





'''simple docstring'''

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=False      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       OmegaConf.load(__UpperCAmelCase      )
   if display:
      print(yaml.dump(OmegaConf.to_container(__UpperCAmelCase      )      )      )
   return config

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=None							,	__UpperCAmelCase=None      )						->					Optional[Any]:

   '''simple docstring'''







   if conf_path is None:
      __SCREAMING_SNAKE_CASE      							=       """./model_checkpoints/vqgan_only.yaml"""
   __SCREAMING_SNAKE_CASE      							=       load_config(__UpperCAmelCase							,	display=__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       VQModel(**config.model.params      )
   if ckpt_path is None:
      __SCREAMING_SNAKE_CASE      							=       """./model_checkpoints/vqgan_only.pt"""
   __SCREAMING_SNAKE_CASE      							=       torch.load(__UpperCAmelCase							,	map_location=__UpperCAmelCase      )
   if ".ckpt" in ckpt_path:
      __SCREAMING_SNAKE_CASE      							=       sd["""state_dict"""]
   model.load_state_dict(__UpperCAmelCase							,	strict=__UpperCAmelCase      )
   model.to(__UpperCAmelCase      )
   del sd
   return model

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       model.encode(__UpperCAmelCase      )
   print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""      )
   __SCREAMING_SNAKE_CASE      							=       model.decode(__UpperCAmelCase      )
   return xrec

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=False      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       string.rsplit("""."""							,	1      )
   if reload:
      __SCREAMING_SNAKE_CASE      							=       importlib.import_module(__UpperCAmelCase      )
      importlib.reload(__UpperCAmelCase      )
   return getattr(importlib.import_module(__UpperCAmelCase							,	package=__UpperCAmelCase      )							,	cls      )

def 						__magic_name__       (							__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   if "target" not in config:
      raise KeyError("""Expected key `target` to instantiate."""      )
   return get_obj_from_str(config["""target"""]      )(**config.get("""params"""							,	{}      )      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=True							,	__UpperCAmelCase=True      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       instantiate_from_config(__UpperCAmelCase      )
   if sd is not None:
      model.load_state_dict(__UpperCAmelCase      )
   if gpu:
      model.cuda()
   if eval_mode:
      model.eval()
   return {"model": model}







def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   if ckpt:
      __SCREAMING_SNAKE_CASE      							=       torch.load(__UpperCAmelCase							,	map_location="""cpu"""      )
      __SCREAMING_SNAKE_CASE      							=       pl_sd["""global_step"""]
      print(f"""loaded model from global step {global_step}."""      )
   else:
      __SCREAMING_SNAKE_CASE      							=       {"""state_dict""": None}
      __SCREAMING_SNAKE_CASE      							=       None
   __SCREAMING_SNAKE_CASE      							=       load_model_from_config(config.model							,	pl_sd["""state_dict"""]							,	gpu=__UpperCAmelCase							,	eval_mode=__UpperCAmelCase      )["""model"""]
   return model, global_step












'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a        =						logging.get_logger(__name__)

a        =						{
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}







class __a (   _snake_case			):
   __UpperCamelCase   : Tuple	      =	'sew'





