




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()
a													=			logging.get_logger(__name__)

a													=			{
    "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",
}
a													=			[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Dict:
      for attribute in key.split("""."""       ):
            _UpperCAmelCase      =			getattr(snake_case			,     snake_case       )

      if weight_type is not None:
            _UpperCAmelCase      =			getattr(snake_case			,     snake_case       ).shape
      else:
            _UpperCAmelCase      =			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":
            _UpperCAmelCase      =			value
      elif weight_type == "weight_g":
            _UpperCAmelCase      =			value
      elif weight_type == "weight_v":
            _UpperCAmelCase      =			value
      elif weight_type == "bias":
            _UpperCAmelCase      =			value
      else:
            _UpperCAmelCase      =			value

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				List[Any]:
      _UpperCAmelCase      =			[]
      _UpperCAmelCase      =			fairseq_model.state_dict()

      _UpperCAmelCase      =			hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            _UpperCAmelCase      =			False
            if "conv_layers" in name:
                  load_conv_layer(
                      snake_case			,     snake_case			,     snake_case			,     snake_case			,     hf_model.config.feat_extract_norm == """group"""			,     )
                  _UpperCAmelCase      =			True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""       )[-1] == name.split("""."""       )[0]:
                              _UpperCAmelCase      =			True
                              if "*" in mapped_key:
                                    _UpperCAmelCase      =			name.split(snake_case       )[0].split("""."""       )[-2]
                                    _UpperCAmelCase      =			mapped_key.replace("""*"""			,     snake_case       )
                              if "weight_g" in name:
                                    _UpperCAmelCase      =			"""weight_g"""
                              elif "weight_v" in name:
                                    _UpperCAmelCase      =			"""weight_v"""
                              elif "bias" in name and "relative_attention_bias" not in name:
                                    _UpperCAmelCase      =			"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    _UpperCAmelCase      =			"""weight"""
                              else:
                                    _UpperCAmelCase      =			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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Any:
      _UpperCAmelCase      =			full_name.split("""conv_layers."""       )[-1]
      _UpperCAmelCase      =			name.split("""."""       )
      _UpperCAmelCase      =			int(items[0]       )
      _UpperCAmelCase      =			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."
                  )
                  _UpperCAmelCase      =			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."
                  )
                  _UpperCAmelCase      =			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."
                  )
                  _UpperCAmelCase      =			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."
                  )
                  _UpperCAmelCase      =			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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case=None       )						->				List[Any]:
      # load the pre-trained checkpoints
      _UpperCAmelCase      =			torch.load(snake_case       )
      _UpperCAmelCase      =			WavLMConfigOrig(checkpoint["""cfg"""]       )
      _UpperCAmelCase      =			WavLMOrig(snake_case       )
      model.load_state_dict(checkpoint["""model"""]       )
      model.eval()

      if config_path is not None:
            _UpperCAmelCase      =			WavLMConfig.from_pretrained(snake_case       )
      else:
            _UpperCAmelCase      =			WavLMConfig()

      _UpperCAmelCase      =			WavLMModel(snake_case       )

      recursively_load_weights(snake_case			,     snake_case       )

      hf_wavlm.save_pretrained(snake_case       )


if __name__ == "__main__":
    a													=			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")
    a													=			parser.parse_args()
    convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





from __future__ import annotations

from scipy.special import comb  # type: ignore




class       _A				:






		def __init__(    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			list_of_points
								# Degree determines the flexibility of the curve.
								# Degree = 1 will produce a straight line.
								_UpperCAmelCase      =			len(_SCREAMING_SNAKE_CASE			) - 1






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								assert 0 <= t <= 1, "Time t must be between 0 and 1."
								_UpperCAmelCase      =			[]
								for i in range(len(self.list_of_points			)			):
														# basis function for each i
														output_values.append(
														    comb(self.degree						,		_SCREAMING_SNAKE_CASE			) * ((1 - t) ** (self.degree - i)) * (t**i)			)
								# the basis must sum up to 1 for it to produce a valid Bezier curve.
								assert round(sum(_SCREAMING_SNAKE_CASE			)						,		5			) == 1
								return output_values






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								assert 0 <= t <= 1, "Time t must be between 0 and 1."

								_UpperCAmelCase      =			self.basis_function(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			0.0
								_UpperCAmelCase      =			0.0
								for i in range(len(self.list_of_points			)			):
														# For all points, sum up the product of i-th basis function and i-th point.
														x += basis_function[i] * self.list_of_points[i][0]
														y += basis_function[i] * self.list_of_points[i][1]
								return (x, y)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE = 0.01			):
								from matplotlib import pyplot as plt  # type: ignore

								_UpperCAmelCase      =			[]  # x coordinates of points to plot
								_UpperCAmelCase      =			[]  # y coordinates of points to plot

								_UpperCAmelCase      =			0.0
								while t <= 1:
														_UpperCAmelCase      =			self.bezier_curve_function(_SCREAMING_SNAKE_CASE			)
														to_plot_x.append(value[0]			)
														to_plot_y.append(value[1]			)
														t += step_size

								_UpperCAmelCase      =			[i[0] for i in self.list_of_points]
								_UpperCAmelCase      =			[i[1] for i in self.list_of_points]

								plt.plot(
								    _SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		color="""blue"""						,		label="""Curve of Degree """ + str(self.degree			)						,		)
								plt.scatter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		color="""red"""						,		label="""Control Points"""			)
								plt.legend()
								plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
				BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
				BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3