






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()
SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)

SCREAMING_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''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[str]   ,  lowerCamelCase_       :       int   ,  lowerCamelCase_       :       Any   ,  lowerCamelCase_       :       Optional[int]   ,  lowerCamelCase_       :       Dict				):
			for attribute in key.split('.'				):
						__a						:  Dict									=     getattr(lowerCamelCase_   ,  lowerCamelCase_				)

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

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

def        UpperCAmelCase__		(						lowerCamelCase_       :       Optional[int]   ,  lowerCamelCase_       :       Union[str, Any]   ,  lowerCamelCase_       :       Tuple				):
			__a						:  str									=     []
			__a						:  int									=     fairseq_model.state_dict()

			__a						:  Dict									=     hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						__a						:  Tuple									=     False
						if "conv_layers" in name:
									load_conv_layer(
									    lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  hf_model.config.feat_extract_norm == 'group'   ,  )
									__a						:  List[Any]									=     True
						else:
									for key, mapped_key in MAPPING.items():
												__a						:  List[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):
															__a						:  List[str]									=     True
															if "*" in mapped_key:
																		__a						:  Optional[Any]									=     name.split(lowerCamelCase_				)[0].split('.'				)[-2]
																		__a						:  Optional[int]									=     mapped_key.replace('*'   ,  lowerCamelCase_				)
															if "weight_g" in name:
																		__a						:  Optional[int]									=     'weight_g'
															elif "weight_v" in name:
																		__a						:  List[str]									=     'weight_v'
															elif "weight" in name:
																		__a						:  str									=     'weight'
															elif "bias" in name:
																		__a						:  Dict									=     'bias'
															else:
																		__a						:  Tuple									=     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_       :       List[Any]   ,  lowerCamelCase_       :       int   ,  lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Union[str, Any]				):
			__a						:  Union[str, Any]									=     full_name.split('conv_layers.'				)[-1]
			__a						:  Union[str, Any]									=     name.split('.'				)
			__a						:  List[Any]									=     int(items[0]				)
			__a						:  List[str]									=     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.'''
									)
									__a						:  List[str]									=     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.'''
									)
									__a						:  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."
									)
									__a						:  str									=     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.'''
									)
									__a						:  List[str]									=     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_       :       Optional[Any]   ,  lowerCamelCase_       :       Union[str, Any]   ,  lowerCamelCase_       :       Dict=None   ,  lowerCamelCase_       :       Tuple=None   ,  lowerCamelCase_       :       Union[str, Any]=True				):
			if config_path is not None:
						__a						:  Optional[Any]									=     HubertConfig.from_pretrained(lowerCamelCase_				)
			else:
						__a						:  str									=     HubertConfig()

			if is_finetuned:
						if dict_path:
									__a						:  Dict									=     Dictionary.load(lowerCamelCase_				)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									__a						:  List[Any]									=     target_dict.pad_index
									__a						:  List[Any]									=     target_dict.bos_index
									__a						:  Tuple									=     target_dict.eos_index
									__a						:  List[str]									=     len(target_dict.symbols				)
									__a						:  Union[str, Any]									=     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_				)
									__a						:  Optional[int]									=     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_   ,  )
									__a						:  Dict									=     True if config.feat_extract_norm == 'layer' else False
									__a						:  Tuple									=     WavaVecaFeatureExtractor(
									    feature_size=1   ,  sampling_rate=1_6_0_0_0   ,  padding_value=0   ,  do_normalize=lowerCamelCase_   ,  return_attention_mask=lowerCamelCase_   ,  )
									__a						:  List[Any]									=     WavaVecaProcessor(feature_extractor=lowerCamelCase_   ,  tokenizer=lowerCamelCase_				)
									processor.save_pretrained(lowerCamelCase_				)

						__a						:  Union[str, Any]									=     HubertForCTC(lowerCamelCase_				)
			else:
						__a						:  int									=     HubertModel(lowerCamelCase_				)

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

			__a						:  Optional[Any]									=     model[0].eval()

			recursively_load_weights(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)

			hf_wavavec.save_pretrained(lowerCamelCase_				)


