



import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_lowercase		:				Optional[int]      		=logging.get_logger(__name__)

_lowercase		:				Tuple      		={
    "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",
}
_lowercase		:				Dict      		=[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       Dict						,       _lowercase    :       Dict						,       _lowercase    :       Optional[int]						,       _lowercase    :       str)							->	Optional[int]:







			"""simple docstring"""
			for attribute in key.split("""."""):
						a__       :						int							=  getattr(_lowercase						,       _lowercase)

			if weight_type is not None:
						a__       :						Union[str, Any]							=  getattr(_lowercase						,       _lowercase).shape
			else:
						a__       :						Union[str, Any]							=  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__       :						Any							=  value
			elif weight_type == "weight_g":
						a__       :						List[Any]							=  value
			elif weight_type == "weight_v":
						a__       :						Tuple							=  value
			elif weight_type == "bias":
						a__       :						Dict							=  value
			else:
						a__       :						int							=  value

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

def      lowerCAmelCase_					(  _lowercase    :       Dict						,       _lowercase    :       Optional[int])							->	Optional[int]:







			"""simple docstring"""
			a__       :						List[Any]							=  []
			a__       :						Tuple							=  fairseq_model.state_dict()

			a__       :						str							=  hf_model.unispeech_sat.feature_extractor

			for name, value in fairseq_dict.items():
						a__       :						Optional[Any]							=  False
						if "conv_layers" in name:
									load_conv_layer(
									    _lowercase						,       _lowercase						,       _lowercase						,       _lowercase						,       hf_model.config.feat_extract_norm == """group"""						,       )
									a__       :						Tuple							=  True
						else:
									for key, mapped_key in MAPPING.items():
												a__       :						Union[str, Any]							=  """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__       :						List[Any]							=  True
															if "*" in mapped_key:
																		a__       :						Dict							=  name.split(_lowercase)[0].split(""".""")[-2]
																		a__       :						Optional[int]							=  mapped_key.replace("""*"""						,       _lowercase)
															if "weight_g" in name:
																		a__       :						Optional[int]							=  """weight_g"""
															elif "weight_v" in name:
																		a__       :						Any							=  """weight_v"""
															elif "bias" in name:
																		a__       :						Dict							=  """bias"""
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		a__       :						Tuple							=  """weight"""
															else:
																		a__       :						List[str]							=  None
															set_recursively(_lowercase						,       _lowercase						,       _lowercase						,       _lowercase						,       _lowercase)
												continue
						if not is_used:
									unused_weights.append(_lowercase)

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

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str						,       _lowercase    :       List[str]						,       _lowercase    :       Dict						,       _lowercase    :       int)							->	List[str]:







			"""simple docstring"""
			a__       :						Dict							=  full_name.split("""conv_layers.""")[-1]
			a__       :						Tuple							=  name.split(""".""")
			a__       :						Optional[int]							=  int(items[0])
			a__       :						Tuple							=  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__       :						Optional[Any]							=  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__       :						Optional[int]							=  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__       :						List[Any]							=  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__       :						Optional[int]							=  value
									logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
			else:
						unused_weights.append(_lowercase)

@torch.no_grad()
def      lowerCAmelCase_					(  _lowercase    :       Any						,       _lowercase    :       Dict						,       _lowercase    :       int=None						,       _lowercase    :       Dict=None						,       _lowercase    :       Optional[Any]=True)							->	Optional[int]:







			"""simple docstring"""
			if config_path is not None:
						a__       :						Union[str, Any]							=  UniSpeechSatConfig.from_pretrained(_lowercase)
			else:
						a__       :						List[str]							=  UniSpeechSatConfig()

			a__       :						Union[str, Any]							=  """"""

			if is_finetuned:
						a__       :						Dict							=  UniSpeechSatForCTC(_lowercase)
			else:
						a__       :						List[str]							=  UniSpeechSatForPreTraining(_lowercase)

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

			recursively_load_weights(_lowercase						,       _lowercase)

			hf_wavavec.save_pretrained(_lowercase)


if __name__ == "__main__":
		_lowercase		:				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"
		)
		_lowercase		:				Tuple      		=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 string import ascii_uppercase

_lowercase		:				str      		={char: i for i, char in enumerate(ascii_uppercase)}
_lowercase		:				Dict      		=dict(enumerate(ascii_uppercase))

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	str:







			"""simple docstring"""
			a__       :						Any							=  len(_lowercase)
			a__       :						Optional[int]							=  0
			while True:
						if x == i:
									a__       :						Optional[Any]							=  0
						if len(_lowercase) == len(_lowercase):
									break
						key += key[i]
						i += 1
			return key

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	str:







			"""simple docstring"""
			a__       :						Tuple							=  """"""
			a__       :						str							=  0
			for letter in message:
						if letter == " ":
									cipher_text += " "
						else:
									a__       :						List[str]							=  (dicta[letter] - dicta[key_new[i]]) % 26
									i += 1
									cipher_text += dicta[x]
			return cipher_text

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	str:







			"""simple docstring"""
			a__       :						int							=  """"""
			a__       :						int							=  0
			for letter in cipher_text:
						if letter == " ":
									or_txt += " "
						else:
									a__       :						Dict							=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
									i += 1
									or_txt += dicta[x]
			return or_txt

def      lowerCAmelCase_					(  )							->	None:







			"""simple docstring"""
			a__       :						List[Any]							=  """THE GERMAN ATTACK"""
			a__       :						List[Any]							=  """SECRET"""
			a__       :						Tuple							=  generate_key(_lowercase						,       _lowercase)
			a__       :						str							=  cipher_text(_lowercase						,       _lowercase)
			print(F'''Encrypted Text = {s}''')
			print(F'''Original Text = {original_text(_lowercase						,       _lowercase)}''')


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()




