



"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCAmelCase_   						=   {
    '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',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      str:
		for attribute in key.split('''.'''	):
				lowercase__   :    Union[str, Any]				=			getattr(__lowerCamelCase						,     __lowerCamelCase	)

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

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



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Union[str, Any]:
		lowercase__   :    int				=			[]
		lowercase__   :    Tuple				=			fairseq_model.state_dict()

		lowercase__   :    Union[str, Any]				=			hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				lowercase__   :    Union[str, Any]				=			False
				if "conv_layers" in name:
						load_conv_layer(
						    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     hf_model.config.feat_extract_norm == '''group'''						,     )
						lowercase__   :    Tuple				=			True
				else:
						for key, mapped_key in MAPPING.items():
								lowercase__   :    str				=			'''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

								if key in name or (key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0] and not is_finetuned):
										lowercase__   :    Tuple				=			True
										if "*" in mapped_key:
												lowercase__   :    List[Any]				=			name.split(__lowerCamelCase	)[0].split('''.'''	)[-2]
												lowercase__   :    Any				=			mapped_key.replace('''*'''						,     __lowerCamelCase	)
										if "weight_g" in name:
												lowercase__   :    Optional[Any]				=			'''weight_g'''
										elif "weight_v" in name:
												lowercase__   :    List[str]				=			'''weight_v'''
										elif "weight" in name:
												lowercase__   :    Optional[Any]				=			'''weight'''
										elif "bias" in name:
												lowercase__   :    Any				=			'''bias'''
										else:
												lowercase__   :    List[str]				=			None
										set_recursively(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
								continue
				if not is_used:
						unused_weights.append(__lowerCamelCase	)

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



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



@torch.no_grad()
def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=True	)							->      Any:
		if config_path is not None:
				lowercase__   :    int				=			HubertConfig.from_pretrained(__lowerCamelCase	)
		else:
				lowercase__   :    int				=			HubertConfig()

		if is_finetuned:
				if dict_path:
						lowercase__   :    Optional[int]				=			Dictionary.load(__lowerCamelCase	)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						lowercase__   :    Optional[Any]				=			target_dict.pad_index
						lowercase__   :    List[str]				=			target_dict.bos_index
						lowercase__   :    int				=			target_dict.eos_index
						lowercase__   :    List[Any]				=			len(target_dict.symbols	)
						lowercase__   :    Tuple				=			os.path.join(__lowerCamelCase						,     '''vocab.json'''	)
						if not os.path.isdir(__lowerCamelCase	):
								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__lowerCamelCase	)	)
								return
						os.makedirs(__lowerCamelCase						,     exist_ok=__lowerCamelCase	)
						with open(__lowerCamelCase						,     '''w'''						,     encoding='''utf-8'''	) as vocab_handle:
								json.dump(target_dict.indices						,     __lowerCamelCase	)
						lowercase__   :    Dict				=			WavaVecaCTCTokenizer(
						    __lowerCamelCase						,     unk_token=target_dict.unk_word						,     pad_token=target_dict.pad_word						,     bos_token=target_dict.bos_word						,     eos_token=target_dict.eos_word						,     word_delimiter_token='''|'''						,     do_lower_case=__lowerCamelCase						,     )
						lowercase__   :    Any				=			True if config.feat_extract_norm == '''layer''' else False
						lowercase__   :    Tuple				=			WavaVecaFeatureExtractor(
						    feature_size=1						,     sampling_rate=1_60_00						,     padding_value=0						,     do_normalize=__lowerCamelCase						,     return_attention_mask=__lowerCamelCase						,     )
						lowercase__   :    Tuple				=			WavaVecaProcessor(feature_extractor=__lowerCamelCase						,     tokenizer=__lowerCamelCase	)
						processor.save_pretrained(__lowerCamelCase	)

				lowercase__   :    str				=			HubertForCTC(__lowerCamelCase	)
		else:
				lowercase__   :    int				=			HubertModel(__lowerCamelCase	)

		if is_finetuned:
				lowercase__		,			lowercase__		,			lowercase__   :    Optional[Any]				=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]						,     arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
		else:
				lowercase__		,			lowercase__		,			lowercase__   :    Optional[Any]				=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

		lowercase__   :    Optional[int]				=			model[0].eval()

		recursively_load_weights(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)

		hf_wavavec.save_pretrained(__lowerCamelCase	)


if __name__ == "__main__":
							lowerCAmelCase_   						=   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'
							)
							lowerCAmelCase_   						=   parser.parse_args()
							convert_hubert_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)








"""simple docstring"""




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       int			=					(IPNDMScheduler,)
 UpperCamelCase					:       int			=					(('''num_inference_steps''', 50),)


 def _lowercase	(     self		:						Union[str, Any]							,						**UpperCAmelCase__		:						Tuple							)					->       int:
   _a  :       Optional[int]             =	{"""num_train_timesteps""": 1000}
   config.update(**UpperCAmelCase__							)
   return config