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







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


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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[List, PIL.Image.Image, torch.Tensor]							)	->     int:
							warnings.warn(
							    """The preprocess method is deprecated and will be removed in a future version. Please"""
							    """ use VaeImageProcessor.preprocess instead"""      ,					UpperCAmelCase__      ,					)
							if isinstance(UpperCAmelCase__      ,					torch.Tensor							):
														return image
							elif isinstance(UpperCAmelCase__      ,					PIL.Image.Image							):
														lowerCamelCase_					=       [image]

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

														lowerCamelCase_					=       [np.array(i.resize((w, h)      ,					resample=PIL_INTERPOLATION["""lanczos"""]							)							)[None, :] for i in image]
														lowerCamelCase_					=       np.concatenate(UpperCAmelCase__      ,					axis=0							)
														lowerCamelCase_					=       np.array(UpperCAmelCase__							).astype(np.floataa							) / 2_5_5.0
														lowerCamelCase_					=       image.transpose(0      ,					3      ,					1      ,					2							)
														lowerCamelCase_					=       2.0 * image - 1.0
														lowerCamelCase_					=       torch.from_numpy(UpperCAmelCase__							)
							elif isinstance(image[0]      ,					torch.Tensor							):
														lowerCamelCase_					=       torch.cat(UpperCAmelCase__      ,					dim=0							)
							return image



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[List, PIL.Image.Image, torch.Tensor]							)	->     Any:
							if isinstance(UpperCAmelCase__      ,					torch.Tensor							):
														return mask
							elif isinstance(UpperCAmelCase__      ,					PIL.Image.Image							):
														lowerCamelCase_					=       [mask]

							if isinstance(mask[0]      ,					PIL.Image.Image							):
														lowerCamelCase_	, lowerCamelCase_					=       mask[0].size
														lowerCamelCase_	, lowerCamelCase_					=       (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
														lowerCamelCase_					=       [np.array(m.convert("""L"""							).resize((w, h)      ,					resample=PIL_INTERPOLATION["""nearest"""]							)							)[None, :] for m in mask]
														lowerCamelCase_					=       np.concatenate(UpperCAmelCase__      ,					axis=0							)
														lowerCamelCase_					=       mask.astype(np.floataa							) / 2_5_5.0
														lowerCamelCase_					=       0
														lowerCamelCase_					=       1
														lowerCamelCase_					=       torch.from_numpy(UpperCAmelCase__							)
							elif isinstance(mask[0]      ,					torch.Tensor							):
														lowerCamelCase_					=       torch.cat(UpperCAmelCase__      ,					dim=0							)
							return mask





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     42
					SCREAMING_SNAKE_CASE           =     42

					def __init__(						self       :  Optional[int] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict ):
												super().__init__()
												self.register_modules(unet=__UpperCamelCase ,       scheduler=__UpperCamelCase )



					@torch.no_grad()
					def __call__(						self       :  List[str] ,       __UpperCamelCase       :  Union[torch.Tensor, PIL.Image.Image] ,       __UpperCamelCase       :  Union[torch.Tensor, PIL.Image.Image] ,       __UpperCamelCase       :  int = 2_5_0 ,       __UpperCamelCase       :  float = 0.0 ,       __UpperCamelCase       :  int = 1_0 ,       __UpperCamelCase       :  int = 1_0 ,       __UpperCamelCase       :  Optional[Union[torch.Generator, List[torch.Generator]]] = None ,       __UpperCamelCase       :  Optional[str] = "pil" ,       __UpperCamelCase       :  bool = True ,       ):
												lowerCamelCase_					=       image

												lowerCamelCase_					=       _preprocess_image(__UpperCamelCase )
												lowerCamelCase_					=       original_image.to(device=self.device ,       dtype=self.unet.dtype )
												lowerCamelCase_					=       _preprocess_mask(__UpperCamelCase )
												lowerCamelCase_					=       mask_image.to(device=self.device ,       dtype=self.unet.dtype )

												lowerCamelCase_					=       original_image.shape[0]

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

												lowerCamelCase_					=       original_image.shape
												lowerCamelCase_					=       randn_tensor(__UpperCamelCase ,       generator=__UpperCamelCase ,       device=self.device ,       dtype=self.unet.dtype )

												# set step values
												self.scheduler.set_timesteps(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       self.device )
												lowerCamelCase_					=       eta

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

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

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

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase )





