


'''simple docstring'''






import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
 import jax
 import jax.numpy as jnp

 from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class  __magic_name__							(		unittest.TestCase    ):






       def __init__(      self  ,							snake_case  ,							snake_case=3  ,							snake_case=3_2  ,							snake_case=3  ,							snake_case=1_0  ,							snake_case=[1_0, 2_0, 3_0, 4_0]  ,							snake_case=[1, 1, 2, 1]  ,							snake_case=True  ,							snake_case=True  ,							snake_case="relu"  ,							snake_case=3  ,							snake_case=None  ,							)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =parent
          _UpperCAmelCase       :       Tuple							   =batch_size
          _UpperCAmelCase       :       int							   =image_size
          _UpperCAmelCase       :       Optional[Any]							   =num_channels
          _UpperCAmelCase       :       Optional[int]							   =embeddings_size
          _UpperCAmelCase       :       Optional[Any]							   =hidden_sizes
          _UpperCAmelCase       :       Optional[Any]							   =depths
          _UpperCAmelCase       :       Dict							   =is_training
          _UpperCAmelCase       :       List[str]							   =use_labels
          _UpperCAmelCase       :       Optional[Any]							   =hidden_act
          _UpperCAmelCase       :       Tuple							   =num_labels
          _UpperCAmelCase       :       Any							   =scope
          _UpperCAmelCase       :       Dict							   =len(snake_case)






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          _UpperCAmelCase       :       List[str]							   =self.get_config()

          return config, pixel_values






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          return RegNetConfig(
              num_channels=self.num_channels  ,							embeddings_size=self.embeddings_size  ,							hidden_sizes=self.hidden_sizes  ,							depths=self.depths  ,							hidden_act=self.hidden_act  ,							num_labels=self.num_labels  ,							image_size=self.image_size  ,							)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =FlaxRegNetModel(config=snake_case)
          _UpperCAmelCase       :       str							   =model(snake_case)

          # Output shape (b, c, h, w)
          self.parent.assertEqual(
              result.last_hidden_state.shape  ,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)  ,							)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   =self.num_labels
          _UpperCAmelCase       :       Optional[int]							   =FlaxRegNetForImageClassification(config=snake_case)
          _UpperCAmelCase       :       Tuple							   =model(snake_case)
          self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.num_labels))






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =self.prepare_config_and_inputs()
          _UpperCAmelCase   ,	_UpperCAmelCase       :       Dict							   =config_and_inputs
          _UpperCAmelCase       :       Dict							   ={'pixel_values': pixel_values}
          return config, inputs_dict






@require_flax
class  __magic_name__							(		lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

       UpperCAmelCase										=False
       UpperCAmelCase										=False
       UpperCAmelCase										=False






       def       lowerCAmelCase		(      self)						->  None:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =FlaxRegNetModelTester(self)
          _UpperCAmelCase       :       Dict							   =ConfigTester(self  ,							config_class=snake_case  ,							has_text_modality=snake_case)






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          return






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case)






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case)






       @unittest.skip(reason='RegNet does not use inputs_embeds')
       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          pass






       @unittest.skip(reason='RegNet does not support input and output embeddings')
       def       lowerCAmelCase		(      self)						->  List[Any]:




          '''simple docstring'''
          pass






       def       lowerCAmelCase		(      self)						->  List[Any]:




          '''simple docstring'''
          _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[Any]							   =self.model_tester.prepare_config_and_inputs_for_common()

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

             _UpperCAmelCase       :       List[str]							   =['pixel_values']
             self.assertListEqual(arg_names[:1]  ,							snake_case)






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          def check_hidden_states_output(snake_case  ,							snake_case  ,							snake_case):
             _UpperCAmelCase       :       List[Any]							   =model_class(snake_case)

             _UpperCAmelCase       :       str							   =model(**self._prepare_for_class(snake_case  ,							snake_case))

             _UpperCAmelCase       :       str							   =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             _UpperCAmelCase       :       List[Any]							   =self.model_tester.num_stages
             self.assertEqual(len(snake_case)  ,							expected_num_stages + 1)

