






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

_a      	=  {
    '''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''',
}
_a      	=  [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  Union[str, Any]:





						"""simple docstring"""



						for attribute in key.split('.'	):
												_UpperCAmelCase									=			getattr(__lowerCAmelCase ,		__lowerCAmelCase	)

						if weight_type is not None:
												_UpperCAmelCase									=			getattr(__lowerCAmelCase ,		__lowerCAmelCase	).shape
						else:
												_UpperCAmelCase									=			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":
												_UpperCAmelCase									=			value
						elif weight_type == "weight_g":
												_UpperCAmelCase									=			value
						elif weight_type == "weight_v":
												_UpperCAmelCase									=			value
						elif weight_type == "bias":
												_UpperCAmelCase									=			value
						else:
												_UpperCAmelCase									=			value

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


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			[]
						_UpperCAmelCase									=			fairseq_model.state_dict()

						_UpperCAmelCase									=			hf_model.unispeech_sat.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCAmelCase									=			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		hf_model.config.feat_extract_norm == 'group' ,		)
																		_UpperCAmelCase									=			True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCAmelCase									=			'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
																														_UpperCAmelCase									=			True
																														if "*" in mapped_key:
																																				_UpperCAmelCase									=			name.split(__lowerCAmelCase	)[0].split('.'	)[-2]
																																				_UpperCAmelCase									=			mapped_key.replace('*' ,		__lowerCAmelCase	)
																														if "weight_g" in name:
																																				_UpperCAmelCase									=			'weight_g'
																														elif "weight_v" in name:
																																				_UpperCAmelCase									=			'weight_v'
																														elif "bias" in name:
																																				_UpperCAmelCase									=			'bias'
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				_UpperCAmelCase									=			'weight'
																														else:
																																				_UpperCAmelCase									=			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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  List[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			full_name.split('conv_layers.'	)[-1]
						_UpperCAmelCase									=			name.split('.'	)
						_UpperCAmelCase									=			int(items[0]	)
						_UpperCAmelCase									=			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."""	)
																		_UpperCAmelCase									=			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."""	)
																		_UpperCAmelCase									=			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."""	)
																		_UpperCAmelCase									=			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."""	)
																		_UpperCAmelCase									=			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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=None ,		__lowerCAmelCase=None ,		__lowerCAmelCase=True	)->  int:





						"""simple docstring"""



						if config_path is not None:
												_UpperCAmelCase									=			UniSpeechSatConfig.from_pretrained(__lowerCAmelCase	)
						else:
												_UpperCAmelCase									=			UniSpeechSatConfig()

						_UpperCAmelCase									=			''

						if is_finetuned:
												_UpperCAmelCase									=			UniSpeechSatForCTC(__lowerCAmelCase	)
						else:
												_UpperCAmelCase									=			UniSpeechSatForPreTraining(__lowerCAmelCase	)

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

						recursively_load_weights(__lowerCAmelCase ,		__lowerCAmelCase	)

						hf_wavavec.save_pretrained(__lowerCAmelCase	)


if __name__ == "__main__":
		_a      	=  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'''
		)
		_a      	=  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
		)

import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
      from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


UpperCamelCase  							=						logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase  							=						256



class   snake_case_       (		__A       ):
   __A	:		str         =				["melgan"]






   def __init__(      self				:     str	,						lowercase_				:     SpectrogramNotesEncoder	,						lowercase_				:     SpectrogramContEncoder	,						lowercase_				:     TaFilmDecoder	,						lowercase_				:     DDPMScheduler	,						lowercase_				:     OnnxRuntimeModel if is_onnx_available() else Any	,						)					->						None:
       super().__init__()

       # From MELGAN
       lowercase__ :   List[Any]    					=				math.log(1E-5  )  # Matches MelGAN training.
       lowercase__ :   str    					=				4.0  # Largest value for most examples
       lowercase__ :   Any    					=				1_28

       self.register_modules(
           notes_encoder=lowercase_	,						continuous_encoder=lowercase_	,						decoder=lowercase_	,						scheduler=lowercase_	,						melgan=lowercase_	,						)






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     List[str]=(-1.0, 1.0)	,						lowercase_				:     Dict=False  )					->						Optional[Any]:
       lowercase__					,   lowercase__ :   int    					=				output_range
       if clip:
           lowercase__ :   Optional[Any]    					=				torch.clip(lowercase_	,						self.min_value	,						self.max_value  )
       # Scale to [0, 1].
       lowercase__ :   List[str]    					=				(features - self.min_value) / (self.max_value - self.min_value)
       # Scale to [min_out, max_out].
       return zero_one * (max_out - min_out) + min_out






   def       __UpperCamelCase     (      self				:     Optional[int]	,						lowercase_				:     List[str]	,						lowercase_				:     List[str]=(-1.0, 1.0)	,						lowercase_				:     List[Any]=False  )					->						Union[str, Any]:
       lowercase__					,   lowercase__ :   Tuple    					=				input_range
       lowercase__ :   Optional[Any]    					=				torch.clip(lowercase_	,						lowercase_	,						lowercase_  ) if clip else outputs
       # Scale to [0, 1].
       lowercase__ :   Union[str, Any]    					=				(outputs - min_out) / (max_out - min_out)
       # Scale to [self.min_value, self.max_value].
       return zero_one * (self.max_value - self.min_value) + self.min_value






