code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import argparse
  import json
  import os
  
  import fairseq
  import torch
  from fairseq.data import Dictionary
  
  # Register SEW's fairseq modules
  from sew_asapp import tasks  # noqa: F401
  
  from transformers import (
      SEWDConfig,
      SEWDForCTC,
      SEWDModel,
      WavaVecaCTCTokenizer,
      WavaVecaFeatureExtractor,
      WavaVecaProcessor,
      logging,
  )
  
  
  logging.set_verbosity_info()
  __lowerCamelCase       :       Tuple				   =			logging.get_logger(__name__)
  
  __lowerCamelCase       :       Dict				   =			{
      "post_extract_proj": "feature_projection",
      "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
      "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
      "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
      "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
      "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
      "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
      "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
      "output.dense": "encoder.encoder.layer.*.output.dense",
      "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
      "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
      "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
      "encoder.upsample.0": "encoder.upsample.projection",
      "encoder.layer_norm": "encoder.layer_norm",
      "w2v_model.layer_norm": "layer_norm",
      "w2v_encoder.proj": "lm_head",
      "mask_emb": "masked_spec_embed",
  }
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_,	snake_case_,	snake_case_	)			->							int:
  				for attribute in key.split('''.'''	):
  								A__     :							Union[str, Any]   				=getattr(UpperCamelCase__,	UpperCamelCase__	)
  
  				if weight_type is not None:
  								A__     :							List[Any]   				=getattr(UpperCamelCase__,	UpperCamelCase__	).shape
  				else:
  								A__     :							Optional[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":
  								A__     :							Optional[int]   				=value
  				elif weight_type == "weight_g":
  								A__     :							str   				=value
  				elif weight_type == "weight_v":
  								A__     :							int   				=value
  				elif weight_type == "bias":
  								A__     :							Optional[Any]   				=value
  				else:
  								A__     :							Union[str, Any]   				=value
  
  				logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							Any:
  				A__     :							Optional[Any]   				=[]
  				A__     :							Optional[int]   				=fairseq_model.state_dict()
  
  				A__     :							Any   				=hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor
  
  				for name, value in fairseq_dict.items():
  								A__     :							Optional[int]   				=False
  								if "conv_layers" in name:
  												load_conv_layer(
  												    UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	hf_model.config.feat_extract_norm == '''group''',	)
  												A__     :							Optional[Any]   				=True
  								else:
  												for key, mapped_key in MAPPING.items():
  																A__     :							str   				="sew_d." + 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]:
  																				A__     :							Dict   				=True
  																				if "*" in mapped_key:
  																								A__     :							Tuple   				=name.split(UpperCamelCase__	)[0].split('''.'''	)[-2]
  																								if not layer_index.isnumeric():
  																												continue
  																								A__     :							List[Any]   				=mapped_key.replace('''*''',	UpperCamelCase__	)
  																				if "weight_g" in name:
  																								A__     :							Dict   				="weight_g"
  																				elif "weight_v" in name:
  																								A__     :							Dict   				="weight_v"
  																				elif "weight" in name:
  																								A__     :							Optional[int]   				="weight"
  																				elif "bias" in name:
  																								A__     :							Any   				="bias"
  																				else:
  																								A__     :							List[str]   				=None
  																				set_recursively(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__	)
  																continue
  								if not is_used:
  												unused_weights.append(UpperCamelCase__	)
  
  				logger.warning(f'Unused weights: {unused_weights}'	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_,	snake_case_,	snake_case_	)			->							Tuple:
  				A__     :							Any   				=full_name.split('''conv_layers.'''	)[-1]
  				A__     :							Union[str, Any]   				=name.split('''.'''	)
  				A__     :							Union[str, Any]   				=int(items[0]	)
  				A__     :							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.'
  												)
  												A__     :							Optional[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.'
  												)
  												A__     :							Optional[int]   				=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__     :							Union[str, 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.'
  												)
  												A__     :							int   				=value
  												logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'	)
  				else:
  								unused_weights.append(UpperCamelCase__	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							Optional[Any]:
  				A__     :							Any   				=SEWDConfig()
  				if is_finetuned:
  								A__     :							List[str]   				=model.wav_encoder.wav_model.cfg
  				else:
  								A__     :							Tuple   				=model.cfg
  
