
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()
lowercase__		:List[Any]			  =   logging.get_logger(__name__)

lowercase__		:List[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",
    "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",
}
lowercase__		:int			  =   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					for attribute in key.split('''.'''		):
										lowercase						     =						getattr(lowerCAmelCase__			,      lowerCAmelCase__		)

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

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



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						[]
					lowercase						     =						fairseq_model.state_dict()

					lowercase						     =						hf_model.feature_extractor
					lowercase						     =						hf_model.adapter

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

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



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						full_name.split('''conv_layers.'''		)[-1]
					lowercase						     =						name.split('''.'''		)
					lowercase						     =						int(items[0]		)
					lowercase						     =						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.'
															)
															lowercase						     =						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.'
															)
															lowercase						     =						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."
															)
															lowercase						     =						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.'
															)
															lowercase						     =						value
															logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
					else:
										unused_weights.append(lowerCAmelCase__		)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						full_name.split('''adaptor.'''		)[-1]
					lowercase						     =						name.split('''.'''		)

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



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase     ,						lowercase						     =						emb.weight.shape
					lowercase						     =						nn.Linear(lowerCAmelCase__			,      lowerCAmelCase__			,      bias=lowerCAmelCase__		)
					lowercase						     =						emb.weight.data
					return lin_layer



@torch.no_grad()
def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      ):
					'''simple docstring'''
					lowercase						     =						WavaVecaConfig.from_pretrained(
					    lowerCAmelCase__			,      add_adapter=lowerCAmelCase__			,      adapter_stride=lowerCAmelCase__			,      adapter_kernel_size=lowerCAmelCase__			,      use_auth_token=lowerCAmelCase__			,      output_hidden_size=lowerCAmelCase__			,      )
					lowercase						     =						MBartConfig.from_pretrained(lowerCAmelCase__		)

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

					# load feature extractor
					lowercase						     =						WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase__			,      use_auth_token=lowerCAmelCase__		)

					# set weights for wav2vec2 encoder
					lowercase						     =						WavaVecaModel(lowerCAmelCase__		)

					recursively_load_weights_wavaveca(model.encoder			,      lowerCAmelCase__		)

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

					lowercase						     =						SpeechEncoderDecoderModel(encoder=lowerCAmelCase__			,      decoder=lowerCAmelCase__		)
					lowercase						     =						False

					lowercase						     =						MBartaaTokenizer(lowerCAmelCase__		)
					tokenizer.save_pretrained(lowerCAmelCase__		)

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

					lowercase						     =						tokenizer.eos_token_id
					lowercase						     =						25_0004
					lowercase						     =						tokenizer.eos_token_id

					lowercase						     =						SpeechEncoderDecoderConfig.from_dict(lowerCAmelCase__		)

					hf_wavavec.save_pretrained(lowerCAmelCase__		)
					feature_extractor.save_pretrained(lowerCAmelCase__		)


if __name__ == "__main__":
							lowercase__		:Union[str, 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")

							lowercase__		:Dict			  =   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,
							)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__		:Dict			  =   {
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:List[str]			  =   [
							    "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "LukeForEntityClassification",
							    "LukeForEntityPairClassification",
							    "LukeForEntitySpanClassification",
							    "LukeForMultipleChoice",
							    "LukeForQuestionAnswering",
							    "LukeForSequenceClassification",
							    "LukeForTokenClassification",
							    "LukeForMaskedLM",
							    "LukeModel",
							    "LukePreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
							from .tokenization_luke import LukeTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_luke import (
														    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LukeForEntityClassification,
														    LukeForEntityPairClassification,
														    LukeForEntitySpanClassification,
														    LukeForMaskedLM,
														    LukeForMultipleChoice,
														    LukeForQuestionAnswering,
														    LukeForSequenceClassification,
														    LukeForTokenClassification,
														    LukeModel,
														    LukePreTrainedModel,
														)

else:
							import sys

							lowercase__		:Tuple			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




