






"""simple docstring"""
import importlib

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

def 							snake_case (  A__      ,A__=False		):
			UpperCAmelCase_	:					Any     	=   OmegaConf.load(A__		)
			if display:
						print(yaml.dump(OmegaConf.to_container(A__		)		)		)
			return config

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

def 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   model.encode(A__		)
			print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""		)
			UpperCAmelCase_	:					Tuple     	=   model.decode(A__		)
			return xrec

def 							snake_case (  A__      ,A__=False		):
			UpperCAmelCase_	,      UpperCAmelCase_	:					Dict     	=   string.rsplit("."      ,1		)
			if reload:
						UpperCAmelCase_	:					Optional[int]     	=   importlib.import_module(A__		)
						importlib.reload(A__		)
			return getattr(importlib.import_module(A__      ,package=A__		)      ,cls		)

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

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











"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor







class      UpperCamelCase_			(unittest.TestCase	):





	def __init__( self   :					Optional[int]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					List[str]=13      ,      lowerCAmelCase_   :					Union[str, Any]=3      ,      lowerCAmelCase_   :					Union[str, Any]=224      ,      lowerCAmelCase_   :					List[Any]=30      ,      lowerCAmelCase_   :					Any=400      ,      lowerCAmelCase_   :					Any=True      ,      lowerCAmelCase_   :					Dict=None      ,      lowerCAmelCase_   :					int=True      ,      lowerCAmelCase_   :					Any=[0.5, 0.5, 0.5]      ,      lowerCAmelCase_   :					str=[0.5, 0.5, 0.5]      ,      )		->     Dict:
				UpperCAmelCase_	:					int     	=   size if size is not None else {"height": 18, "width": 18}
				UpperCAmelCase_	:					List[Any]     	=   parent
				UpperCAmelCase_	:					int     	=   batch_size
				UpperCAmelCase_	:					Optional[int]     	=   num_channels
				UpperCAmelCase_	:					Dict     	=   image_size
				UpperCAmelCase_	:					Union[str, Any]     	=   min_resolution
				UpperCAmelCase_	:					List[str]     	=   max_resolution
				UpperCAmelCase_	:					Dict     	=   do_resize
				UpperCAmelCase_	:					Optional[int]     	=   size
				UpperCAmelCase_	:					List[str]     	=   do_normalize
				UpperCAmelCase_	:					List[Any]     	=   image_mean
				UpperCAmelCase_	:					Dict     	=   image_std





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Union[str, Any]:
				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				}







@require_torch
@require_vision
class      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							ViTImageProcessor if is_vision_available() else None





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Optional[Any]:
				UpperCAmelCase_	:					Optional[Any]     	=   EfficientFormerImageProcessorTester(self		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     List[Any]:
				return self.image_proc_tester.prepare_image_processor_dict()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     str:
				UpperCAmelCase_	:					Tuple     	=   self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "image_mean"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "image_std"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "do_normalize"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "do_resize"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "size"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     List[str]:
				pass





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     int:
				# Initialize image_processor
				UpperCAmelCase_	:					str     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random PIL images
				UpperCAmelCase_	:					int     	=   prepare_image_inputs(self.image_proc_tester      ,      equal_resolution=lowerCAmelCase_		)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_      ,      Image.Image		)

				# Test not batched input
				UpperCAmelCase_	:					Optional[Any]     	=   image_processor(image_inputs[0]      ,      return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )      ,      )

				# Test batched
				UpperCAmelCase_	:					str     	=   image_processor(lowerCAmelCase_      ,      return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )      ,      )





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     List[Any]:
				# Initialize image_processor
				UpperCAmelCase_	:					List[str]     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				UpperCAmelCase_	:					List[str]     	=   prepare_image_inputs(self.image_proc_tester      ,      equal_resolution=lowerCAmelCase_      ,      numpify=lowerCAmelCase_		)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_      ,      np.ndarray		)

				# Test not batched input
				UpperCAmelCase_	:					str     	=   image_processor(image_inputs[0]      ,      return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )      ,      )

				# Test batched
				UpperCAmelCase_	:					Dict     	=   image_processor(lowerCAmelCase_      ,      return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )      ,      )





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     List[Any]:
				# Initialize image_processor
				UpperCAmelCase_	:					Optional[Any]     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				UpperCAmelCase_	:					Dict     	=   prepare_image_inputs(self.image_proc_tester      ,      equal_resolution=lowerCAmelCase_      ,      torchify=lowerCAmelCase_		)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_      ,      torch.Tensor		)

				# Test not batched input
				UpperCAmelCase_	:					Optional[int]     	=   image_processor(image_inputs[0]      ,      return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )      ,      )

				# Test batched
				UpperCAmelCase_	:					Optional[int]     	=   image_processor(lowerCAmelCase_      ,      return_tensors="pt"		).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,      (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )      ,      )