 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						Any=0							,						**UpperCAmelCase__		:						Optional[Any]							)					->       Union[str, Any]:
   _a  :       Optional[int]             =	dict(self.forward_default_kwargs							)
   _a  :       Dict             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)
   _a  :       Optional[Any]             =	self.dummy_sample
   _a  :       Union[str, Any]             =	0.1 * sample
   _a  :       Union[str, Any]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

   for scheduler_class in self.scheduler_classes:
     _a  :       Optional[int]             =	self.get_scheduler_config(**UpperCAmelCase__							)
     _a  :       Union[str, Any]             =	scheduler_class(**UpperCAmelCase__							)
     scheduler.set_timesteps(UpperCAmelCase__							)
     # copy over dummy past residuals
     _a  :       Any             =	dummy_past_residuals[:]

     if time_step is None:
       _a  :       str             =	scheduler.timesteps[len(scheduler.timesteps							) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(UpperCAmelCase__							)
       _a  :       Union[str, Any]             =	scheduler_class.from_pretrained(UpperCAmelCase__							)
       new_scheduler.set_timesteps(UpperCAmelCase__							)
       # copy over dummy past residuals
       _a  :       Optional[Any]             =	dummy_past_residuals[:]

     _a  :       List[Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       str             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

     _a  :       Optional[int]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Tuple             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"


 def _lowercase	(     self		:						Tuple							)					->       List[str]:
   pass


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						List[str]=0							,						**UpperCAmelCase__		:						Optional[Any]							)					->       List[Any]:
   _a  :       Optional[Any]             =	dict(self.forward_default_kwargs							)
   _a  :       Optional[Any]             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)
   _a  :       Optional[Any]             =	self.dummy_sample
   _a  :       List[Any]             =	0.1 * sample
   _a  :       Optional[Any]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

   for scheduler_class in self.scheduler_classes:
     _a  :       Union[str, Any]             =	self.get_scheduler_config()
     _a  :       Optional[Any]             =	scheduler_class(**UpperCAmelCase__							)
     scheduler.set_timesteps(UpperCAmelCase__							)

     # copy over dummy past residuals (must be after setting timesteps)
     _a  :       Any             =	dummy_past_residuals[:]

     if time_step is None:
       _a  :       List[Any]             =	scheduler.timesteps[len(scheduler.timesteps							) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(UpperCAmelCase__							)
       _a  :       Any             =	scheduler_class.from_pretrained(UpperCAmelCase__							)
       # copy over dummy past residuals
       new_scheduler.set_timesteps(UpperCAmelCase__							)

       # copy over dummy past residual (must be after setting timesteps)
       _a  :       Optional[Any]             =	dummy_past_residuals[:]

     _a  :       List[str]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Tuple             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

     _a  :       Union[str, Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       int             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"


 def _lowercase	(     self		:						str							,						**UpperCAmelCase__		:						Any							)					->       List[str]:
   _a  :       Optional[int]             =	self.scheduler_classes[0]
   _a  :       Optional[Any]             =	self.get_scheduler_config(**UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       int             =	10
   _a  :       List[Any]             =	self.dummy_model()
   _a  :       str             =	self.dummy_sample_deter
   scheduler.set_timesteps(UpperCAmelCase__							)

   for i, t in enumerate(scheduler.timesteps							):
     _a  :       str             =	model(UpperCAmelCase__							,						UpperCAmelCase__							)
     _a  :       List[Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							).prev_sample

   for i, t in enumerate(scheduler.timesteps							):
     _a  :       Union[str, Any]             =	model(UpperCAmelCase__							,						UpperCAmelCase__							)
     _a  :       Any             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							).prev_sample

   return sample


 def _lowercase	(     self		:						int							)					->       str:
   _a  :       Dict             =	dict(self.forward_default_kwargs							)

   _a  :       int             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)

   for scheduler_class in self.scheduler_classes:
     _a  :       Optional[int]             =	self.get_scheduler_config()
     _a  :       Tuple             =	scheduler_class(**UpperCAmelCase__							)

     _a  :       Tuple             =	self.dummy_sample
     _a  :       Optional[Any]             =	0.1 * sample

     if num_inference_steps is not None and hasattr(UpperCAmelCase__							,						"""set_timesteps"""							):
       scheduler.set_timesteps(UpperCAmelCase__							)
     elif num_inference_steps is not None and not hasattr(UpperCAmelCase__							,						"""set_timesteps"""							):
       _a  :       List[str]             =	num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     _a  :       Union[str, Any]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
     _a  :       Optional[Any]             =	dummy_past_residuals[:]

     _a  :       Optional[Any]             =	scheduler.timesteps[5]
     _a  :       str             =	scheduler.timesteps[6]

     _a  :       Optional[int]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Union[str, Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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

     _a  :       Tuple             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       List[str]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

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


 def _lowercase	(     self		:						List[str]							)					->       List[str]:
   for timesteps in [100, 1000]:
     self.check_over_configs(num_train_timesteps=UpperCAmelCase__							,						time_step=UpperCAmelCase__							)


 def _lowercase	(     self		:						List[str]							)					->       List[str]:
   for t, num_inference_steps in zip([1, 5, 10]							,						[10, 50, 100]							):
     self.check_over_forward(num_inference_steps=UpperCAmelCase__							,						time_step=UpperCAmelCase__							)







 def _lowercase	(     self		:						int							)					->       List[Any]:
   _a  :       str             =	self.full_loop()
   _a  :       List[Any]             =	torch.mean(torch.abs(UpperCAmelCase__							)							)

   assert abs(result_mean.item() - 2540529							) < 10

