





'''simple docstring'''







import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

snake_case_     :       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",
    "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",
    "mask_emb": "masked_spec_embed",
}
snake_case_     :       int						=  [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Union[str, Any],			SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			Dict,			SCREAMING_SNAKE_CASE__      :			Optional[int],			SCREAMING_SNAKE_CASE__      :			List[Any]   ) ->	int:
						for attribute in key.split('''.'''   ):
												UpperCAmelCase_			:      str     						=  getattr(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )

						if weight_type is not None:
												UpperCAmelCase_			:      List[str]     						=  getattr(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ).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[Any]     						=  value
						elif weight_type == "weight_g":
												UpperCAmelCase_			:      str     						=  value
						elif weight_type == "weight_v":
												UpperCAmelCase_			:      Tuple     						=  value
						elif weight_type == "bias":
												UpperCAmelCase_			:      str     						=  value
						else:
												UpperCAmelCase_			:      Optional[int]     						=  value

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





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			Tuple   ) ->	List[Any]:
						UpperCAmelCase_			:      List[Any]     						=  []
						UpperCAmelCase_			:      Tuple     						=  fairseq_model.state_dict()

						UpperCAmelCase_			:      Tuple     						=  hf_model.feature_extractor
						UpperCAmelCase_			:      Union[str, Any]     						=  hf_model.adapter

						for name, value in fairseq_dict.items():
												UpperCAmelCase_			:      Union[str, Any]     						=  False
												if "conv_layers" in name:
																		load_conv_layer(
																		    SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			hf_model.config.feat_extract_norm == '''group''',			)
																		UpperCAmelCase_			:      List[str]     						=  True
												elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']   ):
																		load_adapter(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
																		UpperCAmelCase_			:      List[Any]     						=  True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split('''w2v_model.'''   )[-1] == name.split('''.'''   )[0]:
																														UpperCAmelCase_			:      Union[str, Any]     						=  True
																														if "*" in mapped_key:
																																				UpperCAmelCase_			:      List[str]     						=  name.split(SCREAMING_SNAKE_CASE__   )[0].split('''.'''   )[-2]
																																				UpperCAmelCase_			:      Any     						=  mapped_key.replace('''*''',			SCREAMING_SNAKE_CASE__   )
																														if "weight_g" in name:
																																				UpperCAmelCase_			:      Dict     						=  '''weight_g'''
																														elif "weight_v" in name:
																																				UpperCAmelCase_			:      List[Any]     						=  '''weight_v'''
																														elif "bias" in name:
																																				UpperCAmelCase_			:      Dict     						=  '''bias'''
																														elif "weight" in name:
																																				UpperCAmelCase_			:      Dict     						=  '''weight'''
																														else:
																																				UpperCAmelCase_			:      Optional[int]     						=  None
																														set_recursively(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
																								continue
												if not is_used:
																		unused_weights.append(SCREAMING_SNAKE_CASE__   )

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





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Dict,			SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			Optional[int],			SCREAMING_SNAKE_CASE__      :			Optional[Any]   ) ->	Dict:
						UpperCAmelCase_			:      Any     						=  full_name.split('''conv_layers.'''   )[-1]
						UpperCAmelCase_			:      Union[str, Any]     						=  name.split('''.'''   )
						UpperCAmelCase_			:      int     						=  int(items[0]   )
						UpperCAmelCase_			:      List[Any]     						=  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_			:      Dict     						=  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_			:      Dict     						=  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_			:      List[Any]     						=  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_			:      Dict     						=  value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
						else:
												unused_weights.append(SCREAMING_SNAKE_CASE__   )





