
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler






class     a_       (			unittest.TestCase							):



       """simple docstring"""
       # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
       __SCREAMING_SNAKE_CASE       :					List[Any]						=  (ScoreSdeVeScheduler,)
       __SCREAMING_SNAKE_CASE       :					Dict						=  ()






       @property
       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Any				      = 4
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = 3
           SCREAMING_SNAKE_CASE				:				List[str]				      = 8
           SCREAMING_SNAKE_CASE				:				List[str]				      = 8

           SCREAMING_SNAKE_CASE				:				Dict				      = torch.rand((batch_size, num_channels, height, width)   )

           return sample






       @property
       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Any				      = 4
           SCREAMING_SNAKE_CASE				:				List[str]				      = 3
           SCREAMING_SNAKE_CASE				:				Dict				      = 8
           SCREAMING_SNAKE_CASE				:				Dict				      = 8

           SCREAMING_SNAKE_CASE				:				Tuple				      = batch_size * num_channels * height * width
           SCREAMING_SNAKE_CASE				:				Any				      = torch.arange(a__   )
           SCREAMING_SNAKE_CASE				:				int				      = sample.reshape(a__				,       a__				,       a__				,       a__   )
           SCREAMING_SNAKE_CASE				:				Dict				      = sample / num_elems
           SCREAMING_SNAKE_CASE				:				str				      = sample.permute(3				,       0				,       1				,       2   )

           return sample






       def 	__lowerCAmelCase     (		self   )					->Tuple:
           def model(_lowerCamelCase				,       _lowerCamelCase				,       *_lowerCamelCase   ):
               return sample * t / (t + 1)

           return model






       def 	__lowerCAmelCase     (		self				,       **_lowerCamelCase   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				Dict				      = {
               '''num_train_timesteps''': 2000,
               '''snr''': 0.1_5,
               '''sigma_min''': 0.0_1,
               '''sigma_max''': 1348,
               '''sampling_eps''': 1e-5,
           }

           config.update(**a__   )
           return config






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase=0				,       **_lowerCamelCase   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				Dict				      = dict(self.forward_default_kwargs   )

           for scheduler_class in self.scheduler_classes:
               SCREAMING_SNAKE_CASE				:				List[Any]				      = self.dummy_sample
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = 0.1 * sample

               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.get_scheduler_config(**a__   )
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = scheduler_class(**a__   )

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(a__   )
                   SCREAMING_SNAKE_CASE				:				List[str]				      = scheduler_class.from_pretrained(a__   )

               SCREAMING_SNAKE_CASE				:				Tuple				      = scheduler.step_pred(
                   a__				,       a__				,       a__				,       generator=torch.manual_seed(0   )				,       **a__   ).prev_sample
               SCREAMING_SNAKE_CASE				:				List[Any]				      = new_scheduler.step_pred(
                   a__				,       a__				,       a__				,       generator=torch.manual_seed(0   )				,       **a__   ).prev_sample

               assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

               SCREAMING_SNAKE_CASE				:				Tuple				      = scheduler.step_correct(a__				,       a__				,       generator=torch.manual_seed(0   )				,       **a__   ).prev_sample
               SCREAMING_SNAKE_CASE				:				str				      = new_scheduler.step_correct(
                   a__				,       a__				,       generator=torch.manual_seed(0   )				,       **a__   ).prev_sample

               assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler correction are not identical"






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase=0				,       **_lowerCamelCase   )					->int:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = dict(self.forward_default_kwargs   )
           kwargs.update(a__   )

           for scheduler_class in self.scheduler_classes:
               SCREAMING_SNAKE_CASE				:				Dict				      = self.dummy_sample
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = 0.1 * sample

               SCREAMING_SNAKE_CASE				:				List[str]				      = self.get_scheduler_config()
               SCREAMING_SNAKE_CASE				:				Any				      = scheduler_class(**a__   )

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(a__   )
                   SCREAMING_SNAKE_CASE				:				Dict				      = scheduler_class.from_pretrained(a__   )

