


import random
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():
      import PIL

      from transformers import FlavaImageProcessor
      from transformers.image_utils import PILImageResampling
      from transformers.models.flava.image_processing_flava import (
          FLAVA_CODEBOOK_MEAN,
          FLAVA_CODEBOOK_STD,
          FLAVA_IMAGE_MEAN,
          FLAVA_IMAGE_STD,
      )
else:
      _snake_case   = _snake_case   = _snake_case   = _snake_case   = None




class    A_   ( unittest.TestCase     ):







 """simple docstring"""





 def __init__(			self					: Union[str, Any]       ,    _a					: Optional[Any]       ,    _a					: List[Any]=7       ,    _a					: Dict=3       ,    _a					: Optional[Any]=3_0       ,    _a					: Dict=4_0_0       ,    _a					: List[Any]=True       ,    _a					: List[str]=None       ,    _a					: List[Any]=True       ,    _a					: Union[str, Any]=None       ,    _a					: Tuple=None       ,    _a					: Optional[int]=True       ,    _a					: List[str]=1 / 2_5_5       ,    _a					: List[str]=True       ,    _a					: Tuple=FLAVA_IMAGE_MEAN       ,    _a					: Optional[int]=FLAVA_IMAGE_STD       ,    _a					: Any=1_4       ,    _a					: int=7_5       ,    _a					: List[Any]=None       ,    _a					: List[str]=1_6       ,    _a					: str=0.3       ,    _a					: Dict=None       ,    _a					: Any=True       ,    _a					: List[str]=None       ,    _a					: str=None       ,    _a					: Tuple=True       ,    _a					: Optional[int]=None       ,    _a					: List[Any]=True       ,    _a					: Optional[int]=True       ,    _a					: int=FLAVA_CODEBOOK_MEAN       ,    _a					: str=FLAVA_CODEBOOK_STD       ,    )    ->			Tuple:
      UpperCAmelCase						:				List[Any]		  =			size if size is not None else {"height": 2_2_4, "width": 2_2_4}
      UpperCAmelCase						:				List[Any]		  =			crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      UpperCAmelCase						:				int		  =			codebook_size if codebook_size is not None else {"height": 1_1_2, "width": 1_1_2}
      UpperCAmelCase						:				Dict		  =			codebook_crop_size if codebook_crop_size is not None else {"height": 1_1_2, "width": 1_1_2}

      UpperCAmelCase						:				Any		  =			parent
      UpperCAmelCase						:				Optional[Any]		  =			batch_size
      UpperCAmelCase						:				Dict		  =			num_channels
      UpperCAmelCase						:				str		  =			do_resize
      UpperCAmelCase						:				List[str]		  =			do_rescale
      UpperCAmelCase						:				List[str]		  =			rescale_factor
      UpperCAmelCase						:				Optional[int]		  =			min_resolution
      UpperCAmelCase						:				List[str]		  =			max_resolution
      UpperCAmelCase						:				Any		  =			size
      UpperCAmelCase						:				str		  =			resample if resample is not None else PILImageResampling.BICUBIC
      UpperCAmelCase						:				Optional[Any]		  =			do_normalize
      UpperCAmelCase						:				List[Any]		  =			image_mean
      UpperCAmelCase						:				Union[str, Any]		  =			image_std
      UpperCAmelCase						:				Any		  =			do_center_crop
      UpperCAmelCase						:				str		  =			crop_size

      UpperCAmelCase						:				Optional[int]		  =			input_size_patches
      UpperCAmelCase						:				Optional[int]		  =			total_mask_patches
      UpperCAmelCase						:				int		  =			mask_group_max_patches
      UpperCAmelCase						:				Optional[Any]		  =			mask_group_min_patches
      UpperCAmelCase						:				List[str]		  =			mask_group_min_aspect_ratio
      UpperCAmelCase						:				Any		  =			mask_group_max_aspect_ratio

