




"""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()
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        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Dict:


			"""simple docstring"""
			for attribute in key.split("."				):
						__UpperCAmelCase						:			Optional[int]												=       getattr(UpperCamelCase					,							UpperCamelCase				)

			if weight_type is not None:
						__UpperCAmelCase						:			Optional[int]												=       getattr(UpperCamelCase					,							UpperCamelCase				).shape
			else:
						__UpperCAmelCase						:			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":
						__UpperCAmelCase						:			List[str]												=       value
			elif weight_type == "weight_g":
						__UpperCAmelCase						:			Dict												=       value
			elif weight_type == "weight_v":
						__UpperCAmelCase						:			Optional[Any]												=       value
			elif weight_type == "bias":
						__UpperCAmelCase						:			Optional[int]												=       value
			else:
						__UpperCAmelCase						:			Dict												=       value

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

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      Any:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       []
			__UpperCAmelCase						:			Union[str, Any]												=       fairseq_model.state_dict()

			__UpperCAmelCase						:			Dict												=       hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						__UpperCAmelCase						:			List[Any]												=       False
						if "conv_layers" in name:
									load_conv_layer(
									    UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							hf_model.config.feat_extract_norm == "group"					,							)
									__UpperCAmelCase						:			Any												=       True
						else:
									for key, mapped_key in MAPPING.items():
												if key in name or key.split("w2v_model."				)[-1] == name.split("."				)[0]:
															__UpperCAmelCase						:			Optional[Any]												=       True
															if "*" in mapped_key:
																		__UpperCAmelCase						:			Optional[Any]												=       name.split(UpperCamelCase				)[0].split("."				)[-2]
																		__UpperCAmelCase						:			Union[str, Any]												=       mapped_key.replace("*"					,							UpperCamelCase				)
															if "weight_g" in name:
																		__UpperCAmelCase						:			Dict												=       "weight_g"
															elif "weight_v" in name:
																		__UpperCAmelCase						:			List[Any]												=       "weight_v"
															elif "bias" in name and "relative_attention_bias" not in name:
																		__UpperCAmelCase						:			Optional[int]												=       "bias"
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		__UpperCAmelCase						:			int												=       "weight"
															else:
																		__UpperCAmelCase						:			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					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Optional[int]:


			"""simple docstring"""
			__UpperCAmelCase						:			List[str]												=       full_name.split("conv_layers."				)[-1]
			__UpperCAmelCase						:			Tuple												=       name.split("."				)
			__UpperCAmelCase						:			List[str]												=       int(items[0]				)
			__UpperCAmelCase						:			Tuple												=       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						:			Union[str, Any]												=       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						:			Tuple												=       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						:			Optional[int]												=       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						:			List[str]												=       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					,							UpperCamelCase					,							UpperCamelCase=None				)      ->      List[Any]:


			"""simple docstring"""
			# load the pre-trained checkpoints
			__UpperCAmelCase						:			Optional[int]												=       torch.load(UpperCamelCase				)
			__UpperCAmelCase						:			Optional[int]												=       WavLMConfigOrig(checkpoint["cfg"]				)
			__UpperCAmelCase						:			List[str]												=       WavLMOrig(UpperCamelCase				)
			model.load_state_dict(checkpoint["model"]				)
			model.eval()

			if config_path is not None:
						__UpperCAmelCase						:			Optional[Any]												=       WavLMConfig.from_pretrained(UpperCamelCase				)
			else:
						__UpperCAmelCase						:			str												=       WavLMConfig()

			__UpperCAmelCase						:			str												=       WavLMModel(UpperCamelCase				)

			recursively_load_weights(UpperCamelCase					,							UpperCamelCase				)

			hf_wavlm.save_pretrained(UpperCamelCase				)


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)






'''simple docstring'''


# Function to print upper half of diamond (pyramid)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     for i in range(0       ,					SCREAMING_SNAKE_CASE							):
          for _ in range(0       ,					n - i - 1							):  # printing spaces
               print(" "       ,					end=""							)
          for _ in range(0       ,					i + 1							):  # printing stars
               print("* "       ,					end=""							)
          print()


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     for i in range(SCREAMING_SNAKE_CASE       ,					0       ,					-1							):
          for _ in range(SCREAMING_SNAKE_CASE       ,					0       ,					-1							):  # printing stars
               print("* "       ,					end=""							)
          print()
          for _ in range(n - i + 1       ,					0       ,					-1							):  # printing spaces
               print(" "       ,					end=""							)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if n <= 0:
          print("       ...       ....        nothing printing :("							)
          return
     floyd(SCREAMING_SNAKE_CASE							)  # upper half
     reverse_floyd(SCREAMING_SNAKE_CASE							)  # lower half


if __name__ == "__main__":
      print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
      print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
      _UpperCamelCase 							=  1
      while K:
            _UpperCamelCase 							=  int(input("""enter the number and , and see the magic : """))
            print()
            pretty_print(user_number)
            _UpperCamelCase 							=  int(input("""press 0 to exit... and 1 to continue..."""))

      print("""Good Bye...""")

