"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
     import tensorflow as tf

     from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor
class 						__A							:






     def __init__(		self    ,					a__    ,					a__=13    ,					a__=30    ,					a__=2    ,					a__=3    ,					a__=True    ,					a__=True    ,					a__=32    ,					a__=2    ,					a__=4    ,					a__=37    ,					a__="gelu"    ,					a__=0.1    ,					a__=0.1    ,					a__=10    ,					a__=0.0_2    ,					a__=3    ,					a__=None    ,					):
       _lowerCAmelCase						:	Optional[int]        =					parent
       _lowerCAmelCase						:	Optional[int]        =					batch_size
       _lowerCAmelCase						:	Any        =					image_size
       _lowerCAmelCase						:	str        =					patch_size
       _lowerCAmelCase						:	Tuple        =					num_channels
       _lowerCAmelCase						:	Any        =					is_training
       _lowerCAmelCase						:	List[Any]        =					use_labels
       _lowerCAmelCase						:	List[Any]        =					hidden_size
       _lowerCAmelCase						:	Any        =					num_hidden_layers
       _lowerCAmelCase						:	Union[str, Any]        =					num_attention_heads
       _lowerCAmelCase						:	Optional[Any]        =					intermediate_size
       _lowerCAmelCase						:	List[str]        =					hidden_act
       _lowerCAmelCase						:	Tuple        =					hidden_dropout_prob
       _lowerCAmelCase						:	List[str]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Dict        =					type_sequence_label_size
       _lowerCAmelCase						:	str        =					initializer_range
       _lowerCAmelCase						:	Union[str, Any]        =					scope

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






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

       _lowerCAmelCase						:	Any        =					self.get_config()

       return config, pixel_values, labels






     def    __A  (		self  ):
       return ViTConfig(
           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    ,					is_decoder=a__    ,					initializer_range=self.initializer_range    ,					)






     def    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Optional[Any]        =					TFViTModel(config=a__  )
       _lowerCAmelCase						:	Optional[int]        =					model(a__    ,					training=a__  )
       self.parent.assertEqual(result.last_hidden_state.shape    ,					(self.batch_size, self.seq_length, self.hidden_size)  )

       # Test with an image with different size than the one specified in config.
       _lowerCAmelCase						:	Dict        =					self.image_size // 2
       _lowerCAmelCase						:	Optional[int]        =					pixel_values[:, :, :image_size, :image_size]
       _lowerCAmelCase						:	Tuple        =					model(a__    ,					interpolate_pos_encoding=a__    ,					training=a__  )
       _lowerCAmelCase						:	Union[str, Any]        =					(image_size // self.patch_size) ** 2 + 1
       self.parent.assertEqual(result.last_hidden_state.shape    ,					(self.batch_size, seq_length, self.hidden_size)  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	List[str]        =					self.type_sequence_label_size
       _lowerCAmelCase						:	Tuple        =					TFViTForImageClassification(a__  )
       _lowerCAmelCase						:	List[Any]        =					model(a__    ,					labels=a__    ,					training=a__  )
       self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.type_sequence_label_size)  )

       # Test with an image with different size than the one specified in config.
       _lowerCAmelCase						:	List[Any]        =					self.image_size // 2
       _lowerCAmelCase						:	Dict        =					pixel_values[:, :, :image_size, :image_size]
       _lowerCAmelCase						:	Optional[Any]        =					model(a__    ,					interpolate_pos_encoding=a__    ,					training=a__  )
       self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.type_sequence_label_size)  )

       # test greyscale images
       _lowerCAmelCase						:	List[str]        =					1
       _lowerCAmelCase						:	List[str]        =					TFViTForImageClassification(a__  )
       _lowerCAmelCase						:	Optional[Any]        =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
       _lowerCAmelCase						:	List[str]        =					model(a__  )
       self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.type_sequence_label_size)  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					self.prepare_config_and_inputs()
       _lowerCAmelCase						:	Optional[int]        =					config_and_inputs
       _lowerCAmelCase						:	List[Any]        =					{"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_tf
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      Union[str, Any]		 =		(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
     _UpperCamelCase							:      Dict		 =		(
         {"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
         if is_tf_available()
         else {}
     )

     _UpperCamelCase							:      Dict		 =		False
     _UpperCamelCase							:      Any		 =		False
     _UpperCamelCase							:      Union[str, Any]		 =		False






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					TFViTModelTester(self  )
       _lowerCAmelCase						:	Optional[Any]        =					ConfigTester(self    ,					config_class=a__    ,					has_text_modality=a__    ,					hidden_size=37  )






     def    __A  (		self  ):
       self.config_tester.run_common_tests()






     @unittest.skip(reason="""ViT does not use inputs_embeds"""  )
     def    __A  (		self  ):
       pass






     @unittest.skip(reason="""ViT does not use inputs_embeds"""  )
     def    __A  (		self  ):
       pass






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[Any]        =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:	Dict        =					model_class(a__  )
         self.assertIsInstance(model.get_input_embeddings()    ,					(tf.keras.layers.Layer)  )
         _lowerCAmelCase						:	List[str]        =					model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(a__    ,					tf.keras.layers.Layer  )  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:	Dict        =					model_class(a__  )
         _lowerCAmelCase						:	Any        =					inspect.signature(model.call  )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCAmelCase						:	List[Any]        =					[*signature.parameters.keys()]

         _lowerCAmelCase						:	Dict        =					["""pixel_values"""]
         self.assertListEqual(arg_names[:1]    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[Any]        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[Any]        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a__  )






     @slow
     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					TFViTModel.from_pretrained("""google/vit-base-patch16-224"""  )
       self.assertIsNotNone(a__  )







def 							SCREAMING_SNAKE_CASE					(      )	->				Optional[Any]:
  _lowerCAmelCase						:	int        =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
  return image






@require_tf
@require_vision
class 						__A							(			unittest.TestCase		):






     @cached_property
     def    __A  (		self  ):
       return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""  ) if is_vision_available() else None






     @slow
     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""  )

       _lowerCAmelCase						:	Optional[int]        =					self.default_image_processor
       _lowerCAmelCase						:	Optional[int]        =					prepare_img()
       _lowerCAmelCase						:	Optional[Any]        =					image_processor(images=a__    ,					return_tensors="""tf"""  )

       # forward pass
       _lowerCAmelCase						:	Tuple        =					model(**a__  )

       # verify the logits
       _lowerCAmelCase						:	Union[str, Any]        =					tf.TensorShape((1, 1000)  )
       self.assertEqual(outputs.logits.shape    ,					a__  )

       _lowerCAmelCase						:	Optional[Any]        =					tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]  )

       tf.debugging.assert_near(outputs.logits[0, :3]    ,					a__    ,					atol=1e-4  )






"""simple docstring"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	bool = False     )	->				str:

  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected string as input, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )
  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected boolean as use_pascal parameter, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )

  _lowerCAmelCase						:	str        =					input_str.split("""_"""     )

  _lowerCAmelCase						:	Optional[int]        =					0 if use_pascal else 1

  _lowerCAmelCase						:	List[Any]        =					words[start_index:]

  _lowerCAmelCase						:	Union[str, Any]        =					[word[0].upper() + word[1:] for word in words_to_capitalize]

  _lowerCAmelCase						:	List[Any]        =					"""""" if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]     )


if __name__ == "__main__":
     from doctest import testmod

     testmod()




