


import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
 from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class   A__					:







    """simple docstring"""

    def __init__(       self			,					__snake_case			,					__snake_case=1_3			,					__snake_case=3_2			,					__snake_case=2			,					__snake_case=3			,					__snake_case=1_6			,					__snake_case=[1, 2, 1]			,					__snake_case=[2, 2, 4]			,					__snake_case=2			,					__snake_case=2.0			,					__snake_case=True			,					__snake_case=0.0			,					__snake_case=0.0			,					__snake_case=0.1			,					__snake_case="gelu"			,					__snake_case=False			,					__snake_case=True			,					__snake_case=0.02			,					__snake_case=1E-5			,					__snake_case=True			,					__snake_case=None			,					__snake_case=True			,					__snake_case=1_0			,					__snake_case=8			,					):
         snake_case		 =				parent
         snake_case		 =				batch_size
         snake_case		 =				image_size
         snake_case		 =				patch_size
         snake_case		 =				num_channels
         snake_case		 =				embed_dim
         snake_case		 =				depths
         snake_case		 =				num_heads
         snake_case		 =				window_size
         snake_case		 =				mlp_ratio
         snake_case		 =				qkv_bias
         snake_case		 =				hidden_dropout_prob
         snake_case		 =				attention_probs_dropout_prob
         snake_case		 =				drop_path_rate
         snake_case		 =				hidden_act
         snake_case		 =				use_absolute_embeddings
         snake_case		 =				patch_norm
         snake_case		 =				layer_norm_eps
         snake_case		 =				initializer_range
         snake_case		 =				is_training
         snake_case		 =				scope
         snake_case		 =				use_labels
         snake_case		 =				type_sequence_label_size
         snake_case		 =				encoder_stride
    def 		a_			(       self    ):
         snake_case		 =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

         snake_case		 =				None
         if self.use_labels:
              snake_case		 =				ids_tensor([self.batch_size]			,					self.type_sequence_label_size    )

         snake_case		 =				self.get_config()

         return config, pixel_values, labels
    def 		a_			(       self    ):
         return SwinvaConfig(
             image_size=self.image_size			,					patch_size=self.patch_size			,					num_channels=self.num_channels			,					embed_dim=self.embed_dim			,					depths=self.depths			,					num_heads=self.num_heads			,					window_size=self.window_size			,					mlp_ratio=self.mlp_ratio			,					qkv_bias=self.qkv_bias			,					hidden_dropout_prob=self.hidden_dropout_prob			,					attention_probs_dropout_prob=self.attention_probs_dropout_prob			,					drop_path_rate=self.drop_path_rate			,					hidden_act=self.hidden_act			,					use_absolute_embeddings=self.use_absolute_embeddings			,					path_norm=self.patch_norm			,					layer_norm_eps=self.layer_norm_eps			,					initializer_range=self.initializer_range			,					encoder_stride=self.encoder_stride			,					)
    def 		a_			(       self			,					__snake_case			,					__snake_case			,					__snake_case    ):
         snake_case		 =				SwinvaModel(config=__UpperCamelCase    )
         model.to(__UpperCamelCase    )
         model.eval()
         snake_case		 =				model(__UpperCamelCase    )

         snake_case		 =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
         snake_case		 =				int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

         self.parent.assertEqual(result.last_hidden_state.shape			,					(self.batch_size, expected_seq_len, expected_dim)    )
    def 		a_			(       self			,					__snake_case			,					__snake_case			,					__snake_case    ):
         snake_case		 =				SwinvaForMaskedImageModeling(config=__UpperCamelCase    )
         model.to(__UpperCamelCase    )
         model.eval()
         snake_case		 =				model(__UpperCamelCase    )
         self.parent.assertEqual(
             result.logits.shape			,					(self.batch_size, self.num_channels, self.image_size, self.image_size)    )

         # test greyscale images
         snake_case		 =				1
         snake_case		 =				SwinvaForMaskedImageModeling(__UpperCamelCase    )
         model.to(__UpperCamelCase    )
         model.eval()

         snake_case		 =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
         snake_case		 =				model(__UpperCamelCase    )
         self.parent.assertEqual(result.logits.shape			,					(self.batch_size, 1, self.image_size, self.image_size)    )
    def 		a_			(       self			,					__snake_case			,					__snake_case			,					__snake_case    ):
         snake_case		 =				self.type_sequence_label_size
         snake_case		 =				SwinvaForImageClassification(__UpperCamelCase    )
         model.to(__UpperCamelCase    )
         model.eval()
         snake_case		 =				model(__UpperCamelCase			,					labels=__UpperCamelCase    )
         self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.type_sequence_label_size)    )




    def 		a_			(       self    ):
         snake_case		 =				self.prepare_config_and_inputs()
         snake_case ,  snake_case ,  snake_case		 =				config_and_inputs
         snake_case		 =				{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
class   A__					(    lowercase_       ,					lowercase_       ,					unittest.TestCase	):







