
import importlib

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


def 				A	( lowercase   ,	lowercase=False			)       ->  Any:
		'''simple docstring'''

		UpperCamelCase          =					OmegaConf.load(a__			)
		if display:
				print(yaml.dump(OmegaConf.to_container(a__			)			)			)
		return config


def 				A	( lowercase   ,	lowercase=None   ,	lowercase=None			)       ->  str:
		'''simple docstring'''

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


def 				A	( lowercase   ,	lowercase			)       ->  str:
		'''simple docstring'''

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


def 				A	( lowercase   ,	lowercase=False			)       ->  Optional[int]:
		'''simple docstring'''

		UpperCamelCase     ,  UpperCamelCase          =					string.rsplit('.'   ,	1			)
		if reload:
				UpperCamelCase          =					importlib.import_module(a__			)
				importlib.reload(a__			)
		return getattr(importlib.import_module(a__   ,	package=a__			)   ,	cls			)


def 				A	( lowercase			)       ->  Any:
		'''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 				A	( lowercase   ,	lowercase   ,	lowercase=True   ,	lowercase=True			)       ->  List[Any]:
		'''simple docstring'''

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


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  Optional[int]:
		'''simple docstring'''

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


import os
import sys
import unittest


_UpperCAmelCase :			str              =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_UpperCAmelCase :			Tuple              =	os.path.join(git_repo_path, "src", "diffusers")


class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					find_backend('    if not is_torch_available():'	)
				self.assertEqual(A_    ,				'torch'	)

				# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
				# self.assertEqual(backend_with_underscore, "tensorflow_text")

				UpperCamelCase          =					find_backend('    if not (is_torch_available() and is_transformers_available()):'	)
				self.assertEqual(A_    ,				'torch_and_transformers'	)

				# double_backend_with_underscore = find_backend(
				#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
				# )
				# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

				UpperCamelCase          =					find_backend(
				    '    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'	)
				self.assertEqual(A_    ,				'torch_and_transformers_and_onnx'	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					read_init()
				# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
				self.assertIn('torch'    ,				A_	)
				self.assertIn('torch_and_transformers'    ,				A_	)
				self.assertIn('flax_and_transformers'    ,				A_	)
				self.assertIn('torch_and_transformers_and_onnx'    ,				A_	)

				# Likewise, we can't assert on the exact content of a key
				self.assertIn('UNet2DModel'    ,				objects['torch']	)
				self.assertIn('FlaxUNet2DConditionModel'    ,				objects['flax']	)
				self.assertIn('StableDiffusionPipeline'    ,				objects['torch_and_transformers']	)
				self.assertIn('FlaxStableDiffusionPipeline'    ,				objects['flax_and_transformers']	)
				self.assertIn('LMSDiscreteScheduler'    ,				objects['torch_and_scipy']	)
				self.assertIn('OnnxStableDiffusionPipeline'    ,				objects['torch_and_transformers_and_onnx']	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					create_dummy_object('CONSTANT'    ,				'\'torch\''	)
				self.assertEqual(A_    ,				'\nCONSTANT = None\n'	)

				UpperCamelCase          =					create_dummy_object('function'    ,				'\'torch\''	)
				self.assertEqual(
				    A_    ,				'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'	)

				UpperCamelCase          =					'\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n'
				UpperCamelCase          =					create_dummy_object('FakeClass'    ,				'\'torch\''	)
				self.assertEqual(A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					'# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n'
				UpperCamelCase          =					create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}	)
				self.assertEqual(dummy_files['torch']    ,				A_	)
