


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 A  (   unittest.TestCase     ):






      def __init__(					self						:     Optional[Any]			,   lowercase_						:     List[str]			,   lowercase_						:     List[Any]=13			,   lowercase_						:     int=30			,   lowercase_						:     Any=2			,   lowercase_						:     Dict=3			,   lowercase_						:     Any=True			,   lowercase_						:     Optional[int]=True			,   lowercase_						:     Tuple=32			,   lowercase_						:     List[Any]=5			,   lowercase_						:     Union[str, Any]=4			,   lowercase_						:     Optional[int]=37			,   lowercase_						:     List[str]="gelu"			,   lowercase_						:     List[Any]=0.1			,   lowercase_						:     str=0.1			,   lowercase_						:     Tuple=10			,   lowercase_						:     List[Any]=0.02			,   )					->						Union[str, Any]:
             """simple docstring"""




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

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






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

             _lowerCamelCase       :		Optional[int]		=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			,   )

             return config, pixel_values






      def        lowerCamelCase       (					self						:     str			,   lowercase_						:     str			,   lowercase_						:     Optional[Any] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=FlaxViTModel(config=__a )
             _lowerCamelCase       :		str		=model(__a )
             # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
             _lowerCamelCase       :		Optional[int]		=(self.image_size, self.image_size)
             _lowerCamelCase       :		List[str]		=(self.patch_size, self.patch_size)
             _lowerCamelCase       :		List[str]		=(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        lowerCamelCase       (					self						:     List[str]			,   lowercase_						:     Optional[int]			,   lowercase_						:     Any )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=self.type_sequence_label_size
             _lowerCamelCase       :		List[Any]		=FlaxViTForImageClassification(config=__a )
             _lowerCamelCase       :		Tuple		=model(__a )
             self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.type_sequence_label_size) )

             # test greyscale images
             _lowerCamelCase       :		Union[str, Any]		=1
             _lowerCamelCase       :		str		=FlaxViTForImageClassification(__a )

             _lowerCamelCase       :		Optional[Any]		=floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
             _lowerCamelCase       :		Dict		=model(__a )






      def        lowerCamelCase       (					self						:     str )					->						Union[str, Any]:
             """simple docstring"""




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




@require_flax
class A  (   lowerCAmelCase__    ,							unittest.TestCase     ):
      UpperCamelCase__							:  int      =(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






      def        lowerCamelCase       (					self						:     int )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=FlaxViTModelTester(self )
             _lowerCamelCase       :		Union[str, Any]		=ConfigTester(self			,   config_class=__a			,   has_text_modality=__a			,   hidden_size=37 )






      def        lowerCamelCase       (					self						:     Any )					->						Union[str, Any]:
             """simple docstring"""




             self.config_tester.run_common_tests()






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__a )






      def        lowerCamelCase       (					self						:     List[str] )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*__a )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Dict:
             """simple docstring"""




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

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

                    _lowerCamelCase       :		str		=['pixel_values']
                    self.assertListEqual(arg_names[:1]			,   __a )






      def        lowerCamelCase       (					self						:     Dict )					->						Dict:
             """simple docstring"""




             _lowerCamelCase	,     _lowerCamelCase       :		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__ ):
                           _lowerCamelCase       :		Dict		=self._prepare_for_class(__a			,   __a )
                           _lowerCamelCase       :		Optional[int]		=model_class(__a )

                           @jax.jit
                           def model_jitted(lowercase_						:     Optional[int]			,   **lowercase_						:     List[str] ):
                                  return model(pixel_values=__a			,   **__a )

                           with self.subTest('JIT Enabled' ):
                                  _lowerCamelCase       :		int		=model_jitted(**__a ).to_tuple()

                           with self.subTest('JIT Disabled' ):
                                  with jax.disable_jit():
                                         _lowerCamelCase       :		Optional[Any]		=model_jitted(**__a ).to_tuple()

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






      @slow
      def        lowerCamelCase       (					self						:     Optional[int] )					->						Union[str, Any]:
             """simple docstring"""




             for model_class_name in self.all_model_classes:
                    _lowerCamelCase       :		Optional[Any]		=model_class_name.from_pretrained('google/vit-base-patch16-224' )
                    _lowerCamelCase       :		str		=model(np.ones((1, 3, 224, 224) ) )
                    self.assertIsNotNone(__a )






"""simple docstring"""


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =["""image_processor""", """tokenizer"""]
							__UpperCAmelCase						:    Optional[Any]				      ="""CLIPImageProcessor"""
							__UpperCAmelCase						:    Union[str, Any]				      =("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")

							def __init__(     self    ,   __a=None    ,   __a=None    ,   **__a	):
										__lowerCAmelCase				    =			None
										if "feature_extractor" in kwargs:
													warnings.warn(
													    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
													    " instead."    ,   __a    ,   )
													__lowerCAmelCase				    =			kwargs.pop("feature_extractor"	)

										__lowerCAmelCase				    =			image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
													raise ValueError("You need to specify an `image_processor`."	)
										if tokenizer is None:
													raise ValueError("You need to specify a `tokenizer`."	)

										super().__init__(__a    ,   __a	)

							def __call__(     self    ,   __a=None    ,   __a=None    ,   __a=None    ,   **__a	):

										if text is None and images is None:
													raise ValueError("You have to specify either text or images. Both cannot be none."	)

										if text is not None:
													__lowerCAmelCase				    =			self.tokenizer(__a    ,   return_tensors=__a    ,   **__a	)

										if images is not None:
													__lowerCAmelCase				    =			self.image_processor(__a    ,   return_tensors=__a    ,   **__a	)

										if text is not None and images is not None:
													__lowerCAmelCase				    =			image_features.pixel_values
													return encoding
										elif text is not None:
													return encoding
										else:
													return BatchEncoding(data=dict(**__a	)    ,   tensor_type=__a	)

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.batch_decode(*__a    ,   **__a	)

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.decode(*__a    ,   **__a	)



							@property
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.tokenizer.model_input_names
										__lowerCAmelCase				    =			self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)




