
import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    DebertaForMaskedLM,
			    DebertaForQuestionAnswering,
			    DebertaForSequenceClassification,
			    DebertaForTokenClassification,
			    DebertaModel,
			)
			from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST





class       _lowerCAmelCase   (      __a			):




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=13							,							_UpperCamelCase=7							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=99							,							_UpperCamelCase=32							,							_UpperCamelCase=5							,							_UpperCamelCase=4							,							_UpperCamelCase=37							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=512							,							_UpperCamelCase=16							,							_UpperCamelCase=2							,							_UpperCamelCase=0.02							,							_UpperCamelCase=False							,							_UpperCamelCase=True							,							_UpperCamelCase="None"							,							_UpperCamelCase=3							,							_UpperCamelCase=4							,							_UpperCamelCase=None							,							)							-> int:
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      seq_length
							lowerCAmelCase_         =      is_training
							lowerCAmelCase_         =      use_input_mask
							lowerCAmelCase_         =      use_token_type_ids
							lowerCAmelCase_         =      use_labels
							lowerCAmelCase_         =      vocab_size
							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_         =      max_position_embeddings
							lowerCAmelCase_         =      type_vocab_size
							lowerCAmelCase_         =      type_sequence_label_size
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      num_labels
							lowerCAmelCase_         =      num_choices
							lowerCAmelCase_         =      relative_attention
							lowerCAmelCase_         =      position_biased_input
							lowerCAmelCase_         =      pos_att_type
							lowerCAmelCase_         =      scope




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size	)

							lowerCAmelCase_         =      None
							if self.use_input_mask:
										lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length]							,							vocab_size=2	)

							lowerCAmelCase_         =      None
							if self.use_token_type_ids:
										lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length]							,							self.type_vocab_size	)

							lowerCAmelCase_         =      None
							lowerCAmelCase_         =      None
							lowerCAmelCase_         =      None
							if self.use_labels:
										lowerCAmelCase_         =      ids_tensor([self.batch_size]							,							self.type_sequence_label_size	)
										lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length]							,							self.num_labels	)
										lowerCAmelCase_         =      ids_tensor([self.batch_size]							,							self.num_choices	)

							lowerCAmelCase_         =      self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




				def 	__a		(   self	)							-> Optional[Any]:
							return DebertaConfig(
							    vocab_size=self.vocab_size							,							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							,							max_position_embeddings=self.max_position_embeddings							,							type_vocab_size=self.type_vocab_size							,							initializer_range=self.initializer_range							,							relative_attention=self.relative_attention							,							position_biased_input=self.position_biased_input							,							pos_att_type=self.pos_att_type							,							)




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      self.get_config()
							lowerCAmelCase_         =      300
							return config




				def 	__a		(   self							,							_UpperCamelCase	)							-> Any:
							self.parent.assertListEqual(list(result.loss.size()	)							,							[]	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      DebertaModel(config=_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      model(_UpperCamelCase							,							attention_mask=_UpperCamelCase							,							token_type_ids=_UpperCamelCase	)[0]
							lowerCAmelCase_         =      model(_UpperCamelCase							,							token_type_ids=_UpperCamelCase	)[0]
							lowerCAmelCase_         =      model(_UpperCamelCase	)[0]

							self.parent.assertListEqual(list(sequence_output.size()	)							,							[self.batch_size, self.seq_length, self.hidden_size]	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> int:
							lowerCAmelCase_         =      DebertaForMaskedLM(config=_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      model(_UpperCamelCase							,							attention_mask=_UpperCamelCase							,							token_type_ids=_UpperCamelCase							,							labels=_UpperCamelCase	)

							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> List[Any]:
							lowerCAmelCase_         =      self.num_labels
							lowerCAmelCase_         =      DebertaForSequenceClassification(_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      model(_UpperCamelCase							,							attention_mask=_UpperCamelCase							,							token_type_ids=_UpperCamelCase							,							labels=_UpperCamelCase	)
							self.parent.assertListEqual(list(result.logits.size()	)							,							[self.batch_size, self.num_labels]	)
							self.check_loss_output(_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Tuple:
							lowerCAmelCase_         =      self.num_labels
							lowerCAmelCase_         =      DebertaForTokenClassification(config=_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      model(_UpperCamelCase							,							attention_mask=_UpperCamelCase							,							token_type_ids=_UpperCamelCase							,							labels=_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.num_labels)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Tuple:
							lowerCAmelCase_         =      DebertaForQuestionAnswering(config=_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      model(
							    _UpperCamelCase							,							attention_mask=_UpperCamelCase							,							token_type_ids=_UpperCamelCase							,							start_positions=_UpperCamelCase							,							end_positions=_UpperCamelCase							,							)
							self.parent.assertEqual(result.start_logits.shape							,							(self.batch_size, self.seq_length)	)
							self.parent.assertEqual(result.end_logits.shape							,							(self.batch_size, self.seq_length)	)