      UpperCAmelCase						:				Optional[Any]		  =			codebook_do_resize
      UpperCAmelCase						:				List[str]		  =			codebook_size
      UpperCAmelCase						:				Optional[Any]		  =			codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
      UpperCAmelCase						:				List[Any]		  =			codebook_do_center_crop
      UpperCAmelCase						:				List[str]		  =			codebook_crop_size
      UpperCAmelCase						:				List[str]		  =			codebook_do_map_pixels
      UpperCAmelCase						:				Dict		  =			codebook_do_normalize
      UpperCAmelCase						:				Any		  =			codebook_image_mean
      UpperCAmelCase						:				int		  =			codebook_image_std
 def   _UpperCamelCase    (			self					: Any				)    ->			str:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "resample": self.resample,
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "input_size_patches": self.input_size_patches,
          "total_mask_patches": self.total_mask_patches,
          "mask_group_max_patches": self.mask_group_max_patches,
          "mask_group_min_patches": self.mask_group_min_patches,
          "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
          "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
          "codebook_do_resize": self.codebook_do_resize,
          "codebook_size": self.codebook_size,
          "codebook_resample": self.codebook_resample,
          "codebook_do_center_crop": self.codebook_do_center_crop,
          "codebook_crop_size": self.codebook_crop_size,
          "codebook_do_map_pixels": self.codebook_do_map_pixels,
          "codebook_do_normalize": self.codebook_do_normalize,
          "codebook_image_mean": self.codebook_image_mean,
          "codebook_image_std": self.codebook_image_std,
      }
 def   _UpperCamelCase    (			self					: Tuple				)    ->			Any:
      return (self.size["height"], self.size["width"])
 def   _UpperCamelCase    (			self					: Optional[int]				)    ->			int:
      return (
          (self.input_size_patches, self.input_size_patches)
          if not isinstance(self.input_size_patches       ,    _a				)
          else self.input_size_patches
      )

 def   _UpperCamelCase    (			self					: List[Any]				)    ->			Any:
      return (self.codebook_size["height"], self.codebook_size["width"])




@require_torch
@require_vision
class    A_   ( a_      ,						unittest.TestCase     ):







 """simple docstring"""





 _UpperCamelCase				: Union[str, Any]      =					FlavaImageProcessor if is_vision_available() else None
 _UpperCamelCase				: Tuple      =					None
 def   _UpperCamelCase    (			self					: List[Any]				)    ->			Dict:
      UpperCAmelCase						:				Union[str, Any]		  =			FlavaImageProcessingTester(self				)
 @property
 def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			Any:
      return self.image_processor_tester.prepare_image_processor_dict()
 def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			Tuple:
      UpperCAmelCase						:				List[Any]		  =			self.image_processing_class(**self.image_processor_dict				)
      self.assertTrue(hasattr(_a       ,    "image_mean"				)				)
      self.assertTrue(hasattr(_a       ,    "image_std"				)				)
      self.assertTrue(hasattr(_a       ,    "do_normalize"				)				)
      self.assertTrue(hasattr(_a       ,    "do_resize"				)				)
      self.assertTrue(hasattr(_a       ,    "resample"				)				)
      self.assertTrue(hasattr(_a       ,    "crop_size"				)				)
      self.assertTrue(hasattr(_a       ,    "do_center_crop"				)				)
      self.assertTrue(hasattr(_a       ,    "do_rescale"				)				)
      self.assertTrue(hasattr(_a       ,    "rescale_factor"				)				)
      self.assertTrue(hasattr(_a       ,    "masking_generator"				)				)
      self.assertTrue(hasattr(_a       ,    "codebook_do_resize"				)				)
      self.assertTrue(hasattr(_a       ,    "codebook_size"				)				)
      self.assertTrue(hasattr(_a       ,    "codebook_resample"				)				)
      self.assertTrue(hasattr(_a       ,    "codebook_do_center_crop"				)				)
      self.assertTrue(hasattr(_a       ,    "codebook_crop_size"				)				)
      self.assertTrue(hasattr(_a       ,    "codebook_do_map_pixels"				)				)
      self.assertTrue(hasattr(_a       ,    "codebook_do_normalize"				)				)
      self.assertTrue(hasattr(_a       ,    "codebook_image_mean"				)				)
      self.assertTrue(hasattr(_a       ,    "codebook_image_std"				)				)
 def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			int:
      UpperCAmelCase						:				Optional[int]		  =			self.image_processing_class.from_dict(self.image_processor_dict				)
      self.assertEqual(image_processor.size       ,    {"height": 2_2_4, "width": 2_2_4}				)
      self.assertEqual(image_processor.crop_size       ,    {"height": 2_2_4, "width": 2_2_4}				)
      self.assertEqual(image_processor.codebook_size       ,    {"height": 1_1_2, "width": 1_1_2}				)
      self.assertEqual(image_processor.codebook_crop_size       ,    {"height": 1_1_2, "width": 1_1_2}				)

