
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       __A:

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

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

												lowerCamelCase_							  =					self.get_config()

												return config, pixel_values, labels

					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					{
												    """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=__UpperCamelCase   ,					initializer_range=self.initializer_range   ,					backbone_featmap_shape=self.backbone_featmap_shape   ,					backbone_config=__UpperCamelCase   ,					)

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					ViTHybridModel(config=__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												lowerCamelCase_							  =					model(__UpperCamelCase   )
												self.parent.assertEqual(result.last_hidden_state.shape   ,					(self.batch_size, self.seq_length, self.hidden_size)   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Dict   ):
												lowerCamelCase_							  =					self.type_sequence_label_size
												lowerCamelCase_							  =					ViTHybridForImageClassification(__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					labels=__UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.type_sequence_label_size)   )



					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					self.prepare_config_and_inputs()
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					config_and_inputs
												lowerCamelCase_							  =					{"""pixel_values""": pixel_values}
												return config, inputs_dict





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

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					ViTHybridModelTester(self   )
												lowerCamelCase_							  =					ConfigTester(self   ,					config_class=__UpperCamelCase   ,					has_text_modality=__UpperCamelCase   ,					hidden_size=3_7   )

					def    lowercase__      (	self					:     Tuple   ):
												self.config_tester.run_common_tests()

					@unittest.skip(reason="""ViT does not use inputs_embeds"""   )
					def    lowercase__      (	self					:     Any   ):
												pass

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

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

					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

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

																			lowerCamelCase_							  =					["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase   )

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

												lowerCamelCase_							  =					_config_zero_init(__UpperCamelCase   )
												for model_class in self.all_model_classes:
																			lowerCamelCase_							  =					model_class(config=__UpperCamelCase   )
																			# Skip the check for the backbone
																			for name, module in model.named_modules():
																										if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																																	lowerCamelCase_							  =					[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    lowercase__      (	self					:     List[str]   ):
												for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_							  =					ViTHybridModel.from_pretrained(__UpperCamelCase   )
																			self.assertIsNotNone(__UpperCamelCase   )



def      __lowerCAmelCase						(							)  ->    Tuple:
							lowerCamelCase_							  =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
							return image





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

					@cached_property
					def    lowercase__      (	self					:     Dict   ):
												return (
												    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
												    if is_vision_available()
												    else None
												)

					@slow
					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(
												    __UpperCamelCase   )

												lowerCamelCase_							  =					self.default_image_processor
												lowerCamelCase_							  =					prepare_img()
												lowerCamelCase_							  =					image_processor(images=__UpperCamelCase   ,					return_tensors="""pt"""   ).to(__UpperCamelCase   )

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

												# verify the logits
												lowerCamelCase_							  =					torch.Size((1, 1_0_0_0)   )
												self.assertEqual(outputs.logits.shape   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					torch.tensor([-1.9090, -0.4993, -0.2389]   ).to(__UpperCamelCase   )

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



					@slow
					@require_accelerate
					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""   )
												lowerCamelCase_							  =					ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""   ,					device_map="""auto"""   )

												lowerCamelCase_							  =					prepare_img()

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

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







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''image_processor''', '''tokenizer''']
					SCREAMING_SNAKE_CASE			    =    '''BlipImageProcessor'''
					SCREAMING_SNAKE_CASE			    =    '''AutoTokenizer'''

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					False
												super().__init__(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					self.image_processor

					def __call__(	self					:     Any   ,					__UpperCamelCase					:     ImageInput = None   ,					__UpperCamelCase					:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Union[bool, str, PaddingStrategy] = False   ,					__UpperCamelCase					:     Union[bool, str, TruncationStrategy] = None   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     int = 0   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					**__UpperCamelCase					:     Optional[Any]   ,					):
												if images is None and text is None:
																			raise ValueError("""You have to specify either images or text."""   )

												# Get only text
												if images is None:
																			lowerCamelCase_							  =					self.tokenizer
																			lowerCamelCase_							  =					self.tokenizer(
																			    text=__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					stride=__UpperCamelCase   ,					pad_to_multiple_of=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					return_overflowing_tokens=__UpperCamelCase   ,					return_special_tokens_mask=__UpperCamelCase   ,					return_offsets_mapping=__UpperCamelCase   ,					return_token_type_ids=__UpperCamelCase   ,					return_length=__UpperCamelCase   ,					verbose=__UpperCamelCase   ,					return_tensors=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
																			return text_encoding

												# add pixel_values
												lowerCamelCase_							  =					self.image_processor(__UpperCamelCase   ,					return_tensors=__UpperCamelCase   )

												if text is not None:
																			lowerCamelCase_							  =					self.tokenizer(
																			    text=__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					stride=__UpperCamelCase   ,					pad_to_multiple_of=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					return_overflowing_tokens=__UpperCamelCase   ,					return_special_tokens_mask=__UpperCamelCase   ,					return_offsets_mapping=__UpperCamelCase   ,					return_token_type_ids=__UpperCamelCase   ,					return_length=__UpperCamelCase   ,					verbose=__UpperCamelCase   ,					return_tensors=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
												else:
																			lowerCamelCase_							  =					None

												if text_encoding is not None:
																			encoding_image_processor.update(__UpperCamelCase   )

												return encoding_image_processor

					def    lowercase__      (	self					:     Any   ,					*__UpperCamelCase					:     Union[str, Any]   ,					**__UpperCamelCase					:     List[Any]   ):
												return self.tokenizer.batch_decode(*__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					*__UpperCamelCase					:     List[Any]   ,					**__UpperCamelCase					:     int   ):
												return self.tokenizer.decode(*__UpperCamelCase   ,					**__UpperCamelCase   )



					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					self.tokenizer.model_input_names
												lowerCamelCase_							  =					self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