               SCREAMING_SNAKE_CASE				:				Tuple				      = scheduler.step_pred(
                   a__				,       a__				,       a__				,       generator=torch.manual_seed(0   )				,       **a__   ).prev_sample
               SCREAMING_SNAKE_CASE				:				int				      = new_scheduler.step_pred(
                   a__				,       a__				,       a__				,       generator=torch.manual_seed(0   )				,       **a__   ).prev_sample

               assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = scheduler.step_correct(a__				,       a__				,       generator=torch.manual_seed(0   )				,       **a__   ).prev_sample
               SCREAMING_SNAKE_CASE				:				int				      = new_scheduler.step_correct(
                   a__				,       a__				,       generator=torch.manual_seed(0   )				,       **a__   ).prev_sample

               assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler correction are not identical"






       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           for timesteps in [10, 100, 1000]:
               self.check_over_configs(num_train_timesteps=a__   )






       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]				,       [1, 100, 1000]   ):
               self.check_over_configs(sigma_min=a__				,       sigma_max=a__   )






       def 	__lowerCAmelCase     (		self   )					->str:
           for t in [0.1, 0.5, 0.7_5]:
               self.check_over_forward(time_step=a__   )






       def 	__lowerCAmelCase     (		self   )					->Any:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = dict(self.forward_default_kwargs   )

           SCREAMING_SNAKE_CASE				:				Tuple				      = self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE				:				str				      = self.get_scheduler_config()
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = scheduler_class(**a__   )

           SCREAMING_SNAKE_CASE				:				Any				      = 3

           SCREAMING_SNAKE_CASE				:				Tuple				      = self.dummy_model()
           SCREAMING_SNAKE_CASE				:				str				      = self.dummy_sample_deter

           scheduler.set_sigmas(a__   )
           scheduler.set_timesteps(a__   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.manual_seed(0   )

           for i, t in enumerate(scheduler.timesteps   ):
               SCREAMING_SNAKE_CASE				:				str				      = scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps   ):
                   with torch.no_grad():
                       SCREAMING_SNAKE_CASE				:				List[str]				      = model(a__				,       a__   )
                   SCREAMING_SNAKE_CASE				:				str				      = scheduler.step_correct(a__				,       a__				,       generator=a__				,       **a__   ).prev_sample

               with torch.no_grad():
                   SCREAMING_SNAKE_CASE				:				Dict				      = model(a__				,       a__   )

               SCREAMING_SNAKE_CASE				:				Optional[int]				      = scheduler.step_pred(a__				,       a__				,       a__				,       generator=a__				,       **a__   )
               SCREAMING_SNAKE_CASE				:				str				      = output.prev_sample, output.prev_sample_mean

           SCREAMING_SNAKE_CASE				:				Any				      = torch.sum(torch.abs(a__   )   )
           SCREAMING_SNAKE_CASE				:				Dict				      = torch.mean(torch.abs(a__   )   )

           assert np.isclose(result_sum.item()				,       143_7275_8528.0   )
           assert np.isclose(result_mean.item()				,       1871_4530.0   )






       def 	__lowerCAmelCase     (		self   )					->Any:
           SCREAMING_SNAKE_CASE				:				str				      = dict(self.forward_default_kwargs   )

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = kwargs.pop('''num_inference_steps'''				,       a__   )

           for scheduler_class in self.scheduler_classes:
               SCREAMING_SNAKE_CASE				:				Dict				      = self.get_scheduler_config()
               SCREAMING_SNAKE_CASE				:				str				      = scheduler_class(**a__   )

               SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.dummy_sample
               SCREAMING_SNAKE_CASE				:				Any				      = 0.1 * sample

               if num_inference_steps is not None and hasattr(a__				,       '''set_timesteps'''   ):
                   scheduler.set_timesteps(a__   )
               elif num_inference_steps is not None and not hasattr(a__				,       '''set_timesteps'''   ):
                   SCREAMING_SNAKE_CASE				:				Optional[Any]				      = num_inference_steps

               SCREAMING_SNAKE_CASE				:				List[Any]				      = scheduler.step_pred(a__				,       0				,       a__				,       generator=torch.manual_seed(0   )				,       **a__   ).prev_sample
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = scheduler.step_pred(a__				,       1				,       a__				,       generator=torch.manual_seed(0   )				,       **a__   ).prev_sample

               self.assertEqual(output_a.shape				,       sample.shape   )
               self.assertEqual(output_a.shape				,       output_a.shape   )






'''simple docstring'''





import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
       from PIL import Image
else:






       class     lowercase  :






             @staticmethod
             def  A__(*a__			,	**a__       )							-> Union[str, Any]:
                pass






