

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							:




				def __init__(   self ,  snake_case ,  snake_case=13 ,  snake_case=30 ,  snake_case=2 ,  snake_case=3 ,  snake_case=True ,  snake_case=True ,  snake_case=32 ,  snake_case=5 ,  snake_case=4 ,  snake_case=37 ,  snake_case="gelu" ,  snake_case=0.1 ,  snake_case=0.1 ,  snake_case=10 ,  snake_case=0.02 ,  snake_case=None ,  ):
											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

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




				def        a				(   self			):
											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        a				(   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        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       ViTMSNModel(config=snake_case			)
											model.to(snake_case			)
											model.eval()
											snake_case_     							=       model(snake_case			)
											self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size)			)




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       self.type_sequence_label_size
											snake_case_     							=       ViTMSNForImageClassification(snake_case			)
											model.to(snake_case			)
											model.eval()
											snake_case_     							=       model(snake_case ,  labels=snake_case			)
											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_     							=       1
											snake_case_     							=       ViTMSNForImageClassification(snake_case			)
											model.to(snake_case			)
											model.eval()

											snake_case_     							=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
											snake_case_     							=       model(snake_case			)
											self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.type_sequence_label_size)			)




				def        a				(   self			):
											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 					lowercase							(			lowercase_      ,    lowercase_      ,    unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     (
				    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     False
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     False
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     False
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     False




				def        a				(   self			):
											snake_case_     							=       ViTMSNModelTester(self			)
											snake_case_     							=       ConfigTester(self ,  config_class=snake_case ,  has_text_modality=snake_case ,  hidden_size=37			)




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




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




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




				def        a				(   self			):
											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(snake_case			)
																		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] ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case			)




				def        a				(   self			):
											snake_case_     							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*snake_case			)




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





def    __lowerCamelCase      (					):







							'''simple docstring'''






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







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




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




				@slow
				def        a				(   self			):
											torch.manual_seed(2			)
											snake_case_     							=       ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'			).to(snake_case			)

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

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

											# verify the logits
											snake_case_     							=       torch.Size((1, 1000)			)
											self.assertEqual(outputs.logits.shape ,  snake_case			)

											snake_case_     							=       torch.tensor([-0.08_03, -0.44_54, -0.23_75]			).to(snake_case			)

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







import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_UpperCAmelCase						:  Dict									=			"""sshleifer/bart-tiny-random"""
_UpperCAmelCase						:  Optional[Any]									=			"""patrickvonplaten/t5-tiny-random"""







@require_torch
class 					lowercase							(			unittest.TestCase     ):




				@cached_property
				def        a				(   self			):
											return AutoConfig.from_pretrained(snake_case			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=1			)
											self.assertEqual(student.config.num_hidden_layers ,  1			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=snake_case			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=snake_case			)
											self.assertEqual(student.config.encoder_layers ,  1			)
											self.assertEqual(student.config.decoder_layers ,  self.teacher_config.encoder_layers			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=1			)
											self.assertEqual(student.config.encoder_layers ,  1			)
											self.assertEqual(student.config.decoder_layers ,  1			)




				def        a				(   self			):
											with self.assertRaises(snake_case			):
																		create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=snake_case ,  d=snake_case			)




