





import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     LevitForImageClassification,
     LevitForImageClassificationWithTeacher,
     LevitModel,
 )
 from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self							):
         _A     =							self.config_class(**self.inputs_dict							)
         self.parent.assertTrue(hasattr(a_				,     'hidden_sizes'							)							)
         self.parent.assertTrue(hasattr(a_				,     'num_attention_heads'							)							)







class      lowerCamelCase:
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_=13				,     snake_case_=64				,     snake_case_=3				,     snake_case_=3				,     snake_case_=2				,     snake_case_=1				,     snake_case_=16				,     snake_case_=[128, 256, 384]				,     snake_case_=[4, 6, 8]				,     snake_case_=[2, 3, 4]				,     snake_case_=[16, 16, 16]				,     snake_case_=0				,     snake_case_=[2, 2, 2]				,     snake_case_=[2, 2, 2]				,     snake_case_=0.02				,     snake_case_=True				,     snake_case_=True				,     snake_case_=2				,     ):
         _A     =							parent
         _A     =							batch_size
         _A     =							image_size
         _A     =							num_channels
         _A     =							kernel_size
         _A     =							stride
         _A     =							padding
         _A     =							hidden_sizes
         _A     =							num_attention_heads
         _A     =							depths
         _A     =							key_dim
         _A     =							drop_path_rate
         _A     =							patch_size
         _A     =							attention_ratio
         _A     =							mlp_ratio
         _A     =							initializer_range
         _A     =							[
             ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
             ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
         ]
         _A     =							is_training
         _A     =							use_labels
         _A     =							num_labels
         _A     =							initializer_range




     def        lowerCAmelCase__		(    self							):
         _A     =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         _A     =							None
         if self.use_labels:
             _A     =							ids_tensor([self.batch_size]				,     self.num_labels							)

         _A     =							self.get_config()
         return config, pixel_values, labels




     def        lowerCAmelCase__		(    self							):
         return LevitConfig(
             image_size=self.image_size				,     num_channels=self.num_channels				,     kernel_size=self.kernel_size				,     stride=self.stride				,     padding=self.padding				,     patch_size=self.patch_size				,     hidden_sizes=self.hidden_sizes				,     num_attention_heads=self.num_attention_heads				,     depths=self.depths				,     key_dim=self.key_dim				,     drop_path_rate=self.drop_path_rate				,     mlp_ratio=self.mlp_ratio				,     attention_ratio=self.attention_ratio				,     initializer_range=self.initializer_range				,     down_ops=self.down_ops				,     )




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							LevitModel(config=a_							)
         model.to(a_							)
         model.eval()
         _A     =							model(a_							)
         _A     =							(self.image_size, self.image_size)
         _A     =							image_size[0], image_size[1]
         for _ in range(4							):
             _A     =							floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
             _A     =							floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
         self.parent.assertEqual(
             result.last_hidden_state.shape				,     (self.batch_size, ceil(height / 4							) * ceil(width / 4							), self.hidden_sizes[-1])				,     )




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_labels
         _A     =							LevitForImageClassification(a_							)
         model.to(a_							)
         model.eval()
         _A     =							model(a_				,     labels=a_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.prepare_config_and_inputs()
         _A     =							config_and_inputs
         _A     =							{"pixel_values": pixel_values}
         return config, inputs_dict







@require_torch
class      lowerCamelCase(						__snake_case			, __snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			(
         (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
         if is_torch_available()
         else ()
     )
     __magic_name__   =			(
         {
             'feature-extraction': LevitModel,
             'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
         }
         if is_torch_available()
         else {}
     )

     __magic_name__   =			False
     __magic_name__   =			False
     __magic_name__   =			False
     __magic_name__   =			False
     __magic_name__   =			False




     def        lowerCAmelCase__		(    self							):
         _A     =							LevitModelTester(self							)
         _A     =							ConfigTester(self				,     config_class=a_				,     has_text_modality=a_				,     hidden_size=37							)




     def        lowerCAmelCase__		(    self							):
         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()




     def        lowerCAmelCase__		(    self							):
         return




     @unittest.skip(reason='Levit does not use inputs_embeds'							)
     def        lowerCAmelCase__		(    self							):
         pass




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




     @unittest.skip(reason='Levit does not output attentions'							)
     def        lowerCAmelCase__		(    self							):
         pass




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             _A     =							model_class(a_							)
             _A     =							inspect.signature(model.forward							)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             _A     =							[*signature.parameters.keys()]

             _A     =							["pixel_values"]
             self.assertListEqual(arg_names[:1]				,     a_							)




     def        lowerCAmelCase__		(    self							):
         def check_hidden_states_output(snake_case_				,     snake_case_				,     snake_case_							):
             _A     =							model_class(a_							)
             model.to(a_							)
             model.eval()

             with torch.no_grad():
                 _A     =							model(**self._prepare_for_class(a_				,     a_							)							)

