(__snake_case     ,						2						) for name in output_blocks[i]]
  _lowercase							      ={}

  for layer in output_block_layers:
   _lowercase      ,    _lowercase							      =layer.split('''.'''						)[0], shave_segments(__snake_case     ,						1						)
   if layer_id in output_block_list:
    output_block_list[layer_id].append(__snake_case						)
   else:
    _lowercase							      =[layer_name]

  if len(__snake_case						) > 1:
   _lowercase							      =[key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
   _lowercase							      =[key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

   _lowercase							      =renew_resnet_paths(__snake_case						)
   _lowercase							      =renew_resnet_paths(__snake_case						)

   _lowercase							      ={'''old''': F"output_blocks.{i}.0", '''new''': F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
   assign_to_checkpoint(__snake_case     ,						__snake_case     ,						__snake_case     ,						additional_replacements=[meta_path]     ,						config=__snake_case						)

   if ["conv.weight", "conv.bias"] in output_block_list.values():
    _lowercase							      =list(output_block_list.values()						).index(['''conv.weight''', '''conv.bias''']						)
    _lowercase							      =checkpoint[
        F"output_blocks.{i}.{index}.conv.weight"
    ]
    _lowercase							      =checkpoint[
        F"output_blocks.{i}.{index}.conv.bias"
    ]

    # Clear attentions as they have been attributed above.
    if len(__snake_case						) == 2:
     _lowercase							      =[]

   if len(__snake_case						):
    _lowercase							      =renew_attention_paths(__snake_case						)
    _lowercase							      ={
        '''old''': F"output_blocks.{i}.1",
        '''new''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
    }
    _lowercase							      ={
        F"output_blocks.{i}.1.qkv.bias": {
            '''key''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
            '''query''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
            '''value''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
        },
        F"output_blocks.{i}.1.qkv.weight": {
            '''key''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
            '''query''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
            '''value''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
        },
    }
    assign_to_checkpoint(
        __snake_case     ,						__snake_case     ,						__snake_case     ,						additional_replacements=[meta_path]     ,						attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions						) else None     ,						config=__snake_case     ,						)
  else:
   _lowercase							      =renew_resnet_paths(__snake_case     ,						n_shave_prefix_segments=1						)
   for path in resnet_0_paths:
    _lowercase							      ='''.'''.join(['''output_blocks''', str(__snake_case						), path['''old''']]						)
    _lowercase							      ='''.'''.join(['''up_blocks''', str(__snake_case						), '''resnets''', str(__snake_case						), path['''new''']]						)

    _lowercase							      =checkpoint[old_path]

 return new_checkpoint


if __name__ == "__main__":
  UpperCAmelCase__   	=							argparse.ArgumentParser()

  parser.add_argument(
      '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
  )

  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The config json file corresponding to the architecture.''',
  )

  parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

  UpperCAmelCase__   	=							parser.parse_args()

  UpperCAmelCase__   	=							torch.load(args.checkpoint_path)

  with open(args.config_file) as f:
    UpperCAmelCase__   	=							json.loads(f.read())

  UpperCAmelCase__   	=							convert_ldm_checkpoint(checkpoint, config)

  if "ldm" in config:
    del config["ldm"]

  UpperCAmelCase__   	=							UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    UpperCAmelCase__   	=							DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
    UpperCAmelCase__   	=							VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

    UpperCAmelCase__   	=							LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__   	=							logging.get_logger(__name__)

