


import importlib

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






def 							__lowercase   ( __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				List[Any]=False   ):
							a__             =     OmegaConf.load(__lowerCAmelCase   )
							if display:
														print(yaml.dump(OmegaConf.to_container(__lowerCAmelCase   )   )   )
							return config






def 							__lowercase   ( __lowerCAmelCase							:				Optional[int]					,    __lowerCAmelCase							:				Tuple=None					,    __lowerCAmelCase							:				Union[str, Any]=None   ):
							if conf_path is None:
														a__             =     './model_checkpoints/vqgan_only.yaml'
							a__             =     load_config(__lowerCAmelCase					,    display=__lowerCAmelCase   )
							a__             =     VQModel(**config.model.params   )
							if ckpt_path is None:
														a__             =     './model_checkpoints/vqgan_only.pt'
							a__             =     torch.load(__lowerCAmelCase					,    map_location=__lowerCAmelCase   )
							if ".ckpt" in ckpt_path:
														a__             =     sd['state_dict']
							model.load_state_dict(__lowerCAmelCase					,    strict=__lowerCAmelCase   )
							model.to(__lowerCAmelCase   )
							del sd
							return model






def 							__lowercase   ( __lowerCAmelCase							:				List[str]					,    __lowerCAmelCase							:				str   ):
							a__      ,					a__      ,					a__             =     model.encode(__lowerCAmelCase   )
							print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'   )
							a__             =     model.decode(__lowerCAmelCase   )
							return xrec






def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Any=False   ):
							a__      ,					a__             =     string.rsplit('.'					,    1   )
							if reload:
														a__             =     importlib.import_module(__lowerCAmelCase   )
														importlib.reload(__lowerCAmelCase   )
							return getattr(importlib.import_module(__lowerCAmelCase					,    package=__lowerCAmelCase   )					,    cls   )






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






def 							__lowercase   ( __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				Any=True					,    __lowerCAmelCase							:				Union[str, Any]=True   ):
							a__             =     instantiate_from_config(__lowerCAmelCase   )
							if sd is not None:
														model.load_state_dict(__lowerCAmelCase   )
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}






def 							__lowercase   ( __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				Optional[int]					,    __lowerCAmelCase							:				Any   ):
							# load the specified checkpoint
							if ckpt:
														a__             =     torch.load(__lowerCAmelCase					,    map_location='cpu'   )
														a__             =     pl_sd['global_step']
														print(F'loaded model from global step {global_step}.'   )
							else:
														a__             =     {'state_dict': None}
														a__             =     None
							a__             =     load_model_from_config(config.model					,    pl_sd['state_dict']					,    gpu=__lowerCAmelCase					,    eval_mode=__lowerCAmelCase   )['model']
							return model, global_step








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case							:       Union[str, Any]     			=      logging.get_logger(__name__)

snake_case							:       str     			=      {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    str          =			'''mobilenet_v2'''
					def __init__(      self   :Optional[Any]			,__snake_case   :List[Any]=3			,__snake_case   :Any=2_24			,__snake_case   :Dict=1.0			,__snake_case   :str=8			,__snake_case   :Union[str, Any]=8			,__snake_case   :Optional[Any]=6			,__snake_case   :str=32			,__snake_case   :Tuple=True			,__snake_case   :Union[str, Any]=True			,__snake_case   :Any="relu6"			,__snake_case   :List[str]=True			,__snake_case   :Dict=0.8			,__snake_case   :Optional[int]=0.02			,__snake_case   :Tuple=0.0_01			,__snake_case   :Dict=2_55			,**__snake_case   :Dict			,)					->    Any:
												super().__init__(**__snake_case      )

												if depth_multiplier <= 0:
																			raise ValueError('depth_multiplier must be greater than zero.'      )

												a__             =     num_channels
												a__             =     image_size
												a__             =     depth_multiplier
												a__             =     depth_divisible_by
												a__             =     min_depth
												a__             =     expand_ratio
												a__             =     output_stride
												a__             =     first_layer_is_expansion
												a__             =     finegrained_output
												a__             =     hidden_act
												a__             =     tf_padding
												a__             =     classifier_dropout_prob
												a__             =     initializer_range
												a__             =     layer_norm_eps
												a__             =     semantic_loss_ignore_index







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    Tuple          =			version.parse('''1.11'''      )
					@property
					def  lowerCamelCase__(      self   :Any      )					->    Mapping[str, Mapping[int, str]]:
												return OrderedDict([('pixel_values', {0: 'batch'})]      )
					@property
					def  lowerCamelCase__(      self   :List[str]      )					->    Mapping[str, Mapping[int, str]]:
												if self.task == "image-classification":
																			return OrderedDict([('logits', {0: 'batch'})]      )
												else:
																			return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )



					@property
					def  lowerCamelCase__(      self   :Any      )					->    float:
												return 1E-4




