



"""simple docstring"""

from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch




@require_torch
@require_tf
class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Union[str, Any]									=       SMALL_MODEL_IDENTIFIER
             __a		:		List[Any]									=       """pt"""
             __a		:		Union[str, Any]									=       """tf"""



      def     lowerCAmelCase__(self						,     _lowercase							):





             '''simple docstring'''

             __a		:		Tuple									=       AutoModel.from_pretrained(self.test_model							)
             model_pt.save_pretrained(_lowercase							)



      def     lowerCAmelCase__(self						,     _lowercase							):





             '''simple docstring'''

             __a		:		List[str]									=       TFAutoModel.from_pretrained(self.test_model						,     from_pt=_lowercase							)
             model_tf.save_pretrained(_lowercase							)



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Any									=       """mock_framework"""

             # Framework provided - return whatever the user provides
             __a		:		str									=       FeaturesManager.determine_framework(self.test_model						,     _lowercase							)
             self.assertEqual(_lowercase						,     _lowercase							)

             # Local checkpoint and framework provided - return provided framework
             # PyTorch checkpoint
             with TemporaryDirectory() as local_pt_ckpt:
                    self._setup_pt_ckpt(_lowercase							)
                    __a		:		int									=       FeaturesManager.determine_framework(_lowercase						,     _lowercase							)
                    self.assertEqual(_lowercase						,     _lowercase							)

             # TensorFlow checkpoint
             with TemporaryDirectory() as local_tf_ckpt:
                    self._setup_tf_ckpt(_lowercase							)
                    __a		:		List[str]									=       FeaturesManager.determine_framework(_lowercase						,     _lowercase							)
                    self.assertEqual(_lowercase						,     _lowercase							)



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             with TemporaryDirectory() as local_pt_ckpt:
                    self._setup_pt_ckpt(_lowercase							)
                    __a		:		List[Any]									=       FeaturesManager.determine_framework(_lowercase							)
                    self.assertEqual(_lowercase						,     self.framework_pt							)

             # TensorFlow checkpoint
             with TemporaryDirectory() as local_tf_ckpt:
                    self._setup_tf_ckpt(_lowercase							)
                    __a		:		str									=       FeaturesManager.determine_framework(_lowercase							)
                    self.assertEqual(_lowercase						,     self.framework_tf							)

             # Invalid local checkpoint
             with TemporaryDirectory() as local_invalid_ckpt:
                    with self.assertRaises(_lowercase							):
                           __a		:		str									=       FeaturesManager.determine_framework(_lowercase							)





      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		List[str]									=       MagicMock(return_value=_lowercase							)
             with patch("""transformers.onnx.features.is_tf_available"""						,     _lowercase							):
                    __a		:		Dict									=       FeaturesManager.determine_framework(self.test_model							)
                    self.assertEqual(_lowercase						,     self.framework_pt							)

             # PyTorch not in environment -> use TensorFlow
             __a		:		Tuple									=       MagicMock(return_value=_lowercase							)
             with patch("""transformers.onnx.features.is_torch_available"""						,     _lowercase							):
                    __a		:		Union[str, Any]									=       FeaturesManager.determine_framework(self.test_model							)
                    self.assertEqual(_lowercase						,     self.framework_tf							)

             # Both in environment -> use PyTorch
             __a		:		List[Any]									=       MagicMock(return_value=_lowercase							)
             __a		:		List[Any]									=       MagicMock(return_value=_lowercase							)
             with patch("""transformers.onnx.features.is_tf_available"""						,     _lowercase							), patch(
                 """transformers.onnx.features.is_torch_available"""						,     _lowercase							):
                    __a		:		int									=       FeaturesManager.determine_framework(self.test_model							)
                    self.assertEqual(_lowercase						,     self.framework_pt							)

             # Both not in environment -> raise error
             __a		:		int									=       MagicMock(return_value=_lowercase							)
             __a		:		List[str]									=       MagicMock(return_value=_lowercase							)
             with patch("""transformers.onnx.features.is_tf_available"""						,     _lowercase							), patch(
                 """transformers.onnx.features.is_torch_available"""						,     _lowercase							):
                    with self.assertRaises(_lowercase							):
                           __a		:		Optional[Any]									=       FeaturesManager.determine_framework(self.test_model							)






"""simple docstring"""

import importlib

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





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=False					):
							__a		:		Dict									=       OmegaConf.load(_lowerCamelCase					)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase					)					)					)
							return config





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





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[str]					):
							__a							,     __a							,     __a		:		Tuple									=       model.encode(_lowerCamelCase					)
							print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''					)
							__a		:		Union[str, Any]									=       model.decode(_lowerCamelCase					)
							return xrec





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]=False					):
							__a							,     __a		:		Optional[Any]									=       string.rsplit("""."""  ,       1					)
							if reload:
														__a		:		Optional[Any]									=       importlib.import_module(_lowerCamelCase					)
														importlib.reload(_lowerCamelCase					)
							return getattr(importlib.import_module(_lowerCamelCase  ,       package=_lowerCamelCase					)  ,       cls					)





def       __magic_name__    (   _lowerCamelCase				: Any					):
							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__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int=True  ,       _lowerCamelCase				: int=True					):
							__a		:		Union[str, Any]									=       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       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int					):
							# load the specified checkpoint
							if ckpt:
														__a		:		List[str]									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
														__a		:		Any									=       pl_sd["""global_step"""]
														print(F'''loaded model from global step {global_step}.'''					)
							else:
														__a		:		List[Any]									=       {"""state_dict""": None}
														__a		:		Any									=       None
							__a		:		Union[str, Any]									=       load_model_from_config(config.model  ,       pl_sd["""state_dict"""]  ,       gpu=_lowerCamelCase  ,       eval_mode=_lowerCamelCase					)["""model"""]
							return model, global_step

