
import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_UpperCAmelCase      :				Tuple          = logging.get_logger(__name__)

_UpperCAmelCase      :				str          = {
    "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",
}
_UpperCAmelCase      :				Any          = [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]


def    A (      lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		for attribute in key.split('.'				):
				UpperCamelCase						=  getattr(lowercase					, lowercase				)

		if weight_type is not None:
				UpperCamelCase						=  getattr(lowercase					, lowercase				).shape
		else:
				UpperCamelCase						=  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":
				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    A (      lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase						=  []
		UpperCamelCase						=  fairseq_model.state_dict()

		UpperCamelCase						=  hf_model.unispeech_sat.feature_extractor

		for name, value in fairseq_dict.items():
				UpperCamelCase						=  False
				if "conv_layers" in name:
						load_conv_layer(
						    lowercase					, lowercase					, lowercase					, lowercase					, hf_model.config.feat_extract_norm == 'group'					, )
						UpperCamelCase						=  True
				else:
						for key, mapped_key in MAPPING.items():
								UpperCamelCase						=  '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
										UpperCamelCase						=  True
										if "*" in mapped_key:
												UpperCamelCase						=  name.split(lowercase				)[0].split('.'				)[-2]
												UpperCamelCase						=  mapped_key.replace('*'					, lowercase				)
										if "weight_g" in name:
												UpperCamelCase						=  'weight_g'
										elif "weight_v" in name:
												UpperCamelCase						=  'weight_v'
										elif "bias" in name:
												UpperCamelCase						=  'bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												UpperCamelCase						=  'weight'
										else:
												UpperCamelCase						=  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    A (      lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		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:
						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.'''				)
						UpperCamelCase						=  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.'''				)
						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:
						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.'''				)
						UpperCamelCase						=  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.'''				)
						UpperCamelCase						=  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    A (      lowercase					, lowercase					, lowercase=None					, lowercase=None					, lowercase=True				)     ->						List[Any]:
		'''simple docstring'''

		if config_path is not None:
				UpperCamelCase						=  UniSpeechSatConfig.from_pretrained(lowercase				)
		else:
				UpperCamelCase						=  UniSpeechSatConfig()

		UpperCamelCase						=  ''

		if is_finetuned:
				UpperCamelCase						=  UniSpeechSatForCTC(lowercase				)
		else:
				UpperCamelCase						=  UniSpeechSatForPreTraining(lowercase				)

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

		recursively_load_weights(lowercase					, lowercase				)

		hf_wavavec.save_pretrained(lowercase				)


if __name__ == "__main__":
					_UpperCAmelCase      :				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"
					)
					_UpperCAmelCase      :				Dict          = 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
					)




import re


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		if len(re.findall('[ATCG]'					, lowercase				)				) != len(lowercase				):
				raise ValueError('Invalid Strand'				)

		return dna.translate(dna.maketrans('ATCG'					, 'TAGC'				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
