
'''simple docstring'''







from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     ['''vqvae''']






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__()
											self.register_modules(unet=__SCREAMING_SNAKE_CASE				,							scheduler=__SCREAMING_SNAKE_CASE				,							mel=__SCREAMING_SNAKE_CASE				,							vqvae=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return 50 if isinstance(self.scheduler				,							__SCREAMING_SNAKE_CASE		) else 10_00






					@torch.no_grad()
					def __call__(   self				,							__SCREAMING_SNAKE_CASE = 1				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = 0				,							__SCREAMING_SNAKE_CASE = 0				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = 0				,							__SCREAMING_SNAKE_CASE = 0				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = 0				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE=True				,							):


											"""simple docstring"""





											lowercase_	:     str													=       steps or self.get_default_steps()
											self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[Any]													=       step_generator or generator
											# For backwards compatibility
											if type(self.unet.config.sample_size		) == int:
																	lowercase_	:     str													=       (self.unet.config.sample_size, self.unet.config.sample_size)
											if noise is None:
																	lowercase_	:     Union[str, Any]													=       randn_tensor(
																	    (
																	        batch_size,
																	        self.unet.config.in_channels,
																	        self.unet.config.sample_size[0],
																	        self.unet.config.sample_size[1],
																	    )				,							generator=__SCREAMING_SNAKE_CASE				,							device=self.device				,							)
											lowercase_	:     str													=       noise
											lowercase_	:     Any													=       None

											if audio_file is not None or raw_audio is not None:
																	self.mel.load_audio(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     int													=       self.mel.audio_slice_to_image(__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     int													=       np.frombuffer(input_image.tobytes()				,							dtype='''uint8'''		).reshape(
																	    (input_image.height, input_image.width)		)
																	lowercase_	:     Dict													=       (input_image / 2_55) * 2 - 1
																	lowercase_	:     Optional[Any]													=       torch.tensor(input_image[np.newaxis, :, :]				,							dtype=torch.float		).to(self.device		)

																	if self.vqvae is not None:
																							lowercase_	:     Optional[int]													=       self.vqvae.encode(torch.unsqueeze(__SCREAMING_SNAKE_CASE				,							0		)		).latent_dist.sample(
																							    generator=__SCREAMING_SNAKE_CASE		)[0]
																							lowercase_	:     str													=       self.vqvae.config.scaling_factor * input_images

																	if start_step > 0:
																							lowercase_	:     str													=       self.scheduler.add_noise(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							self.scheduler.timesteps[start_step - 1]		)

																	lowercase_	:     str													=       (
																	    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
																	)
																	lowercase_	:     Union[str, Any]													=       int(mask_start_secs * pixels_per_second		)
																	lowercase_	:     Union[str, Any]													=       int(mask_end_secs * pixels_per_second		)
																	lowercase_	:     List[Any]													=       self.scheduler.add_noise(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							torch.tensor(self.scheduler.timesteps[start_step:]		)		)

											for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]		)		):
																	if isinstance(self.unet				,							__SCREAMING_SNAKE_CASE		):
																							lowercase_	:     Any													=       self.unet(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)['''sample''']
																	else:
																							lowercase_	:     int													=       self.unet(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)['''sample''']

																	if isinstance(self.scheduler				,							__SCREAMING_SNAKE_CASE		):
																							lowercase_	:     str													=       self.scheduler.step(
																							    model_output=__SCREAMING_SNAKE_CASE				,							timestep=__SCREAMING_SNAKE_CASE				,							sample=__SCREAMING_SNAKE_CASE				,							eta=__SCREAMING_SNAKE_CASE				,							generator=__SCREAMING_SNAKE_CASE				,							)['''prev_sample''']
																	else:
																							lowercase_	:     Tuple													=       self.scheduler.step(
																							    model_output=__SCREAMING_SNAKE_CASE				,							timestep=__SCREAMING_SNAKE_CASE				,							sample=__SCREAMING_SNAKE_CASE				,							generator=__SCREAMING_SNAKE_CASE				,							)['''prev_sample''']

																	if mask is not None:
																							if mask_start > 0:
																													lowercase_	:     Dict													=       mask[:, step, :, :mask_start]
																							if mask_end > 0:
																													lowercase_	:     Tuple													=       mask[:, step, :, -mask_end:]

											if self.vqvae is not None:
																	# 0.18215 was scaling factor used in training to ensure unit variance
																	lowercase_	:     Optional[Any]													=       1 / self.vqvae.config.scaling_factor * images
																	lowercase_	:     Union[str, Any]													=       self.vqvae.decode(__SCREAMING_SNAKE_CASE		)['''sample''']