             _A     =							outputs.hidden_states

             _A     =							len(self.model_tester.depths							) + 1
             self.assertEqual(len(a_							)				,     a_							)

             _A     =							(self.model_tester.image_size, self.model_tester.image_size)
             _A     =							image_size[0], image_size[1]
             for _ in range(4							):
                 _A     =							floor(
                     (
                         (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                         / self.model_tester.stride
                     )
                     + 1							)
                 _A     =							floor(
                     (
                         (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                         / self.model_tester.stride
                     )
                     + 1							)
             # verify the first hidden states (first block)
             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]							)				,     [
                     height * width,
                     self.model_tester.hidden_sizes[0],
                 ]				,     )

         _A     =							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             _A     =							True
             check_hidden_states_output(a_				,     a_				,     a_							)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             _A     =							True

             check_hidden_states_output(a_				,     a_				,     a_							)




     @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
     def        lowerCAmelCase__		(    self							):
         pass




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_=False							):
         _A     =							super()._prepare_for_class(a_				,     a_				,     return_labels=a_							)

         if return_labels:
             if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                 del inputs_dict["labels"]

         return inputs_dict




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*a_							)




     def        lowerCAmelCase__		(    self							):
         if not self.model_tester.is_training:
             return

         _A     =							self.model_tester.prepare_config_and_inputs_for_common()
         _A     =							True

         for model_class in self.all_model_classes:
             # LevitForImageClassificationWithTeacher supports inference-only
             if (
                 model_class in get_values(a_							)
                 or model_class.__name__ == "LevitForImageClassificationWithTeacher"
             ):
                 continue
             _A     =							model_class(a_							)
             model.to(a_							)
             model.train()
             _A     =							self._prepare_for_class(a_				,     a_				,     return_labels=a_							)
             _A     =							model(**a_							).loss
             loss.backward()




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
             return

         _A     =							False
         _A     =							True

         for model_class in self.all_model_classes:
             if model_class in get_values(a_							) or not model_class.supports_gradient_checkpointing:
                 continue
             # LevitForImageClassificationWithTeacher supports inference-only
             if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                 continue
             _A     =							model_class(a_							)
             model.gradient_checkpointing_enable()
             model.to(a_							)
             model.train()
             _A     =							self._prepare_for_class(a_				,     a_				,     return_labels=a_							)
             _A     =							model(**a_							).loss
             loss.backward()




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs_for_common()

         _A     =							[
             {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
             {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
             {"title": "regression", "num_labels": 1, "dtype": torch.float},
         ]

         for model_class in self.all_model_classes:
             if (
                 model_class
                 not in [
                     *get_values(a_							),
                 ]
                 or model_class.__name__ == "LevitForImageClassificationWithTeacher"
             ):
                 continue

             for problem_type in problem_types:
                 with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"							):
                     _A     =							problem_type["title"]
                     _A     =							problem_type["num_labels"]

                     _A     =							model_class(a_							)
                     model.to(a_							)
                     model.train()

                     _A     =							self._prepare_for_class(a_				,     a_				,     return_labels=a_							)

                     if problem_type["num_labels"] > 1:
                         _A     =							inputs["labels"].unsqueeze(1							).repeat(1				,     problem_type['num_labels']							)

                     _A     =							inputs["labels"].to(problem_type['dtype']							)

                     # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                     # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                     # they have the same size." which is a symptom something in wrong for the regression problem.
                     # See https://github.com/huggingface/transformers/issues/11780
                     with warnings.catch_warnings(record=a_							) as warning_list:
                         _A     =							model(**a_							).loss
                     for w in warning_list:
                         if "Using a target size that is different to the input size" in str(w.message							):
                             raise ValueError(
                                 F"Something is going wrong in the regression problem: intercepted {w.message}"							)

                     loss.backward()




     @slow
     def        lowerCAmelCase__		(    self							):
         for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A     =							LevitModel.from_pretrained(a_							)
             self.assertIsNotNone(a_							)





def 				__lowerCAmelCase(       )							-> List[Any]:




    """simple docstring"""




    _A     =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
    return image







@require_torch
@require_vision
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @cached_property
     def        lowerCAmelCase__		(    self							):
         return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)




     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
             a_							)

         _A     =							self.default_image_processor
         _A     =							prepare_img()
         _A     =							image_processor(images=a_				,     return_tensors='pt'							).to(a_							)

         # forward pass
         with torch.no_grad():
             _A     =							model(**a_							)

         # verify the logits
         _A     =							torch.Size((1, 1000)							)
         self.assertEqual(outputs.logits.shape				,     a_							)

         _A     =							torch.tensor([1.0448, -0.3745, -1.8317]							).to(a_							)

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









'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .multicontrolnet import MultiControlNetModel
					from .pipeline_controlnet import StableDiffusionControlNetPipeline
					from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
					from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
					from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


