





"""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 		__snake_case :





	"""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=0.0_2 ,						__lowerCamelCase=None ,						):



					'''simple docstring'''




					__A   :						Union[str, Any]									=					parent
					__A   :						Optional[Any]									=					batch_size
					__A   :						Any									=					image_size
					__A   :						str									=					patch_size
					__A   :						List[Any]									=					num_channels
					__A   :						Optional[Any]									=					is_training
					__A   :						List[str]									=					use_labels
					__A   :						int									=					hidden_size
					__A   :						Dict									=					num_hidden_layers
					__A   :						List[Any]									=					num_attention_heads
					__A   :						Optional[int]									=					intermediate_size
					__A   :						List[Any]									=					hidden_act
					__A   :						Optional[int]									=					hidden_dropout_prob
					__A   :						Union[str, Any]									=					attention_probs_dropout_prob
					__A   :						int									=					type_sequence_label_size
					__A   :						Optional[Any]									=					initializer_range
					__A   :						str									=					scope

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

	def  UpperCamelCase__(   self  ):



					'''simple docstring'''




					__A   :						List[str]									=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

					__A   :						Optional[Any]									=					None
					if self.use_labels:
									__A   :						Any									=					ids_tensor([self.batch_size] ,						self.type_sequence_label_size  )

					__A   :						str									=					self.get_config()

					return config, pixel_values, labels

	def  UpperCamelCase__(   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  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



					'''simple docstring'''




					__A   :						Optional[int]									=					ViTMSNModel(config=__lowerCamelCase  )
					model.to(__lowerCamelCase  )
					model.eval()
					__A   :						Optional[int]									=					model(__lowerCamelCase  )
					self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)  )

	def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



					'''simple docstring'''




					__A   :						str									=					self.type_sequence_label_size
					__A   :						List[Any]									=					ViTMSNForImageClassification(__lowerCamelCase  )
					model.to(__lowerCamelCase  )
					model.eval()
					__A   :						Dict									=					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
					__A   :						str									=					1
					__A   :						Dict									=					ViTMSNForImageClassification(__lowerCamelCase  )
					model.to(__lowerCamelCase  )
					model.eval()

					__A   :						List[str]									=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
					__A   :						Dict									=					model(__lowerCamelCase  )
					self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.type_sequence_label_size)  )







	def  UpperCamelCase__(   self  ):



					'''simple docstring'''




					__A   :						Optional[int]									=					self.prepare_config_and_inputs()
					__A   :						int									=					config_and_inputs
					__A   :						Union[str, Any]									=					{'''pixel_values''': pixel_values}
					return config, inputs_dict






@require_torch
class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





	"""simple docstring"""


	_lowerCamelCase				=					(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
	_lowerCamelCase				=					(
	    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_lowerCamelCase				=					False
	_lowerCamelCase				=					False
	_lowerCamelCase				=					False
	_lowerCamelCase				=					False

	def  UpperCamelCase__(   self  ):



					'''simple docstring'''




					__A   :						Tuple									=					ViTMSNModelTester(self  )
					__A   :						Union[str, Any]									=					ConfigTester(self ,						config_class=__lowerCamelCase ,						has_text_modality=__lowerCamelCase ,						hidden_size=37  )

	def  UpperCamelCase__(   self  ):



					'''simple docstring'''




					self.config_tester.run_common_tests()

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



					'''simple docstring'''




					pass

	def  UpperCamelCase__(   self  ):



					'''simple docstring'''




					__A   :						Dict									=					self.model_tester.prepare_config_and_inputs_for_common()

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

	def  UpperCamelCase__(   self  ):



					'''simple docstring'''




					__A   :						str									=					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									__A   :						Dict									=					model_class(__lowerCamelCase  )
									__A   :						Optional[Any]									=					inspect.signature(model.forward  )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__A   :						Any									=					[*signature.parameters.keys()]

									__A   :						str									=					['''pixel_values''']
									self.assertListEqual(arg_names[:1] ,						__lowerCamelCase  )

	def  UpperCamelCase__(   self  ):



					'''simple docstring'''




					__A   :						int									=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowerCamelCase  )

	def  UpperCamelCase__(   self  ):



					'''simple docstring'''




					__A   :						Optional[int]									=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase  )







	@slow
	def  UpperCamelCase__(   self  ):



					'''simple docstring'''




					for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A   :						Optional[int]									=					ViTMSNModel.from_pretrained(__lowerCamelCase  )
									self.assertIsNotNone(__lowerCamelCase  )







def 		__lowercase					(       )       ->Tuple:



				'''simple docstring'''







				__A   :						Optional[Any]									=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
				return image






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





	"""simple docstring"""


	@cached_property
	def  UpperCamelCase__(   self  ):



					'''simple docstring'''




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







	@slow
	def  UpperCamelCase__(   self  ):



					'''simple docstring'''




					torch.manual_seed(2  )
					__A   :						Union[str, Any]									=					ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''  ).to(__lowerCamelCase  )

					__A   :						List[str]									=					self.default_image_processor
					__A   :						Dict									=					prepare_img()
					__A   :						Optional[int]									=					image_processor(images=__lowerCamelCase ,						return_tensors='''pt'''  ).to(__lowerCamelCase  )

					# forward pass
					with torch.no_grad():
									__A   :						Union[str, Any]									=					model(**__lowerCamelCase  )

					# verify the logits
					__A   :						Optional[Any]									=					torch.Size((1, 1000)  )
					self.assertEqual(outputs.logits.shape ,						__lowerCamelCase  )

					__A   :						Tuple									=					torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]  ).to(__lowerCamelCase  )

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











"""simple docstring"""



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_			=      {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""GPTNeoXTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTNeoXForCausalLM""",
           """GPTNeoXForQuestionAnswering""",
           """GPTNeoXForSequenceClassification""",
           """GPTNeoXForTokenClassification""",
           """GPTNeoXLayer""",
           """GPTNeoXModel""",
           """GPTNeoXPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neox import (
                  GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoXForCausalLM,
                  GPTNeoXForQuestionAnswering,
                  GPTNeoXForSequenceClassification,
                  GPTNeoXForTokenClassification,
                  GPTNeoXLayer,
                  GPTNeoXModel,
                  GPTNeoXPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