											lowercase_	:     str													=       (images / 2 + 0.5).clamp(0				,							1		)
											lowercase_	:     Union[str, Any]													=       images.cpu().permute(0				,							2				,							3				,							1		).numpy()
											lowercase_	:     Any													=       (images * 2_55).round().astype('''uint8'''		)
											lowercase_	:     Union[str, Any]													=       list(
											    (Image.fromarray(_[:, :, 0]		) for _ in images)
											    if images.shape[3] == 1
											    else (Image.fromarray(__SCREAMING_SNAKE_CASE				,							mode='''RGB'''		).convert('''L'''		) for _ in images)		)

											lowercase_	:     Any													=       [self.mel.image_to_audio(__SCREAMING_SNAKE_CASE		) for _ in images]
											if not return_dict:
																	return images, (self.mel.get_sample_rate(), audios)

											return BaseOutput(**AudioPipelineOutput(np.array(__SCREAMING_SNAKE_CASE		)[:, np.newaxis, :]		)				,							**ImagePipelineOutput(__SCREAMING_SNAKE_CASE		)		)






					@torch.no_grad()
					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = 50		):


											"""simple docstring"""





											assert isinstance(self.scheduler				,							__SCREAMING_SNAKE_CASE		)
											self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[str]													=       np.array(
											    [np.frombuffer(image.tobytes()				,							dtype='''uint8'''		).reshape((1, image.height, image.width)		) for image in images]		)
											lowercase_	:     Tuple													=       (sample / 2_55) * 2 - 1
											lowercase_	:     List[Any]													=       torch.Tensor(__SCREAMING_SNAKE_CASE		).to(self.device		)

											for t in self.progress_bar(torch.flip(self.scheduler.timesteps				,							(0,)		)		):
																	lowercase_	:     List[str]													=       t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
																	lowercase_	:     Union[str, Any]													=       self.scheduler.alphas_cumprod[t]
																	lowercase_	:     List[str]													=       (
																	    self.scheduler.alphas_cumprod[prev_timestep]
																	    if prev_timestep >= 0
																	    else self.scheduler.final_alpha_cumprod
																	)
																	lowercase_	:     Optional[int]													=       1 - alpha_prod_t
																	lowercase_	:     List[Any]													=       self.unet(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)['''sample''']
																	lowercase_	:     int													=       (1 - alpha_prod_t_prev) ** 0.5 * model_output
																	lowercase_	:     str													=       (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
																	lowercase_	:     List[Any]													=       sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

											return sample






					@staticmethod
					def 	_snake_case    (   __SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       acos(torch.dot(torch.flatten(__SCREAMING_SNAKE_CASE		)				,							torch.flatten(__SCREAMING_SNAKE_CASE		)		) / torch.norm(__SCREAMING_SNAKE_CASE		) / torch.norm(__SCREAMING_SNAKE_CASE		)		)
											return sin((1 - alpha) * theta		) * xa / sin(__SCREAMING_SNAKE_CASE		) + sin(alpha * theta		) * xa / sin(__SCREAMING_SNAKE_CASE		)










import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
snake_case__					:							Dict													=     logging.get_logger(__name__)

snake_case__					:							Optional[Any]													=     {
    '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__					:							Optional[int]													=     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]



def _a				(	lowerCamelCase:   List[Any]					,				lowerCamelCase:   Any					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   Any					,				lowerCamelCase:   int )	->						List[str]:







      '''simple docstring'''






      for attribute in key.split('''.''' ):
            __A          =				getattr(lowerCamelCase					,				lowerCamelCase )

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

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



def _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   Optional[int] )	->						Tuple:







      '''simple docstring'''






      __A          =				[]
      __A          =				fairseq_model.state_dict()

      __A          =				hf_model.unispeech_sat.feature_extractor

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







      '''simple docstring'''






      __A          =				full_name.split('''conv_layers.''' )[-1]
      __A          =				name.split('''.''' )
      __A          =				int(items[0] )
      __A          =				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.""" )
                  __A          =				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.""" )
                  __A          =				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.""" )
                  __A          =				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.""" )
                  __A          =				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:   Tuple					,				lowerCamelCase:   int					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[int]=True )	->						List[Any]:







      '''simple docstring'''






      if config_path is not None:
            __A          =				UniSpeechSatConfig.from_pretrained(lowerCamelCase )
      else:
            __A          =				UniSpeechSatConfig()

      __A          =				''''''

      if is_finetuned:
            __A          =				UniSpeechSatForCTC(lowerCamelCase )
      else:
            __A          =				UniSpeechSatForPreTraining(lowerCamelCase )

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

      recursively_load_weights(lowerCamelCase					,				lowerCamelCase )

      hf_wavavec.save_pretrained(lowerCamelCase )


if __name__ == "__main__":
       snake_case__					:							Tuple													=     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__					:							Any													=     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
       )





