





import inspect
import unittest

from transformers import ResNetConfig, 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.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor



class 				_lowerCAmelCase			(	unittest.TestCase							):


  '''simple docstring'''







  def __init__(					self					:    List[str]					, UpperCamelCase					:    Tuple					, UpperCamelCase					:    List[str]=3					, UpperCamelCase					:    Union[str, Any]=32					, UpperCamelCase					:    Dict=3					, UpperCamelCase					:    str=10					, UpperCamelCase					:    str=[10, 20, 30, 40]					, UpperCamelCase					:    Any=[1, 1, 2, 1]					, UpperCamelCase					:    Optional[Any]=True					, UpperCamelCase					:    List[Any]=True					, UpperCamelCase					:    Any="relu"					, UpperCamelCase					:    int=3					, UpperCamelCase					:    Union[str, Any]=None					, ):


    '''simple docstring'''





    _snake_case      : Tuple														=		parent
    _snake_case      : List[Any]														=		batch_size
    _snake_case      : Union[str, Any]														=		image_size
    _snake_case      : Optional[Any]														=		num_channels
    _snake_case      : List[Any]														=		embeddings_size
    _snake_case      : Any														=		hidden_sizes
    _snake_case      : Union[str, Any]														=		depths
    _snake_case      : Tuple														=		is_training
    _snake_case      : Dict														=		use_labels
    _snake_case      : Union[str, Any]														=		hidden_act
    _snake_case      : str														=		num_labels
    _snake_case      : List[str]														=		scope
    _snake_case      : Optional[int]														=		len(UpperCamelCase							)







  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    _snake_case      : List[Any]														=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

    _snake_case      : Any														=		self.get_config()

    return config, pixel_values







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


    '''simple docstring'''





    return ResNetConfig(
        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 UpperCamelCase_       (					self					:    Dict					, UpperCamelCase					:    Dict					, UpperCamelCase					:    Union[str, Any]							):


    '''simple docstring'''





    _snake_case      : Any														=		FlaxResNetModel(config=UpperCamelCase							)
    _snake_case      : Optional[int]														=		model(UpperCamelCase							)

    # Output shape (b, c, h, w)
    self.parent.assertEqual(
        result.last_hidden_state.shape					, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					, )







  def UpperCamelCase_       (					self					:    Optional[Any]					, UpperCamelCase					:    Optional[int]					, UpperCamelCase					:    str							):


    '''simple docstring'''





    _snake_case      : Dict														=		self.num_labels
    _snake_case      : Any														=		FlaxResNetForImageClassification(config=UpperCamelCase							)
    _snake_case      : List[Any]														=		model(UpperCamelCase							)
    self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_labels)							)







  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    _snake_case      : Any														=		self.prepare_config_and_inputs()
    _snake_case							,    _snake_case      : Optional[int]														=		config_and_inputs
    _snake_case      : Any														=		{'pixel_values': pixel_values}
    return config, inputs_dict




@require_flax
class 				_lowerCAmelCase			(	UpperCAmelCase_							,			unittest.TestCase							):


  '''simple docstring'''
  a_							:				List[Any]					    =(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

  a_							:				Union[str, Any]					    =False
  a_							:				int					    =False
  a_							:				List[str]					    =False







  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    _snake_case      : Any														=		FlaxResNetModelTester(self							)
    _snake_case      : List[str]														=		ConfigTester(self					, config_class=UpperCamelCase					, has_text_modality=UpperCamelCase							)







  def UpperCamelCase_       (					self					:    Dict							):


    '''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 UpperCamelCase_       (					self					:    Union[str, Any]							):


    '''simple docstring'''





    return







  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    _snake_case      : Optional[Any]														=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase							)







  def UpperCamelCase_       (					self					:    Optional[int]							):


    '''simple docstring'''





    _snake_case      : Dict														=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCamelCase							)







  @unittest.skip(reason='ResNet does not use inputs_embeds'							)
  def UpperCamelCase_       (					self					:    Any							):


    '''simple docstring'''





    pass







  @unittest.skip(reason='ResNet does not support input and output embeddings'							)
  def UpperCamelCase_       (					self					:    Optional[Any]							):


    '''simple docstring'''





    pass







  def UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : Tuple														=		self.model_tester.prepare_config_and_inputs_for_common()

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

      _snake_case      : Dict														=		['pixel_values']
      self.assertListEqual(arg_names[:1]					, UpperCamelCase							)







  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    def check_hidden_states_output(UpperCamelCase					:    List[str]					, UpperCamelCase					:    int					, UpperCamelCase					:    Optional[Any]							):
      _snake_case      : Optional[Any]														=		model_class(UpperCamelCase							)

      _snake_case      : Union[str, Any]														=		model(**self._prepare_for_class(UpperCamelCase					, UpperCamelCase							)							)

      _snake_case      : Union[str, Any]														=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

      _snake_case      : str														=		self.model_tester.num_stages
      self.assertEqual(len(UpperCamelCase							)					, expected_num_stages + 1							)







  @unittest.skip(reason='ResNet does not use feedforward chunking'							)
  def UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    pass







  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : Any														=		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      : Tuple														=		self._prepare_for_class(UpperCamelCase					, UpperCamelCase							)
        _snake_case      : Tuple														=		model_class(UpperCamelCase							)

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

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

        with self.subTest('JIT Disabled'							):
          with jax.disable_jit():
            _snake_case      : Any														=		model_jitted(**UpperCamelCase							).to_tuple()

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




def 			lowerCamelCase_		(					)->						Optional[int]:
  _snake_case      : Dict														=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
  return image







@require_flax
class 				_lowerCAmelCase			(	unittest.TestCase							):


  '''simple docstring'''







  @cached_property
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    return AutoImageProcessor.from_pretrained('microsoft/resnet-50'							) if is_vision_available() else None







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


    '''simple docstring'''





    _snake_case      : List[str]														=		FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'							)

    _snake_case      : Optional[int]														=		self.default_image_processor
    _snake_case      : int														=		prepare_img()
    _snake_case      : int														=		image_processor(images=UpperCamelCase					, return_tensors='np'							)

    _snake_case      : List[Any]														=		model(**UpperCamelCase							)

    # verify the logits
    _snake_case      : Optional[int]														=		(1, 10_00)
    self.assertEqual(outputs.logits.shape					, UpperCamelCase							)

    _snake_case      : List[Any]														=		jnp.array([-11.10_69, -9.78_77, -8.37_77]							)

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











def 			lowerCamelCase_		(					lowerCAmelCase:		list			,							lowerCAmelCase:		list )->						list:
  _snake_case      : int														=		len(lowerCAmelCase )
  _snake_case      : int														=		len(lowerCAmelCase )
  _snake_case      : int														=		(
      first_input_list_length
      if first_input_list_length > second_input_list_length
      else second_input_list_length
  )
  _snake_case      : list														=		[]
  for char_count in range(lowerCAmelCase ):
    if char_count < first_input_list_length:
      output_result_list.append(first_input_list[char_count] )
    if char_count < second_input_list_length:
      output_result_list.append(second_input_list[char_count] )

  return output_result_list


if __name__ == "__main__":
      print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)




