




"""simple docstring"""

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a							      =     {
    '''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a							      =     ['''VivitImageProcessor''']


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a							      =     [
           '''VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''VivitModel''',
           '''VivitPreTrainedModel''',
           '''VivitForVideoClassification''',
       ]


if TYPE_CHECKING:
       from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vivit import VivitImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vivit import (
                  VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VivitForVideoClassification,
                  VivitModel,
                  VivitPreTrainedModel,
              )


else:
       import sys

       a							      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



"""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        _UpperCAmelCase:



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






							'''simple docstring'''
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    batch_size
							_UpperCamelCase									=    image_size
							_UpperCamelCase									=    patch_size
							_UpperCamelCase									=    num_channels
							_UpperCamelCase									=    is_training
							_UpperCamelCase									=    use_labels
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    num_attention_heads
							_UpperCamelCase									=    intermediate_size
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    hidden_dropout_prob
							_UpperCamelCase									=    attention_probs_dropout_prob
							_UpperCamelCase									=    type_sequence_label_size
							_UpperCamelCase									=    initializer_range
							_UpperCamelCase									=    scope
							_UpperCamelCase									=    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
							_UpperCamelCase									=    (self.image_size // 32) ** 2
							_UpperCamelCase									=    num_patches + 1



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

							_UpperCamelCase									=    self.get_config()

							return config, pixel_values, labels



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''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=__a							,						initializer_range=self.initializer_range							,						backbone_featmap_shape=self.backbone_featmap_shape							,						backbone_config=__a							,						)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.type_sequence_label_size
							_UpperCamelCase									=    ViTHybridForImageClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						labels=__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.type_sequence_label_size))




		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.prepare_config_and_inputs()
							_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    config_and_inputs
							_UpperCamelCase									=    {'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
		lowercase__         = (
		    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
		    if is_torch_available()
		    else {}
		)
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						has_text_modality=__a							,						hidden_size=37)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		@unittest.skip(reason='''ViT does not use inputs_embeds''')
		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							pass



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(__a)
												self.assertIsInstance(model.get_input_embeddings()							,						(nn.Module))
												_UpperCamelCase									=    model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__a							,						nn.Linear))



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

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

												_UpperCamelCase									=    ['''pixel_values''']
												self.assertListEqual(arg_names[:1]							,						__a)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCamelCase									=    _config_zero_init(__a)
							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(config=__a)
												# Skip the check for the backbone
												for name, module in model.named_modules():
																	if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																						_UpperCamelCase									=    [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 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase									=    ViTHybridModel.from_pretrained(__a)
												self.assertIsNotNone(__a)





def  lowerCamelCase__						(     )				->						Tuple:


					"""simple docstring"""





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






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



		@cached_property
		def 		UpperCAmelCase				(   self)			->			Tuple:






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



		@slow
		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
							    __a)

							_UpperCamelCase									=    self.default_image_processor
							_UpperCamelCase									=    prepare_img()
							_UpperCamelCase									=    image_processor(images=__a							,						return_tensors='''pt''').to(__a)

							# forward pass
							with torch.no_grad():
												_UpperCamelCase									=    model(**__a)

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

							_UpperCamelCase									=    torch.tensor([-1.9090, -0.4993, -0.2389]).to(__a)

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




		@slow
		@require_accelerate
		def 		UpperCAmelCase				(   self)			->			Any:






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

							_UpperCamelCase									=    prepare_img()

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

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






