


'''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       :
 def __init__(	self ,			__magic_name__ ,			__magic_name__=13 ,			__magic_name__=30 ,			__magic_name__=2 ,			__magic_name__=3 ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=32 ,			__magic_name__=5 ,			__magic_name__=4 ,			__magic_name__=37 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=10 ,			__magic_name__=0.0_2 ,			__magic_name__=None ,			)   ->							Dict:
      _a      =							parent
      _a      =							batch_size
      _a      =							image_size
      _a      =							patch_size
      _a      =							num_channels
      _a      =							is_training
      _a      =							use_labels
      _a      =							hidden_size
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							intermediate_size
      _a      =							hidden_act
      _a      =							hidden_dropout_prob
      _a      =							attention_probs_dropout_prob
      _a      =							type_sequence_label_size
      _a      =							initializer_range
      _a      =							scope

      # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      _a      =							(image_size // patch_size) ** 2
      _a      =							num_patches + 1
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

      _a      =							None
      if self.use_labels:
           _a      =							ids_tensor([self.batch_size] ,			self.type_sequence_label_size  )

      _a      =							self.get_config()

      return config, pixel_values, labels
 def 							__UpperCAmelCase						(	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 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							List[Any]:
      _a      =							ViTMSNModel(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__  )
      self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Union[str, Any]:
      _a      =							self.type_sequence_label_size
      _a      =							ViTMSNForImageClassification(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			labels=__magic_name__  )
      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      =							1
      _a      =							ViTMSNForImageClassification(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()

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




 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							self.prepare_config_and_inputs()
      _a ,				_a ,				_a      =							config_and_inputs
      _a      =							{'pixel_values': pixel_values}
      return config, inputs_dict





@require_torch
class 	a       (       _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _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  )   ->							int:
      _a      =							ViTMSNModelTester(self  )
      _a      =							ConfigTester(self ,			config_class=__magic_name__ ,			has_text_modality=__magic_name__ ,			hidden_size=37  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      self.config_tester.run_common_tests()
 @unittest.skip(reason='ViTMSN does not use inputs_embeds'  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      pass
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a ,				_a      =							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a      =							model_class(__magic_name__  )
           self.assertIsInstance(model.get_input_embeddings() ,			(nn.Module)  )
           _a      =							model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__magic_name__ ,			nn.Linear  )  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a ,				_a      =							self.model_tester.prepare_config_and_inputs_for_common()

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

           _a      =							['pixel_values']
           self.assertListEqual(arg_names[:1] ,			__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__magic_name__  )




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							int:
      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a      =							ViTMSNModel.from_pretrained(__magic_name__  )
           self.assertIsNotNone(__magic_name__  )






def 		_A			()				->					Dict:




     '''simple docstring'''



     _a      =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
     return image






@require_torch
@require_vision
class 	a       (       unittest.TestCase    ):
 @cached_property
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'  ) if is_vision_available() else None




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							int:
      torch.manual_seed(2  )
      _a      =							ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'  ).to(__magic_name__  )

      _a      =							self.default_image_processor
      _a      =							prepare_img()
      _a      =							image_processor(images=__magic_name__ ,			return_tensors='pt'  ).to(__magic_name__  )

      # forward pass
      with torch.no_grad():
           _a      =							model(**__magic_name__  )

      # verify the logits
      _a      =							torch.Size((1, 10_00)  )
      self.assertEqual(outputs.logits.shape ,			__magic_name__  )

      _a      =							torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]  ).to(__magic_name__  )

      self.assertTrue(torch.allclose(outputs.logits[0, :3] ,			__magic_name__ ,			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_   :      List[Any]				    =      {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[Any]				    =      [
     "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_   :      Optional[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