   def __init__(					self				:			str				,lowerCamelCase				:			Any=32				,lowerCamelCase				:			str=768				,lowerCamelCase				:			str=12				,lowerCamelCase				:			Union[str, Any]=12				,lowerCamelCase				:			Union[str, Any]=3072				,lowerCamelCase				:			int=2				,lowerCamelCase				:			Union[str, Any]="gelu"				,lowerCamelCase				:			Tuple=0.1				,lowerCamelCase				:			Union[str, Any]=0.1				,lowerCamelCase				:			Union[str, Any]=0.1				,lowerCamelCase				:			Any=0.0				,lowerCamelCase				:			Optional[Any]=0.1				,lowerCamelCase				:			Union[str, Any]=0.1				,lowerCamelCase				:			Optional[Any]=0.02				,lowerCamelCase				:			List[str]=1E-5				,lowerCamelCase				:			Tuple="group"				,lowerCamelCase				:			Optional[Any]="gelu"				,lowerCamelCase				:			List[str]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,lowerCamelCase				:			Any=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,lowerCamelCase				:			Dict=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,lowerCamelCase				:			Optional[int]=False				,lowerCamelCase				:			Dict=128				,lowerCamelCase				:			Union[str, Any]=16				,lowerCamelCase				:			List[Any]=True				,lowerCamelCase				:			List[Any]=0.05				,lowerCamelCase				:			Optional[int]=10				,lowerCamelCase				:			Any=2				,lowerCamelCase				:			Any=0.0				,lowerCamelCase				:			Tuple=10				,lowerCamelCase				:			str=0				,lowerCamelCase				:			Tuple="mean"				,lowerCamelCase				:			int=False				,lowerCamelCase				:			Dict=False				,lowerCamelCase				:			Optional[int]=256				,lowerCamelCase				:			str=0				,lowerCamelCase				:			Tuple=1				,lowerCamelCase				:			Tuple=2				,**lowerCamelCase				:			Union[str, Any]				,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase				,pad_token_id=lowerCamelCase				,bos_token_id=lowerCamelCase				,eos_token_id=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       hidden_size
      __SCREAMING_SNAKE_CASE      							=       feat_extract_norm
      __SCREAMING_SNAKE_CASE      							=       feat_extract_activation
      __SCREAMING_SNAKE_CASE      							=       list(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       list(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       list(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       conv_bias
      __SCREAMING_SNAKE_CASE      							=       num_conv_pos_embeddings
      __SCREAMING_SNAKE_CASE      							=       num_conv_pos_embedding_groups
      __SCREAMING_SNAKE_CASE      							=       len(self.conv_dim    )
      __SCREAMING_SNAKE_CASE      							=       num_hidden_layers
      __SCREAMING_SNAKE_CASE      							=       intermediate_size
      __SCREAMING_SNAKE_CASE      							=       squeeze_factor
      __SCREAMING_SNAKE_CASE      							=       hidden_act
      __SCREAMING_SNAKE_CASE      							=       num_attention_heads
      __SCREAMING_SNAKE_CASE      							=       hidden_dropout
      __SCREAMING_SNAKE_CASE      							=       attention_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_dropout
      __SCREAMING_SNAKE_CASE      							=       feat_proj_dropout
      __SCREAMING_SNAKE_CASE      							=       final_dropout
      __SCREAMING_SNAKE_CASE      							=       layerdrop
      __SCREAMING_SNAKE_CASE      							=       layer_norm_eps
      __SCREAMING_SNAKE_CASE      							=       initializer_range
      __SCREAMING_SNAKE_CASE      							=       vocab_size

      if (
          (len(self.conv_stride    ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
          or (len(self.conv_dim    ) != self.num_feat_extract_layers)
      ):
         raise ValueError(
             """Configuration for convolutional layers is incorrect."""
             """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
             f"""but is `len(config.conv_dim) = {len(self.conv_dim    )}`, `len(config.conv_stride)"""
             f"""= {len(self.conv_stride    )}`, `len(config.conv_kernel) = {len(self.conv_kernel    )}`."""    )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      __SCREAMING_SNAKE_CASE      							=       apply_spec_augment
      __SCREAMING_SNAKE_CASE      							=       mask_time_prob
      __SCREAMING_SNAKE_CASE      							=       mask_time_length
      __SCREAMING_SNAKE_CASE      							=       mask_time_min_masks
      __SCREAMING_SNAKE_CASE      							=       mask_feature_prob
      __SCREAMING_SNAKE_CASE      							=       mask_feature_length
      __SCREAMING_SNAKE_CASE      							=       mask_feature_min_masks

      # ctc loss
      __SCREAMING_SNAKE_CASE      							=       ctc_loss_reduction
      __SCREAMING_SNAKE_CASE      							=       ctc_zero_infinity

      # sequence classification
      __SCREAMING_SNAKE_CASE      							=       use_weighted_layer_sum
      __SCREAMING_SNAKE_CASE      							=       classifier_proj_size





   @property
   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      return functools.reduce(operator.mul				,self.conv_stride				,1    )





