




'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class        lowercase_      (						lowerCAmelCase_							):
     A_											=			42
     A_											=			42





     def __init__(     self     :					Optional[int]     ,							__lowerCamelCase     :					UNetaDModel     ,							__lowerCamelCase     :					KarrasVeScheduler			):
           super().__init__()
           self.register_modules(unet=__lowerCamelCase     ,							scheduler=__lowerCamelCase			)





     @torch.no_grad()
     def __call__(     self     :					int     ,							__lowerCamelCase     :					int = 1     ,							__lowerCamelCase     :					int = 50     ,							__lowerCamelCase     :					Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,							__lowerCamelCase     :					Optional[str] = "pil"     ,							__lowerCamelCase     :					bool = True     ,							**__lowerCamelCase     :					Dict     ,							):
           snake_case__       :   str	      =    self.unet.config.sample_size
           snake_case__       :   Optional[int]	      =    (batch_size, 3, img_size, img_size)

           snake_case__       :   Any	      =    self.unet

           # sample x_0 ~ N(0, sigma_0^2 * I)
           snake_case__       :   Tuple	      =    randn_tensor(__lowerCamelCase     ,							generator=__lowerCamelCase     ,							device=self.device			) * self.scheduler.init_noise_sigma

           self.scheduler.set_timesteps(__lowerCamelCase			)

           for t in self.progress_bar(self.scheduler.timesteps			):
                 # here sigma_t == t_i from the paper
                 snake_case__       :   List[str]	      =    self.scheduler.schedule[t]
                 snake_case__       :   List[Any]	      =    self.scheduler.schedule[t - 1] if t > 0 else 0

                 # 1. Select temporarily increased noise level sigma_hat
                 # 2. Add new noise to move from sample_i to sample_hat
                 snake_case__      ,							snake_case__       :   Union[str, Any]	      =    self.scheduler.add_noise_to_input(__lowerCamelCase     ,							__lowerCamelCase     ,							generator=__lowerCamelCase			)

                 # 3. Predict the noise residual given the noise magnitude `sigma_hat`
                 # The model inputs and output are adjusted by following eq. (213) in [1].
                 snake_case__       :   List[Any]	      =    (sigma_hat / 2) * model((sample_hat + 1) / 2     ,							sigma_hat / 2			).sample

                 # 4. Evaluate dx/dt at sigma_hat
                 # 5. Take Euler step from sigma to sigma_prev
                 snake_case__       :   Any	      =    self.scheduler.step(__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase			)

                 if sigma_prev != 0:
                       # 6. Apply 2nd order correction
                       # The model inputs and output are adjusted by following eq. (213) in [1].
                       snake_case__       :   str	      =    (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2     ,							sigma_prev / 2			).sample
                       snake_case__       :   List[str]	      =    self.scheduler.step_correct(
                           __lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							step_output.prev_sample     ,							step_output['derivative']     ,							)
                 snake_case__       :   Union[str, Any]	      =    step_output.prev_sample

           snake_case__       :   Dict	      =    (sample / 2 + 0.5).clamp(0     ,							1			)
           snake_case__       :   Union[str, Any]	      =    sample.cpu().permute(0     ,							2     ,							3     ,							1			).numpy()
           if output_type == "pil":
                 snake_case__       :   Any	      =    self.numpy_to_pil(__lowerCamelCase			)

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=__lowerCamelCase			)






'''simple docstring'''






import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
      from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import MobileNetVaImageProcessor






class        lowercase_      (						lowerCAmelCase_							):





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   Union[str, Any]	      =    self.config_class(**self.inputs_dict			)
           self.parent.assertTrue(hasattr(__lowerCamelCase     ,							'tf_padding'			)			)
           self.parent.assertTrue(hasattr(__lowerCamelCase     ,							'depth_multiplier'			)			)






class        lowercase_      :





