





'''simple docstring'''


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowerCAmelCase_ : Dict	  =       logging.get_logger(__name__)

lowerCAmelCase_ : Dict	  =       {
    "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_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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
lowerCAmelCase_ : Optional[Any]	  =       [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]







def  _lowerCamelCase	(__lowerCamelCase	:  Optional[Any]       ,	__lowerCamelCase	:  Tuple       ,	__lowerCamelCase	:  List[str]       ,	__lowerCamelCase	:  Tuple       ,	__lowerCamelCase	:  str  ) ->     List[Any]:
			for attribute in key.split("."  ):
						a__	 =			getattr(_UpperCamelCase       ,	_UpperCamelCase  )

			if weight_type is not None:
						a__	 =			getattr(_UpperCamelCase       ,	_UpperCamelCase  ).shape
			else:
						a__	 =			hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    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__	 =			value
			elif weight_type == "weight_g":
						a__	 =			value
			elif weight_type == "weight_v":
						a__	 =			value
			elif weight_type == "bias":
						a__	 =			value
			else:
						a__	 =			value

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







def  _lowerCamelCase	(__lowerCamelCase	:  int       ,	__lowerCamelCase	:  List[str]  ) ->     Any:
			a__	 =			[]
			a__	 =			fairseq_model.state_dict()

			a__	 =			hf_model.unispeech_sat.feature_extractor

			for name, value in fairseq_dict.items():
						a__	 =			False
						if "conv_layers" in name:
									load_conv_layer(
									    _UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	hf_model.config.feat_extract_norm == "group"       ,	)
									a__	 =			True
						else:
									for key, mapped_key in MAPPING.items():
												a__	 =			"unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
												if key in name or key.split("w2v_model."  )[-1] == name.split("."  )[0]:
															if "layer_norm_for_extract" in name and (".".join(name.split("."  )[:-1]  ) != key):
																		# special case since naming is very similar
																		continue
															a__	 =			True
															if "*" in mapped_key:
																		a__	 =			name.split(_UpperCamelCase  )[0].split("."  )[-2]
																		a__	 =			mapped_key.replace("*"       ,	_UpperCamelCase  )
															if "weight_g" in name:
																		a__	 =			"weight_g"
															elif "weight_v" in name:
																		a__	 =			"weight_v"
															elif "bias" in name:
																		a__	 =			"bias"
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		a__	 =			"weight"
															else:
																		a__	 =			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  _lowerCamelCase	(__lowerCamelCase	:  Optional[Any]       ,	__lowerCamelCase	:  Union[str, Any]       ,	__lowerCamelCase	:  Union[str, Any]       ,	__lowerCamelCase	:  Any       ,	__lowerCamelCase	:  List[str]  ) ->     Optional[int]:
			a__	 =			full_name.split("conv_layers."  )[-1]
			a__	 =			name.split("."  )
			a__	 =			int(items[0]  )
			a__	 =			int(items[1]  )

			if type_id == 0:
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''  )
									a__	 =			value
									logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''  )
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''  )
									a__	 =			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:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''  )
									a__	 =			value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''  )
									a__	 =			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  _lowerCamelCase	(__lowerCamelCase	:  Tuple       ,	__lowerCamelCase	:  int       ,	__lowerCamelCase	:  int=None       ,	__lowerCamelCase	:  Any=None       ,	__lowerCamelCase	:  Dict=True  ) ->     int:
			if config_path is not None:
						a__	 =			UniSpeechSatConfig.from_pretrained(_UpperCamelCase  )
			else:
						a__	 =			UniSpeechSatConfig()

			a__	 =			""

			if is_finetuned:
						a__	 =			UniSpeechSatForCTC(_UpperCamelCase  )
			else:
						a__	 =			UniSpeechSatForPreTraining(_UpperCamelCase  )

			a__ ,					a__ ,					a__	 =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]       ,	arg_overrides={"data": "/".join(dict_path.split("/"  )[:-1]  )}  )
			a__	 =			model[0].eval()

			recursively_load_weights(_UpperCamelCase       ,	_UpperCamelCase  )

			hf_wavavec.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
		lowerCAmelCase_ : Any	  =       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("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
		parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
		parser.add_argument(
		    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
		)
		lowerCAmelCase_ : Optional[Any]	  =       parser.parse_args()
		convert_unispeech_sat_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)








from __future__ import annotations





def 		a__      (		_UpperCamelCase      :			list[float]				):
   if len(_UpperCamelCase				) < 2:
      raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''				)
   if any(i <= 0 for i in nums				):
      raise ValueError('''All values must be greater than 0'''				)
   __lowerCamelCase    				=     nums.copy()
   copy_nums.sort()
   return copy_nums[-1] < sum(copy_nums[:-1]				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