    """simple docstring"""

    __magic_name__			  =		(
        (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
    )
    __magic_name__			  =		(
        {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
        if is_torch_available()
        else {}
    )

    __magic_name__			  =		False
    __magic_name__			  =		False
    __magic_name__			  =		False
    __magic_name__			  =		False
    def 		a_			(       self    ):
         snake_case		 =				SwinvaModelTester(self    )
         snake_case		 =				ConfigTester(self			,					config_class=__UpperCamelCase			,					embed_dim=3_7    )
    def 		a_			(       self    ):
         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 		a_			(       self    ):
         snake_case		 =				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__UpperCamelCase    )
    @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''    )
    def 		a_			(       self    ):
         pass
    @unittest.skip(reason='''Swinv2 does not use inputs_embeds'''    )
    def 		a_			(       self    ):
         pass
    def 		a_			(       self    ):
         snake_case ,  snake_case		 =				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case		 =				model_class(__UpperCamelCase    )
              self.assertIsInstance(model.get_input_embeddings()			,					(nn.Module)    )
              snake_case		 =				model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(__UpperCamelCase			,					nn.Linear    )    )
    def 		a_			(       self    ):
         snake_case ,  snake_case		 =				self.model_tester.prepare_config_and_inputs_for_common()

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

              snake_case		 =				['''pixel_values''']
              self.assertListEqual(arg_names[:1]			,					__UpperCamelCase    )
    def 		a_			(       self    ):
         snake_case ,  snake_case		 =				self.model_tester.prepare_config_and_inputs_for_common()
         snake_case		 =				True

         for model_class in self.all_model_classes:
              snake_case		 =				True
              snake_case		 =				False
              snake_case		 =				True
              snake_case		 =				model_class(__UpperCamelCase    )
              model.to(__UpperCamelCase    )
              model.eval()
              with torch.no_grad():
                   snake_case		 =				model(**self._prepare_for_class(__UpperCamelCase			,					__UpperCamelCase    )    )
              snake_case		 =				outputs.attentions
              snake_case		 =				len(self.model_tester.depths    )
              self.assertEqual(len(__UpperCamelCase    )			,					__UpperCamelCase    )

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              snake_case		 =				True
              snake_case		 =				config.window_size**2
              snake_case		 =				model_class(__UpperCamelCase    )
              model.to(__UpperCamelCase    )
              model.eval()
              with torch.no_grad():
                   snake_case		 =				model(**self._prepare_for_class(__UpperCamelCase			,					__UpperCamelCase    )    )
              snake_case		 =				outputs.attentions
              self.assertEqual(len(__UpperCamelCase    )			,					__UpperCamelCase    )

              self.assertListEqual(
                  list(attentions[0].shape[-3:]    )			,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,					)
              snake_case		 =				len(__UpperCamelCase    )

              # Check attention is always last and order is fine
              snake_case		 =				True
              snake_case		 =				True
              snake_case		 =				model_class(__UpperCamelCase    )
              model.to(__UpperCamelCase    )
              model.eval()
              with torch.no_grad():
                   snake_case		 =				model(**self._prepare_for_class(__UpperCamelCase			,					__UpperCamelCase    )    )

              if hasattr(self.model_tester			,					'''num_hidden_states_types'''    ):
                   snake_case		 =				self.model_tester.num_hidden_states_types
              else:
                   # also another +1 for reshaped_hidden_states
                   snake_case		 =				2
              self.assertEqual(out_len + added_hidden_states			,					len(__UpperCamelCase    )    )

              snake_case		 =				outputs.attentions

              self.assertEqual(len(__UpperCamelCase    )			,					__UpperCamelCase    )

              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]    )			,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,					)
    def 		a_			(       self			,					__snake_case			,					__snake_case			,					__snake_case			,					__snake_case    ):
         snake_case		 =				model_class(__UpperCamelCase    )
         model.to(__UpperCamelCase    )
         model.eval()

         with torch.no_grad():
              snake_case		 =				model(**self._prepare_for_class(__UpperCamelCase			,					__UpperCamelCase    )    )

         snake_case		 =				outputs.hidden_states

         snake_case		 =				getattr(
             self.model_tester			,					'''expected_num_hidden_layers'''			,					len(self.model_tester.depths    ) + 1    )
         self.assertEqual(len(__UpperCamelCase    )			,					__UpperCamelCase    )

