

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

__snake_case			:     int										=      {
    '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			:     List[str]										=      [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def 						__lowerCamelCase	(       __snake_case					:		Union[str, Any],		__snake_case					:		int,		__snake_case					:		List[Any],		__snake_case					:		Union[str, Any],		__snake_case					:		Tuple	)	->							str:






						"""simple docstring"""





						for attribute in key.split("""."""	):
												A__					:       List[str]     =getattr(__snake_case,		__snake_case	)

						if weight_type is not None:
												A__					:       List[str]     =getattr(__snake_case,		__snake_case	).shape
						else:
												A__					:       int     =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":
												A__					:       str     =value
						elif weight_type == "weight_g":
												A__					:       List[Any]     =value
						elif weight_type == "weight_v":
												A__					:       Dict     =value
						elif weight_type == "bias":
												A__					:       Dict     =value
						else:
												A__					:       Optional[int]     =value

						logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."	)
def 						__lowerCamelCase	(       __snake_case					:		Union[str, Any],		__snake_case					:		str	)	->							Any:






						"""simple docstring"""





						A__					:       Union[str, Any]     =[]
						A__					:       Optional[int]     =fairseq_model.state_dict()

						A__					:       str     =hf_model.feature_extractor
						A__					:       str     =hf_model.adapter

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

						logger.warning(f"Unused weights: {unused_weights}"	)
def 						__lowerCamelCase	(       __snake_case					:		int,		__snake_case					:		Union[str, Any],		__snake_case					:		List[str],		__snake_case					:		Any,		__snake_case					:		Union[str, Any]	)	->							List[str]:






						"""simple docstring"""





						A__					:       Optional[Any]     =full_name.split("""conv_layers."""	)[-1]
						A__					:       int     =name.split("""."""	)
						A__					:       List[Any]     =int(items[0]	)
						A__					:       List[str]     =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."
																		)
																		A__					:       List[Any]     =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."
																		)
																		A__					:       Tuple     =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."
																		)
																		A__					:       Tuple     =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."
																		)
																		A__					:       List[Any]     =value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."	)
						else:
												unused_weights.append(__snake_case	)
def 						__lowerCamelCase	(       __snake_case					:		Dict,		__snake_case					:		Optional[int],		__snake_case					:		int,		__snake_case					:		Optional[int]	)	->							Tuple:






						"""simple docstring"""





						A__					:       Tuple     =full_name.split("""adaptor."""	)[-1]
						A__					:       Optional[Any]     =name.split("""."""	)

						if items[1].isdigit():
												A__					:       int     =int(items[1]	)
						else:
												A__					:       Optional[Any]     =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."
																								A__					:       int     =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."
																								A__					:       Union[str, Any]     =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."
																								A__					:       Optional[int]     =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."
																								A__					:       Optional[int]     =value
																								logger.info(f"Adapter proj layer weight was initialized from {full_name}."	)
						elif isinstance(__snake_case,		__snake_case	):
												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."
																		A__					:       Optional[Any]     =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."
																		A__					:       List[Any]     =value
																		logger.info(f"Adapter layer {layer_id} bias was initialized from {full_name}."	)
						else:
												unused_weights.append(__snake_case	)
def 						__lowerCamelCase	(       __snake_case					:		List[str]	)	->							Optional[Any]:






						"""simple docstring"""





						A__   ,						A__					:       Optional[Any]     =emb.weight.shape
						A__					:       int     =nn.Linear(__snake_case,		__snake_case,		bias=__snake_case	)
						A__					:       List[str]     =emb.weight.data
						return lin_layer
@torch.no_grad()
def 						__lowerCamelCase	(       __snake_case					:		Tuple,		__snake_case					:		Tuple,		__snake_case					:		Union[str, Any],		__snake_case					:		Optional[int],		__snake_case					:		str,		__snake_case					:		Dict,		__snake_case					:		List[Any],		__snake_case					:		List[str],		__snake_case					:		List[Any],		__snake_case					:		Dict,		__snake_case					:		List[Any],		)	->							Dict:






