




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       :      str ,   __snake_case       :      str ,   __snake_case       :      List[str]=13 ,   __snake_case       :      Union[str, Any]=30 ,   __snake_case       :      Dict=2 ,   __snake_case       :      str=3 ,   __snake_case       :      List[Any]=True ,   __snake_case       :      Any=True ,   __snake_case       :      Tuple=32 ,   __snake_case       :      List[Any]=5 ,   __snake_case       :      Any=4 ,   __snake_case       :      Optional[int]=37 ,   __snake_case       :      Tuple="gelu" ,   __snake_case       :      Optional[int]=0.1 ,   __snake_case       :      Optional[Any]=0.1 ,   __snake_case       :      Dict=10 ,   __snake_case       :      int=0.02 ,   ):
									UpperCAmelCase_										=					parent
									UpperCAmelCase_										=					batch_size
									UpperCAmelCase_										=					image_size
									UpperCAmelCase_										=					patch_size
									UpperCAmelCase_										=					num_channels
									UpperCAmelCase_										=					is_training
									UpperCAmelCase_										=					use_labels
									UpperCAmelCase_										=					hidden_size
									UpperCAmelCase_										=					num_hidden_layers
									UpperCAmelCase_										=					num_attention_heads
									UpperCAmelCase_										=					intermediate_size
									UpperCAmelCase_										=					hidden_act
									UpperCAmelCase_										=					hidden_dropout_prob
									UpperCAmelCase_										=					attention_probs_dropout_prob
									UpperCAmelCase_										=					type_sequence_label_size
									UpperCAmelCase_										=					initializer_range

									# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									UpperCAmelCase_										=					(image_size // patch_size) ** 2
									UpperCAmelCase_										=					num_patches + 1
					def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
									UpperCAmelCase_										=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

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

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




					def   lowerCamelCase_       (     self       :      List[Any]    ):
									UpperCAmelCase_										=					self.prepare_config_and_inputs()
									(
									    UpperCAmelCase_
									)										=					config_and_inputs
									UpperCAmelCase_										=					{'pixel_values': pixel_values}
									return config, inputs_dict



@require_flax
class       a					(       snake_case_     ,       unittest.TestCase      ):

					'''simple docstring'''





					lowerCAmelCase    :	int       		=  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
					def   lowerCamelCase_       (     self       :      Dict    ):
									UpperCAmelCase_										=					FlaxViTModelTester(self    )
									UpperCAmelCase_										=					ConfigTester(self ,   config_class=_A ,   has_text_modality=_A ,   hidden_size=37    )
					def   lowerCamelCase_       (     self       :      Optional[Any]    ):
									self.config_tester.run_common_tests()
					def   lowerCamelCase_       (     self       :      Dict    ):
									UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_A    )
					def   lowerCamelCase_       (     self       :      Tuple    ):
									UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_A    )
					def   lowerCamelCase_       (     self       :      str    ):
									UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs_for_common()

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

													UpperCAmelCase_										=					['pixel_values']
													self.assertListEqual(arg_names[:1] ,   _A    )
					def   lowerCamelCase_       (     self       :      Dict    ):
									UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs_for_common()

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

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

																	with self.subTest('''JIT Enabled'''    ):
																					UpperCAmelCase_										=					model_jitted(**_A    ).to_tuple()

																	with self.subTest('''JIT Disabled'''    ):
																					with jax.disable_jit():
																									UpperCAmelCase_										=					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]    ):


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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase      			=						{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						['ConditionalDetrFeatureExtractor']
 _lowerCamelCase      			=						['ConditionalDetrImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
     'ConditionalDetrForObjectDetection',
     'ConditionalDetrForSegmentation',
     'ConditionalDetrModel',
     'ConditionalDetrPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

 _lowerCamelCase      			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





