




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format







class     __lowerCAmelCase :

			"""simple docstring"""







			snake_case_   		=       None


			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    self.feature_extraction_class(**self.feat_extract_dict							)
							__lowerCamelCase		       =    json.loads(feat_extract.to_json_string()							)
							for key, value in self.feat_extract_dict.items():
											self.assertEqual(obj[key]			,						lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.feature_extraction_class(**self.feat_extract_dict							)

							with tempfile.TemporaryDirectory() as tmpdirname:
											__lowerCamelCase		       =    os.path.join(lowerCamelCase__			,						'feat_extract.json'							)
											feat_extract_first.to_json_file(lowerCamelCase__							)
											__lowerCamelCase		       =    self.feature_extraction_class.from_json_file(lowerCamelCase__							)

							self.assertEqual(feat_extract_second.to_dict()			,						feat_extract_first.to_dict()							)


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    self.feature_extraction_class(**self.feat_extract_dict							)

							with tempfile.TemporaryDirectory() as tmpdirname:
											__lowerCamelCase		       =    feat_extract_first.save_pretrained(lowerCamelCase__							)[0]
											check_json_file_has_correct_format(lowerCamelCase__							)
											__lowerCamelCase		       =    self.feature_extraction_class.from_pretrained(lowerCamelCase__							)

							self.assertEqual(feat_extract_second.to_dict()			,						feat_extract_first.to_dict()							)






			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.feature_extraction_class()
							self.assertIsNotNone(lowerCamelCase__							)








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()
__A        =    logging.get_logger(__name__)

__A        =    {
    "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",
}
__A        =    [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 			lowerCamelCase_		(       UpperCamelCase__				:      Any ,     UpperCamelCase__				:      Any ,     UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Optional[int]      )     ->       Tuple:





				"""simple docstring"""


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

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

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      Tuple      )     ->       Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    fairseq_model.state_dict()

				__lowerCamelCase		       =    hf_model.feature_extractor
				__lowerCamelCase		       =    hf_model.adapter

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





				"""simple docstring"""


				__lowerCamelCase		       =    full_name.split('conv_layers.'      )[-1]
				__lowerCamelCase		       =    name.split('.'      )
				__lowerCamelCase		       =    int(items[0]      )
				__lowerCamelCase		       =    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."""
												)
												__lowerCamelCase		       =    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."""
												)
												__lowerCamelCase		       =    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."
												)
												__lowerCamelCase		       =    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."""
												)
												__lowerCamelCase		       =    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__				:      Tuple ,     UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      Any ,     UpperCamelCase__				:      int      )     ->       Union[str, Any]:





				"""simple docstring"""


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

				if items[1].isdigit():
								__lowerCamelCase		       =    int(items[1]      )
				else:
								__lowerCamelCase		       =    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."""
																__lowerCamelCase		       =    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."""
																__lowerCamelCase		       =    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."""
																__lowerCamelCase		       =    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."""
																__lowerCamelCase		       =    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."""
												__lowerCamelCase		       =    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."""
												__lowerCamelCase		       =    value
												logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""      )
				else:
								unused_weights.append(UpperCamelCase__      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      Tuple      )     ->       Tuple:





				"""simple docstring"""


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







@torch.no_grad()
def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Optional[Any] ,     )     ->       str:





				"""simple docstring"""


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

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

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

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

				recursively_load_weights_wavaveca(model.encoder ,     UpperCamelCase__      )

				# load decoder weights
				__lowerCamelCase		       =    MBartForCausalLM(UpperCamelCase__      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    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}"""      )

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

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

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

				__lowerCamelCase		       =    tokenizer.eos_token_id
				__lowerCamelCase		       =    25_0004
				__lowerCamelCase		       =    tokenizer.eos_token_id

				__lowerCamelCase		       =    SpeechEncoderDecoderConfig.from_dict(UpperCamelCase__      )

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


if __name__ == "__main__":
					__A        =    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")

					__A        =    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,
					)