						"""simple docstring"""





						A__					:       Any     =WavaVecaConfig.from_pretrained(
						    __snake_case,		add_adapter=__snake_case,		adapter_stride=__snake_case,		adapter_kernel_size=__snake_case,		use_auth_token=__snake_case,		output_hidden_size=__snake_case,		)
						A__					:       Tuple     =MBartConfig.from_pretrained(__snake_case	)

						# load model
						A__   ,						A__   ,						A__					:       Tuple     =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,
						    },		)
						A__					:       str     =model[0].eval()

						# load feature extractor
						A__					:       Optional[int]     =WavaVecaFeatureExtractor.from_pretrained(__snake_case,		use_auth_token=__snake_case	)

						# set weights for wav2vec2 encoder
						A__					:       Optional[Any]     =WavaVecaModel(__snake_case	)

						recursively_load_weights_wavaveca(model.encoder,		__snake_case	)

						# load decoder weights
						A__					:       int     =MBartForCausalLM(__snake_case	)
						A__   ,						A__					:       Optional[int]     =hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),		strict=__snake_case	)
						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}"	)

						A__					:       int     =SpeechEncoderDecoderModel(encoder=__snake_case,		decoder=__snake_case	)
						A__					:       Any     =False

						A__					:       Dict     =MBartaaTokenizer(__snake_case	)
						tokenizer.save_pretrained(__snake_case	)

						A__					:       Any     =hf_wavavec.config.to_dict()
						A__					:       List[str]     =tokenizer.pad_token_id
						A__					:       int     =tokenizer.bos_token_id
						A__					:       List[str]     =tokenizer.eos_token_id
						A__					:       Tuple     ="""mbart50"""
						A__					:       str     ="""wav2vec2"""

						A__					:       Optional[int]     =tokenizer.eos_token_id
						A__					:       Optional[int]     =250_004
						A__					:       Optional[Any]     =tokenizer.eos_token_id

						A__					:       List[Any]     =SpeechEncoderDecoderConfig.from_dict(__snake_case	)

						hf_wavavec.save_pretrained(__snake_case	)
						feature_extractor.save_pretrained(__snake_case	)


if __name__ == "__main__":
		__snake_case			:     int										=      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')

		__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,
		)




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	(CMStochasticIterativeScheduler,)
			__snake_case        =	10


			def lowercase__						(				self	:						List[str]		,      **lowerCAmelCase_	:						Dict			)      ->		Dict:





									'''simple docstring'''
									A__					:       int     ={
									    """num_train_timesteps""": 2_01,
									    """sigma_min""": 0.002,
									    """sigma_max""": 80.0,
									}

									config.update(**lowerCAmelCase_			)
									return config


			def lowercase__						(				self	:						Optional[Any]			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Dict     =10

									A__					:       str     =self.get_scheduler_config()
									A__					:       Any     =self.scheduler_classes[0](**lowerCAmelCase_			)

									scheduler.set_timesteps(lowerCAmelCase_			)

									A__					:       List[Any]     =scheduler.timesteps[0]
									A__					:       Union[str, Any]     =scheduler.timesteps[1]

									A__					:       Optional[int]     =self.dummy_sample
									A__					:       Union[str, Any]     =0.1 * sample

									A__					:       Dict     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			).prev_sample
									A__					:       int     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			).prev_sample

									self.assertEqual(output_a.shape		,      sample.shape			)
									self.assertEqual(output_a.shape		,      output_a.shape			)


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





									'''simple docstring'''
									for timesteps in [10, 50, 1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=lowerCAmelCase_			)


			def lowercase__						(				self	:						Tuple			)      ->		Optional[int]:





									'''simple docstring'''
									for clip_denoised in [True, False]:
															self.check_over_configs(clip_denoised=lowerCAmelCase_			)


			def lowercase__						(				self	:						List[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.scheduler_classes[0]
									A__					:       Dict     =self.get_scheduler_config()
									A__					:       Any     =scheduler_class(**lowerCAmelCase_			)

