

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()
_UpperCAmelCase	:     Dict		       = logging.get_logger(__name__)

_UpperCAmelCase	:     List[Any]		       = {
    """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""",
}
_UpperCAmelCase	:     Any		       = [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							for attribute in key.split('.'   ):
														snake_case_           =					getattr(UpperCamelCase__     ,							UpperCamelCase__   )

							if weight_type is not None:
														snake_case_           =					getattr(UpperCamelCase__     ,							UpperCamelCase__   ).shape
							else:
														snake_case_           =					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":
														snake_case_           =					value
							elif weight_type == "weight_g":
														snake_case_           =					value
							elif weight_type == "weight_v":
														snake_case_           =					value
							elif weight_type == "bias":
														snake_case_           =					value
							else:
														snake_case_           =					value

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





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					[]
							snake_case_           =					fairseq_model.state_dict()

							snake_case_           =					hf_model.feature_extractor
							snake_case_           =					hf_model.adapter

							for name, value in fairseq_dict.items():
														snake_case_           =					False
														if "conv_layers" in name:
																					load_conv_layer(
																					    UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							hf_model.config.feat_extract_norm == 'group'     ,							)
																					snake_case_           =					True
														elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']   ):
																					load_adapter(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
																					snake_case_           =					True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
																																			snake_case_           =					True
																																			if "*" in mapped_key:
																																										snake_case_           =					name.split(UpperCamelCase__   )[0].split('.'   )[-2]
																																										snake_case_           =					mapped_key.replace('*'     ,							UpperCamelCase__   )
																																			if "weight_g" in name:
																																										snake_case_           =					'weight_g'
																																			elif "weight_v" in name:
																																										snake_case_           =					'weight_v'
																																			elif "bias" in name:
																																										snake_case_           =					'bias'
																																			elif "weight" in name:
																																										snake_case_           =					'weight'
																																			else:
																																										snake_case_           =					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			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					full_name.split('conv_layers.'   )[-1]
							snake_case_           =					name.split('.'   )
							snake_case_           =					int(items[0]   )
							snake_case_           =					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.'''
																					)
																					snake_case_           =					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.'''
																					)
																					snake_case_           =					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."
																					)
																					snake_case_           =					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.'''
																					)
																					snake_case_           =					value
																					logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''   )
							else:
														unused_weights.append(UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					full_name.split('adaptor.'   )[-1]
							snake_case_           =					name.split('.'   )

							if items[1].isdigit():
														snake_case_           =					int(items[1]   )
							else:
														snake_case_           =					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.'''
																												snake_case_           =					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.'''
																												snake_case_           =					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.'''
																												snake_case_           =					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.'''
																												snake_case_           =					value
																												logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''   )
							elif isinstance(UpperCamelCase__     ,							UpperCamelCase__   ):
														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.'''
																					snake_case_           =					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.'''
																					snake_case_           =					value
																					logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''   )
							else:
														unused_weights.append(UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_       ,					snake_case_           =					emb.weight.shape
							snake_case_           =					nn.Linear(UpperCamelCase__     ,							UpperCamelCase__     ,							bias=UpperCamelCase__   )
							snake_case_           =					emb.weight.data
							return lin_layer





@torch.no_grad()
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							):







							'''simple docstring'''






							snake_case_           =					WavaVecaConfig.from_pretrained(
							    UpperCamelCase__     ,							add_adapter=UpperCamelCase__     ,							adapter_stride=UpperCamelCase__     ,							adapter_kernel_size=UpperCamelCase__     ,							use_auth_token=UpperCamelCase__     ,							output_hidden_size=UpperCamelCase__     ,							)
							snake_case_           =					MBartConfig.from_pretrained(UpperCamelCase__   )

							# load model
							snake_case_       ,					snake_case_       ,					snake_case_           =					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,
							    }     ,							)
							snake_case_           =					model[0].eval()

							# load feature extractor
							snake_case_           =					WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase__     ,							use_auth_token=UpperCamelCase__   )

							# set weights for wav2vec2 encoder
							snake_case_           =					WavaVecaModel(UpperCamelCase__   )

							recursively_load_weights_wavaveca(model.encoder     ,							UpperCamelCase__   )

							# load decoder weights
							snake_case_           =					MBartForCausalLM(UpperCamelCase__   )
							snake_case_       ,					snake_case_           =					hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()     ,							strict=UpperCamelCase__   )
							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}'''   )

							snake_case_           =					SpeechEncoderDecoderModel(encoder=UpperCamelCase__     ,							decoder=UpperCamelCase__   )
							snake_case_           =					False

							snake_case_           =					MBartaaTokenizer(UpperCamelCase__   )
							tokenizer.save_pretrained(UpperCamelCase__   )

							snake_case_           =					hf_wavavec.config.to_dict()
							snake_case_           =					tokenizer.pad_token_id
							snake_case_           =					tokenizer.bos_token_id
							snake_case_           =					tokenizer.eos_token_id
							snake_case_           =					'mbart50'
							snake_case_           =					'wav2vec2'

							snake_case_           =					tokenizer.eos_token_id
							snake_case_           =					250004
							snake_case_           =					tokenizer.eos_token_id

							snake_case_           =					SpeechEncoderDecoderConfig.from_dict(UpperCamelCase__   )

							hf_wavavec.save_pretrained(UpperCamelCase__   )
							feature_extractor.save_pretrained(UpperCamelCase__   )


if __name__ == "__main__":
					_UpperCAmelCase	:     List[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_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=1024, type=int, help="""encoder output dim""")
					parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""")

					_UpperCAmelCase	:     Any		       = 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,
					)







def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					(
							    first_str_length if first_str_length > second_str_length else second_str_length
							)
							snake_case_           =					[]
							for char_count in range(UpperCamelCase__   ):
														if char_count < first_str_length:
																					output_list.append(first_str[char_count]   )
														if char_count < second_str_length:
																					output_list.append(second_str[char_count]   )
							return "".join(UpperCamelCase__   )


if __name__ == "__main__":
					print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)