      UpperCAmelCase						:				List[Any]		  =			self.image_processing_class.from_dict(
          self.image_processor_dict       ,    size=4_2       ,    crop_size=8_4       ,    codebook_size=3_3       ,    codebook_crop_size=6_6				)
      self.assertEqual(image_processor.size       ,    {"height": 4_2, "width": 4_2}				)
      self.assertEqual(image_processor.crop_size       ,    {"height": 8_4, "width": 8_4}				)
      self.assertEqual(image_processor.codebook_size       ,    {"height": 3_3, "width": 3_3}				)
      self.assertEqual(image_processor.codebook_crop_size       ,    {"height": 6_6, "width": 6_6}				)
 def   _UpperCamelCase    (			self					: str				)    ->			Optional[int]:
      pass
 def   _UpperCamelCase    (			self					: List[str]				)    ->			Union[str, Any]:
      # Initialize image_processing
      UpperCAmelCase						:				List[str]		  =			self.image_processing_class(**self.image_processor_dict				)
      # create random PIL images
      UpperCAmelCase						:				str		  =			prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=_a				)
      for image in image_inputs:
           self.assertIsInstance(_a       ,    PIL.Image.Image				)

      # Test not batched input
      UpperCAmelCase						:				Dict		  =			image_processing(image_inputs[0]       ,    return_tensors="pt"				)

      # Test no bool masked pos
      self.assertFalse("bool_masked_pos" in encoded_images				)

      UpperCAmelCase  , UpperCAmelCase						:				Tuple		  =			self.image_processor_tester.get_expected_image_size()

      self.assertEqual(
          encoded_images.pixel_values.shape       ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,    )

      # Test batched
      UpperCAmelCase						:				Optional[Any]		  =			image_processing(_a       ,    return_tensors="pt"				)
      UpperCAmelCase  , UpperCAmelCase						:				Optional[int]		  =			self.image_processor_tester.get_expected_image_size()

      # Test no bool masked pos
      self.assertFalse("bool_masked_pos" in encoded_images				)

      self.assertEqual(
          encoded_images.pixel_values.shape       ,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )       ,    )
 def   _UpperCamelCase    (			self					: Tuple       ,    _a					: List[Any]       ,    _a					: Tuple				)    ->			int:
      # Initialize image_processing
      UpperCAmelCase						:				Any		  =			self.image_processing_class(**self.image_processor_dict				)
      # create random tensors
      UpperCAmelCase						:				List[str]		  =			prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=_a       ,    **_a				)
      for image in image_inputs:
           self.assertIsInstance(_a       ,    _a				)

      # Test not batched input
      UpperCAmelCase						:				Tuple		  =			image_processing(image_inputs[0]       ,    return_tensors="pt"				)

      UpperCAmelCase  , UpperCAmelCase						:				List[str]		  =			self.image_processor_tester.get_expected_image_size()
      self.assertEqual(
          encoded_images.pixel_values.shape       ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,    )

      UpperCAmelCase						:				int		  =			image_processing(_a       ,    return_image_mask=_a       ,    return_tensors="pt"				)

      UpperCAmelCase  , UpperCAmelCase						:				str		  =			self.image_processor_tester.get_expected_image_size()
      self.assertEqual(
          encoded_images.pixel_values.shape       ,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )       ,    )

      UpperCAmelCase  , UpperCAmelCase						:				Optional[Any]		  =			self.image_processor_tester.get_expected_mask_size()
      self.assertEqual(
          encoded_images.bool_masked_pos.shape       ,    (
              self.image_processor_tester.batch_size,
              expected_height,
              expected_width,
          )       ,    )

      # Test batched
      UpperCAmelCase						:				str		  =			image_processing(_a       ,    return_tensors="pt"				).pixel_values

      UpperCAmelCase  , UpperCAmelCase						:				List[str]		  =			self.image_processor_tester.get_expected_image_size()
      self.assertEqual(
          encoded_images.shape       ,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )       ,    )