									A__					:       int     =1
									scheduler.set_timesteps(lowerCAmelCase_			)
									A__					:       Any     =scheduler.timesteps

									A__					:       Optional[int]     =torch.manual_seed(0			)

									A__					:       List[Any]     =self.dummy_model()
									A__					:       Optional[int]     =self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(lowerCAmelCase_			):
															# 1. scale model input
															A__					:       Optional[Any]     =scheduler.scale_model_input(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 2. predict noise residual
															A__					:       Union[str, Any]     =model(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 3. predict previous sample x_t-1
															A__					:       Tuple     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      generator=lowerCAmelCase_			).prev_sample

															A__					:       Dict     =pred_prev_sample

									A__					:       Union[str, Any]     =torch.sum(torch.abs(lowerCAmelCase_			)			)
									A__					:       Optional[Any]     =torch.mean(torch.abs(lowerCAmelCase_			)			)

									assert abs(result_sum.item() - 192.7614			) < 1e-2
									assert abs(result_mean.item() - 0.2510			) < 1e-3


			def lowercase__						(				self	:						Dict			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Dict     =self.scheduler_classes[0]
									A__					:       Dict     =self.get_scheduler_config()
									A__					:       Tuple     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Tuple     =[1_06, 0]
									scheduler.set_timesteps(timesteps=lowerCAmelCase_			)
									A__					:       List[Any]     =scheduler.timesteps

									A__					:       Optional[Any]     =torch.manual_seed(0			)

									A__					:       int     =self.dummy_model()
									A__					:       int     =self.dummy_sample_deter * scheduler.init_noise_sigma

									for t in timesteps:
															# 1. scale model input
															A__					:       Any     =scheduler.scale_model_input(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 2. predict noise residual
															A__					:       List[str]     =model(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 3. predict previous sample x_t-1
															A__					:       Dict     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      generator=lowerCAmelCase_			).prev_sample

															A__					:       Union[str, Any]     =pred_prev_sample

									A__					:       Union[str, Any]     =torch.sum(torch.abs(lowerCAmelCase_			)			)
									A__					:       List[Any]     =torch.mean(torch.abs(lowerCAmelCase_			)			)

									assert abs(result_sum.item() - 347.6357			) < 1e-2
									assert abs(result_mean.item() - 0.4527			) < 1e-3


			def lowercase__						(				self	:						Optional[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.scheduler_classes[0]
									A__					:       Union[str, Any]     =self.get_scheduler_config()
									A__					:       List[Any]     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Tuple     =[39, 30, 12, 15, 0]

									with self.assertRaises(lowerCAmelCase_		,      msg="""`timesteps` must be in descending order."""			):
															scheduler.set_timesteps(timesteps=lowerCAmelCase_			)


			def lowercase__						(				self	:						Union[str, Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.scheduler_classes[0]
									A__					:       List[str]     =self.get_scheduler_config()
									A__					:       Tuple     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Dict     =[39, 30, 12, 1, 0]
									A__					:       int     =len(lowerCAmelCase_			)

									with self.assertRaises(lowerCAmelCase_		,      msg="""Can only pass one of `num_inference_steps` or `timesteps`."""			):
															scheduler.set_timesteps(num_inference_steps=lowerCAmelCase_		,      timesteps=lowerCAmelCase_			)



			def lowercase__						(				self	:						Any			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.scheduler_classes[0]
									A__					:       Any     =self.get_scheduler_config()
									A__					:       Optional[int]     =scheduler_class(**lowerCAmelCase_			)

									A__					:       List[str]     =[scheduler.config.num_train_timesteps]

									with self.assertRaises(
									    lowerCAmelCase_		,      msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""		,      ):
															scheduler.set_timesteps(timesteps=lowerCAmelCase_			)