         # Swinv2 has a different seq_length
         snake_case		 =				(
             config.patch_size
             if isinstance(config.patch_size			,					collections.abc.Iterable    )
             else (config.patch_size, config.patch_size)
         )

         snake_case		 =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]    )			,					[num_patches, self.model_tester.embed_dim]			,					)

         snake_case		 =				outputs.reshaped_hidden_states
         self.assertEqual(len(__UpperCamelCase    )			,					__UpperCamelCase    )

         snake_case ,  snake_case ,  snake_case ,  snake_case		 =				reshaped_hidden_states[0].shape
         snake_case		 =				(
             reshaped_hidden_states[0].view(__UpperCamelCase			,					__UpperCamelCase			,					height * width    ).permute(0			,					2			,					1    )
         )
         self.assertListEqual(
             list(reshaped_hidden_states.shape[-2:]    )			,					[num_patches, self.model_tester.embed_dim]			,					)
    def 		a_			(       self    ):
         snake_case ,  snake_case		 =				self.model_tester.prepare_config_and_inputs_for_common()

         snake_case		 =				(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size			,					collections.abc.Iterable    )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes:
              snake_case		 =				True
              self.check_hidden_states_output(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase    )

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

              self.check_hidden_states_output(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase    )
    def 		a_			(       self    ):
         snake_case ,  snake_case		 =				self.model_tester.prepare_config_and_inputs_for_common()
         snake_case		 =				3

         snake_case		 =				(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size			,					collections.abc.Iterable    )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )
         snake_case		 =				(
             config.patch_size
             if isinstance(config.patch_size			,					collections.abc.Iterable    )
             else (config.patch_size, config.patch_size)
         )

         snake_case		 =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         snake_case		 =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
              snake_case		 =				True
              self.check_hidden_states_output(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					(padded_height, padded_width)    )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              snake_case		 =				True
              self.check_hidden_states_output(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					(padded_height, padded_width)    )
    def 		a_			(       self    ):
         snake_case		 =				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase    )
    def 		a_			(       self    ):
         snake_case		 =				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase    )
    @slow
    def 		a_			(       self    ):
         for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case		 =				SwinvaModel.from_pretrained(__UpperCamelCase    )
              self.assertIsNotNone(__UpperCamelCase    )




    def 		a_			(       self    ):
         snake_case ,  snake_case		 =				self.model_tester.prepare_config_and_inputs_for_common()

         snake_case		 =				_config_zero_init(__UpperCamelCase    )
         for model_class in self.all_model_classes:
              snake_case		 =				model_class(config=__UpperCamelCase    )
              for name, param in model.named_parameters():
                   if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                        self.assertIn(
                            ((param.data.mean() * 1E9).round() / 1E9).item()			,					[0.0, 1.0]			,					msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''			,					)






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







    """simple docstring"""

    @cached_property
    def 		a_			(       self    ):
         return (
             AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''    )
             if is_vision_available()
             else None
         )




    @slow
    def 		a_			(       self    ):
         snake_case		 =				SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''    ).to(
             __UpperCamelCase    )
         snake_case		 =				self.default_image_processor

         snake_case		 =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
         snake_case		 =				image_processor(images=__UpperCamelCase			,					return_tensors='''pt'''    ).to(__UpperCamelCase    )

         # forward pass
         with torch.no_grad():
              snake_case		 =				model(**__UpperCamelCase    )

         # verify the logits
         snake_case		 =				torch.Size((1, 1_0_0_0)    )
         self.assertEqual(outputs.logits.shape			,					__UpperCamelCase    )
         snake_case		 =				torch.tensor([-0.3947, -0.4306, 0.0026]    ).to(__UpperCamelCase    )
         self.assertTrue(torch.allclose(outputs.logits[0, :3]			,					__UpperCamelCase			,					atol=1E-4    )    )











"""simple docstring"""
import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:






		class 					_UpperCAmelCase   :


							@staticmethod
							def 	lowerCamelCase       (					*__UpperCamelCase			:List[Any]     ,       **__UpperCamelCase			:List[Any]		):
														pass






def 					A__				(			UpperCamelCase       ):
							A		=  hashlib.mda(image.tobytes()       )
							return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					_UpperCAmelCase   (	unittest.TestCase ):
					UpperCamelCase      =							MODEL_FOR_DEPTH_ESTIMATION_MAPPING


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Optional[int]		):
												A		=  DepthEstimationPipeline(model=__UpperCamelCase     ,       image_processor=__UpperCamelCase		)
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
												self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}     ,       __UpperCamelCase		)
												import datasets

												A		=  datasets.load_dataset("hf-internal-testing/fixtures_image_utils"     ,       "image"     ,       split="test"		)
												A		=  depth_estimator(
												    [
												        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(
												    [
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												    ]     ,       __UpperCamelCase     ,       )


					@require_tf
					@unittest.skip("Depth estimation is not implemented in TF"		)
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												pass


					@slow
					@require_torch
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  "Intel/dpt-large"
												A		=  pipeline("depth-estimation"     ,       model=__UpperCamelCase		)
												A		=  depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
												A		=  hashimage(outputs["depth"]		)

												# This seems flaky.
												# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
												self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()		)     ,       29.304		)
												self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()		)     ,       2.662		)


					@require_torch
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												# This is highly irregular to have no small tests.
												self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)


