"""simple docstring"""
import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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 ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
					from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image



class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=13		,    lowerCAmelCase=64		,    lowerCAmelCase=2		,    lowerCAmelCase=3		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=32		,    lowerCAmelCase=5		,    lowerCAmelCase=4		,    lowerCAmelCase=37		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=10		,    lowerCAmelCase=0.02		,    lowerCAmelCase=[1, 16, 4, 4]		,    lowerCAmelCase=None		,    ):






										"""simple docstring"""

										snake_case								=			parent
										snake_case								=			batch_size
										snake_case								=			image_size
										snake_case								=			patch_size
										snake_case								=			num_channels
										snake_case								=			is_training
										snake_case								=			use_labels
										snake_case								=			hidden_size
										snake_case								=			num_hidden_layers
										snake_case								=			num_attention_heads
										snake_case								=			intermediate_size
										snake_case								=			hidden_act
										snake_case								=			hidden_dropout_prob
										snake_case								=			attention_probs_dropout_prob
										snake_case								=			type_sequence_label_size
										snake_case								=			initializer_range
										snake_case								=			scope
										snake_case								=			backbone_featmap_shape

										# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										# the number of patches is based on the feature map of the backbone, which by default uses an output stride
										# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
										snake_case								=			(self.image_size // 32) ** 2
										snake_case								=			num_patches + 1

				def 		snake_case (				self    ):






										"""simple docstring"""

										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 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			{
										    'global_padding': 'same',
										    'layer_type': 'bottleneck',
										    'depths': [3, 4, 9],
										    'out_features': ['stage1', 'stage2', 'stage3'],
										    'embedding_dynamic_padding': True,
										    'hidden_sizes': [4, 8, 16, 32],
										    'num_groups': 2,
										}

										return ViTHybridConfig(
										    image_size=self.image_size		,    patch_size=self.patch_size		,    num_channels=self.num_channels		,    hidden_size=self.hidden_size		,    num_hidden_layers=self.num_hidden_layers		,    num_attention_heads=self.num_attention_heads		,    intermediate_size=self.intermediate_size		,    hidden_act=self.hidden_act		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    is_decoder=lowerCAmelCase		,    initializer_range=self.initializer_range		,    backbone_featmap_shape=self.backbone_featmap_shape		,    backbone_config=lowerCAmelCase		,    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			ViTHybridModel(config=lowerCAmelCase    )
										model.to(lowerCAmelCase    )
										model.eval()
										snake_case								=			model(lowerCAmelCase    )
										self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.type_sequence_label_size
										snake_case								=			ViTHybridForImageClassification(lowerCAmelCase    )
										model.to(lowerCAmelCase    )
										model.eval()
										snake_case								=			model(lowerCAmelCase		,    labels=lowerCAmelCase    )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										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   lowerCAmelCase_    (			lowerCAmelCase     ,    lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Optional[Any]			  =						(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
				_lowerCAmelCase     :    Optional[Any]			  =						(
				    {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
				    if is_torch_available()
				    else {}
				)
				_lowerCAmelCase     :    List[Any]			  =						False
				_lowerCAmelCase     :    str			  =						False
				_lowerCAmelCase     :    Optional[Any]			  =						False

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			ViTHybridModelTester(self    )
										snake_case								=			ConfigTester(self		,    config_class=lowerCAmelCase		,    has_text_modality=lowerCAmelCase		,    hidden_size=37    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				@unittest.skip(reason='ViT does not use inputs_embeds'    )
				def 		snake_case (				self    ):






										"""simple docstring"""

										pass

				def 		snake_case (				self    ):






										"""simple docstring"""

										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(lowerCAmelCase    )
																self.assertIsInstance(model.get_input_embeddings()		,    (nn.Module)    )
																snake_case								=			model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(lowerCAmelCase		,    nn.Linear    )    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										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(lowerCAmelCase    )
																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]		,    lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case					,snake_case								=			self.model_tester.prepare_config_and_inputs_for_common()

										snake_case								=			_config_zero_init(lowerCAmelCase    )
										for model_class in self.all_model_classes:
																snake_case								=			model_class(config=lowerCAmelCase    )
																# Skip the check for the backbone
																for name, module in model.named_modules():
																						if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																												snake_case								=			[F"""{name}.{key}""" for key in module.state_dict().keys()]
																												break

																for name, param in model.named_parameters():
																						if param.requires_grad:
																												if name in backbone_params:
																																		continue
																												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"""		,    )

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case								=			ViTHybridModel.from_pretrained(lowerCAmelCase    )
																self.assertIsNotNone(lowerCAmelCase    )





def    lowerCAmelCase__    (			)					->				str:





						"""simple docstring"""






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



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



				"""simple docstring"""

				@cached_property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return (
										    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
										    if is_vision_available()
										    else None
										)

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(
										    lowerCAmelCase    )

										snake_case								=			self.default_image_processor
										snake_case								=			prepare_img()
										snake_case								=			image_processor(images=lowerCAmelCase		,    return_tensors='pt'    ).to(lowerCAmelCase    )

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

										# verify the logits
										snake_case								=			torch.Size((1, 10_00)    )
										self.assertEqual(outputs.logits.shape		,    lowerCAmelCase    )

										snake_case								=			torch.tensor([-1.90_90, -0.49_93, -0.23_89]    ).to(lowerCAmelCase    )

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

				@slow
				@require_accelerate
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'    )
										snake_case								=			ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'		,    device_map='auto'    )

										snake_case								=			prepare_img()

										snake_case								=			image_processor(images=lowerCAmelCase		,    return_tensors='pt'    )
										snake_case								=			model(**lowerCAmelCase    )
										snake_case								=			outputs.logits
										# model predicts one of the 1000 ImageNet classes
										snake_case								=			logits.argmax(-1    ).item()

										self.assertTrue(model.config.idalabel[predicted_class_idx]		,    'tabby, tabby cat'    )



"""simple docstring"""
from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = False		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			path_or_paths
										snake_case								=			split if split or isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else 'train'
										snake_case								=			features
										snake_case								=			cache_dir
										snake_case								=			keep_in_memory
										snake_case								=			streaming
										snake_case								=			num_proc
										snake_case								=			kwargs

				@abstractmethod
				def 		snake_case (				self    ):






										"""simple docstring"""

										pass





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = False		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			features
										snake_case								=			cache_dir
										snake_case								=			keep_in_memory
										snake_case								=			streaming
										snake_case								=			num_proc
										snake_case								=			kwargs

				@abstractmethod
				def 		snake_case (				self    ):






										"""simple docstring"""

										pass


