

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Union[str, Any]														=					(KDPMaDiscreteScheduler,)
    __lowerCamelCase						:	Tuple														=					10





    def 				snake_case_    (				self						,       **a):
         lowercase__   :       str				=	{
             'num_train_timesteps': 1100,
             'beta_start': 0.0_001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**a)
         return config





    def 				snake_case_    (				self):
         for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=a)





    def 				snake_case_    (				self):
         for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						,       [0.0_002, 0.002, 0.02]):
              self.check_over_configs(beta_start=a						,       beta_end=a)





    def 				snake_case_    (				self):
         for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=a)





    def 				snake_case_    (				self):
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=a)





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.scheduler_classes[0]
         lowercase__   :       Dict				=	self.get_scheduler_config(prediction_type='v_prediction')
         lowercase__   :       Optional[Any]				=	scheduler_class(**a)

         scheduler.set_timesteps(self.num_inference_steps)

         lowercase__   :       Optional[int]				=	self.dummy_model()
         lowercase__   :       Union[str, Any]				=	self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase__   :       List[Any]				=	sample.to(a)

         for i, t in enumerate(scheduler.timesteps):
              lowercase__   :       List[str]				=	scheduler.scale_model_input(a						,       a)

              lowercase__   :       int				=	model(a						,       a)

              lowercase__   :       Union[str, Any]				=	scheduler.step(a						,       a						,       a)
              lowercase__   :       str				=	output.prev_sample

         lowercase__   :       List[Any]				=	torch.sum(torch.abs(a))
         lowercase__   :       Optional[Any]				=	torch.mean(torch.abs(a))

         if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 4.6934e-07) < 1e-2
              assert abs(result_mean.item() - 6.1112e-10) < 1e-3
         else:
              # CUDA
              assert abs(result_sum.item() - 4.693428650170972e-07) < 1e-2
              assert abs(result_mean.item() - 0.0_002) < 1e-3





    def 				snake_case_    (				self):
         if torch_device == "mps":
              return
         lowercase__   :       Tuple				=	self.scheduler_classes[0]
         lowercase__   :       Optional[int]				=	self.get_scheduler_config()
         lowercase__   :       Optional[Any]				=	scheduler_class(**a)

         scheduler.set_timesteps(self.num_inference_steps)

         lowercase__   :       int				=	self.dummy_model()
         lowercase__   :       Optional[int]				=	self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase__   :       List[Any]				=	sample.to(a)

         for i, t in enumerate(scheduler.timesteps):
              lowercase__   :       List[str]				=	scheduler.scale_model_input(a						,       a)

              lowercase__   :       Union[str, Any]				=	model(a						,       a)

              lowercase__   :       Dict				=	scheduler.step(a						,       a						,       a)
              lowercase__   :       Tuple				=	output.prev_sample

         lowercase__   :       Any				=	torch.sum(torch.abs(a))
         lowercase__   :       Any				=	torch.mean(torch.abs(a))

         if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 20.4_125) < 1e-2
              assert abs(result_mean.item() - 0.0_266) < 1e-3
         else:
              # CUDA
              assert abs(result_sum.item() - 20.4_125) < 1e-2
              assert abs(result_mean.item() - 0.0_266) < 1e-3





    def 				snake_case_    (				self):
         if torch_device == "mps":
              return
         lowercase__   :       str				=	self.scheduler_classes[0]
         lowercase__   :       Optional[Any]				=	self.get_scheduler_config()
         lowercase__   :       str				=	scheduler_class(**a)

         scheduler.set_timesteps(self.num_inference_steps						,       device=a)

         lowercase__   :       Optional[Any]				=	self.dummy_model()
         lowercase__   :       Optional[Any]				=	self.dummy_sample_deter.to(a) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
              lowercase__   :       Any				=	scheduler.scale_model_input(a						,       a)

              lowercase__   :       str				=	model(a						,       a)

              lowercase__   :       Dict				=	scheduler.step(a						,       a						,       a)
              lowercase__   :       Union[str, Any]				=	output.prev_sample

         lowercase__   :       Optional[Any]				=	torch.sum(torch.abs(a))
         lowercase__   :       Optional[int]				=	torch.mean(torch.abs(a))

         if str(a).startswith('cpu'):
              # The following sum varies between 148 and 156 on mps. Why?
              assert abs(result_sum.item() - 20.4_125) < 1e-2
              assert abs(result_mean.item() - 0.0_266) < 1e-3
         else:
              # CUDA
              assert abs(result_sum.item() - 20.4_125) < 1e-2
              assert abs(result_mean.item() - 0.0_266) < 1e-3




