
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  ):





     def __init__(self				:   Any    ,   _A				:   Union[str, Any]    ,   _A				:   List[str]=1_3    ,   _A				:   List[str]=3_0    ,   _A				:   Tuple=2    ,   _A				:   List[Any]=3    ,   _A				:   Dict=True    ,   _A				:   Union[str, Any]=True    ,   _A				:   Tuple=3_2    ,   _A				:   Any=5    ,   _A				:   Optional[int]=4    ,   _A				:   Optional[int]=3_7    ,   _A				:   List[Any]="gelu"    ,   _A				:   Tuple=0.1    ,   _A				:   Tuple=0.1    ,   _A				:   Dict=1_0    ,   _A				:   str=0.02    ,   )						->						Optional[Any]:
         snake_case								= parent
         snake_case								= batch_size
         snake_case								= image_size
         snake_case								= patch_size
         snake_case								= num_channels
         snake_case								= is_training
         snake_case								= use_labels
         snake_case								= hidden_size
         snake_case								= num_hidden_layers
         snake_case								= num_attention_heads
         snake_case								= intermediate_size
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= type_sequence_label_size
         snake_case								= initializer_range

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





     def 		UpperCAmelCase(self				:   Dict       )						->						Optional[int]:
         snake_case								= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         snake_case								= 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 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   Union[str, Any]    ,   _A				:   str       )						->						Union[str, Any]:
         snake_case								= FlaxViTModel(config=_A       )
         snake_case								= model(_A       )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         snake_case								= (self.image_size, self.image_size)
         snake_case								= (self.patch_size, self.patch_size)
         snake_case								= (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 		UpperCAmelCase(self				:   Optional[int]    ,   _A				:   List[Any]    ,   _A				:   Optional[Any]       )						->						Optional[Any]:
         snake_case								= self.type_sequence_label_size
         snake_case								= FlaxViTForImageClassification(config=_A       )
         snake_case								= model(_A       )
         self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)       )

         # test greyscale images
         snake_case								= 1
         snake_case								= FlaxViTForImageClassification(_A       )

         snake_case								= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         snake_case								= model(_A       )





     def 		UpperCAmelCase(self				:   List[Any]       )						->						Dict:
         snake_case								= self.prepare_config_and_inputs()
         (
             (
             snake_case
         )					,							(
             snake_case
         )					,							
         )								= config_and_inputs
         snake_case								= {"pixel_values": pixel_values}
         return config, inputs_dict







@require_flax
class 					lowerCamelCase		(  A_							,			unittest.TestCase  ):
     UpperCAmelCase__    :							Optional[int]									=		(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						None:
         snake_case								= FlaxViTModelTester(self       )
         snake_case								= ConfigTester(self    ,   config_class=_A    ,   has_text_modality=_A    ,   hidden_size=3_7       )





     def 		UpperCAmelCase(self				:   Tuple       )						->						str:
         self.config_tester.run_common_tests()





     def 		UpperCAmelCase(self				:   str       )						->						Optional[int]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_A       )





     def 		UpperCAmelCase(self				:   Tuple       )						->						Union[str, Any]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_A       )





     def 		UpperCAmelCase(self				:   Tuple       )						->						str:
         snake_case					,							snake_case								= self.model_tester.prepare_config_and_inputs_for_common()

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

             snake_case								= ["pixel_values"]
             self.assertListEqual(arg_names[:1]    ,   _A       )





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						str:
         snake_case					,							snake_case								= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__       ):
                 snake_case								= self._prepare_for_class(_A    ,   _A       )
                 snake_case								= model_class(_A       )

                 @jax.jit
                 def model_jitted(_A				:   Dict    ,   **_A				:   Optional[Any]       ):
                     return model(pixel_values=_A    ,   **_A       )

                 with self.subTest("JIT Enabled"       ):
                     snake_case								= model_jitted(**_A       ).to_tuple()

                 with self.subTest("JIT Disabled"       ):
                     with jax.disable_jit():
                         snake_case								= 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 		UpperCAmelCase(self				:   int       )						->						Optional[Any]:
         for model_class_name in self.all_model_classes:
             snake_case								= model_class_name.from_pretrained("google/vit-base-patch16-224"       )
             snake_case								= model(np.ones((1, 3, 2_2_4, 2_2_4)       )       )
             self.assertIsNotNone(_A       )



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A   =			{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A   =			[
       "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LiltForQuestionAnswering",
       "LiltForSequenceClassification",
       "LiltForTokenClassification",
       "LiltModel",
       "LiltPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_lilt import (
          LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
          LiltForQuestionAnswering,
          LiltForSequenceClassification,
          LiltForTokenClassification,
          LiltModel,
          LiltPreTrainedModel,
      )

else:
   import sys

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

