






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_				:







    def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=1_3       ,					__lowerCAmelCase=6_4       ,					__lowerCAmelCase=2       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=3_2       ,					__lowerCAmelCase=5       ,					__lowerCAmelCase=4       ,					__lowerCAmelCase=3_7       ,					__lowerCAmelCase="gelu"       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=1_0       ,					__lowerCAmelCase=0.02       ,					__lowerCAmelCase=[1, 1_6, 4, 4]       ,					__lowerCAmelCase=None       ,					):
        """simple docstring"""
        __magic_name__		:str        =      parent
        __magic_name__		:List[Any]        =      batch_size
        __magic_name__		:int        =      image_size
        __magic_name__		:int        =      patch_size
        __magic_name__		:Tuple        =      num_channels
        __magic_name__		:Dict        =      is_training
        __magic_name__		:int        =      use_labels
        __magic_name__		:List[str]        =      hidden_size
        __magic_name__		:Any        =      num_hidden_layers
        __magic_name__		:Optional[int]        =      num_attention_heads
        __magic_name__		:Tuple        =      intermediate_size
        __magic_name__		:List[str]        =      hidden_act
        __magic_name__		:Union[str, Any]        =      hidden_dropout_prob
        __magic_name__		:int        =      attention_probs_dropout_prob
        __magic_name__		:Optional[Any]        =      type_sequence_label_size
        __magic_name__		:Tuple        =      initializer_range
        __magic_name__		:Dict        =      scope
        __magic_name__		:str        =      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
        __magic_name__		:List[Any]        =      (self.image_size // 3_2) ** 2
        __magic_name__		:List[str]        =      num_patches + 1







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Any        =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

        __magic_name__		:List[str]        =      self.get_config()

        return config, pixel_values, labels







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Optional[Any]        =      {
            '''global_padding''': '''same''',
            '''layer_type''': '''bottleneck''',
            '''depths''': [3, 4, 9],
            '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
            '''embedding_dynamic_padding''': True,
            '''hidden_sizes''': [4, 8, 1_6, 3_2],
            '''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 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
        """simple docstring"""
        __magic_name__		:Tuple        =      ViTHybridModel(config=lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()
        __magic_name__		:int        =      model(lowerCAmelCase_     )
        self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size)     )







    def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
        """simple docstring"""
        __magic_name__		:Dict        =      self.type_sequence_label_size
        __magic_name__		:List[str]        =      ViTHybridForImageClassification(lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()
        __magic_name__		:Optional[Any]        =      model(lowerCAmelCase_       ,					labels=lowerCAmelCase_     )
        self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.type_sequence_label_size)     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Dict        =      self.prepare_config_and_inputs()
        __magic_name__     ,				__magic_name__     ,				__magic_name__		:Optional[int]        =      config_and_inputs
        __magic_name__		:Dict        =      {'''pixel_values''': pixel_values}
        return config, inputs_dict







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







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Tuple        =      ViTHybridModelTester(self     )
        __magic_name__		:Optional[int]        =      ConfigTester(self       ,					config_class=lowerCAmelCase_       ,					has_text_modality=lowerCAmelCase_       ,					hidden_size=3_7     )







    def 			A	(						self     ):
        """simple docstring"""
        self.config_tester.run_common_tests()







    @unittest.skip(reason='''ViT does not use inputs_embeds'''     )
    def 			A	(						self     ):
        """simple docstring"""
        pass







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__     ,				__magic_name__		:str        =      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __magic_name__		:Optional[Any]        =      model_class(lowerCAmelCase_     )
            self.assertIsInstance(model.get_input_embeddings()       ,					(nn.Module)     )
            __magic_name__		:List[str]        =      model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(lowerCAmelCase_       ,					nn.Linear     )     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__     ,				__magic_name__		:int        =      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __magic_name__		:Tuple        =      model_class(lowerCAmelCase_     )
            __magic_name__		:List[Any]        =      inspect.signature(model.forward     )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __magic_name__		:Dict        =      [*signature.parameters.keys()]

            __magic_name__		:List[str]        =      ['''pixel_values''']
            self.assertListEqual(arg_names[:1]       ,					lowerCAmelCase_     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Tuple        =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Any        =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__     ,				__magic_name__		:List[Any]        =      self.model_tester.prepare_config_and_inputs_for_common()

        __magic_name__		:List[Any]        =      _config_zero_init(lowerCAmelCase_     )
        for model_class in self.all_model_classes:
            __magic_name__		:Optional[int]        =      model_class(config=lowerCAmelCase_     )
            # Skip the check for the backbone
            for name, module in model.named_modules():
                if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                    __magic_name__		:Dict        =      [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 			A	(						self     ):
        """simple docstring"""







        for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __magic_name__		:Tuple        =      ViTHybridModel.from_pretrained(lowerCAmelCase_     )
            self.assertIsNotNone(lowerCAmelCase_     )







def      __lowercase					( ):







    """simple docstring"""






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

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







    @cached_property
    def 			A	(						self     ):
        """simple docstring"""
        return (
            ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
            if is_vision_available()
            else None
        )







    @slow
    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:List[Any]        =      ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(
            lowerCAmelCase_     )

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

        # forward pass
        with torch.no_grad():
            __magic_name__		:Optional[int]        =      model(**lowerCAmelCase_     )

        # verify the logits
        __magic_name__		:Union[str, Any]        =      torch.Size((1, 1_0_0_0)     )
        self.assertEqual(outputs.logits.shape       ,					lowerCAmelCase_     )

        __magic_name__		:int        =      torch.tensor([-1.9090, -0.4993, -0.2389]     ).to(lowerCAmelCase_     )

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







    @slow
    @require_accelerate
    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Any        =      ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384'''     )
        __magic_name__		:Union[str, Any]        =      ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''       ,					device_map='''auto'''     )

        __magic_name__		:Dict        =      prepare_img()

        __magic_name__		:Optional[Any]        =      image_processor(images=lowerCAmelCase_       ,					return_tensors='''pt'''     )
        __magic_name__		:List[str]        =      model(**lowerCAmelCase_     )
        __magic_name__		:List[Any]        =      outputs.logits
        # model predicts one of the 1000 ImageNet classes
        __magic_name__		:List[Any]        =      logits.argmax(-1     ).item()

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














from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url

@pytest.mark.parametrize('''repo_id''',  ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''',  ['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.mark.parametrize('''revision''',  [None, '''v2'''] )
def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      hf_hub_url(repo_id=snake_case,  path=snake_case,  revision=snake_case )
				assert url == f'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(snake_case )}'''