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{
    '''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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''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''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
snake_case_        =			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   Dict      ):



     '''simple docstring'''





     for attribute in key.split('.'      ):
          lowercase__   :       Optional[Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

     if weight_type is not None:
          lowercase__   :       Union[str, Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ).shape
     else:
          lowercase__   :       Optional[int]				=	hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              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__   :       Optional[int]				=	value
     elif weight_type == "weight_g":
          lowercase__   :       Union[str, Any]				=	value
     elif weight_type == "weight_v":
          lowercase__   :       Tuple				=	value
     elif weight_type == "bias":
          lowercase__   :       Any				=	value
     else:
          lowercase__   :       Union[str, Any]				=	value

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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





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

     lowercase__   :       Optional[int]				=	hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          lowercase__   :       Tuple				=	False
          if "conv_layers" in name:
               load_conv_layer(
                   SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					hf_model.config.feat_extract_norm == 'group'     ,					)
               lowercase__   :       Optional[int]				=	True
          else:
               for key, mapped_key in MAPPING.items():
                    lowercase__   :       List[str]				=	'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
                         if "layer_norm_for_extract" in name and (".".join(name.split('.'      )[:-1]      ) != key):
                              # special case since naming is very similar
                              continue
                         lowercase__   :       Tuple				=	True
                         if "*" in mapped_key:
                              lowercase__   :       Any				=	name.split(SCREAMING_SNAKE_CASE_      )[0].split('.'      )[-2]
                              lowercase__   :       Optional[Any]				=	mapped_key.replace('*'     ,					SCREAMING_SNAKE_CASE_      )
                         if "weight_g" in name:
                              lowercase__   :       int				=	'weight_g'
                         elif "weight_v" in name:
                              lowercase__   :       Any				=	'weight_v'
                         elif "bias" in name:
                              lowercase__   :       str				=	'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              lowercase__   :       Union[str, Any]				=	'weight'
                         else:
                              lowercase__   :       Union[str, Any]				=	None
                         set_recursively(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
                    continue
          if not is_used:
               unused_weights.append(SCREAMING_SNAKE_CASE_      )

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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	full_name.split('conv_layers.'      )[-1]
     lowercase__   :       Dict				=	name.split('.'      )
     lowercase__   :       List[str]				=	int(items[0]      )
     lowercase__   :       Any				=	int(items[1]      )

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        f"""{full_name} has size {value.shape}, but"""
                        f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""      )
               lowercase__   :       List[Any]				=	value
               logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        f"""{full_name} has size {value.shape}, but"""
                        f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""      )
               lowercase__   :       Any				=	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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        f"""{full_name} has size {value.shape}, but"""
                        f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."""      )
               lowercase__   :       str				=	value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        f"""{full_name} has size {value.shape}, but"""
                        f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""      )
               lowercase__   :       int				=	value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
     else:
          unused_weights.append(SCREAMING_SNAKE_CASE_      )

@torch.no_grad()
def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   List[str]=None     ,					SCREAMING_SNAKE_CASE_		:   List[Any]=None     ,					SCREAMING_SNAKE_CASE_		:   int=True      ):



     '''simple docstring'''





     if config_path is not None:
          lowercase__   :       Any				=	UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE_      )
     else:
          lowercase__   :       Dict				=	UniSpeechSatConfig()

     lowercase__   :       str				=	''

     if is_finetuned:
          lowercase__   :       Any				=	UniSpeechSatForCTC(SCREAMING_SNAKE_CASE_      )
     else:
          lowercase__   :       Optional[Any]				=	UniSpeechSatForPreTraining(SCREAMING_SNAKE_CASE_      )

     lowercase__				,			lowercase__				,			lowercase__   :       List[Any]				=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]     ,					arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
     lowercase__   :       List[str]				=	model[0].eval()

     recursively_load_weights(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

     hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
       snake_case_        =			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_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
       parser.add_argument(
           '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
       )
       snake_case_        =			parser.parse_args()
       convert_unispeech_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )

