


import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__UpperCamelCase    :						Optional[Any]     =							'src/diffusers'
__UpperCamelCase    :						Any     =							'.'


# This is to make sure the diffusers module imported is the one in the repo.
__UpperCamelCase    :						Optional[Any]     =							importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(DIFFUSERS_PATH, '__init__.py'),
    submodule_search_locations=[DIFFUSERS_PATH],
)
__UpperCamelCase    :						List[Any]     =							spec.loader.load_module()





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




      return line.startswith(__UpperCamelCase      ) or len(__UpperCamelCase      ) <= 1 or re.search(r"""^\s*\)(\s*->.*:|:)\s*$"""						,	__UpperCamelCase      ) is not None





def   _UpperCAmelCase       (     UpperCAmelCase      :			str      ):
      """simple docstring"""




      __lowerCamelCase     :      Dict       						=						object_name.split("""."""      )
      __lowerCamelCase     :      Any       						=						0

      # First let's find the module where our object lives.
      __lowerCamelCase     :      Dict       						=						parts[i]
      while i < len(__UpperCamelCase      ) and not os.path.isfile(os.path.join(__UpperCamelCase						,	f"""{module}.py"""      )      ):
            i += 1
            if i < len(__UpperCamelCase      ):
                  __lowerCamelCase     :      str       						=						os.path.join(__UpperCamelCase						,	parts[i]      )
      if i >= len(__UpperCamelCase      ):
            raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}."""      )

      with open(os.path.join(__UpperCamelCase						,	f"""{module}.py"""      )						,	"""r"""						,	encoding="""utf-8"""						,	newline="""\n"""      ) as f:
            __lowerCamelCase     :      int       						=						f.readlines()

      # Now let's find the class / func in the code!
      __lowerCamelCase     :      str       						=						""""""
      __lowerCamelCase     :      Optional[int]       						=						0
      for name in parts[i + 1 :]:
            while (
                line_index < len(__UpperCamelCase      ) and re.search(rf"""^{indent}(class|def)\s+{name}(\(|\:)"""						,	lines[line_index]      ) is None
            ):
                  line_index += 1
            indent += "    "
            line_index += 1

      if line_index >= len(__UpperCamelCase      ):
            raise ValueError(f""" {object_name} does not match any function or class in {module}."""      )

      # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
      __lowerCamelCase     :      Optional[Any]       						=						line_index
      while line_index < len(__UpperCamelCase      ) and _should_continue(lines[line_index]						,	__UpperCamelCase      ):
            line_index += 1
      # Clean up empty lines at the end (if any).
      while len(lines[line_index - 1]      ) <= 1:
            line_index -= 1

      __lowerCamelCase     :      int       						=						lines[start_index:line_index]
      return "".join(__UpperCamelCase      )


__UpperCamelCase    :						Tuple     =							re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
__UpperCamelCase    :						Union[str, Any]     =							re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
__UpperCamelCase    :						Any     =							re.compile(R'<FILL\s+[^>]*>')





def   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple      ):
      """simple docstring"""




      __lowerCamelCase     :      Union[str, Any]       						=						code.split("""\n"""      )
      __lowerCamelCase     :      Any       						=						0
      while idx < len(__UpperCamelCase      ) and len(lines[idx]      ) == 0:
            idx += 1
      if idx < len(__UpperCamelCase      ):
            return re.search(r"""^(\s*)\S"""						,	lines[idx]      ).groups()[0]
      return ""





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




      __lowerCamelCase     :      Optional[Any]       						=						len(get_indent(__UpperCamelCase      )      ) > 0
      if has_indent:
            __lowerCamelCase     :      Tuple       						=						f"""class Bla:\n{code}"""
      __lowerCamelCase     :      Tuple       						=						black.Mode(target_versions={black.TargetVersion.PYaa}						,	line_length=119						,	preview=__UpperCamelCase      )
      __lowerCamelCase     :      Optional[Any]       						=						black.format_str(__UpperCamelCase						,	mode=__UpperCamelCase      )
      __lowerCamelCase						, __lowerCamelCase     :      Union[str, Any]       						=						style_docstrings_in_code(__UpperCamelCase      )
      return result[len("""class Bla:\n"""      ) :] if has_indent else result





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Tuple=False      ):
      """simple docstring"""




      with open(__UpperCamelCase						,	"""r"""						,	encoding="""utf-8"""						,	newline="""\n"""      ) as f:
            __lowerCamelCase     :      Dict       						=						f.readlines()
      __lowerCamelCase     :      Union[str, Any]       						=						[]
      __lowerCamelCase     :      int       						=						0
      # Not a for loop cause `lines` is going to change (if `overwrite=True`).
      while line_index < len(__UpperCamelCase      ):
            __lowerCamelCase     :      List[Any]       						=						_re_copy_warning.search(lines[line_index]      )
            if search is None:
                  line_index += 1
                  continue