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Any   ) ->	Union[str, Any]:
						UpperCAmelCase_			:      Dict     						=  full_name.split('''adaptor.'''   )[-1]
						UpperCAmelCase_			:      Union[str, Any]     						=  name.split('''.'''   )

						if items[1].isdigit():
												UpperCAmelCase_			:      Union[str, Any]     						=  int(items[1]   )
						else:
												UpperCAmelCase_			:      Tuple     						=  None

						if "adaptor" not in full_name:
												if "proj_ln" in full_name:
																		# has to be layer norm
																		if "bias" in name:
																								assert (
																								    value.shape == adapter.proj_layer_norm.bias.data.shape
																								), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
																								UpperCAmelCase_			:      int     						=  value
																								logger.info(F"""Adapter proj layer norm bias was initialized from {full_name}."""   )
																		if "weight" in name:
																								assert (
																								    value.shape == adapter.proj_layer_norm.weight.data.shape
																								), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
																								UpperCAmelCase_			:      Optional[Any]     						=  value
												else:
																		# has to be projection layer
																		if "bias" in name:
																								assert (
																								    value.shape == adapter.proj.bias.data.shape
																								), F"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
																								UpperCAmelCase_			:      List[str]     						=  value
																								logger.info(F"""Adapter proj layer bias was initialized from {full_name}."""   )
																		if "weight" in name:
																								assert (
																								    value.shape == adapter.proj.weight.data.shape
																								), F"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
																								UpperCAmelCase_			:      int     						=  value
																								logger.info(F"""Adapter proj layer weight was initialized from {full_name}."""   )
						elif isinstance(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ):
												if "bias" in name:
																		assert (
																		    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																		), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
																		UpperCAmelCase_			:      Tuple     						=  value
																		logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""   )
												elif "weight" in name:
																		assert (
																		    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																		), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
																		UpperCAmelCase_			:      List[Any]     						=  value
																		logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""   )
						else:
												unused_weights.append(SCREAMING_SNAKE_CASE__   )





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str   ) ->	Dict:
						UpperCAmelCase_						,       UpperCAmelCase_			:      Dict     						=  emb.weight.shape
						UpperCAmelCase_			:      Any     						=  nn.Linear(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			bias=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Optional[int]     						=  emb.weight.data
						return lin_layer





@torch.no_grad()
def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			List[Any],			SCREAMING_SNAKE_CASE__      :			Union[str, Any],			SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			List[Any],			SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			int,			) ->	Any:
						UpperCAmelCase_			:      Optional[Any]     						=  WavaVecaConfig.from_pretrained(
						    SCREAMING_SNAKE_CASE__,			add_adapter=SCREAMING_SNAKE_CASE__,			adapter_stride=SCREAMING_SNAKE_CASE__,			adapter_kernel_size=SCREAMING_SNAKE_CASE__,			use_auth_token=SCREAMING_SNAKE_CASE__,			output_hidden_size=SCREAMING_SNAKE_CASE__,			)
						UpperCAmelCase_			:      Optional[Any]     						=  MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__   )

						# load model
						UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path],			arg_overrides={
						        '''config_yaml''': config_yaml_path,
						        '''data''': '''/'''.join(dict_path.split('''/'''   )[:-1]   ),
						        '''w2v_path''': checkpoint_path,
						        '''load_pretrained_decoder_from''': None,
						    },			)
						UpperCAmelCase_			:      Any     						=  model[0].eval()

						# load feature extractor
						UpperCAmelCase_			:      List[str]     						=  WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__,			use_auth_token=SCREAMING_SNAKE_CASE__   )

						# set weights for wav2vec2 encoder
						UpperCAmelCase_			:      int     						=  WavaVecaModel(SCREAMING_SNAKE_CASE__   )

						recursively_load_weights_wavaveca(model.encoder,			SCREAMING_SNAKE_CASE__   )

						# load decoder weights
						UpperCAmelCase_			:      Union[str, Any]     						=  MBartForCausalLM(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),			strict=SCREAMING_SNAKE_CASE__   )
						logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}"""   )
						logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""   )

						UpperCAmelCase_			:      int     						=  SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__,			decoder=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      List[str]     						=  False

