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 __snake_case       (		unittest.TestCase	):




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   parent
     SCREAMING_SNAKE_CASE__    							=   batch_size
     SCREAMING_SNAKE_CASE__    							=   image_size
     SCREAMING_SNAKE_CASE__    							=   patch_size
     SCREAMING_SNAKE_CASE__    							=   num_channels
     SCREAMING_SNAKE_CASE__    							=   is_training
     SCREAMING_SNAKE_CASE__    							=   use_labels
     SCREAMING_SNAKE_CASE__    							=   hidden_size
     SCREAMING_SNAKE_CASE__    							=   num_hidden_layers
     SCREAMING_SNAKE_CASE__    							=   num_attention_heads
     SCREAMING_SNAKE_CASE__    							=   intermediate_size
     SCREAMING_SNAKE_CASE__    							=   hidden_act
     SCREAMING_SNAKE_CASE__    							=   hidden_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   type_sequence_label_size
     SCREAMING_SNAKE_CASE__    							=   initializer_range

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




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

     SCREAMING_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=_lowercase ,			initializer_range=self.initializer_range ,			)

     return config, pixel_values




   def 			__a		(					self    :       Dict ,			_lowercase    :       str ,			_lowercase    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   FlaxViTModel(config=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase   )
     # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
     SCREAMING_SNAKE_CASE__    							=   (self.image_size, self.image_size)
     SCREAMING_SNAKE_CASE__    							=   (self.patch_size, self.patch_size)
     SCREAMING_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 			__a		(					self    :       Any ,			_lowercase    :       Optional[int] ,			_lowercase    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.type_sequence_label_size
     SCREAMING_SNAKE_CASE__    							=   FlaxViTForImageClassification(config=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase   )
     self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.type_sequence_label_size)   )

     # test greyscale images
     SCREAMING_SNAKE_CASE__    							=   1
     SCREAMING_SNAKE_CASE__    							=   FlaxViTForImageClassification(_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase   )




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE__  
     )							,  (
         SCREAMING_SNAKE_CASE__  
     )							,    
     )    							=   config_and_inputs
     SCREAMING_SNAKE_CASE__    							=   {"""pixel_values""": pixel_values}
     return config, inputs_dict







@require_flax
class __snake_case       (		lowerCamelCase_     ,	unittest.TestCase	):
   lowerCAmelCase_														=							(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   FlaxViTModelTester(self   )
     SCREAMING_SNAKE_CASE__    							=   ConfigTester(self ,			config_class=_lowercase ,			has_text_modality=_lowercase ,			hidden_size=37   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowercase   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowercase   )




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs_for_common()

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

       SCREAMING_SNAKE_CASE__    							=   ["""pixel_values"""]
       self.assertListEqual(arg_names[:1] ,			_lowercase   )




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,  SCREAMING_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__   ):
         SCREAMING_SNAKE_CASE__    							=   self._prepare_for_class(_lowercase ,			_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   model_class(_lowercase   )

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

         with self.subTest("""JIT Enabled"""   ):
           SCREAMING_SNAKE_CASE__    							=   model_jitted(**_lowercase   ).to_tuple()

         with self.subTest("""JIT Disabled"""   ):
           with jax.disable_jit():
             SCREAMING_SNAKE_CASE__    							=   model_jitted(**_lowercase   ).to_tuple()

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




   @slow
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     for model_class_name in self.all_model_classes:
       SCREAMING_SNAKE_CASE__    							=   model_class_name.from_pretrained("""google/vit-base-patch16-224"""   )
       SCREAMING_SNAKE_CASE__    							=   model(np.ones((1, 3, 2_24, 2_24)   )   )
       self.assertIsNotNone(_lowercase   )

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase    :				List[Any]							     =	logging.get_logger(__name__)

__lowerCamelCase    :				str							     =	{
    '''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/resolve/main/config.json''',
}







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"timesformer"




   def __init__(					self    :       Any ,			_lowercase    :       Tuple=2_24 ,			_lowercase    :       Tuple=16 ,			_lowercase    :       List[Any]=3 ,			_lowercase    :       str=8 ,			_lowercase    :       Optional[Any]=7_68 ,			_lowercase    :       List[str]=12 ,			_lowercase    :       List[str]=12 ,			_lowercase    :       Dict=30_72 ,			_lowercase    :       List[str]="gelu" ,			_lowercase    :       List[str]=0.0 ,			_lowercase    :       List[str]=0.0 ,			_lowercase    :       Tuple=0.02 ,			_lowercase    :       List[Any]=1E-6 ,			_lowercase    :       List[str]=True ,			_lowercase    :       Dict="divided_space_time" ,			_lowercase    :       Union[str, Any]=0 ,			**_lowercase    :       Union[str, Any] ,			):



     """simple docstring"""






     super().__init__(**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   image_size
     SCREAMING_SNAKE_CASE__    							=   patch_size
     SCREAMING_SNAKE_CASE__    							=   num_channels
     SCREAMING_SNAKE_CASE__    							=   num_frames

     SCREAMING_SNAKE_CASE__    							=   hidden_size
     SCREAMING_SNAKE_CASE__    							=   num_hidden_layers
     SCREAMING_SNAKE_CASE__    							=   num_attention_heads
     SCREAMING_SNAKE_CASE__    							=   intermediate_size
     SCREAMING_SNAKE_CASE__    							=   hidden_act
     SCREAMING_SNAKE_CASE__    							=   hidden_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   initializer_range
     SCREAMING_SNAKE_CASE__    							=   layer_norm_eps
     SCREAMING_SNAKE_CASE__    							=   qkv_bias

     SCREAMING_SNAKE_CASE__    							=   attention_type
     SCREAMING_SNAKE_CASE__    							=   drop_path_rate
