




'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_							:	Optional[int]	     =		logging.get_logger(__name__)

UpperCAmelCase_							:	Any	     =		{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''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''',
}
UpperCAmelCase_							:	Tuple	     =		[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any] ,						_lowerCamelCase				:  str ,						_lowerCamelCase				:  Optional[Any] ,						_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  List[str]						)-> Optional[int]:




     '''simple docstring'''





     for attribute in key.split('''.'''						):
          __snake_case            =	getattr(_lowerCamelCase ,						_lowerCamelCase						)

     if weight_type is not None:
          __snake_case            =	getattr(_lowerCamelCase ,						_lowerCamelCase						).shape
     else:
          __snake_case            =	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":
          __snake_case            =	value
     elif weight_type == "weight_g":
          __snake_case            =	value
     elif weight_type == "weight_v":
          __snake_case            =	value
     elif weight_type == "bias":
          __snake_case            =	value
     elif weight_type == "running_mean":
          __snake_case            =	value
     elif weight_type == "running_var":
          __snake_case            =	value
     elif weight_type == "num_batches_tracked":
          __snake_case            =	value
     elif weight_type == "inv_freq":
          __snake_case            =	value
     else:
          __snake_case            =	value

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







def       _UpperCamelCase			(_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  str						)-> int:




     '''simple docstring'''





     __snake_case            =	[]
     __snake_case            =	fairseq_model.state_dict()

     __snake_case            =	hf_model.wavaveca_conformer.feature_extractor

     for name, value in fairseq_dict.items():
          __snake_case            =	False
          if "conv_layers" in name:
               load_conv_layer(
                   _lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase ,						hf_model.config.feat_extract_norm == '''group''' ,						)
               __snake_case            =	True
          else:
               for key, mapped_key in MAPPING.items():
                    __snake_case            =	'''wav2vec2_conformer.''' + 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]:
                         __snake_case            =	True
                         if "*" in mapped_key:
                              __snake_case            =	name.split(_lowerCamelCase						)[0].split('''.'''						)[-2]
                              __snake_case            =	mapped_key.replace('''*''' ,						_lowerCamelCase						)
                         if "pos_bias_u" in name:
                              __snake_case            =	None
                         elif "pos_bias_v" in name:
                              __snake_case            =	None
                         elif "weight_g" in name:
                              __snake_case            =	'''weight_g'''
                         elif "weight_v" in name:
                              __snake_case            =	'''weight_v'''
                         elif "bias" in name:
                              __snake_case            =	'''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              __snake_case            =	'''weight'''
                         elif "running_mean" in name:
                              __snake_case            =	'''running_mean'''
                         elif "inv_freq" in name:
                              __snake_case            =	'''inv_freq'''
                         elif "running_var" in name:
                              __snake_case            =	'''running_var'''
                         elif "num_batches_tracked" in name:
                              __snake_case            =	'''num_batches_tracked'''
                         else:
                              __snake_case            =	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				:  Optional[Any] ,						_lowerCamelCase				:  Optional[Any] ,						_lowerCamelCase				:  Any ,						_lowerCamelCase				:  List[str] ,						_lowerCamelCase				:  List[str]						)-> Optional[Any]:




     '''simple docstring'''





     __snake_case            =	full_name.split('''conv_layers.'''						)[-1]
     __snake_case            =	name.split('''.'''						)
     __snake_case            =	int(items[0]						)
     __snake_case            =	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.'''						)
               __snake_case            =	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.'''						)
               __snake_case            =	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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''						)
               __snake_case            =	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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''						)
               __snake_case            =	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				:  List[Any] ,						_lowerCamelCase				:  int ,						_lowerCamelCase				:  int=None ,						_lowerCamelCase				:  Union[str, Any]=None ,						_lowerCamelCase				:  str=True						)-> Optional[Any]:




     '''simple docstring'''





     if config_path is not None:
          __snake_case            =	WavaVecaConformerConfig.from_pretrained(_lowerCamelCase ,						hidden_act='''swish'''						)
     else:
          __snake_case            =	WavaVecaConformerConfig()

     if "rope" in checkpoint_path:
          __snake_case            =	'''rotary'''

     if is_finetuned:
          if dict_path:
               __snake_case            =	Dictionary.load(_lowerCamelCase						)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               __snake_case            =	target_dict.pad_index
               __snake_case            =	target_dict.bos_index
               __snake_case            =	target_dict.eos_index
               __snake_case            =	len(target_dict.symbols						)
               __snake_case            =	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						)
               __snake_case            =	target_dict.indices

               # fairseq has the <pad> and <s> switched
               __snake_case            =	0
               __snake_case            =	1
               with open(_lowerCamelCase ,						'''w''' ,						encoding='''utf-8'''						) as vocab_handle:
                    json.dump(_lowerCamelCase ,						_lowerCamelCase						)
               __snake_case            =	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 ,						)
               __snake_case            =	True if config.feat_extract_norm == '''layer''' else False
               __snake_case            =	WavaVecaFeatureExtractor(
                   feature_size=1 ,						sampling_rate=1_60_00 ,						padding_value=0 ,						do_normalize=_lowerCamelCase ,						return_attention_mask=_lowerCamelCase ,						)
               __snake_case            =	WavaVecaProcessor(feature_extractor=_lowerCamelCase ,						tokenizer=_lowerCamelCase						)
               processor.save_pretrained(_lowerCamelCase						)

