






'''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()
_UpperCAmelCase			:		List[str]   				=    logging.get_logger(__name__)

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





def 	lowerCAmelCase_		(lowercase__						:       int     ,					lowercase__						:       Any     ,					lowercase__						:       Optional[Any]     ,					lowercase__						:       List[Any]     ,					lowercase__						:       Dict       )		->    int:


				'''simple docstring'''




				for attribute in key.split('''.'''       ):
								lowerCAmelCase__      			=						getattr(lowercase__     ,					lowercase__       )

				if weight_type is not None:
								lowerCAmelCase__      			=						getattr(lowercase__     ,					lowercase__       ).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_		(lowercase__						:       str     ,					lowercase__						:       Any       )		->    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(
												    lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__     ,					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(lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__       )
												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(lowercase__       )[0].split('''.'''       )[-2]
																								lowerCAmelCase__      			=						mapped_key.replace('''*'''     ,					lowercase__       )
																				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(lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__       )
																continue
								if not is_used:
												unused_weights.append(lowercase__       )

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





def 	lowerCAmelCase_		(lowercase__						:       Optional[int]     ,					lowercase__						:       Tuple     ,					lowercase__						:       Dict     ,					lowercase__						:       Union[str, Any]     ,					lowercase__						:       List[Any]       )		->    str:


				'''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(lowercase__       )





def 	lowerCAmelCase_		(lowercase__						:       Optional[int]     ,					lowercase__						:       Optional[int]     ,					lowercase__						:       Tuple     ,					lowercase__						:       Tuple       )		->    List[str]:


				'''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(lowercase__     ,					lowercase__       ):
								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(lowercase__       )





def 	lowerCAmelCase_		(lowercase__						:       Union[str, Any]       )		->    Optional[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						emb.weight.shape
				lowerCAmelCase__      			=						nn.Linear(lowercase__     ,					lowercase__     ,					bias=lowercase__       )
				lowerCAmelCase__      			=						emb.weight.data
				return lin_layer





@torch.no_grad()
def 	lowerCAmelCase_		(lowercase__						:       List[Any]     ,					lowercase__						:       Optional[int]     ,					lowercase__						:       int     ,					lowercase__						:       List[Any]     ,					lowercase__						:       Dict     ,					lowercase__						:       int     ,					lowercase__						:       Optional[int]     ,					lowercase__						:       List[Any]     ,					lowercase__						:       Optional[Any]     ,					lowercase__						:       Tuple     ,					lowercase__						:       int     ,					)		->    str:


				'''simple docstring'''




				lowerCAmelCase__      			=						WavaVecaConfig.from_pretrained(
				    lowercase__     ,					add_adapter=lowercase__     ,					adapter_stride=lowercase__     ,					adapter_kernel_size=lowercase__     ,					use_auth_token=lowercase__     ,					output_hidden_size=lowercase__     ,					)
				lowerCAmelCase__      			=						MBartConfig.from_pretrained(lowercase__       )

				# load model
				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(lowercase__     ,					use_auth_token=lowercase__       )

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

				recursively_load_weights_wavaveca(model.encoder     ,					lowercase__       )

				# load decoder weights
				lowerCAmelCase__      			=						MBartForCausalLM(lowercase__       )
				lowerCAmelCase__      			=						hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()     ,					strict=lowercase__       )
				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=lowercase__     ,					decoder=lowercase__       )
				lowerCAmelCase__      			=						False

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

				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_00_04
				lowerCAmelCase__      			=						tokenizer.eos_token_id

				lowerCAmelCase__      			=						SpeechEncoderDecoderConfig.from_dict(lowercase__       )

				hf_wavavec.save_pretrained(lowercase__       )
				feature_extractor.save_pretrained(lowercase__       )


if __name__ == "__main__":
			_UpperCAmelCase			:		List[str]   				=    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=1_024, type=int, help="encoder output dim")
			parser.add_argument("--start_token_id", default=250_004, type=int, help="`decoder_start_token_id` of model config")

			_UpperCAmelCase			:		Union[str, 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,
			)













import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:Tuple		      =    ['image_processor', 'tokenizer']
							UpperCamelCase_		:Tuple		      =    'ViTImageProcessor'
							UpperCamelCase_		:Dict		      =    ('CLIPTokenizer', 'CLIPTokenizerFast')




							def __init__(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any]	):
											lowerCAmelCase__      			=						None
											if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
															lowerCAmelCase__      			=						kwargs.pop('''feature_extractor'''	)

											lowerCAmelCase__      			=						image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
															raise ValueError('''You need to specify an `image_processor`.'''	)
											if tokenizer is None:
															raise ValueError('''You need to specify a `tokenizer`.'''	)

											super().__init__(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def __call__(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Any=None ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=None ,							**SCREAMING_SNAKE_CASE_     :   Optional[int]	):
											if text is None and visual_prompt is None and images is None:
															raise ValueError('''You have to specify either text, visual prompt or images.'''	)

											if text is not None and visual_prompt is not None:
															raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''	)

											if text is not None:
															lowerCAmelCase__      			=						self.tokenizer(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if visual_prompt is not None:
															lowerCAmelCase__      			=						self.image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if images is not None:
															lowerCAmelCase__      			=						self.image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if visual_prompt is not None and images is not None:
															lowerCAmelCase__      			=						{
															    '''pixel_values''': image_features.pixel_values,
															    '''conditional_pixel_values''': prompt_features.pixel_values,
															}
															return encoding
											elif text is not None and images is not None:
															lowerCAmelCase__      			=						image_features.pixel_values
															return encoding
											elif text is not None:
															return encoding
											elif visual_prompt is not None:
															lowerCAmelCase__      			=						{
															    '''conditional_pixel_values''': prompt_features.pixel_values,
															}
															return encoding
											else:
															return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_	) ,							tensor_type=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Union[str, Any] ,							*SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							**SCREAMING_SNAKE_CASE_     :   str	):
											return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Optional[int] ,							*SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							**SCREAMING_SNAKE_CASE_     :   Tuple	):
											return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							@property
							def 						__snake_case					(   self     :   str	):
											warnings.warn(
											    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
											return self.image_processor_class




							@property
							def 						__snake_case					(   self     :   Tuple	):
											warnings.warn(
											    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
											return self.image_processor