      # Test masking
      UpperCAmelCase						:				Optional[Any]		  =			image_processing(_a       ,    return_image_mask=_a       ,    return_tensors="pt"				)

      UpperCAmelCase  , UpperCAmelCase						:				Optional[Any]		  =			self.image_processor_tester.get_expected_image_size()
      self.assertEqual(
          encoded_images.pixel_values.shape       ,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )       ,    )

      UpperCAmelCase  , UpperCAmelCase						:				Any		  =			self.image_processor_tester.get_expected_mask_size()
      self.assertEqual(
          encoded_images.bool_masked_pos.shape       ,    (
              self.image_processor_tester.batch_size,
              expected_height,
              expected_width,
          )       ,    )
 def   _UpperCamelCase    (			self					: Tuple				)    ->			Optional[int]:
      self._test_call_framework(np.ndarray       ,    prepare_kwargs={"numpify": True}				)
 def   _UpperCamelCase    (			self					: List[Any]				)    ->			Tuple:
      self._test_call_framework(torch.Tensor       ,    prepare_kwargs={"torchify": True}				)
 def   _UpperCamelCase    (			self					: List[str]				)    ->			Dict:
      # Initialize image_processing
      random.seed(1_2_3_4				)
      UpperCAmelCase						:				List[Any]		  =			self.image_processing_class(**self.image_processor_dict				)
      UpperCAmelCase						:				str		  =			prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=_a       ,    torchify=_a				)

      # Test not batched input
      UpperCAmelCase						:				Optional[Any]		  =			image_processing(image_inputs[0]       ,    return_image_mask=_a       ,    return_tensors="pt"				)
      self.assertEqual(encoded_images.bool_masked_pos.sum().item()       ,    7_5				)

 def   _UpperCamelCase    (			self					: Union[str, Any]				)    ->			Any:
      # Initialize image_processing
      UpperCAmelCase						:				Dict		  =			self.image_processing_class(**self.image_processor_dict				)
      # create random PIL images
      UpperCAmelCase						:				Dict		  =			prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=_a				)
      for image in image_inputs:
           self.assertIsInstance(_a       ,    PIL.Image.Image				)

      # Test not batched input
      UpperCAmelCase						:				Dict		  =			image_processing(image_inputs[0]       ,    return_codebook_pixels=_a       ,    return_tensors="pt"				)
      UpperCAmelCase  , UpperCAmelCase						:				Union[str, Any]		  =			self.image_processor_tester.get_expected_codebook_image_size()
      self.assertEqual(
          encoded_images.codebook_pixel_values.shape       ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,    )

      # Test batched
      UpperCAmelCase						:				Any		  =			image_processing(_a       ,    return_codebook_pixels=_a       ,    return_tensors="pt"				)
      UpperCAmelCase  , UpperCAmelCase						:				List[str]		  =			self.image_processor_tester.get_expected_codebook_image_size()
      self.assertEqual(
          encoded_images.codebook_pixel_values.shape       ,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )       ,    )







import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: Any      =					(EulerAncestralDiscreteScheduler,)
 _UpperCamelCase				: Union[str, Any]      =					10
 def   _UpperCamelCase    (			self					: int       ,    **_a					: str				)    ->			Dict:
      UpperCAmelCase						:				List[Any]		  =			{
          "num_train_timesteps": 1_1_0_0,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
      }

      config.update(**_a				)
      return config
 def   _UpperCamelCase    (			self					: str				)    ->			Union[str, Any]:
      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=_a				)
 def   _UpperCamelCase    (			self					: Tuple				)    ->			int:
      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=_a       ,    beta_end=_a				)
 def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			List[str]:
      for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=_a				)
 def   _UpperCamelCase    (			self					: Dict				)    ->			str:
      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=_a				)
 def   _UpperCamelCase    (			self					: Any				)    ->			Optional[Any]:
      UpperCAmelCase						:				int		  =			self.scheduler_classes[0]
      UpperCAmelCase						:				Optional[int]		  =			self.get_scheduler_config()
      UpperCAmelCase						:				List[Any]		  =			scheduler_class(**_a				)

      scheduler.set_timesteps(self.num_inference_steps				)

      UpperCAmelCase						:				Dict		  =			torch.manual_seed(0				)

