


import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
     import jax

     from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel






class     _lowerCamelCase					(   unittest.TestCase     ):




       """simple docstring"""



       def __init__( self							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE=13							,			_SCREAMING_SNAKE_CASE=30							,			_SCREAMING_SNAKE_CASE=2							,			_SCREAMING_SNAKE_CASE=3							,			_SCREAMING_SNAKE_CASE=True							,			_SCREAMING_SNAKE_CASE=True							,			_SCREAMING_SNAKE_CASE=32							,			_SCREAMING_SNAKE_CASE=5							,			_SCREAMING_SNAKE_CASE=4							,			_SCREAMING_SNAKE_CASE=37							,			_SCREAMING_SNAKE_CASE="gelu"							,			_SCREAMING_SNAKE_CASE=0.1							,			_SCREAMING_SNAKE_CASE=0.1							,			_SCREAMING_SNAKE_CASE=10							,			_SCREAMING_SNAKE_CASE=0.0_2							,			)->Optional[int]:




         '''simple docstring'''





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

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



       def 			_snake_case			( self						)->List[str]:




         '''simple docstring'''





         A_ :							Any							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

         A_ :							Tuple							=   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=_SCREAMING_SNAKE_CASE							,			initializer_range=self.initializer_range							,			)

         return config, pixel_values



       def 			_snake_case			( self							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE						)->Tuple:




         '''simple docstring'''





         A_ :							List[str]							=   FlaxViTModel(config=_SCREAMING_SNAKE_CASE						)
         A_ :							Dict							=   model(_SCREAMING_SNAKE_CASE						)
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         A_ :							Optional[int]							=   (self.image_size, self.image_size)
         A_ :							List[Any]							=   (self.patch_size, self.patch_size)
         A_ :							Union[str, Any]							=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, num_patches + 1, self.hidden_size)						)



       def 			_snake_case			( self							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE						)->str:




         '''simple docstring'''





         A_ :							Optional[Any]							=   self.type_sequence_label_size
         A_ :							Union[str, Any]							=   FlaxViTForImageClassification(config=_SCREAMING_SNAKE_CASE						)
         A_ :							List[Any]							=   model(_SCREAMING_SNAKE_CASE						)
         self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)						)

         # test greyscale images
         A_ :							Union[str, Any]							=   1
         A_ :							Dict							=   FlaxViTForImageClassification(_SCREAMING_SNAKE_CASE						)

         A_ :							Tuple							=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
         A_ :							Optional[Any]							=   model(_SCREAMING_SNAKE_CASE						)







       def 			_snake_case			( self						)->str:




         '''simple docstring'''





         A_ :							str							=   self.prepare_config_and_inputs()
         (
             (
             A_		
         )	,			(
             A_		
         )	,					
         ) :							Tuple							=   config_and_inputs
         A_ :							Any							=   {'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_flax
class     _lowerCamelCase					(   A__   ,				unittest.TestCase     ):




       """simple docstring"""
       snake_case    =       (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()



       def 			_snake_case			( self						)->Tuple:




         '''simple docstring'''





         A_ :							List[str]							=   FlaxViTModelTester(self						)
         A_ :							str							=   ConfigTester(self							,			config_class=_SCREAMING_SNAKE_CASE							,			has_text_modality=_SCREAMING_SNAKE_CASE							,			hidden_size=37						)



       def 			_snake_case			( self						)->Tuple:




         '''simple docstring'''





         self.config_tester.run_common_tests()



       def 			_snake_case			( self						)->str:




         '''simple docstring'''





         A_ :							List[Any]							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE						)



       def 			_snake_case			( self						)->int:




         '''simple docstring'''





         A_ :							str							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE						)



       def 			_snake_case			( self						)->List[str]:




         '''simple docstring'''





         A_	,			A_ :							Union[str, Any]							=   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           A_ :							Dict							=   model_class(_SCREAMING_SNAKE_CASE						)
           A_ :							Dict							=   inspect.signature(model.__call__						)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           A_ :							Union[str, Any]							=   [*signature.parameters.keys()]

           A_ :							Union[str, Any]							=   ['''pixel_values''']
           self.assertListEqual(arg_names[:1]							,			_SCREAMING_SNAKE_CASE						)



       def 			_snake_case			( self						)->Dict:




         '''simple docstring'''





         A_	,			A_ :							Optional[Any]							=   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__						):
             A_ :							List[Any]							=   self._prepare_for_class(_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE						)
             A_ :							Optional[int]							=   model_class(_SCREAMING_SNAKE_CASE						)

             @jax.jit
             def model_jitted(_SCREAMING_SNAKE_CASE							,			**_SCREAMING_SNAKE_CASE						):
               return model(pixel_values=_SCREAMING_SNAKE_CASE							,			**_SCREAMING_SNAKE_CASE						)

             with self.subTest('''JIT Enabled'''						):
               A_ :							Any							=   model_jitted(**_SCREAMING_SNAKE_CASE						).to_tuple()

             with self.subTest('''JIT Disabled'''						):
               with jax.disable_jit():
                 A_ :							List[str]							=   model_jitted(**_SCREAMING_SNAKE_CASE						).to_tuple()

             self.assertEqual(len(_SCREAMING_SNAKE_CASE						)							,			len(_SCREAMING_SNAKE_CASE						)						)
             for jitted_output, output in zip(_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE						):
               self.assertEqual(jitted_output.shape							,			output.shape						)







       @slow
       def 			_snake_case			( self						)->Dict:




         '''simple docstring'''





         for model_class_name in self.all_model_classes:
           A_ :							Tuple							=   model_class_name.from_pretrained('''google/vit-base-patch16-224'''						)
           A_ :							List[Any]							=   model(np.ones((1, 3, 224, 224)						)						)
           self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)












from typing import Dict

from .base import GenericTensor, Pipeline
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Tuple=None,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       Union[str, Any]=None,    **lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								if tokenize_kwargs is None:
												lowercase__  					=     {}

								if truncation is not None:
												if "truncation" in tokenize_kwargs:
																raise ValueError(
																    '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''    )
												lowercase__  					=     truncation

								lowercase__  					=     tokenize_kwargs

								lowercase__  					=     {}
								if return_tensors is not None:
												lowercase__  					=     return_tensors

								return preprocess_params, {}, postprocess_params




				def 							lowercase__  (	self		:       Tuple,    lowerCamelCase		:       Optional[int],    **lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     self.framework
								lowercase__  					=     self.tokenizer(lowerCamelCase,    return_tensors=lowerCamelCase,    **lowerCamelCase    )
								return model_inputs




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     self.model(**lowerCamelCase    )
								return model_outputs




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       Tuple=False    ):

								'''simple docstring'''
								# [0] is the first available tensor, logits or last_hidden_state.
								if return_tensors:
												return model_outputs[0]
								if self.framework == "pt":
												return model_outputs[0].tolist()
								elif self.framework == "tf":
												return model_outputs[0].numpy().tolist()




				def __call__(	self		:       List[str],    *lowerCamelCase		:       List[Any],    **lowerCamelCase		:       str    ):

								'''simple docstring'''
								return super().__call__(*lowerCamelCase,    **lowerCamelCase    )




