
from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFRoFormerForCausalLM,
			    TFRoFormerForMaskedLM,
			    TFRoFormerForMultipleChoice,
			    TFRoFormerForQuestionAnswering,
			    TFRoFormerForSequenceClassification,
			    TFRoFormerForTokenClassification,
			    TFRoFormerModel,
			)
			from transformers.models.roformer.modeling_tf_roformer import (
			    TFRoFormerSelfAttention,
			    TFRoFormerSinusoidalPositionalEmbedding,
			)





class       _lowerCAmelCase   :




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=13							,							_UpperCamelCase=7							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=99							,							_UpperCamelCase=32							,							_UpperCamelCase=2							,							_UpperCamelCase=4							,							_UpperCamelCase=37							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=512							,							_UpperCamelCase=16							,							_UpperCamelCase=2							,							_UpperCamelCase=0.02							,							_UpperCamelCase=3							,							_UpperCamelCase=4							,							_UpperCamelCase=None							,							)							-> List[str]:
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      13
							lowerCAmelCase_         =      7
							lowerCAmelCase_         =      True
							lowerCAmelCase_         =      True
							lowerCAmelCase_         =      True
							lowerCAmelCase_         =      True
							lowerCAmelCase_         =      99
							lowerCAmelCase_         =      32
							lowerCAmelCase_         =      2
							lowerCAmelCase_         =      4
							lowerCAmelCase_         =      37
							lowerCAmelCase_         =      "gelu"
							lowerCAmelCase_         =      0.1
							lowerCAmelCase_         =      0.1
							lowerCAmelCase_         =      512
							lowerCAmelCase_         =      16
							lowerCAmelCase_         =      2
							lowerCAmelCase_         =      0.02
							lowerCAmelCase_         =      3
							lowerCAmelCase_         =      4
							lowerCAmelCase_         =      None




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

							lowerCAmelCase_         =      None
							if self.use_input_mask:
										lowerCAmelCase_         =      random_attention_mask([self.batch_size, self.seq_length]	)

							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_         =      RoFormerConfig(
							    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							,							return_dict=_UpperCamelCase							,							)

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




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      TFRoFormerModel(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

							lowerCAmelCase_         =      [input_ids, input_mask]
							lowerCAmelCase_         =      model(_UpperCamelCase	)

							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							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Any:
							lowerCAmelCase_         =      True
							lowerCAmelCase_         =      TFRoFormerForCausalLM(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							lowerCAmelCase_         =      model(_UpperCamelCase	)["logits"]
							self.parent.assertListEqual(
							    list(prediction_scores.numpy().shape	)							,							[self.batch_size, self.seq_length, self.vocab_size]	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> int:
							lowerCAmelCase_         =      TFRoFormerForMaskedLM(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							lowerCAmelCase_         =      model(_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	)							-> str:
							lowerCAmelCase_         =      self.num_labels
							lowerCAmelCase_         =      TFRoFormerForSequenceClassification(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> List[str]:
							lowerCAmelCase_         =      self.num_choices
							lowerCAmelCase_         =      TFRoFormerForMultipleChoice(config=_UpperCamelCase	)
							lowerCAmelCase_         =      tf.tile(tf.expand_dims(_UpperCamelCase							,							1	)							,							(1, self.num_choices, 1)	)
							lowerCAmelCase_         =      tf.tile(tf.expand_dims(_UpperCamelCase							,							1	)							,							(1, self.num_choices, 1)	)
							lowerCAmelCase_         =      tf.tile(tf.expand_dims(_UpperCamelCase							,							1	)							,							(1, self.num_choices, 1)	)
							lowerCAmelCase_         =      {
							    "input_ids": multiple_choice_inputs_ids,
							    "attention_mask": multiple_choice_input_mask,
							    "token_type_ids": multiple_choice_token_type_ids,
							}
							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_choices)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Any:
							lowerCAmelCase_         =      self.num_labels
							lowerCAmelCase_         =      TFRoFormerForTokenClassification(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							lowerCAmelCase_         =      model(_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	)							-> Any:
							lowerCAmelCase_         =      TFRoFormerForQuestionAnswering(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							lowerCAmelCase_         =      model(_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	)							-> int:
							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_tf
class       _lowerCAmelCase   (      __a   , __a   , unittest.TestCase			):
				_lowercase     						=(
				    (
				        TFRoFormerModel,
				        TFRoFormerForCausalLM,
				        TFRoFormerForMaskedLM,
				        TFRoFormerForQuestionAnswering,
				        TFRoFormerForSequenceClassification,
				        TFRoFormerForTokenClassification,
				        TFRoFormerForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				_lowercase     						=(
				    {
				        '''feature-extraction''': TFRoFormerModel,
				        '''fill-mask''': TFRoFormerForMaskedLM,
				        '''question-answering''': TFRoFormerForQuestionAnswering,
				        '''text-classification''': TFRoFormerForSequenceClassification,
				        '''text-generation''': TFRoFormerForCausalLM,
				        '''token-classification''': TFRoFormerForTokenClassification,
				        '''zero-shot''': TFRoFormerForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				_lowercase     						=False
				_lowercase     						=False




