





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
A       :   Optional[Any]					= logging.get_logger(__name__)

A       :   Optional[int]					= {
    '''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''',
}
A       :   Union[str, Any]					= [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]







def   __lowerCamelCase   (				__a  :Dict       ,	__a  :Tuple       ,	__a  :Tuple       ,	__a  :Any       ,	__a  :Optional[Any]       )						->							Any:






		"""simple docstring"""

		for attribute in key.split("""."""       ):
				A__     =  getattr(__a       ,	__a       )

		if weight_type is not None:
				A__     =  getattr(__a       ,	__a       ).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   (				__a  :Tuple       ,	__a  :Tuple       )						->							int:






		"""simple docstring"""

		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(
						    __a       ,	__a       ,	__a       ,	__a       ,	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(__a       )[0].split("""."""       )[-2]
												A__     =  mapped_key.replace("""*"""       ,	__a       )
										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(__a       ,	__a       ,	__a       ,	__a       ,	__a       )
								continue
				if not is_used:
						unused_weights.append(__a       )

		logger.warning(F'Unused weights: {unused_weights}'       )







def   __lowerCamelCase   (				__a  :Optional[Any]       ,	__a  :Optional[int]       ,	__a  :Optional[Any]       ,	__a  :Tuple       ,	__a  :Union[str, Any]       )						->							Union[str, Any]:






		"""simple docstring"""

		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(__a       )







@torch.no_grad()
def   __lowerCamelCase   (				__a  :int       ,	__a  :Union[str, Any]       ,	__a  :Optional[int]=None       ,	__a  :Optional[Any]=None       ,	__a  :Tuple=True       )						->							int:






		"""simple docstring"""

		if config_path is not None:
				A__     =  UniSpeechSatConfig.from_pretrained(__a       )
		else:
				A__     =  UniSpeechSatConfig()

		A__     =  """"""

		if is_finetuned:
				A__     =  UniSpeechSatForCTC(__a       )
		else:
				A__     =  UniSpeechSatForPreTraining(__a       )

		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(__a       ,	__a       )

		hf_wavavec.save_pretrained(__a       )


if __name__ == "__main__":
					A       :   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('''--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'''
					)
					A       :   Union[str, 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
					)










def   __lowerCamelCase   (				__a  :int       )						->							Dict:






		"""simple docstring"""

		A__     =  len(__a       )
		A__     =  sum(__a       )

		A__     =  [[False for x in range(s + 1       )] for y in range(n + 1       )]

		for i in range(1       ,	n + 1       ):
				A__     =  True

		for i in range(1       ,	s + 1       ):
				A__     =  False

		for i in range(1       ,	n + 1       ):
				for j in range(1       ,	s + 1       ):
						A__     =  dp[i][j - 1]

						if arr[i - 1] <= j:
								A__     =  dp[i][j] or dp[i - 1][j - arr[i - 1]]

		for j in range(int(s / 2       )       ,	-1       ,	-1       ):
				if dp[n][j] is True:
						A__     =  s - 2 * j
						break

		return diff



