


from __future__ import annotations

from math import gcd





def   _UpperCAmelCase       (     UpperCAmelCase      :			int						,	UpperCAmelCase      :			int = 2						,	UpperCAmelCase      :			int = 1						,	UpperCAmelCase      :			int = 3						,	):
      """simple docstring"""




      if num < 2:
            raise ValueError("""The input value cannot be less than 2"""      )

      # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
      # algorithm struggles to find factors that are divisible by two.
      # As a workaround, we specifically check for two and even inputs.
      #   See: https://math.stackexchange.com/a/2856214/165820
      if num > 2 and num % 2 == 0:
            return 2

      # Pollard's Rho algorithm requires a function that returns pseudorandom
      # values between 0 <= X < ``num``.  It doesn't need to be random in the
      # sense that the output value is cryptographically secure or difficult
      # to calculate, it only needs to be random in the sense that all output
      # values should be equally likely to appear.
      # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
      # However, the success of Pollard's algorithm isn't guaranteed and is
      # determined in part by the initial seed and the chosen random function.
      # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
      # where ``C`` is a value that we can modify between each attempt.
      def rand_fn(UpperCAmelCase      :			int						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			int      ) -> int:
            return (pow(UpperCAmelCase						,	2      ) + step) % modulus

      for _ in range(UpperCAmelCase      ):
            # These track the position within the cycle detection logic.
            __lowerCamelCase     :      str       						=						seed
            __lowerCamelCase     :      Tuple       						=						seed

            while True:
                  # At each iteration, the tortoise moves one step and the hare moves two.
                  __lowerCamelCase     :      Optional[Any]       						=						rand_fn(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
                  __lowerCamelCase     :      Any       						=						rand_fn(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
                  __lowerCamelCase     :      Dict       						=						rand_fn(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )

                  # At some point both the tortoise and the hare will enter a cycle whose
                  # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
                  # the tortoise and hare will end up on the same value modulo ``p``.
                  # We can detect when this happens because the position difference between
                  # the tortoise and the hare will share a common divisor with ``num``.
                  __lowerCamelCase     :      Tuple       						=						gcd(hare - tortoise						,	UpperCAmelCase      )

                  if divisor == 1:
                        # No common divisor yet, just keep searching.
                        continue
                  else:
                        # We found a common divisor!
                        if divisor == num:
                              # Unfortunately, the divisor is ``num`` itself and is useless.
                              break
                        else:
                              # The divisor is a nontrivial factor of ``num``!
                              return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
            __lowerCamelCase     :      str       						=						hare

            # The new step value for the random function can just be incremented.
            # At first the results will be similar to what the old function would
            # have produced, but the value will quickly diverge after a bit.
            step += 1

      # We haven't found a divisor within the requested number of attempts.
      # We were unlucky or ``num`` itself is actually prime.
      return None


if __name__ == "__main__":
    import argparse

    __UpperCamelCase    :						int     =							argparse.ArgumentParser()
    parser.add_argument(
        'num',
        type=int,
        help='The value to find a divisor of',
    )
    parser.add_argument(
        '--attempts',
        type=int,
        default=3,
        help='The number of attempts before giving up',
    )
    __UpperCamelCase    :						str     =							parser.parse_args()

    __UpperCamelCase    :						Optional[int]     =							pollard_rho(args.num, attempts=args.attempts)
    if divisor is None:
        print(F'''{args.num} is probably prime''')
    else:
        __UpperCamelCase    :						Optional[Any]     =							args.num // divisor
        print(F'''{args.num} = {divisor} * {quotient}''')







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)