     def __init__(     self     :					Optional[int]     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					Dict=13     ,							__lowerCamelCase     :					Optional[Any]=3     ,							__lowerCamelCase     :					str=32     ,							__lowerCamelCase     :					str=0.2_5     ,							__lowerCamelCase     :					List[Any]=8     ,							__lowerCamelCase     :					List[Any]=8     ,							__lowerCamelCase     :					Union[str, Any]=6     ,							__lowerCamelCase     :					Optional[Any]=32     ,							__lowerCamelCase     :					Dict=True     ,							__lowerCamelCase     :					str=True     ,							__lowerCamelCase     :					int=True     ,							__lowerCamelCase     :					Tuple="relu6"     ,							__lowerCamelCase     :					List[str]=1280     ,							__lowerCamelCase     :					Optional[Any]=0.1     ,							__lowerCamelCase     :					Tuple=0.0_2     ,							__lowerCamelCase     :					str=True     ,							__lowerCamelCase     :					int=True     ,							__lowerCamelCase     :					int=10     ,							__lowerCamelCase     :					int=None     ,							):
           snake_case__       :   Dict	      =    parent
           snake_case__       :   Union[str, Any]	      =    batch_size
           snake_case__       :   List[Any]	      =    num_channels
           snake_case__       :   List[str]	      =    image_size
           snake_case__       :   Optional[int]	      =    depth_multiplier
           snake_case__       :   int	      =    depth_divisible_by
           snake_case__       :   List[Any]	      =    min_depth
           snake_case__       :   Dict	      =    expand_ratio
           snake_case__       :   Optional[Any]	      =    tf_padding
           snake_case__       :   List[Any]	      =    output_stride
           snake_case__       :   List[str]	      =    first_layer_is_expansion
           snake_case__       :   Optional[Any]	      =    finegrained_output
           snake_case__       :   int	      =    hidden_act
           snake_case__       :   List[Any]	      =    last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier			)
           snake_case__       :   List[str]	      =    classifier_dropout_prob
           snake_case__       :   List[Any]	      =    use_labels
           snake_case__       :   Tuple	      =    is_training
           snake_case__       :   Dict	      =    num_labels
           snake_case__       :   Any	      =    initializer_range
           snake_case__       :   int	      =    scope





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   int	      =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

           snake_case__       :   Optional[int]	      =    None
           snake_case__       :   Optional[int]	      =    None
           if self.use_labels:
                 snake_case__       :   Dict	      =    ids_tensor([self.batch_size]     ,							self.num_labels			)
                 snake_case__       :   List[str]	      =    ids_tensor([self.batch_size, self.image_size, self.image_size]     ,							self.num_labels			)

           snake_case__       :   List[Any]	      =    self.get_config()

           return config, pixel_values, labels, pixel_labels





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           return MobileNetVaConfig(
               num_channels=self.num_channels     ,							image_size=self.image_size     ,							depth_multiplier=self.depth_multiplier     ,							depth_divisible_by=self.depth_divisible_by     ,							min_depth=self.min_depth     ,							expand_ratio=self.expand_ratio     ,							output_stride=self.output_stride     ,							first_layer_is_expansion=self.first_layer_is_expansion     ,							finegrained_output=self.finegrained_output     ,							hidden_act=self.hidden_act     ,							tf_padding=self.tf_padding     ,							classifier_dropout_prob=self.classifier_dropout_prob     ,							initializer_range=self.initializer_range     ,							)





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Union[str, Any]			):
           snake_case__       :   Optional[Any]	      =    MobileNetVaModel(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   List[Any]	      =    model(__lowerCamelCase			)
           self.parent.assertEqual(
               result.last_hidden_state.shape     ,							(
                   self.batch_size,
                   self.last_hidden_size,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )     ,							)
           self.parent.assertEqual(
               result.pooler_output.shape     ,							(self.batch_size, self.last_hidden_size)     ,							)





     def 			_lowerCAmelCase      (     self     :					str     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Dict			):
           snake_case__       :   Optional[int]	      =    self.num_labels
           snake_case__       :   int	      =    MobileNetVaForImageClassification(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   int	      =    model(__lowerCamelCase     ,							labels=__lowerCamelCase			)
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)			)





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Union[str, Any]			):
           snake_case__       :   Tuple	      =    self.num_labels
           snake_case__       :   Optional[Any]	      =    MobileNetVaForSemanticSegmentation(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   Optional[Any]	      =    model(__lowerCamelCase			)
           self.parent.assertEqual(
               result.logits.shape     ,							(
                   self.batch_size,
                   self.num_labels,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )     ,							)
           snake_case__       :   Dict	      =    model(__lowerCamelCase     ,							labels=__lowerCamelCase			)
           self.parent.assertEqual(
               result.logits.shape     ,							(
                   self.batch_size,
                   self.num_labels,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )     ,							)





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   List[Any]	      =    self.prepare_config_and_inputs()
           snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   str	      =    config_and_inputs
           snake_case__       :   Union[str, Any]	      =    {'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class        lowercase_      (						lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase							):
     A_											=			(
         (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     A_											=			(
         {
             "feature-extraction": MobileNetVaModel,
             "image-classification": MobileNetVaForImageClassification,
             "image-segmentation": MobileNetVaForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     A_											=			False
     A_											=			False
     A_											=			False
     A_											=			False





