

import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:Optional[int]	=		(EulerAncestralDiscreteScheduler,)
						_UpperCamelCase:Tuple	=		10







						def        _snake_case				(			self     ,						**_SCREAMING_SNAKE_CASE							)->							Optional[int]:
													lowerCamelCase_								={
													    "num_train_timesteps": 1100,
													    "beta_start": 0.0_0_0_1,
													    "beta_end": 0.0_2,
													    "beta_schedule": "linear",
													}

													config.update(**_SCREAMING_SNAKE_CASE							)
													return config







						def        _snake_case				(			self							)->							Optional[int]:
													for timesteps in [10, 50, 100, 1000]:
																				self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self							)->							str:
													for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]     ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]							):
																				self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE     ,						beta_end=_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self							)->							Optional[int]:
													for schedule in ["linear", "scaled_linear"]:
																				self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self							)->							Union[str, Any]:
													for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self							)->							Any:
													lowerCamelCase_								=self.scheduler_classes[0]
													lowerCamelCase_								=self.get_scheduler_config()
													lowerCamelCase_								=scheduler_class(**_SCREAMING_SNAKE_CASE							)

													scheduler.set_timesteps(self.num_inference_steps							)

													lowerCamelCase_								=torch.manual_seed(0							)

													lowerCamelCase_								=self.dummy_model()
													lowerCamelCase_								=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													lowerCamelCase_								=sample.to(_SCREAMING_SNAKE_CASE							)

													for i, t in enumerate(scheduler.timesteps							):
																				lowerCamelCase_								=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)

																				lowerCamelCase_								=model(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)

																				lowerCamelCase_								=scheduler.step(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						generator=_SCREAMING_SNAKE_CASE							)
																				lowerCamelCase_								=output.prev_sample

													lowerCamelCase_								=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE							)							)
													lowerCamelCase_								=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE							)							)

													assert abs(result_sum.item() - 152.3192							) < 1E-2
													assert abs(result_mean.item() - 0.1_9_8_3							) < 1E-3







						def        _snake_case				(			self							)->							Union[str, Any]:
													lowerCamelCase_								=self.scheduler_classes[0]
													lowerCamelCase_								=self.get_scheduler_config(prediction_type="""v_prediction"""							)
													lowerCamelCase_								=scheduler_class(**_SCREAMING_SNAKE_CASE							)

													scheduler.set_timesteps(self.num_inference_steps							)

													lowerCamelCase_								=torch.manual_seed(0							)

													lowerCamelCase_								=self.dummy_model()
													lowerCamelCase_								=self.dummy_sample_deter * scheduler.init_noise_sigma
													lowerCamelCase_								=sample.to(_SCREAMING_SNAKE_CASE							)

													for i, t in enumerate(scheduler.timesteps							):
																				lowerCamelCase_								=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)

																				lowerCamelCase_								=model(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)

																				lowerCamelCase_								=scheduler.step(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						generator=_SCREAMING_SNAKE_CASE							)
																				lowerCamelCase_								=output.prev_sample

													lowerCamelCase_								=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE							)							)
													lowerCamelCase_								=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE							)							)

													assert abs(result_sum.item() - 108.4439							) < 1E-2
													assert abs(result_mean.item() - 0.1_4_1_2							) < 1E-3







						def        _snake_case				(			self							)->							Optional[Any]:
													lowerCamelCase_								=self.scheduler_classes[0]
													lowerCamelCase_								=self.get_scheduler_config()
													lowerCamelCase_								=scheduler_class(**_SCREAMING_SNAKE_CASE							)

													scheduler.set_timesteps(self.num_inference_steps     ,						device=_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=torch.manual_seed(0							)

													lowerCamelCase_								=self.dummy_model()
													lowerCamelCase_								=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													lowerCamelCase_								=sample.to(_SCREAMING_SNAKE_CASE							)

													for t in scheduler.timesteps:
																				lowerCamelCase_								=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)

																				lowerCamelCase_								=model(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)

																				lowerCamelCase_								=scheduler.step(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						generator=_SCREAMING_SNAKE_CASE							)
																				lowerCamelCase_								=output.prev_sample

													lowerCamelCase_								=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE							)							)
													lowerCamelCase_								=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE							)							)

													assert abs(result_sum.item() - 152.3192							) < 1E-2
													assert abs(result_mean.item() - 0.1_9_8_3							) < 1E-3





import json
import os
import tempfile

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


if is_torch_available():
      import numpy as np
      import torch

if is_vision_available():
      from PIL import Image






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  str=False		,						__lowerCamelCase :  Optional[Any]=False		,						__lowerCamelCase :  int=False  ):



 """simple docstring"""