  				A__     :							Optional[Any]   				=fs_config.conv_bias
  				A__     :							Dict   				=eval(fs_config.conv_feature_layers	)
  				A__     :							str   				=[x[0] for x in conv_layers]
  				A__     :							List[str]   				=[x[1] for x in conv_layers]
  				A__     :							int   				=[x[2] for x in conv_layers]
  				A__     :							Optional[Any]   				="gelu"
  				A__     :							Optional[Any]   				="layer" if fs_config.extractor_mode == "layer_norm" else "group"
  				A__     :							List[str]   				=0.0
  				A__     :							List[str]   				=fs_config.activation_fn.name
  				A__     :							List[Any]   				=fs_config.encoder_embed_dim
  				A__     :							Tuple   				=0.0_2
  				A__     :							Any   				=fs_config.encoder_ffn_embed_dim
  				A__     :							int   				=1e-5
  				A__     :							List[Any]   				=fs_config.encoder_layerdrop
  				A__     :							Dict   				=fs_config.encoder_attention_heads
  				A__     :							int   				=fs_config.conv_pos_groups
  				A__     :							Any   				=fs_config.conv_pos
  				A__     :							Optional[int]   				=len(UpperCamelCase__	)
  				A__     :							Union[str, Any]   				=fs_config.encoder_layers
  				A__     :							List[Any]   				=fs_config.squeeze_factor
  				# DeBERTa-specific parameters:
  				A__     :							List[Any]   				=fs_config.max_position_embeddings
  				A__     :							int   				=fs_config.position_buckets
  				A__     :							Optional[int]   				=fs_config.share_att_key
  				A__     :							str   				=fs_config.relative_attention
  				A__     :							Union[str, Any]   				=fs_config.position_biased_input
  				A__     :							Optional[Any]   				=tuple(fs_config.pos_att_type.split('''|'''	)	)
  				A__     :							str   				=fs_config.norm_rel_ebd
  
  				# take care of any params that are overridden by the Wav2VecCtc model
  				if is_finetuned:
  								A__     :							Tuple   				=model.cfg
  								A__     :							Union[str, Any]   				=fs_config.final_dropout
  								A__     :							List[Any]   				=fs_config.layerdrop
  				A__     :							List[Any]   				=fs_config.activation_dropout
  				A__     :							Optional[int]   				=fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  				A__     :							Any   				=fs_config.attention_dropout
  				A__     :							Dict   				=fs_config.dropout_input
  				A__     :							List[str]   				=fs_config.dropout
  				A__     :							Tuple   				=fs_config.mask_channel_length
  				A__     :							Optional[int]   				=fs_config.mask_channel_prob
  				A__     :							List[Any]   				=fs_config.mask_length
  				A__     :							Tuple   				=fs_config.mask_prob
  
  				A__     :							Dict   				="Wav2Vec2FeatureExtractor"
  				A__     :							List[str]   				="Wav2Vec2CTCTokenizer"
  
  				return config
  
  
  
  @torch.no_grad()
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_=None,	snake_case_=None,	snake_case_=True	)			->							Union[str, Any]:
  
  				if is_finetuned:
  								A__     :							Dict   				=fairseq.checkpoint_utils.load_model_ensemble_and_task(
  								    [checkpoint_path],	arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
  				else:
  								A__     :							List[Any]   				=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)
  
  				if config_path is not None:
  								A__     :							Dict   				=SEWDConfig.from_pretrained(UpperCamelCase__	)
  				else:
  								A__     :							Optional[int]   				=convert_config(model[0],	UpperCamelCase__	)
  				A__     :							Any   				=model[0].eval()
  