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   List[Any]	      =    MobileNetVaModelTester(self			)
           snake_case__       :   str	      =    MobileNetVaConfigTester(self     ,							config_class=__lowerCamelCase     ,							has_text_modality=__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Any			):
           self.config_tester.run_common_tests()





     @unittest.skip(reason='MobileNetV2 does not use inputs_embeds'			)
     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           pass





     @unittest.skip(reason='MobileNetV2 does not support input and output embeddings'			)
     def 			_lowerCAmelCase      (     self     :					int			):
           pass





     @unittest.skip(reason='MobileNetV2 does not output attentions'			)
     def 			_lowerCAmelCase      (     self     :					Tuple			):
           pass





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__      ,							snake_case__       :   Any	      =    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 snake_case__       :   str	      =    model_class(__lowerCamelCase			)
                 snake_case__       :   Optional[int]	      =    inspect.signature(model.forward			)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 snake_case__       :   Union[str, Any]	      =    [*signature.parameters.keys()]

                 snake_case__       :   int	      =    ['pixel_values']
                 self.assertListEqual(arg_names[:1]     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   str	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           def check_hidden_states_output(__lowerCamelCase     :					str     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					List[str]			):
                 snake_case__       :   Tuple	      =    model_class(__lowerCamelCase			)
                 model.to(__lowerCamelCase			)
                 model.eval()

                 with torch.no_grad():
                       snake_case__       :   List[str]	      =    model(**self._prepare_for_class(__lowerCamelCase     ,							__lowerCamelCase			)			)

                 snake_case__       :   List[str]	      =    outputs.hidden_states

                 snake_case__       :   List[Any]	      =    16
                 self.assertEqual(len(__lowerCamelCase			)     ,							__lowerCamelCase			)

           snake_case__      ,							snake_case__       :   Optional[Any]	      =    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 snake_case__       :   List[Any]	      =    True
                 check_hidden_states_output(__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase			)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 snake_case__       :   List[Any]	      =    True

                 check_hidden_states_output(__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   Union[str, Any]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   Tuple	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase			)





     @slow
     def 			_lowerCAmelCase      (     self     :					Dict			):
           for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case__       :   Any	      =    MobileNetVaModel.from_pretrained(__lowerCamelCase			)
                 self.assertIsNotNone(__lowerCamelCase			)






def 		UpperCamelCase__      (		)    ->   Dict:
      snake_case__       :   Tuple	      =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
      return image






@require_torch
@require_vision
class        lowercase_      (						unittest.TestCase							):





     @cached_property
     def 			_lowerCAmelCase      (     self     :					Tuple			):
           return (
               MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'			) if is_vision_available() else None
           )





     @slow
     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Dict	      =    MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'			).to(__lowerCamelCase			)

           snake_case__       :   int	      =    self.default_image_processor
           snake_case__       :   Optional[Any]	      =    prepare_img()
           snake_case__       :   str	      =    image_processor(images=__lowerCamelCase     ,							return_tensors='pt'			).to(__lowerCamelCase			)

           # forward pass
           with torch.no_grad():
                 snake_case__       :   Any	      =    model(**__lowerCamelCase			)

           # verify the logits
           snake_case__       :   Tuple	      =    torch.Size((1, 1001)			)
           self.assertEqual(outputs.logits.shape     ,							__lowerCamelCase			)

           snake_case__       :   int	      =    torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]			).to(__lowerCamelCase			)

           self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,							__lowerCamelCase     ,							atol=1E-4			)			)





     @slow
     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   str	      =    MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'			)
           snake_case__       :   int	      =    model.to(__lowerCamelCase			)

           snake_case__       :   Any	      =    MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'			)

           snake_case__       :   Any	      =    prepare_img()
           snake_case__       :   Dict	      =    image_processor(images=__lowerCamelCase     ,							return_tensors='pt'			).to(__lowerCamelCase			)

           # forward pass
           with torch.no_grad():
                 snake_case__       :   Any	      =    model(**__lowerCamelCase			)
           snake_case__       :   Optional[int]	      =    outputs.logits

           # verify the logits
           snake_case__       :   str	      =    torch.Size((1, 21, 65, 65)			)
           self.assertEqual(logits.shape     ,							__lowerCamelCase			)

           snake_case__       :   Optional[Any]	      =    torch.tensor(
               [
                   [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
                   [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
                   [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
               ]     ,							device=__lowerCamelCase     ,							)

           self.assertTrue(torch.allclose(logits[0, :3, :3, :3]     ,							__lowerCamelCase     ,							atol=1E-4			)			)