 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 __SCREAMING_SNAKE_CASE							:     List[str]							=						[]
 for i in range(image_processor_tester.batch_size  ):
  if equal_resolution:
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor_tester.max_resolution
  else:
   # To avoid getting image width/height 0
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						image_processor_tester.min_resolution
   if getattr(__lowerCamelCase		,						"size_divisor"		,						__lowerCamelCase  ):
    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
    __SCREAMING_SNAKE_CASE							:     str							=						max(image_processor_tester.size_divisor		,						__lowerCamelCase  )
   __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Tuple							=						np.random.choice(np.arange(__lowerCamelCase		,						image_processor_tester.max_resolution  )		,						2  )
  image_inputs.append(
      np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta  )  )

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  __SCREAMING_SNAKE_CASE							:     Dict							=						[Image.fromarray(np.moveaxis(__lowerCamelCase		,						0		,						-1  )  ) for image in image_inputs]

 if torchify:
  __SCREAMING_SNAKE_CASE							:     Dict							=						[torch.from_numpy(__lowerCamelCase  ) for image in image_inputs]

 return image_inputs






def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  List[Any]=10		,						__lowerCamelCase :  Any=10		,						__lowerCamelCase :  int=False		,						__lowerCamelCase :  List[str]=False  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Tuple							=						[]
 for i in range(image_processor_tester.num_frames  ):
  video.append(np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta  )  )

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[Image.fromarray(np.moveaxis(__lowerCamelCase		,						0		,						-1  )  ) for frame in video]

 if torchify:
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						[torch.from_numpy(__lowerCamelCase  ) for frame in video]

 return video






def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Dict=False		,						__lowerCamelCase :  Optional[int]=False		,						__lowerCamelCase :  Any=False  ):



 """simple docstring"""





 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 __SCREAMING_SNAKE_CASE							:     Dict							=						[]
 for i in range(image_processor_tester.batch_size  ):
  if equal_resolution:
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor_tester.max_resolution
  else:
   __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Dict							=						np.random.choice(
       np.arange(image_processor_tester.min_resolution		,						image_processor_tester.max_resolution  )		,						2  )
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						prepare_video(
       image_processor_tester=__lowerCamelCase		,						width=__lowerCamelCase		,						height=__lowerCamelCase		,						numpify=__lowerCamelCase		,						torchify=__lowerCamelCase		,						)
  video_inputs.append(__lowerCamelCase  )

 return video_inputs






class    _SCREAMING_SNAKE_CASE						:
     lowerCAmelCase       =      None





     def 						__snake_case     (							self			:				Dict					)->int:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.image_processing_class(**self.image_processor_dict					)
      __SCREAMING_SNAKE_CASE							:     str							=						json.loads(image_processor.to_json_string()					)
      for key, value in self.image_processor_dict.items():
       self.assertEqual(obj[key]  ,	UpperCamelCase					)





     def 						__snake_case     (							self			:				str					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)

      with tempfile.TemporaryDirectory() as tmpdirname:
       __SCREAMING_SNAKE_CASE							:     Dict							=						os.path.join(UpperCamelCase  ,	"image_processor.json"					)
       image_processor_first.to_json_file(UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class.from_json_file(UpperCamelCase					)

      self.assertEqual(image_processor_second.to_dict()  ,	image_processor_first.to_dict()					)





     def 						__snake_case     (							self			:				Optional[int]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     int							=						self.image_processing_class(**self.image_processor_dict					)

      with tempfile.TemporaryDirectory() as tmpdirname:
       __SCREAMING_SNAKE_CASE							:     Any							=						image_processor_first.save_pretrained(UpperCamelCase					)[0]
       check_json_file_has_correct_format(UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.image_processing_class.from_pretrained(UpperCamelCase					)

      self.assertEqual(image_processor_second.to_dict()  ,	image_processor_first.to_dict()					)





     def 						__snake_case     (							self			:				int					)->Any:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class()
      self.assertIsNotNone(UpperCamelCase					)






     @require_torch
     @require_vision
     def 						__snake_case     (							self			:				Any					)->List[Any]:
      if self.test_cast_dtype is not None:
       # Initialize image_processor
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)

       # create random PyTorch tensors
       __SCREAMING_SNAKE_CASE							:     int							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase  ,	torchify=UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					)
       # for layoutLM compatiblity
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)

       __SCREAMING_SNAKE_CASE							:     int							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to(torch.floataa					)
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)

       __SCREAMING_SNAKE_CASE							:     Dict							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to("cpu"  ,	torch.bfloataa					)
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.bfloataa					)

       with self.assertRaises(UpperCamelCase					):
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to(torch.bfloataa  ,	"cpu"					)

       # Try with text + image feature
       __SCREAMING_SNAKE_CASE							:     Any							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					)
       encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]					)}					)
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						encoding.to(torch.floataa					)

       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)
       self.assertEqual(encoding.input_ids.dtype  ,	torch.long					)