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> List[str]:
							if pipeline_test_casse_name == "TextGenerationPipelineTests":
										return True

							return False




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




				def 	__a		(   self	)							-> List[str]:
							self.config_tester.run_common_tests()




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




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




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_lm_head(*_UpperCamelCase	)




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




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase	)




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




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase	)




				@slow
				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"	)
							self.assertIsNotNone(_UpperCamelCase	)





@require_tf
class       _lowerCAmelCase   (      unittest.TestCase			):




				@slow
				def 	__a		(   self	)							-> Optional[Any]:
							lowerCAmelCase_         =      TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"	)
							lowerCAmelCase_         =      tf.constant([[0, 1, 2, 3, 4, 5]]	)
							lowerCAmelCase_         =      model(_UpperCamelCase	)[0]

							# TODO Replace vocab size
							lowerCAmelCase_         =      50_000

							lowerCAmelCase_         =      [1, 6, vocab_size]
							self.assertEqual(output.shape							,							_UpperCamelCase	)

							print(output[:, :3, :3]	)

							# TODO Replace values below with what was printed above.
							lowerCAmelCase_         =      tf.constant(
							    [
							        [
							            [-0.12053341, -1.0264901, 0.29221946],
							            [-1.5133783, 0.197433, 0.15190607],
							            [-5.0135403, -3.900256, -0.84038764],
							        ]
							    ]	)
							tf.debugging.assert_near(output[:, :3, :3]							,							_UpperCamelCase							,							atol=1e-4	)





@require_tf
class       _lowerCAmelCase   (      unittest.TestCase			):
				_lowercase     						=1E-4




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      tf.constant([[4, 10]]	)
							lowerCAmelCase_         =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=6							,							embedding_dim=6	)

							lowerCAmelCase_         =      emba(input_ids.shape	)
							lowerCAmelCase_         =      tf.constant(
							    [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]	)

							tf.debugging.assert_near(_UpperCamelCase							,							_UpperCamelCase							,							atol=self.tolerance	)




				def 	__a		(   self	)							-> Tuple:
							lowerCAmelCase_         =      tf.constant(
							    [
							        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
							        [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
							        [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
							    ]	)
							lowerCAmelCase_         =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=512							,							embedding_dim=512	)
							emba([2, 16, 512]	)
							lowerCAmelCase_         =      emba.weight[:3, :5]

							tf.debugging.assert_near(_UpperCamelCase							,							_UpperCamelCase							,							atol=self.tolerance	)





@require_tf
class       _lowerCAmelCase   (      unittest.TestCase			):
				_lowercase     						=1E-4




				def 	__a		(   self	)							-> Dict:
							# 2,12,16,64
							lowerCAmelCase_         =      tf.reshape(tf.range(2 * 12 * 16 * 64							,							dtype=tf.floataa	)							,							shape=(2, 12, 16, 64)	) / 100

							lowerCAmelCase_         =      -tf.reshape(tf.range(2 * 12 * 16 * 64							,							dtype=tf.floataa	)							,							shape=(2, 12, 16, 64)	) / 100

							lowerCAmelCase_         =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=32							,							embedding_dim=64	)
							lowerCAmelCase_         =      embed_positions([2, 16, 768]	)[None, None, :, :]

							lowerCAmelCase_      ,    lowerCAmelCase_         =      TFRoFormerSelfAttention.apply_rotary_position_embeddings(
							    _UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)

							lowerCAmelCase_         =      tf.constant(
							    [
							        [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
							        [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
							        [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
							        [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
							        [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
							        [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
							    ]	)
							lowerCAmelCase_         =      tf.constant(
							    [
							        [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
							        [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
							        [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
							        [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
							        [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
							        [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
							    ]	)

							tf.debugging.assert_near(query_layer[0, 0, :6, :8]							,							_UpperCamelCase							,							atol=self.tolerance	)
							tf.debugging.assert_near(key_layer[0, 0, :6, :8]							,							_UpperCamelCase							,							atol=self.tolerance	)





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	)	)



