'''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 				a__:


			'''simple docstring'''






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




					"""simple docstring"""


					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

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



			def 							a_       (   self):




					"""simple docstring"""


					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 							a_       (   self):




					"""simple docstring"""


					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       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  ViTMSNModel(config=__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					lowerCAmelCase   					=  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"""


					lowerCAmelCase   					=  self.type_sequence_label_size
					lowerCAmelCase   					=  ViTMSNForImageClassification(__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					lowerCAmelCase   					=  model(__lowerCAmelCase       ,    labels=__lowerCAmelCase)
					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
					lowerCAmelCase   					=  1
					lowerCAmelCase   					=  ViTMSNForImageClassification(__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()

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







			def 							a_       (   self):




					"""simple docstring"""


					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__(					lowerCAmelCase__ ,						lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     List[Any]      =       (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
			UpperCAmelCase_			:     List[Any]      =       (
			    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
			    if is_torch_available()
			    else {}
			)

			UpperCAmelCase_			:     Optional[Any]      =       False
			UpperCAmelCase_			:     List[str]      =       False
			UpperCAmelCase_			:     Union[str, Any]      =       False
			UpperCAmelCase_			:     Optional[int]      =       False



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ViTMSNModelTester(self)
					lowerCAmelCase   					=  ConfigTester(self       ,    config_class=__lowerCAmelCase       ,    has_text_modality=__lowerCAmelCase       ,    hidden_size=37)



			def 							a_       (   self):




					"""simple docstring"""


					self.config_tester.run_common_tests()



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




					"""simple docstring"""


					pass



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase					,     lowerCAmelCase   					=  self.model_tester.prepare_config_and_inputs_for_common()

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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase					,     lowerCAmelCase   					=  self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							lowerCAmelCase   					=  model_class(__lowerCAmelCase)
							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]       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase)







			@slow
			def 							a_       (   self):




					"""simple docstring"""


					for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase   					=  ViTMSNModel.from_pretrained(__lowerCAmelCase)
							self.assertIsNotNone(__lowerCAmelCase)







def 				snake_case__							(			)    ->	int:

		'''simple docstring'''


		lowerCAmelCase   					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
		return image






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


			'''simple docstring'''






			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small""") if is_vision_available() else None







			@slow
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(2)
					lowerCAmelCase   					=  ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small""").to(__lowerCAmelCase)

					lowerCAmelCase   					=  self.default_image_processor
					lowerCAmelCase   					=  prepare_img()
					lowerCAmelCase   					=  image_processor(images=__lowerCAmelCase       ,    return_tensors="""pt""").to(__lowerCAmelCase)

					# forward pass
					with torch.no_grad():
							lowerCAmelCase   					=  model(**__lowerCAmelCase)

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

					lowerCAmelCase   					=  torch.tensor([-0.0803, -0.4454, -0.2375]).to(__lowerCAmelCase)

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





'''simple docstring'''




from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


__lowercase          =  logging.get_logger(__name__)

__lowercase          =  '''T5Config'''






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     List[str]      =       '''mt5'''
			UpperCAmelCase_			:     Tuple      =       MTaConfig







class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     List[Any]      =       '''mt5'''
			UpperCAmelCase_			:     int      =       MTaConfig







class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Tuple      =       '''mt5'''
			UpperCAmelCase_			:     Union[str, Any]      =       MTaConfig