            # There is some copied code here, let's retrieve the original.
            __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Optional[Any]       						=						search.groups()
            __lowerCamelCase     :      Dict       						=						find_code_in_diffusers(__UpperCamelCase      )
            __lowerCamelCase     :      Any       						=						get_indent(__UpperCamelCase      )

            __lowerCamelCase     :      Optional[int]       						=						line_index + 1 if indent == theoretical_indent else line_index + 2
            __lowerCamelCase     :      int       						=						theoretical_indent
            __lowerCamelCase     :      List[str]       						=						start_index

            # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
            __lowerCamelCase     :      Dict       						=						True
            while line_index < len(__UpperCamelCase      ) and should_continue:
                  line_index += 1
                  if line_index >= len(__UpperCamelCase      ):
                        break
                  __lowerCamelCase     :      List[Any]       						=						lines[line_index]
                  __lowerCamelCase     :      Optional[int]       						=						_should_continue(__UpperCamelCase						,	__UpperCamelCase      ) and re.search(f"""^{indent}# End copy"""						,	__UpperCamelCase      ) is None
            # Clean up empty lines at the end (if any).
            while len(lines[line_index - 1]      ) <= 1:
                  line_index -= 1

            __lowerCamelCase     :      List[Any]       						=						lines[start_index:line_index]
            __lowerCamelCase     :      Any       						=						"""""".join(__UpperCamelCase      )

            # Remove any nested `Copied from` comments to avoid circular copies
            __lowerCamelCase     :      int       						=						[line for line in theoretical_code.split("""\n"""      ) if _re_copy_warning.search(__UpperCamelCase      ) is None]
            __lowerCamelCase     :      Tuple       						=						"""\n""".join(__UpperCamelCase      )

            # Before comparing, use the `replace_pattern` on the original code.
            if len(__UpperCamelCase      ) > 0:
                  __lowerCamelCase     :      Any       						=						replace_pattern.replace("""with"""						,	""""""      ).split(""","""      )
                  __lowerCamelCase     :      Optional[Any]       						=						[_re_replace_pattern.search(__UpperCamelCase      ) for p in patterns]
                  for pattern in patterns:
                        if pattern is None:
                              continue
                        __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Tuple       						=						pattern.groups()
                        __lowerCamelCase     :      Dict       						=						re.sub(__UpperCamelCase						,	__UpperCamelCase						,	__UpperCamelCase      )
                        if option.strip() == "all-casing":
                              __lowerCamelCase     :      Tuple       						=						re.sub(obja.lower()						,	obja.lower()						,	__UpperCamelCase      )
                              __lowerCamelCase     :      List[str]       						=						re.sub(obja.upper()						,	obja.upper()						,	__UpperCamelCase      )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
                  __lowerCamelCase     :      Dict       						=						blackify(lines[start_index - 1] + theoretical_code      )
                  __lowerCamelCase     :      Union[str, Any]       						=						theoretical_code[len(lines[start_index - 1]      ) :]

            # Test for a diff and act accordingly.
            if observed_code != theoretical_code:
                  diffs.append([object_name, start_index]      )
                  if overwrite:
                        __lowerCamelCase     :      str       						=						lines[:start_index] + [theoretical_code] + lines[line_index:]
                        __lowerCamelCase     :      Tuple       						=						start_index + 1

      if overwrite and len(__UpperCamelCase      ) > 0:
            # Warn the user a file has been modified.
            print(f"""Detected changes, rewriting {filename}."""      )
            with open(__UpperCamelCase						,	"""w"""						,	encoding="""utf-8"""						,	newline="""\n"""      ) as f:
                  f.writelines(__UpperCamelCase      )
      return diffs





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[str] = False      ):
      """simple docstring"""




      __lowerCamelCase     :      Optional[Any]       						=						glob.glob(os.path.join(__UpperCamelCase						,	"""**/*.py"""      )						,	recursive=__UpperCamelCase      )
      __lowerCamelCase     :      Union[str, Any]       						=						[]
      for filename in all_files:
            __lowerCamelCase     :      Dict       						=						is_copy_consistent(__UpperCamelCase						,	__UpperCamelCase      )
            diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
      if not overwrite and len(__UpperCamelCase      ) > 0:
            __lowerCamelCase     :      int       						=						"""\n""".join(__UpperCamelCase      )
            raise Exception(
                """Found the following copy inconsistencies:\n"""
                + diff
                + """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."""      )


if __name__ == "__main__":
      __UpperCamelCase    :						int     =							argparse.ArgumentParser()
      parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
      __UpperCamelCase    :						Any     =							parser.parse_args()

      check_copies(args.fix_and_overwrite)







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)



