





'''simple docstring'''




import argparse
import os
import re


lowercase__       : Optional[int]         =       '''src/diffusers'''

# Pattern that looks at the indentation in a line.
lowercase__       : Dict         =       re.compile(r'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowercase__       : List[str]         =       re.compile(r'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowercase__       : Tuple         =       re.compile(r'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
lowercase__       : str         =       re.compile(r'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowercase__       : str         =       re.compile(r'''\[([^\]]+)\]''')






def 			_lowerCAmelCase     (    __snake_case    :					str     )	->	Tuple:
     __A		:					List[Any]						=			_re_indent.search(__snake_case     )
     return "" if search is None else search.groups()[0]






def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]					,				__snake_case    :					str=""					,				__snake_case    :					Any=None					,				__snake_case    :					List[Any]=None     )	->	Optional[int]:
     __A		:					Tuple						=			0
     __A		:					Optional[int]						=			code.split('\n'     )
     if start_prompt is not None:
          while not lines[index].startswith(__snake_case     ):
               index += 1
          __A		:					Optional[int]						=			['\n'.join(lines[:index]     )]
     else:
          __A		:					Any						=			[]

     # We split into blocks until we get to the `end_prompt` (or the end of the block).
     __A		:					Tuple						=			[lines[index]]
     index += 1
     while index < len(__snake_case     ) and (end_prompt is None or not lines[index].startswith(__snake_case     )):
          if len(lines[index]     ) > 0 and get_indent(lines[index]     ) == indent_level:
               if len(__snake_case     ) > 0 and get_indent(current_block[-1]     ).startswith(indent_level + ' '     ):
                    current_block.append(lines[index]     )
                    blocks.append('\n'.join(__snake_case     )     )
                    if index < len(__snake_case     ) - 1:
                         __A		:					Union[str, Any]						=			[lines[index + 1]]
                         index += 1
                    else:
                         __A		:					Union[str, Any]						=			[]
               else:
                    blocks.append('\n'.join(__snake_case     )     )
                    __A		:					Optional[Any]						=			[lines[index]]
          else:
               current_block.append(lines[index]     )
          index += 1

     # Adds current block if it's nonempty.
     if len(__snake_case     ) > 0:
          blocks.append('\n'.join(__snake_case     )     )

     # Add final block after end_prompt if provided.
     if end_prompt is not None and index < len(__snake_case     ):
          blocks.append('\n'.join(lines[index:]     )     )

     return blocks






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	int:

     def _inner(__snake_case    :					List[Any]     ):
          return key(__snake_case     ).lower().replace('_'					,				''     )

     return _inner






def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Any=None     )	->	List[Any]:

     # If no key is provided, we use a noop.
     def noop(__snake_case    :					List[Any]     ):
          return x

     if key is None:
          __A		:					Optional[Any]						=			noop
     # Constants are all uppercase, they go first.
     __A		:					str						=			[obj for obj in objects if key(__snake_case     ).isupper()]
     # Classes are not all uppercase but start with a capital, they go second.
     __A		:					List[str]						=			[obj for obj in objects if key(__snake_case     )[0].isupper() and not key(__snake_case     ).isupper()]
     # Functions begin with a lowercase, they go last.
     __A		:					str						=			[obj for obj in objects if not key(__snake_case     )[0].isupper()]

     __A		:					Tuple						=			ignore_underscore(__snake_case     )
     return sorted(__snake_case					,				key=__snake_case     ) + sorted(__snake_case					,				key=__snake_case     ) + sorted(__snake_case					,				key=__snake_case     )






def 			_lowerCAmelCase     (    __snake_case    :					Optional[int]     )	->	Tuple:

     # This inner function sort imports between [ ].
     def _replace(__snake_case    :					Tuple     ):
          __A		:					List[str]						=			match.groups()[0]
          if "," not in imports:
               return f'[{imports}]'
          __A		:					int						=			[part.strip().replace('"'					,				''     ) for part in imports.split(','     )]
          # We will have a final empty element if the line finished with a comma.
          if len(keys[-1]     ) == 0:
               __A		:					Dict						=			keys[:-1]
          return "[" + ", ".join([f'"{k}"' for k in sort_objects(__snake_case     )]     ) + "]"

