



"""simple docstring"""

import inspect
import unittest

from transformers import ViTMSNConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import ViTMSNForImageClassification, ViTMSNModel
			from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__=13   ,		lowercase__=30   ,		lowercase__=2   ,		lowercase__=3   ,		lowercase__=True   ,		lowercase__=True   ,		lowercase__=32   ,		lowercase__=5   ,		lowercase__=4   ,		lowercase__=37   ,		lowercase__="gelu"   ,		lowercase__=0.1   ,		lowercase__=0.1   ,		lowercase__=10   ,		lowercase__=0.02   ,		lowercase__=None   ,		):
											snake_case_					:		Dict 					=					parent
											snake_case_					:		List[Any] 					=					batch_size
											snake_case_					:		str 					=					image_size
											snake_case_					:		Any 					=					patch_size
											snake_case_					:		str 					=					num_channels
											snake_case_					:		Optional[Any] 					=					is_training
											snake_case_					:		Union[str, Any] 					=					use_labels
											snake_case_					:		Optional[int] 					=					hidden_size
											snake_case_					:		Optional[int] 					=					num_hidden_layers
											snake_case_					:		int 					=					num_attention_heads
											snake_case_					:		List[Any] 					=					intermediate_size
											snake_case_					:		Dict 					=					hidden_act
											snake_case_					:		List[Any] 					=					hidden_dropout_prob
											snake_case_					:		Optional[int] 					=					attention_probs_dropout_prob
											snake_case_					:		List[Any] 					=					type_sequence_label_size
											snake_case_					:		List[Any] 					=					initializer_range
											snake_case_					:		Tuple 					=					scope

											# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
											snake_case_					:		Dict 					=					(image_size // patch_size) ** 2
											snake_case_					:		List[Any] 					=					num_patches + 1







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

											snake_case_					:		str 					=					None
											if self.use_labels:
																snake_case_					:		Optional[int] 					=					ids_tensor([self.batch_size]   ,		self.type_sequence_label_size       )

											snake_case_					:		Any 					=					self.get_config()

											return config, pixel_values, labels







						def 						__UpperCamelCase							(self       ):
											return ViTMSNConfig(
											    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   ,		initializer_range=self.initializer_range   ,		)







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Optional[int] 					=					ViTMSNModel(config=A__       )
											model.to(A__       )
											model.eval()
											snake_case_					:		int 					=					model(A__       )
											self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, self.seq_length, self.hidden_size)       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		str 					=					self.type_sequence_label_size
											snake_case_					:		Optional[int] 					=					ViTMSNForImageClassification(A__       )
											model.to(A__       )
											model.eval()
											snake_case_					:		List[Any] 					=					model(A__   ,		labels=A__       )
											print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""       )
											print("""Labels: {labels}"""       )
											self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.type_sequence_label_size)       )

											# test greyscale images
											snake_case_					:		Optional[int] 					=					1
											snake_case_					:		Union[str, Any] 					=					ViTMSNForImageClassification(A__       )
											model.to(A__       )
											model.eval()

											snake_case_					:		Union[str, Any] 					=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
											snake_case_					:		str 					=					model(A__       )
											self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.type_sequence_label_size)       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		int 					=					self.prepare_config_and_inputs()
											snake_case_					,			snake_case_					,			snake_case_					:		Any 					=					config_and_inputs
											snake_case_					:		Optional[int] 					=					{"""pixel_values""": pixel_values}
											return config, inputs_dict







@require_torch
class    __lowercase     (					_lowerCamelCase					,    _lowerCamelCase					,    unittest.TestCase):







						"""simple docstring"""







						_A		:							int							       =		(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
						_A		:							Union[str, Any]							       =		(
						    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_A		:							List[Any]							       =		False
						_A		:							List[str]							       =		False
						_A		:							Optional[Any]							       =		False
						_A		:							int							       =		False







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					ViTMSNModelTester(self       )
											snake_case_					:		int 					=					ConfigTester(self   ,		config_class=A__   ,		has_text_modality=A__   ,		hidden_size=37       )







						def 						__UpperCamelCase							(self       ):
											self.config_tester.run_common_tests()







						@unittest.skip(reason="""ViTMSN does not use inputs_embeds"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					:		Any 					=					self.model_tester.prepare_config_and_inputs_for_common()

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					:		List[str] 					=					self.model_tester.prepare_config_and_inputs_for_common()

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

																snake_case_					:		Dict 					=					["""pixel_values"""]
																self.assertListEqual(arg_names[:1]   ,		A__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Union[str, Any] 					=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*A__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*A__       )







						@slow
						def 						__UpperCamelCase							(self       ):
											for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case_					:		Any 					=					ViTMSNModel.from_pretrained(A__       )
																self.assertIsNotNone(A__       )







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
					return image







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







						"""simple docstring"""







						@cached_property
						def 						__UpperCamelCase							(self       ):
											return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""       ) if is_vision_available() else None







						@slow
						def 						__UpperCamelCase							(self       ):
											torch.manual_seed(2       )
											snake_case_					:		Optional[int] 					=					ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""       ).to(A__       )

											snake_case_					:		List[Any] 					=					self.default_image_processor
											snake_case_					:		Optional[int] 					=					prepare_img()
											snake_case_					:		Optional[int] 					=					image_processor(images=A__   ,		return_tensors="""pt"""       ).to(A__       )

											# forward pass
											with torch.no_grad():
																snake_case_					:		Any 					=					model(**A__       )

											# verify the logits
											snake_case_					:		Dict 					=					torch.Size((1, 10_00)       )
											self.assertEqual(outputs.logits.shape   ,		A__       )

											snake_case_					:		Union[str, Any] 					=					torch.tensor([-0.0803, -0.4454, -0.2375]       ).to(A__       )

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








"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_			   =					logging.get_logger(__name__)

a_			   =					{
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		"""dpr"""







						def __init__(self   ,		lowercase__=3_05_22   ,		lowercase__=7_68   ,		lowercase__=12   ,		lowercase__=12   ,		lowercase__=30_72   ,		lowercase__="gelu"   ,		lowercase__=0.1   ,		lowercase__=0.1   ,		lowercase__=5_12   ,		lowercase__=2   ,		lowercase__=0.02   ,		lowercase__=1e-12   ,		lowercase__=0   ,		lowercase__="absolute"   ,		lowercase__ = 0   ,		**lowercase__   ,		):
											super().__init__(pad_token_id=lowercase__   ,		**lowercase__       )

											snake_case_					:		List[Any] 					=					vocab_size
											snake_case_					:		List[str] 					=					hidden_size
											snake_case_					:		Tuple 					=					num_hidden_layers
											snake_case_					:		List[Any] 					=					num_attention_heads
											snake_case_					:		int 					=					hidden_act
											snake_case_					:		Dict 					=					intermediate_size
											snake_case_					:		int 					=					hidden_dropout_prob
											snake_case_					:		Any 					=					attention_probs_dropout_prob
											snake_case_					:		str 					=					max_position_embeddings
											snake_case_					:		List[str] 					=					type_vocab_size
											snake_case_					:		List[str] 					=					initializer_range
											snake_case_					:		Optional[int] 					=					layer_norm_eps
											snake_case_					:		Union[str, Any] 					=					projection_dim
											snake_case_					:		str 					=					position_embedding_type