   def       __UpperCamelCase     (      self				:     List[str]	,						lowercase_				:     Any	,						lowercase_				:     Optional[Any]	,						lowercase_				:     Tuple  )					->						List[str]:
       lowercase__ :   Optional[Any]    					=				input_tokens > 0
       lowercase__					,   lowercase__ :   int    					=				self.notes_encoder(
           encoder_input_tokens=lowercase_	,						encoder_inputs_mask=lowercase_  )

       lowercase__					,   lowercase__ :   List[Any]    					=				self.continuous_encoder(
           encoder_inputs=lowercase_	,						encoder_inputs_mask=lowercase_  )

       return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     Optional[int]	,						lowercase_				:     Optional[int]	,						lowercase_				:     str  )					->						Tuple:
       lowercase__ :   Union[str, Any]    					=				noise_time
       if not torch.is_tensor(lowercase_  ):
           lowercase__ :   Optional[Any]    					=				torch.tensor([timesteps]	,						dtype=torch.long	,						device=input_tokens.device  )
       elif torch.is_tensor(lowercase_  ) and len(timesteps.shape  ) == 0:
           lowercase__ :   Optional[Any]    					=				timesteps[None].to(input_tokens.device  )

       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       lowercase__ :   int    					=				timesteps * torch.ones(input_tokens.shape[0]	,						dtype=timesteps.dtype	,						device=timesteps.device  )

       lowercase__ :   str    					=				self.decoder(
           encodings_and_masks=lowercase_	,						decoder_input_tokens=lowercase_	,						decoder_noise_time=lowercase_  )
       return logits







   @torch.no_grad()
   def __call__(      self				:     List[str]	,						lowercase_				:     List[List[int]]	,						lowercase_				:     Optional[torch.Generator] = None	,						lowercase_				:     int = 1_00	,						lowercase_				:     bool = True	,						lowercase_				:     str = "numpy"	,						lowercase_				:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,						lowercase_				:     int = 1	,						)					->						Union[AudioPipelineOutput, Tuple]:
       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(lowercase_	,						lowercase_  ) or callback_steps <= 0)
       ):
           raise ValueError(
               F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
               F''' {type(lowercase_  )}.'''  )

       lowercase__ :   str    					=				np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]	,						dtype=np.floataa  )
       lowercase__ :   Optional[int]    					=				np.zeros([1, 0, self.n_dims]	,						np.floataa  )
       lowercase__ :   str    					=				torch.ones((1, TARGET_FEATURE_LENGTH)	,						dtype=lowercase_	,						device=self.device  )

       for i, encoder_input_tokens in enumerate(lowercase_  ):
           if i == 0:
               lowercase__ :   Union[str, Any]    					=				torch.from_numpy(pred_mel[:1].copy()  ).to(
                   device=self.device	,						dtype=self.decoder.dtype  )
               # The first chunk has no previous context.
               lowercase__ :   List[str]    					=				torch.zeros((1, TARGET_FEATURE_LENGTH)	,						dtype=lowercase_	,						device=self.device  )
           else:
               # The full song pipeline does not feed in a context feature, so the mask
               # will be all 0s after the feature converter. Because we know we're
               # feeding in a full context chunk from the previous prediction, set it
               # to all 1s.
               lowercase__ :   str    					=				ones

           lowercase__ :   str    					=				self.scale_features(
               lowercase_	,						output_range=[-1.0, 1.0]	,						clip=lowercase_  )

           lowercase__ :   str    					=				self.encode(
               input_tokens=torch.IntTensor([encoder_input_tokens]  ).to(device=self.device  )	,						continuous_inputs=lowercase_	,						continuous_mask=lowercase_	,						)

           # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
           lowercase__ :   List[str]    					=				randn_tensor(
               shape=encoder_continuous_inputs.shape	,						generator=lowercase_	,						device=self.device	,						dtype=self.decoder.dtype	,						)

           # set step values
           self.scheduler.set_timesteps(lowercase_  )

           # Denoising diffusion loop
           for j, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
               lowercase__ :   Optional[int]    					=				self.decode(
                   encodings_and_masks=lowercase_	,						input_tokens=lowercase_	,						noise_time=t / self.scheduler.config.num_train_timesteps	,						)

               # Compute previous output: x_t -> x_t-1
               lowercase__ :   Optional[Any]    					=				self.scheduler.step(lowercase_	,						lowercase_	,						lowercase_	,						generator=lowercase_  ).prev_sample

           lowercase__ :   Tuple    					=				self.scale_to_features(lowercase_	,						input_range=[-1.0, 1.0]  )
           lowercase__ :   List[str]    					=				mel[:1]
           lowercase__ :   Optional[int]    					=				mel.cpu().float().numpy()

           lowercase__ :   str    					=				np.concatenate([full_pred_mel, pred_mel[:1]]	,						axis=1  )

           # call the callback, if provided
           if callback is not None and i % callback_steps == 0:
               callback(lowercase_	,						lowercase_  )

           logger.info("Generated segment"	,						lowercase_  )

       if output_type == "numpy" and not is_onnx_available():
           raise ValueError(
               "Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."  )
       elif output_type == "numpy" and self.melgan is None:
           raise ValueError(
               "Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."  )

       if output_type == "numpy":
           lowercase__ :   Union[str, Any]    					=				self.melgan(input_features=full_pred_mel.astype(np.floataa  )  )
       else:
           lowercase__ :   Dict    					=				full_pred_mel

       if not return_dict:
           return (output,)

       return AudioPipelineOutput(audios=lowercase_  )