     __A		:					List[Any]						=			import_statement.split('\n'     )
     if len(__snake_case     ) > 3:
          # Here we have to sort internal imports that are on several lines (one per name):
          # key: [
          #     "object1",
          #     "object2",
          #     ...
          # ]

          # We may have to ignore one or two lines on each side.
          __A		:					Optional[int]						=			2 if lines[1].strip() == '[' else 1
          __A		:					Any						=			[(i, _re_strip_line.search(__snake_case     ).groups()[0]) for i, line in enumerate(lines[idx:-idx]     )]
          __A		:					Optional[int]						=			sort_objects(__snake_case					,				key=lambda __snake_case     : x[1]     )
          __A		:					Any						=			[lines[x[0] + idx] for x in sorted_indices]
          return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]     )
     elif len(__snake_case     ) == 3:
          # Here we have to sort internal imports that are on one separate line:
          # key: [
          #     "object1", "object2", ...
          # ]
          if _re_bracket_content.search(lines[1]     ) is not None:
               __A		:					Union[str, Any]						=			_re_bracket_content.sub(_replace					,				lines[1]     )
          else:
               __A		:					Dict						=			[part.strip().replace('"'					,				''     ) for part in lines[1].split(','     )]
               # We will have a final empty element if the line finished with a comma.
               if len(keys[-1]     ) == 0:
                    __A		:					Tuple						=			keys[:-1]
               __A		:					List[Any]						=			get_indent(lines[1]     ) + ', '.join([f'"{k}"' for k in sort_objects(__snake_case     )]     )
          return "\n".join(__snake_case     )
     else:
          # Finally we have to deal with imports fitting on one line
          __A		:					Optional[Any]						=			_re_bracket_content.sub(_replace					,				__snake_case     )
          return import_statement






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]					,				__snake_case    :					List[Any]=True     )	->	Optional[Any]:
     with open(__snake_case					,				'r'     ) as f:
          __A		:					Dict						=			f.read()

     if "_import_structure" not in code:
          return

     # Blocks of indent level 0
     __A		:					str						=			split_code_in_indented_blocks(
         __snake_case					,				start_prompt='_import_structure = {'					,				end_prompt='if TYPE_CHECKING:'     )

     # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
     for block_idx in range(1					,				len(__snake_case     ) - 1     ):
          # Check if the block contains some `_import_structure`s thingy to sort.
          __A		:					Tuple						=			main_blocks[block_idx]
          __A		:					int						=			block.split('\n'     )

          # Get to the start of the imports.
          __A		:					Tuple						=			0
          while line_idx < len(__snake_case     ) and "_import_structure" not in block_lines[line_idx]:
               # Skip dummy import blocks
               if "import dummy" in block_lines[line_idx]:
                    __A		:					Optional[int]						=			len(__snake_case     )
               else:
                    line_idx += 1
          if line_idx >= len(__snake_case     ):
               continue

          # Ignore beginning and last line: they don't contain anything.
          __A		:					Dict						=			'\n'.join(block_lines[line_idx:-1]     )
          __A		:					int						=			get_indent(block_lines[1]     )
          # Slit the internal block into blocks of indent level 1.
          __A		:					Optional[int]						=			split_code_in_indented_blocks(__snake_case					,				indent_level=__snake_case     )
          # We have two categories of import key: list or _import_structure[key].append/extend
          __A		:					Any						=			_re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
          # Grab the keys, but there is a trap: some lines are empty or just comments.
          __A		:					Dict						=			[(pattern.search(__snake_case     ).groups()[0] if pattern.search(__snake_case     ) is not None else None) for b in internal_blocks]
          # We only sort the lines with a key.
          __A		:					Optional[Any]						=			[(i, key) for i, key in enumerate(__snake_case     ) if key is not None]
          __A		:					Tuple						=			[x[0] for x in sorted(__snake_case					,				key=lambda __snake_case     : x[1]     )]

          # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
          __A		:					str						=			0
          __A		:					Any						=			[]
          for i in range(len(__snake_case     )     ):
               if keys[i] is None:
                    reordered_blocks.append(internal_blocks[i]     )
               else:
                    __A		:					str						=			sort_objects_in_import(internal_blocks[sorted_indices[count]]     )
                    reordered_blocks.append(__snake_case     )
                    count += 1

        # And we put our main block back together with its first and last line.
          __A		:					int						=			'\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]     )

     if code != "\n".join(__snake_case     ):
          if check_only:
               return True
          else:
               print(f'Overwriting {file}.'     )
               with open(__snake_case					,				'w'     ) as f:
                    f.write('\n'.join(__snake_case     )     )






def 			_lowerCAmelCase     (    __snake_case    :					int=True     )	->	Optional[Any]:
     __A		:					Tuple						=			[]
     for root, _, files in os.walk(__snake_case     ):
          if "__init__.py" in files:
               __A		:					List[Any]						=			sort_imports(os.path.join(__snake_case					,				'__init__.py'     )					,				check_only=__snake_case     )
               if result:
                    __A		:					Dict						=			[os.path.join(__snake_case					,				'__init__.py'     )]
     if len(__snake_case     ) > 0:
          raise ValueError(f'Would overwrite {len(__snake_case     )} files, run `make style`.'     )


if __name__ == "__main__":
  lowercase__       : Optional[int]         =       argparse.ArgumentParser()
  parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
  lowercase__       : Union[str, Any]         =       parser.parse_args()

  sort_imports_in_all_inits(check_only=args.check_only)






'''simple docstring'''




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__       : Union[str, Any]         =       logging.get_logger(__name__)

lowercase__       : int         =       {
    '''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__       : Dict         =       [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 			_lowerCAmelCase     (    __snake_case    :					int					,				__snake_case    :					Union[str, Any]					,				__snake_case    :					Tuple					,				__snake_case    :					Any					,				__snake_case    :					List[str]     )	->	Union[str, Any]:
     for attribute in key.split('.'     ):
          __A		:					int						=			getattr(__snake_case					,				__snake_case     )

     if weight_type is not None:
          __A		:					Optional[int]						=			getattr(__snake_case					,				__snake_case     ).shape
     else:
          __A		:					List[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":
          __A		:					Tuple						=			value
     elif weight_type == "weight_g":
          __A		:					Union[str, Any]						=			value
     elif weight_type == "weight_v":
          __A		:					Optional[Any]						=			value
     elif weight_type == "bias":
          __A		:					Optional[int]						=			value
     else:
          __A		:					Optional[int]						=			value

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






def 			_lowerCAmelCase     (    __snake_case    :					Any					,				__snake_case    :					List[str]     )	->	List[Any]:
     __A		:					Optional[Any]						=			[]
     __A		:					Any						=			fairseq_model.state_dict()

     __A		:					Union[str, Any]						=			hf_model.feature_extractor

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

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

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






def 			_lowerCAmelCase     (    __snake_case    :					List[str]					,				__snake_case    :					List[Any]					,				__snake_case    :					Union[str, Any]					,				__snake_case    :					List[str]					,				__snake_case    :					Optional[int]     )	->	int:
     __A		:					int						=			full_name.split('conv_layers.'     )[-1]
     __A		:					List[str]						=			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		:					Optional[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.'
               )
               __A		:					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:
               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		:					Dict						=			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		:					Any						=			value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
     else:
          unused_weights.append(__snake_case     )






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]					,				__snake_case    :					List[str]					,				__snake_case    :					Tuple=None     )	->	Any:
     # load the pre-trained checkpoints
     __A		:					List[str]						=			torch.load(__snake_case     )
     __A		:					Dict						=			WavLMConfigOrig(checkpoint['cfg']     )
     __A		:					Optional[int]						=			WavLMOrig(__snake_case     )
     model.load_state_dict(checkpoint['model']     )
     model.eval()

     if config_path is not None:
          __A		:					List[Any]						=			WavLMConfig.from_pretrained(__snake_case     )
     else:
          __A		:					Dict						=			WavLMConfig()

     __A		:					Optional[Any]						=			WavLMModel(__snake_case     )

     recursively_load_weights(__snake_case					,				__snake_case     )

     hf_wavlm.save_pretrained(__snake_case     )


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