  				A__     :							Tuple   				=True if config.feat_extract_norm == "layer" else False
  				A__     :							List[str]   				=WavaVecaFeatureExtractor(
  				    feature_size=1,	sampling_rate=1_6_0_0_0,	padding_value=0,	do_normalize=UpperCamelCase__,	return_attention_mask=UpperCamelCase__,	)
  
  				if is_finetuned:
  								if dict_path:
  												A__     :							Tuple   				=Dictionary.load(UpperCamelCase__	)
  
  												# important change bos & pad token id since CTC symbol is <pad> and
  												# not <s> as in fairseq
  												A__     :							List[str]   				=target_dict.pad_index
  												A__     :							Dict   				=target_dict.bos_index
  												A__     :							Union[str, Any]   				=target_dict.pad_index
  												A__     :							str   				=target_dict.bos_index
  												A__     :							Any   				=target_dict.eos_index
  												A__     :							Any   				=len(target_dict.symbols	)
  												A__     :							Tuple   				=os.path.join(UpperCamelCase__,	'''vocab.json'''	)
  												if not os.path.isdir(UpperCamelCase__	):
  																logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase__	)	)
  																return
  												os.makedirs(UpperCamelCase__,	exist_ok=UpperCamelCase__	)
  												with open(UpperCamelCase__,	'''w''',	encoding='''utf-8'''	) as vocab_handle:
  																json.dump(target_dict.indices,	UpperCamelCase__	)
  												A__     :							int   				=WavaVecaCTCTokenizer(
  												    UpperCamelCase__,	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=UpperCamelCase__,	)
  												A__     :							List[Any]   				=WavaVecaProcessor(feature_extractor=UpperCamelCase__,	tokenizer=UpperCamelCase__	)
  												processor.save_pretrained(UpperCamelCase__	)
  
  								A__     :							Dict   				=SEWDForCTC(UpperCamelCase__	)
  				else:
  								A__     :							Any   				=SEWDModel(UpperCamelCase__	)
  								feature_extractor.save_pretrained(UpperCamelCase__	)
  
  				recursively_load_weights(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__	)
  
  				hf_model.save_pretrained(UpperCamelCase__	)
  
  
  if __name__ == "__main__":
  						__lowerCamelCase       :       str				   =			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(
  						    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
  						)
  						__lowerCamelCase       :       int				   =			parser.parse_args()
  						convert_sew_checkpoint(
  						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  						)
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  @dataclass
  class 			a      (      UpperCamelCase_   ):
  		__lowercase													=		{
  		    """sld_warmup_steps""": 15,
  		    """sld_guidance_scale""": 20,
  		    """sld_threshold""": 0.0,
  		    """sld_momentum_scale""": 0.0,
  		    """sld_mom_beta""": 0.0,
  		}
  		__lowercase													=		{
  		    """sld_warmup_steps""": 10,
  		    """sld_guidance_scale""": 1_000,
  		    """sld_threshold""": 0.01,
  		    """sld_momentum_scale""": 0.3,
  		    """sld_mom_beta""": 0.4,
  		}
  		__lowercase													=		{
  		    """sld_warmup_steps""": 7,
  		    """sld_guidance_scale""": 2_000,
  		    """sld_threshold""": 0.0_25,
  		    """sld_momentum_scale""": 0.5,
  		    """sld_mom_beta""": 0.7,
  		}
  		__lowercase													=		{
  		    """sld_warmup_steps""": 0,
  		    """sld_guidance_scale""": 5_000,
  		    """sld_threshold""": 1.0,
  		    """sld_momentum_scale""": 0.5,
  		    """sld_mom_beta""": 0.7,
  		}
  
  
  
  
  
  
  
  @dataclass
  class 			a      (      UpperCamelCase_   ):
  		__lowercase													=		42
  		__lowercase													=		42
  		__lowercase													=		42
  		__lowercase													=		42
  
  
  if is_transformers_available() and is_torch_available():
  						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
  						from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  