


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 ):



 """simple docstring"""







 def __init__(		self :	Dict     ,						A_ :	str     ,						A_ :	Tuple=1_3     ,						A_ :	List[str]=3_0     ,						A_ :	Union[str, Any]=2     ,						A_ :	Optional[int]=3     ,						A_ :	int=True     ,						A_ :	str=True     ,						A_ :	Optional[Any]=3_2     ,						A_ :	Any=5     ,						A_ :	Optional[int]=4     ,						A_ :	Tuple=3_7     ,						A_ :	Union[str, Any]="gelu"     ,						A_ :	int=0.1     ,						A_ :	Optional[Any]=0.1     ,						A_ :	Dict=1_0     ,						A_ :	Any=0.02     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    parent
      _lowerCAmelCase   :					Dict        =    batch_size
      _lowerCAmelCase   :					List[str]        =    image_size
      _lowerCAmelCase   :					List[Any]        =    patch_size
      _lowerCAmelCase   :					List[Any]        =    num_channels
      _lowerCAmelCase   :					Optional[Any]        =    is_training
      _lowerCAmelCase   :					Optional[Any]        =    use_labels
      _lowerCAmelCase   :					Optional[Any]        =    hidden_size
      _lowerCAmelCase   :					Any        =    num_hidden_layers
      _lowerCAmelCase   :					List[Any]        =    num_attention_heads
      _lowerCAmelCase   :					str        =    intermediate_size
      _lowerCAmelCase   :					int        =    hidden_act
      _lowerCAmelCase   :					Optional[int]        =    hidden_dropout_prob
      _lowerCAmelCase   :					Optional[int]        =    attention_probs_dropout_prob
      _lowerCAmelCase   :					List[str]        =    type_sequence_label_size
      _lowerCAmelCase   :					Optional[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   :					List[str]        =    num_patches + 1

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

      _lowerCAmelCase   :					Optional[Any]        =    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    __magic_name__       (		self :	List[Any]     ,						A_ :	Optional[Any]     ,						A_ :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    FlaxViTModel(config=A_       )
      _lowerCAmelCase   :					List[str]        =    model(A_       )
      # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
      _lowerCAmelCase   :					Optional[Any]        =    (self.image_size, self.image_size)
      _lowerCAmelCase   :					Dict        =    (self.patch_size, self.patch_size)
      _lowerCAmelCase   :					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    __magic_name__       (		self :	str     ,						A_ :	List[Any]     ,						A_ :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    self.type_sequence_label_size
      _lowerCAmelCase   :					Dict        =    FlaxViTForImageClassification(config=A_       )
      _lowerCAmelCase   :					Any        =    model(A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.type_sequence_label_size)       )

      # test greyscale images
      _lowerCAmelCase   :					str        =    1
      _lowerCAmelCase   :					int        =    FlaxViTForImageClassification(A_       )

      _lowerCAmelCase   :					List[Any]        =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
      _lowerCAmelCase   :					Any        =    model(A_       )


 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




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





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



 """simple docstring"""







 _lowercase     :					int      =						(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    FlaxViTModelTester(self       )
      _lowerCAmelCase   :					str        =    ConfigTester(self     ,						config_class=A_     ,						has_text_modality=A_     ,						hidden_size=3_7       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      self.config_tester.run_common_tests()

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A_       )

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A_       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    self.model_tester.prepare_config_and_inputs_for_common()

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

           _lowerCAmelCase   :					Any        =    ["pixel_values"]
           self.assertListEqual(arg_names[:1]     ,						A_       )

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, 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   :					List[str]        =    self._prepare_for_class(A_     ,						A_       )
                _lowerCAmelCase   :					Optional[int]        =    model_class(A_       )

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

                with self.subTest("JIT Enabled"       ):
                     _lowerCAmelCase   :					Optional[Any]        =    model_jitted(**A_       ).to_tuple()

                with self.subTest("JIT Disabled"       ):
                     with jax.disable_jit():
                          _lowerCAmelCase   :					Optional[int]        =    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    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      for model_class_name in self.all_model_classes:
           _lowerCAmelCase   :					Tuple        =    model_class_name.from_pretrained("google/vit-base-patch16-224"       )
           _lowerCAmelCase   :					Optional[int]        =    model(np.ones((1, 3, 2_2_4, 2_2_4)       )       )
           self.assertIsNotNone(A_       )









import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	Tuple     ,						*A_ :	Optional[int]     ,						**A_ :	int       ):






      '''simple docstring'''




      warnings.warn(
          "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
          " Please use ChineseCLIPImageProcessor instead."     ,						A_     ,						)
      super().__init__(*A_     ,						**A_       )






