




'''simple docstring'''





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   ,_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   ,)				->     int:
          _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 							_lowercase      (					self       )				->     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=_SCREAMING_SNAKE_CASE   ,initializer_range=self.initializer_range   ,)

          return config, pixel_values






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     str:
          _snake_case				= FlaxViTModel(config=_SCREAMING_SNAKE_CASE       )
          _snake_case				= model(_SCREAMING_SNAKE_CASE       )
          # 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 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Any:
          _snake_case				= self.type_sequence_label_size
          _snake_case				= FlaxViTForImageClassification(config=_SCREAMING_SNAKE_CASE       )
          _snake_case				= model(_SCREAMING_SNAKE_CASE       )
          self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.type_sequence_label_size)       )

          # test greyscale images
          _snake_case				= 1
          _snake_case				= FlaxViTForImageClassification(_SCREAMING_SNAKE_CASE       )

          _snake_case				= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
          _snake_case				= model(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Tuple:
          _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   _a							(		__lowerCAmelCase   ,   unittest.TestCase				):
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






     def 							_lowercase      (					self       )				->     None:
          _snake_case				= FlaxViTModelTester(self       )
          _snake_case				= ConfigTester(self   ,config_class=_SCREAMING_SNAKE_CASE   ,has_text_modality=_SCREAMING_SNAKE_CASE   ,hidden_size=37       )






     def 							_lowercase      (					self       )				->     Optional[int]:
          self.config_tester.run_common_tests()






     def 							_lowercase      (					self       )				->     Union[str, Any]:
          _snake_case				= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Tuple:
          _snake_case				= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Optional[Any]:
          _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(_SCREAMING_SNAKE_CASE       )
               _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]   ,_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Optional[int]:
          _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(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
                    _snake_case				= 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"       ):
                         _snake_case				= model_jitted(**_SCREAMING_SNAKE_CASE       ).to_tuple()

                    with self.subTest("JIT Disabled"       ):
                         with jax.disable_jit():
                              _snake_case				= 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 							_lowercase      (					self       )				->     Dict:
          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, 224, 224)       )       )
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE       )








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_		:     List[Any]          =		{
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Optional[Any]          =		[
       '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''GitForCausalLM''',
       '''GitModel''',
       '''GitPreTrainedModel''',
       '''GitVisionModel''',
   ]

if TYPE_CHECKING:
   from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
   from .processing_git import GitProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_git import (
          GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          GitForCausalLM,
          GitModel,
          GitPreTrainedModel,
          GitVisionModel,
      )

else:
   import sys

   UpperCamelCase_		:     List[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