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    
							)         =      config_and_inputs
							lowerCAmelCase_         =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict





@require_torch
class       _lowerCAmelCase   (      __a   , __a   , unittest.TestCase			):
				_lowercase     						=(
				    (
				        DebertaModel,
				        DebertaForMaskedLM,
				        DebertaForSequenceClassification,
				        DebertaForTokenClassification,
				        DebertaForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				_lowercase     						=(
				    {
				        '''feature-extraction''': DebertaModel,
				        '''fill-mask''': DebertaForMaskedLM,
				        '''question-answering''': DebertaForQuestionAnswering,
				        '''text-classification''': DebertaForSequenceClassification,
				        '''token-classification''': DebertaForTokenClassification,
				        '''zero-shot''': DebertaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)

				_lowercase     						=True
				_lowercase     						=False
				_lowercase     						=False
				_lowercase     						=False
				_lowercase     						=False




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      DebertaModelTester(self	)
							lowerCAmelCase_         =      ConfigTester(self							,							config_class=_UpperCamelCase							,							hidden_size=37	)




				def 	__a		(   self	)							-> Union[str, Any]:
							self.config_tester.run_common_tests()




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_model(*_UpperCamelCase	)




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_sequence_classification(*_UpperCamelCase	)




				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_masked_lm(*_UpperCamelCase	)




				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_question_answering(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_token_classification(*_UpperCamelCase	)




				@slow
				def 	__a		(   self	)							-> int:
							for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase_         =      DebertaModel.from_pretrained(_UpperCamelCase	)
										self.assertIsNotNone(_UpperCamelCase	)





@require_torch
@require_sentencepiece
@require_tokenizers
class       _lowerCAmelCase   (      unittest.TestCase			):




				@unittest.skip(reason="Model not available yet"	)
				def 	__a		(   self	)							-> Dict:
							pass




				@slow
				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      DebertaModel.from_pretrained("microsoft/deberta-base"	)

							lowerCAmelCase_         =      torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]	)
							lowerCAmelCase_         =      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
							with torch.no_grad():
										lowerCAmelCase_         =      model(_UpperCamelCase							,							attention_mask=_UpperCamelCase	)[0]
							# compare the actual values for a slice.
							lowerCAmelCase_         =      torch.tensor(
							    [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]	)
							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]							,							_UpperCamelCase							,							atol=1e-4	)							,							f"""{output[:, 1:4, 1:4]}"""	)





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       _lowerCAmelCase   :




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=13							,							_UpperCamelCase=30							,							_UpperCamelCase=2							,							_UpperCamelCase=3							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=32							,							_UpperCamelCase=5							,							_UpperCamelCase=4							,							_UpperCamelCase=37							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=10							,							_UpperCamelCase=0.02							,							_UpperCamelCase=None							,							)							-> List[Any]:
							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	)							-> 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 	__a		(   self	)							-> Optional[Any]:
							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							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      ViTMSNModel(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 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      self.type_sequence_label_size
							lowerCAmelCase_         =      ViTMSNForImageClassification(_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      model(_UpperCamelCase							,							labels=_UpperCamelCase	)
							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(_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()

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




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.prepare_config_and_inputs()
							lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      config_and_inputs
							lowerCAmelCase_         =      {"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class       _lowerCAmelCase   (      __a   , __a   , unittest.TestCase			):
				_lowercase     						=(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				_lowercase     						=(
				    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				_lowercase     						=False
				_lowercase     						=False
				_lowercase     						=False
				_lowercase     						=False




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      ViTMSNModelTester(self	)
							lowerCAmelCase_         =      ConfigTester(self							,							config_class=_UpperCamelCase							,							has_text_modality=_UpperCamelCase							,							hidden_size=37	)




				def 	__a		(   self	)							-> Optional[int]:
							self.config_tester.run_common_tests()




				@unittest.skip(reason="ViTMSN does not use inputs_embeds"	)
				def 	__a		(   self	)							-> str:
							pass




				def 	__a		(   self	)							-> Any:
							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 	__a		(   self	)							-> Optional[Any]:
							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 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase	)




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase	)




				@slow
				def 	__a		(   self	)							-> Union[str, Any]:
							for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase_         =      ViTMSNModel.from_pretrained(_UpperCamelCase	)
										self.assertIsNotNone(_UpperCamelCase	)






def   lowerCamelCase__							(							):




			"""simple docstring"""


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





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




				@cached_property
				def 	__a		(   self	)							-> List[str]:
							return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"	) if is_vision_available() else None




				@slow
				def 	__a		(   self	)							-> int:
							torch.manual_seed(2	)
							lowerCAmelCase_         =      ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"	).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_000)	)
							self.assertEqual(outputs.logits.shape							,							_UpperCamelCase	)

							lowerCAmelCase_         =      torch.tensor([-0.0803, -0.4454, -0.2375]	).to(_UpperCamelCase	)

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