          __snake_case            =	WavaVecaConformerForCTC(_lowerCamelCase						)
     else:
          __snake_case            =	WavaVecaConformerForPreTraining(_lowerCamelCase						)

     if is_finetuned:
          __snake_case       ,     __snake_case       ,     __snake_case            =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path] ,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
     else:
          __snake_case            =	argparse.Namespace(task='''audio_pretraining'''						)
          __snake_case            =	fairseq.tasks.setup_task(_lowerCamelCase						)

          __snake_case       ,     __snake_case       ,     __snake_case            =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,						task=_lowerCamelCase						)

     __snake_case            =	model[0].eval()

     recursively_load_weights(_lowerCamelCase ,						_lowerCamelCase ,						not is_finetuned						)

     hf_wavavec.save_pretrained(_lowerCamelCase						)


if __name__ == "__main__":
     UpperCAmelCase_							:	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_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'''
     )
     UpperCAmelCase_							:	int	     =		parser.parse_args()
     convert_wavaveca_conformer_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )












'''simple docstring'''



import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





     warnings.warn(
         '''The preprocess method is deprecated and will be removed in a future version. Please'''
         ''' use VaeImageProcessor.preprocess instead''' ,						_lowerCamelCase ,						)
     if isinstance(_lowerCamelCase ,						torch.Tensor						):
          return image
     elif isinstance(_lowerCamelCase ,						PIL.Image.Image						):
          __snake_case            =	[image]

     if isinstance(image[0] ,						PIL.Image.Image						):
          __snake_case       ,     __snake_case            =	image[0].size
          __snake_case       ,     __snake_case            =	(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

          __snake_case            =	[np.array(i.resize((w, h) ,						resample=PIL_INTERPOLATION['''lanczos''']						)						)[None, :] for i in image]
          __snake_case            =	np.concatenate(_lowerCamelCase ,						axis=0						)
          __snake_case            =	np.array(_lowerCamelCase						).astype(np.floataa						) / 255.0
          __snake_case            =	image.transpose(0 ,						3 ,						1 ,						2						)
          __snake_case            =	2.0 * image - 1.0
          __snake_case            =	torch.from_numpy(_lowerCamelCase						)
     elif isinstance(image[0] ,						torch.Tensor						):
          __snake_case            =	torch.cat(_lowerCamelCase ,						dim=0						)
     return image







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





     if isinstance(_lowerCamelCase ,						torch.Tensor						):
          return mask
     elif isinstance(_lowerCamelCase ,						PIL.Image.Image						):
          __snake_case            =	[mask]

     if isinstance(mask[0] ,						PIL.Image.Image						):
          __snake_case       ,     __snake_case            =	mask[0].size
          __snake_case       ,     __snake_case            =	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
          __snake_case            =	[np.array(m.convert('''L'''						).resize((w, h) ,						resample=PIL_INTERPOLATION['''nearest''']						)						)[None, :] for m in mask]
          __snake_case            =	np.concatenate(_lowerCamelCase ,						axis=0						)
          __snake_case            =	mask.astype(np.floataa						) / 255.0
          __snake_case            =	0
          __snake_case            =	1
          __snake_case            =	torch.from_numpy(_lowerCamelCase						)
     elif isinstance(mask[0] ,						torch.Tensor						):
          __snake_case            =	torch.cat(_lowerCamelCase ,						dim=0						)
     return mask







class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   UNetaDModel
       __lowercase	:   RePaintScheduler


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




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




       @torch.no_grad()
       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 250     ,	__SCREAMING_SNAKE_CASE = 0.0     ,	__SCREAMING_SNAKE_CASE = 10     ,	__SCREAMING_SNAKE_CASE = 10     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = "pil"     ,	__SCREAMING_SNAKE_CASE = True     ,	)   ->	Union[ImagePipelineOutput, Tuple]:



            '''simple docstring'''




            __snake_case            =	image

            __snake_case            =	_preprocess_image(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	original_image.to(device=self.device     ,	dtype=self.unet.dtype	)
            __snake_case            =	_preprocess_mask(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	mask_image.to(device=self.device     ,	dtype=self.unet.dtype	)

            __snake_case            =	original_image.shape[0]

            # sample gaussian noise to begin the loop
            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) and len(__SCREAMING_SNAKE_CASE	) != batch_size:
                 raise ValueError(
                     F'''You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE	)}, but requested an effective batch'''
                     F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''	)

            __snake_case            =	original_image.shape
            __snake_case            =	randn_tensor(__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE     ,	device=self.device     ,	dtype=self.unet.dtype	)

            # set step values
            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	self.device	)
            __snake_case            =	eta

            __snake_case            =	self.scheduler.timesteps[0] + 1
            __snake_case            =	generator[0] if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) else generator
            for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
                 if t < t_last:
                      # predict the noise residual
                      __snake_case            =	self.unet(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	).sample
                      # compute previous image: x_t -> x_t-1
                      __snake_case            =	self.scheduler.step(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	).prev_sample

                 else:
                      # compute the reverse: x_t-1 -> x_t
                      __snake_case            =	self.scheduler.undo_step(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	t

            __snake_case            =	(image / 2 + 0.5).clamp(0     ,	1	)
            __snake_case            =	image.cpu().permute(0     ,	2     ,	3     ,	1	).numpy()
            if output_type == "pil":
                 __snake_case            =	self.numpy_to_pil(__SCREAMING_SNAKE_CASE	)

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE	)