@is_pipeline_test
@require_torch_or_tf
@require_vision
class     lowercase  ( unittest.TestCase):
      __lowercase					=	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
      __lowercase					=	TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






      def  A__(self			,	a__			,	a__			,	a__       )							-> Dict:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			ImageClassificationPipeline(model=a__			,	image_processor=a__			,	top_k=2       )
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[
             Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ),
             '''http://images.cocodataset.org/val2017/000000039769.jpg''',
         ]
         return image_classifier, examples






      def  A__(self			,	a__			,	a__       )							-> List[Any]:
         SCREAMING_SNAKE_CASE__      :					int							    =			image_classifier('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )

         self.assertEqual(
             a__			,	[
                 {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
             ]			,	)

         import datasets

         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''			,	'''image'''			,	split='''test'''       )

         # Accepts URL + PIL.Image + lists
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			image_classifier(
             [
                 Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ),
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 # RGBA
                 dataset[0]['''file'''],
                 # LA
                 dataset[1]['''file'''],
                 # L
                 dataset[2]['''file'''],
             ]       )
         self.assertEqual(
             a__			,	[
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
             ]			,	)






      @require_torch
      def  A__(self       )							-> List[str]:
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			'''hf-internal-testing/tiny-random-vit'''
         SCREAMING_SNAKE_CASE__      :					Any							    =			pipeline('''image-classification'''			,	model=a__       )

         SCREAMING_SNAKE_CASE__      :					Any							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}]			,	)

         SCREAMING_SNAKE_CASE__      :					Any							    =			image_classifier(
             [
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
             ]			,	top_k=2			,	)
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
             ]			,	)






      @require_tf
      def  A__(self       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			'''hf-internal-testing/tiny-random-vit'''
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			pipeline('''image-classification'''			,	model=a__			,	framework='''tf'''       )

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}]			,	)

         SCREAMING_SNAKE_CASE__      :					Dict							    =			image_classifier(
             [
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
             ]			,	top_k=2			,	)
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
             ]			,	)






      def  A__(self       )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			PreTrainedTokenizer()

         # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			pipeline(
             '''image-classification'''			,	model='''hf-internal-testing/tiny-random-vit'''			,	tokenizer=a__       )

         self.assertIs(image_classifier.tokenizer			,	a__       )






      @slow
      @require_torch
      def  A__(self       )							-> int:
         # Perceiver is not tested by `run_pipeline_test` properly.
         # That is because the type of feature_extractor and model preprocessor need to be kept
         # in sync, which is not the case in the current design
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			pipeline('''image-classification'''			,	model='''deepmind/vision-perceiver-conv'''       )
         SCREAMING_SNAKE_CASE__      :					str							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 {'''score''': 0.43_85, '''label''': '''tabby, tabby cat'''},
                 {'''score''': 0.3_21, '''label''': '''tiger cat'''},
                 {'''score''': 0.05_02, '''label''': '''Egyptian cat'''},
                 {'''score''': 0.01_37, '''label''': '''crib, cot'''},
                 {'''score''': 0.0_07, '''label''': '''radiator'''},
             ]			,	)

         SCREAMING_SNAKE_CASE__      :					str							    =			pipeline('''image-classification'''			,	model='''deepmind/vision-perceiver-fourier'''       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 {'''score''': 0.56_58, '''label''': '''tabby, tabby cat'''},
                 {'''score''': 0.13_09, '''label''': '''tiger cat'''},
                 {'''score''': 0.07_22, '''label''': '''Egyptian cat'''},
                 {'''score''': 0.07_07, '''label''': '''remote control, remote'''},
                 {'''score''': 0.00_82, '''label''': '''computer keyboard, keypad'''},
             ]			,	)

         SCREAMING_SNAKE_CASE__      :					Dict							    =			pipeline('''image-classification'''			,	model='''deepmind/vision-perceiver-learned'''       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 {'''score''': 0.30_22, '''label''': '''tabby, tabby cat'''},
                 {'''score''': 0.23_62, '''label''': '''Egyptian cat'''},
                 {'''score''': 0.18_56, '''label''': '''tiger cat'''},
                 {'''score''': 0.03_24, '''label''': '''remote control, remote'''},
                 {'''score''': 0.00_96, '''label''': '''quilt, comforter, comfort, puff'''},
             ]			,	)