UpperCAmelCase__   	=							{
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}







class       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			'''wavlm'''





     def __init__(self		,     UpperCAmelCase=3_2		,     UpperCAmelCase=7_6_8		,     UpperCAmelCase=1_2		,     UpperCAmelCase=1_2		,     UpperCAmelCase=3_0_7_2		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.02		,     UpperCAmelCase=1e-5		,     UpperCAmelCase="group"		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)		,     UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,     UpperCAmelCase=(1_0, 3, 3, 3, 3, 2, 2)		,     UpperCAmelCase=False		,     UpperCAmelCase=1_2_8		,     UpperCAmelCase=1_6		,     UpperCAmelCase=3_2_0		,     UpperCAmelCase=8_0_0		,     UpperCAmelCase=False		,     UpperCAmelCase=True		,     UpperCAmelCase=0.05		,     UpperCAmelCase=1_0		,     UpperCAmelCase=2		,     UpperCAmelCase=0.0		,     UpperCAmelCase=1_0		,     UpperCAmelCase=3_2_0		,     UpperCAmelCase=2		,     UpperCAmelCase=0.1		,     UpperCAmelCase=1_0_0		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=0.1		,     UpperCAmelCase="mean"		,     UpperCAmelCase=False		,     UpperCAmelCase=False		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)		,     UpperCAmelCase=(5, 3, 3, 1, 1)		,     UpperCAmelCase=(1, 2, 3, 1, 1)		,     UpperCAmelCase=5_1_2		,     UpperCAmelCase=8_0		,     UpperCAmelCase=0		,     UpperCAmelCase=1		,     UpperCAmelCase=2		,     UpperCAmelCase=False		,     UpperCAmelCase=3		,     UpperCAmelCase=2		,     UpperCAmelCase=3		,     UpperCAmelCase=None		,     **UpperCAmelCase		,     )     ->   Optional[Any]:
      super().__init__(**UpperCAmelCase		,     pad_token_id=UpperCAmelCase		,     bos_token_id=UpperCAmelCase		,     eos_token_id=UpperCAmelCase       )
      _lowercase							      =hidden_size
      _lowercase							      =feat_extract_norm
      _lowercase							      =feat_extract_activation
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =conv_bias
      _lowercase							      =num_buckets
      _lowercase							      =max_bucket_distance
      _lowercase							      =num_conv_pos_embeddings
      _lowercase							      =num_conv_pos_embedding_groups
      _lowercase							      =len(self.conv_dim       )
      _lowercase							      =num_hidden_layers
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_act
      _lowercase							      =num_attention_heads
      _lowercase							      =hidden_dropout
      _lowercase							      =attention_dropout
      _lowercase							      =activation_dropout
      _lowercase							      =feat_proj_dropout
      _lowercase							      =final_dropout
      _lowercase							      =layerdrop
      _lowercase							      =layer_norm_eps
      _lowercase							      =initializer_range
      _lowercase							      =num_ctc_classes
      _lowercase							      =vocab_size
      _lowercase							      =do_stable_layer_norm
      _lowercase							      =use_weighted_layer_sum
      _lowercase							      =classifier_proj_size

      if (
          (len(self.conv_stride       ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
          or (len(self.conv_dim       ) != self.num_feat_extract_layers)
      ):
       raise ValueError(
           '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
           ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
           f" {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,"
           f" `len(config.conv_kernel) = {len(self.conv_kernel       )}`."       )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      _lowercase							      =apply_spec_augment
      _lowercase							      =mask_time_prob
      _lowercase							      =mask_time_length
      _lowercase							      =mask_time_min_masks
      _lowercase							      =mask_feature_prob
      _lowercase							      =mask_feature_length

      # parameters for pretraining with codevector quantized representations
      _lowercase							      =num_codevectors_per_group
      _lowercase							      =num_codevector_groups
      _lowercase							      =contrastive_logits_temperature
      _lowercase							      =num_negatives
      _lowercase							      =codevector_dim
      _lowercase							      =proj_codevector_dim
      _lowercase							      =diversity_loss_weight

      # ctc loss
      _lowercase							      =ctc_loss_reduction
      _lowercase							      =ctc_zero_infinity

      # adapter
      _lowercase							      =add_adapter
      _lowercase							      =adapter_kernel_size
      _lowercase							      =adapter_stride
      _lowercase							      =num_adapter_layers
      _lowercase							      =output_hidden_size or hidden_size

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      _lowercase							      =classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =xvector_output_dim





     @property
     def      __A					(self       )     ->   int:
      return functools.reduce(operator.mul		,     self.conv_stride		,     1       )

