






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      :      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""",
}
__snake_case      :      Dict             =       [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]







def 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   str						)					->				List[str]:

						"""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 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   str						)					->				Union[str, 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 		_UpperCamelCase				(      UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Optional[int]						)					->				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 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   Any						)					->				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 		_UpperCamelCase				(      UpperCamelCase_    :   List[str]						)					->				Union[str, Any]:

						"""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 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   str      ,    )					->				Union[str, Any]:

						"""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__":
					__snake_case      :      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=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      :      Tuple             =       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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case      :      Union[str, Any]             =       {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      List[str]             =       ["""ConvBertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      Optional[Any]             =       [
					    """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ConvBertForMaskedLM""",
					    """ConvBertForMultipleChoice""",
					    """ConvBertForQuestionAnswering""",
					    """ConvBertForSequenceClassification""",
					    """ConvBertForTokenClassification""",
					    """ConvBertLayer""",
					    """ConvBertModel""",
					    """ConvBertPreTrainedModel""",
					    """load_tf_weights_in_convbert""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      Union[str, Any]             =       [
					    """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFConvBertForMaskedLM""",
					    """TFConvBertForMultipleChoice""",
					    """TFConvBertForQuestionAnswering""",
					    """TFConvBertForSequenceClassification""",
					    """TFConvBertForTokenClassification""",
					    """TFConvBertLayer""",
					    """TFConvBertModel""",
					    """TFConvBertPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
					from .tokenization_convbert import ConvBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_convbert_fast import ConvBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_convbert import (
										    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ConvBertForMaskedLM,
										    ConvBertForMultipleChoice,
										    ConvBertForQuestionAnswering,
										    ConvBertForSequenceClassification,
										    ConvBertForTokenClassification,
										    ConvBertLayer,
										    ConvBertModel,
										    ConvBertPreTrainedModel,
										    load_tf_weights_in_convbert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_convbert import (
										    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFConvBertForMaskedLM,
										    TFConvBertForMultipleChoice,
										    TFConvBertForQuestionAnswering,
										    TFConvBertForSequenceClassification,
										    TFConvBertForTokenClassification,
										    TFConvBertLayer,
										    TFConvBertModel,
										    TFConvBertPreTrainedModel,
										)


else:
					import sys

					__snake_case      :      Dict             =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