      UpperCAmelCase						:				Union[str, Any]		  =			self.dummy_model()
      UpperCAmelCase						:				Dict		  =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      UpperCAmelCase						:				Optional[Any]		  =			sample.to(_a				)

      for i, t in enumerate(scheduler.timesteps				):
           UpperCAmelCase						:				List[Any]		  =			scheduler.scale_model_input(_a       ,    _a				)

           UpperCAmelCase						:				str		  =			model(_a       ,    _a				)

           UpperCAmelCase						:				int		  =			scheduler.step(_a       ,    _a       ,    _a       ,    generator=_a				)
           UpperCAmelCase						:				Optional[int]		  =			output.prev_sample

      UpperCAmelCase						:				Tuple		  =			torch.sum(torch.abs(_a				)				)
      UpperCAmelCase						:				str		  =			torch.mean(torch.abs(_a				)				)

      assert abs(result_sum.item() - 1_5_2.3_1_9_2				) < 1E-2
      assert abs(result_mean.item() - 0.1_9_8_3				) < 1E-3
 def   _UpperCamelCase    (			self					: Optional[int]				)    ->			Tuple:
      UpperCAmelCase						:				Optional[int]		  =			self.scheduler_classes[0]
      UpperCAmelCase						:				Tuple		  =			self.get_scheduler_config(prediction_type="v_prediction"				)
      UpperCAmelCase						:				str		  =			scheduler_class(**_a				)

      scheduler.set_timesteps(self.num_inference_steps				)

      UpperCAmelCase						:				Dict		  =			torch.manual_seed(0				)

      UpperCAmelCase						:				List[Any]		  =			self.dummy_model()
      UpperCAmelCase						:				Optional[int]		  =			self.dummy_sample_deter * scheduler.init_noise_sigma
      UpperCAmelCase						:				Any		  =			sample.to(_a				)

      for i, t in enumerate(scheduler.timesteps				):
           UpperCAmelCase						:				Union[str, Any]		  =			scheduler.scale_model_input(_a       ,    _a				)

           UpperCAmelCase						:				Union[str, Any]		  =			model(_a       ,    _a				)

           UpperCAmelCase						:				Union[str, Any]		  =			scheduler.step(_a       ,    _a       ,    _a       ,    generator=_a				)
           UpperCAmelCase						:				Optional[int]		  =			output.prev_sample

      UpperCAmelCase						:				Dict		  =			torch.sum(torch.abs(_a				)				)
      UpperCAmelCase						:				Any		  =			torch.mean(torch.abs(_a				)				)

      assert abs(result_sum.item() - 1_0_8.4_4_3_9				) < 1E-2
      assert abs(result_mean.item() - 0.1_4_1_2				) < 1E-3

 def   _UpperCamelCase    (			self					: List[str]				)    ->			Optional[Any]:
      UpperCAmelCase						:				str		  =			self.scheduler_classes[0]
      UpperCAmelCase						:				str		  =			self.get_scheduler_config()
      UpperCAmelCase						:				Union[str, Any]		  =			scheduler_class(**_a				)

      scheduler.set_timesteps(self.num_inference_steps       ,    device=_a				)
      UpperCAmelCase						:				str		  =			torch.manual_seed(0				)

      UpperCAmelCase						:				Optional[int]		  =			self.dummy_model()
      UpperCAmelCase						:				Any		  =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      UpperCAmelCase						:				Optional[Any]		  =			sample.to(_a				)

      for t in scheduler.timesteps:
           UpperCAmelCase						:				Tuple		  =			scheduler.scale_model_input(_a       ,    _a				)

           UpperCAmelCase						:				int		  =			model(_a       ,    _a				)

           UpperCAmelCase						:				Tuple		  =			scheduler.step(_a       ,    _a       ,    _a       ,    generator=_a				)
           UpperCAmelCase						:				str		  =			output.prev_sample

      UpperCAmelCase						:				Tuple		  =			torch.sum(torch.abs(_a				)				)
      UpperCAmelCase						:				Optional[Any]		  =			torch.mean(torch.abs(_a				)				)

      assert abs(result_sum.item() - 1_5_2.3_1_9_2				) < 1E-2
      assert abs(result_mean.item() - 0.1_9_8_3				) < 1E-3