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

          for model_class in self.all_model_classes:
             _UpperCAmelCase       :       List[str]							   =True
             check_hidden_states_output(snake_case  ,							snake_case  ,							snake_case)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             _UpperCAmelCase       :       List[Any]							   =True

             check_hidden_states_output(snake_case  ,							snake_case  ,							snake_case)






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase   ,	_UpperCAmelCase       :       str							   =self.model_tester.prepare_config_and_inputs_for_common()

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

                @jax.jit
                def model_jitted(snake_case  ,							**snake_case):
                   return model(pixel_values=snake_case  ,							**snake_case)

                with self.subTest('JIT Enabled'):
                   _UpperCAmelCase       :       Optional[int]							   =model_jitted(**snake_case).to_tuple()

                with self.subTest('JIT Disabled'):
                   with jax.disable_jit():
                      _UpperCAmelCase       :       Dict							   =model_jitted(**snake_case).to_tuple()

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






def lowerCamelCase__      (						):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
   return image






@require_flax
class  __magic_name__							(		unittest.TestCase    ):






       @cached_property
       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          return AutoImageProcessor.from_pretrained('facebook/regnet-y-040') if is_vision_available() else None






       @slow
       def       lowerCAmelCase		(      self)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040')

          _UpperCAmelCase       :       str							   =self.default_image_processor
          _UpperCAmelCase       :       Tuple							   =prepare_img()
          _UpperCAmelCase       :       str							   =image_processor(images=snake_case  ,							return_tensors='np')

          _UpperCAmelCase       :       str							   =model(**snake_case)

          # verify the logits
          _UpperCAmelCase       :       Optional[int]							   =(1, 1_0_0_0)
          self.assertEqual(outputs.logits.shape  ,							snake_case)

          _UpperCAmelCase       :       str							   =jnp.array([-0.41_80, -1.50_51, -3.48_36])

          self.assertTrue(jnp.allclose(outputs.logits[0, :3]  ,							snake_case  ,							atol=1E-4))





'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase							=logging.get_logger(__name__)

lowercase							={
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="levit"






       def __init__(      self  ,							snake_case=2_2_4  ,							snake_case=3  ,							snake_case=3  ,							snake_case=2  ,							snake_case=1  ,							snake_case=1_6  ,							snake_case=[1_2_8, 2_5_6, 3_8_4]  ,							snake_case=[4, 8, 1_2]  ,							snake_case=[4, 4, 4]  ,							snake_case=[1_6, 1_6, 1_6]  ,							snake_case=0  ,							snake_case=[2, 2, 2]  ,							snake_case=[2, 2, 2]  ,							snake_case=0.02  ,							**snake_case  ,							)						->  Any:




          '''simple docstring'''
          super().__init__(**snake_case)
          _UpperCAmelCase       :       List[str]							   =image_size
          _UpperCAmelCase       :       str							   =num_channels
          _UpperCAmelCase       :       int							   =kernel_size
          _UpperCAmelCase       :       Any							   =stride
          _UpperCAmelCase       :       Tuple							   =padding
          _UpperCAmelCase       :       Optional[int]							   =hidden_sizes
          _UpperCAmelCase       :       List[str]							   =num_attention_heads
          _UpperCAmelCase       :       List[Any]							   =depths
          _UpperCAmelCase       :       Any							   =key_dim
          _UpperCAmelCase       :       List[Any]							   =drop_path_rate
          _UpperCAmelCase       :       int							   =patch_size
          _UpperCAmelCase       :       Tuple							   =attention_ratio
          _UpperCAmelCase       :       Any							   =mlp_ratio
          _UpperCAmelCase       :       Optional[Any]							   =initializer_range
          _UpperCAmelCase       :       str							   =[
              ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
              ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
          ]






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										=version.parse("1.11"    )






       @property
       def       lowerCAmelCase		(      self)						->  Mapping[str, Mapping[int, str]]:




          '''simple docstring'''
          return OrderedDict(
              [
                  ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
              ])






       @property
       def       lowerCAmelCase		(      self)						->  float:




          '''simple docstring'''
          return 1E-4

