
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        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	def __init__(  self     :   str      ,  lowercase     :   str      ,  lowercase     :   int=13      ,  lowercase     :   Tuple=30      ,  lowercase     :   Optional[Any]=2      ,  lowercase     :   Union[str, Any]=3      ,  lowercase     :   List[Any]=True      ,  lowercase     :   List[str]=True      ,  lowercase     :   Tuple=32      ,  lowercase     :   List[str]=5      ,  lowercase     :   Optional[int]=4      ,  lowercase     :   List[Any]=37      ,  lowercase     :   List[str]="gelu"      ,  lowercase     :   Tuple=0.1      ,  lowercase     :   str=0.1      ,  lowercase     :   int=10      ,  lowercase     :   Union[str, Any]=0.02      ,  lowercase     :   str=None      ,  ):


				'''simple docstring'''


				_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     :   str     ):


				'''simple docstring'''


				_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     :   Tuple     ):


				'''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     :   List[Any]      ,  lowercase     :   int      ,  lowercase     :   Optional[Any]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      ViTMSNModel(config=lowercase     )
				model.to(lowercase     )
				model.eval()
				_snake_case          =      model(lowercase     )
				self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)     )



	def 			A							(  self     :   List[str]      ,  lowercase     :   Optional[Any]      ,  lowercase     :   List[Any]      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.type_sequence_label_size
				_snake_case          =      ViTMSNForImageClassification(lowercase     )
				model.to(lowercase     )
				model.eval()
				_snake_case          =      model(lowercase      ,  labels=lowercase     )
				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(lowercase     )
				model.to(lowercase     )
				model.eval()

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





	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_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        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



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

	_UpperCAmelCase	:		Optional[int]          = False
	_UpperCAmelCase	:		str          = False
	_UpperCAmelCase	:		Union[str, Any]          = False
	_UpperCAmelCase	:		Any          = False



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      ViTMSNModelTester(self     )
				_snake_case          =      ConfigTester(self      ,  config_class=lowercase      ,  has_text_modality=lowercase      ,  hidden_size=37     )



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				self.config_tester.run_common_tests()



	@unittest.skip(reason='ViTMSN does not use inputs_embeds'     )
	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				pass



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_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(lowercase     )
							self.assertIsInstance(model.get_input_embeddings()      ,  (nn.Module)     )
							_snake_case          =      model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowercase      ,  nn.Linear     )     )



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_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(lowercase     )
							_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]      ,  lowercase     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase     )



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase     )





	@slow
	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


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






def 	a_		(	)			->  Tuple:
			_snake_case          =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			return image




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





	'''simple docstring'''



	@cached_property
	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


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





	@slow
	def 			A							(  self     :   str     ):


				'''simple docstring'''


				torch.manual_seed(2     )
				_snake_case          =      ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'     ).to(lowercase     )

				_snake_case          =      self.default_image_processor
				_snake_case          =      prepare_img()
				_snake_case          =      image_processor(images=lowercase      ,  return_tensors='pt'     ).to(lowercase     )

				# forward pass
				with torch.no_grad():
							_snake_case          =      model(**lowercase     )

				# verify the logits
				_snake_case          =      torch.Size((1, 1_000)     )
				self.assertEqual(outputs.logits.shape      ,  lowercase     )

				_snake_case          =      torch.tensor([-0.0803, -0.4454, -0.2375]     ).to(lowercase     )

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

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


_lowerCamelCase	:    Tuple          =      logging.get_logger(__name__)

_lowerCamelCase	:    Union[str, Any]          =      {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Any          = "canine"



	def __init__(  self     :   int      ,  lowercase     :   Optional[int]=768      ,  lowercase     :   Tuple=12      ,  lowercase     :   Union[str, Any]=12      ,  lowercase     :   Optional[int]=3_072      ,  lowercase     :   Tuple="gelu"      ,  lowercase     :   Optional[Any]=0.1      ,  lowercase     :   Tuple=0.1      ,  lowercase     :   int=16_384      ,  lowercase     :   Optional[int]=16      ,  lowercase     :   Optional[int]=0.02      ,  lowercase     :   Optional[Any]=1E-12      ,  lowercase     :   Optional[Any]=0      ,  lowercase     :   Dict=0xE000      ,  lowercase     :   Optional[Any]=0xE001      ,  lowercase     :   Union[str, Any]=4      ,  lowercase     :   str=4      ,  lowercase     :   Optional[int]=8      ,  lowercase     :   List[str]=16_384      ,  lowercase     :   Union[str, Any]=128      ,  **lowercase     :   Optional[Any]      ,  ):


				'''simple docstring'''


				super().__init__(pad_token_id=lowercase      ,  bos_token_id=lowercase      ,  eos_token_id=lowercase      ,  **lowercase     )

				_snake_case          =      max_position_embeddings
				_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          =      initializer_range
				_snake_case          =      type_vocab_size
				_snake_case          =      layer_norm_eps

				# Character config:
				_snake_case          =      downsampling_rate
				_snake_case          =      upsampling_kernel_size
				_snake_case          =      num_hash_functions
				_snake_case          =      num_hash_buckets
				_snake_case          =      local_transformer_stride