						UpperCAmelCase_			:      int     						=  MBartaaTokenizer(SCREAMING_SNAKE_CASE__   )
						tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__   )

						UpperCAmelCase_			:      List[str]     						=  hf_wavavec.config.to_dict()
						UpperCAmelCase_			:      Tuple     						=  tokenizer.pad_token_id
						UpperCAmelCase_			:      Optional[int]     						=  tokenizer.bos_token_id
						UpperCAmelCase_			:      Optional[int]     						=  tokenizer.eos_token_id
						UpperCAmelCase_			:      int     						=  '''mbart50'''
						UpperCAmelCase_			:      List[str]     						=  '''wav2vec2'''

						UpperCAmelCase_			:      Dict     						=  tokenizer.eos_token_id
						UpperCAmelCase_			:      List[str]     						=  250004
						UpperCAmelCase_			:      List[str]     						=  tokenizer.eos_token_id

						UpperCAmelCase_			:      Dict     						=  SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE__   )

						hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__   )
						feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
					snake_case_     :       int						=  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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
					parser.add_argument(
					    "--encoder_config_path",
					    default="facebook/wav2vec2-xls-r-1b",
					    type=str,
					    help="Path to hf encoder wav2vec2 checkpoint config",
					)
					parser.add_argument(
					    "--decoder_config_path",
					    default="facebook/mbart-large-50-one-to-many-mmt",
					    type=str,
					    help="Path to hf decoder checkpoint config",
					)
					parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
					parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
					parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
					parser.add_argument("--encoder_output_dim", default=10_24, type=int, help="encoder output dim")
					parser.add_argument("--start_token_id", default=25_00_04, type=int, help="`decoder_start_token_id` of model config")

					snake_case_     :       int						=  parser.parse_args()
					convert_wavaveca_checkpoint(
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.dict_path,
					    args.config_yaml_path,
					    encoder_config_path=args.encoder_config_path,
					    decoder_config_path=args.decoder_config_path,
					    add_adapter=args.add_adapter,
					    adapter_kernel_size=args.adapter_kernel_size,
					    adapter_stride=args.adapter_stride,
					    decoder_start_token_id=args.start_token_id,
					    encoder_output_dim=args.encoder_output_dim,
					)












'''simple docstring'''







def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			list[int]   ) ->	list[list[int]]:
						UpperCAmelCase_			:      int     						=  []
						if len(SCREAMING_SNAKE_CASE__   ) == 1:
												return [nums.copy()]
						for _ in range(len(SCREAMING_SNAKE_CASE__   )   ):
												UpperCAmelCase_			:      List[Any]     						=  nums.pop(0   )
												UpperCAmelCase_			:      Optional[Any]     						=  permute(SCREAMING_SNAKE_CASE__   )
												for perm in permutations:
																		perm.append(SCREAMING_SNAKE_CASE__   )
												result.extend(SCREAMING_SNAKE_CASE__   )
												nums.append(SCREAMING_SNAKE_CASE__   )
						return result





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[str]   ) ->	Any:

						def backtrack(SCREAMING_SNAKE_CASE__      :			Union[str, Any]   ):
												if start == len(SCREAMING_SNAKE_CASE__   ) - 1:
																		output.append(nums[:]   )
												else:
																		for i in range(SCREAMING_SNAKE_CASE__,			len(SCREAMING_SNAKE_CASE__   )   ):
																								UpperCAmelCase_						,       UpperCAmelCase_			:      Tuple     						=  nums[i], nums[start]
																								backtrack(start + 1   )
																								UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  nums[i], nums[start]  # backtrack

						UpperCAmelCase_			:      Optional[int]     						=  []
						backtrack(0   )
						return output


if __name__ == "__main__":
					import doctest

					# use res to print the data in permute2 function
					snake_case_     :       Tuple						=  permutea([1, 2, 3])
					print(res)
					doctest.testmod()





