





"""simple docstring"""
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 	a				(					unittest.TestCase   ):



 def __init__(			self							,    _snake_case							,    _snake_case=3							,    _snake_case=32							,    _snake_case=3							,    _snake_case=10							,    _snake_case=[10, 20, 30, 40]							,    _snake_case=[1, 1, 2, 1]							,    _snake_case=True							,    _snake_case=True							,    _snake_case="relu"							,    _snake_case=3							,    _snake_case=None							,    ):







  """simple docstring"""






  lowerCAmelCase  							=      parent
  lowerCAmelCase  							=      batch_size
  lowerCAmelCase  							=      image_size
  lowerCAmelCase  							=      num_channels
  lowerCAmelCase  							=      embeddings_size
  lowerCAmelCase  							=      hidden_sizes
  lowerCAmelCase  							=      depths
  lowerCAmelCase  							=      is_training
  lowerCAmelCase  							=      use_labels
  lowerCAmelCase  							=      hidden_act
  lowerCAmelCase  							=      num_labels
  lowerCAmelCase  							=      scope
  lowerCAmelCase  							=      len(_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

  lowerCAmelCase  							=      self.get_config()

  return config, pixel_values



 def 					UpperCamelCase__   (			self			):







  """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							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      FlaxResNetModel(config=_snake_case			)
  lowerCAmelCase  							=      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 // 32, self.image_size // 32)							,    )



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      self.num_labels
  lowerCAmelCase  							=      FlaxResNetForImageClassification(config=_snake_case			)
  lowerCAmelCase  							=      model(_snake_case			)
  self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.prepare_config_and_inputs()
  lowerCAmelCase							,lowerCAmelCase  							=      config_and_inputs
  lowerCAmelCase  							=      {'pixel_values': pixel_values}
  return config, inputs_dict







@require_flax
class 	a				(					a__					,      unittest.TestCase   ):
 snake_case__    	=    (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

 snake_case__    	=    False
 snake_case__    	=    False
 snake_case__    	=    False



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      FlaxResNetModelTester(self			)
  lowerCAmelCase  							=      ConfigTester(self							,    config_class=_snake_case							,    has_text_modality=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """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			):







  """simple docstring"""






  return



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*_snake_case			)



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







  """simple docstring"""






  pass



 @unittest.skip(reason='ResNet does not support input and output embeddings'			)
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  pass



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase							,lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs_for_common()

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

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



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  def check_hidden_states_output(_snake_case							,    _snake_case							,    _snake_case			):
   lowerCAmelCase  							=      model_class(_snake_case			)

   lowerCAmelCase  							=      model(**self._prepare_for_class(_snake_case							,    _snake_case			)			)

   lowerCAmelCase  							=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

   lowerCAmelCase  							=      self.model_tester.num_stages
   self.assertEqual(len(_snake_case			)							,    expected_num_stages + 1			)



 @unittest.skip(reason='ResNet does not use feedforward chunking'			)
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  pass



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase							,lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__			):
    lowerCAmelCase  							=      self._prepare_for_class(_snake_case							,    _snake_case			)
    lowerCAmelCase  							=      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'			):
     lowerCAmelCase  							=      model_jitted(**_snake_case			).to_tuple()

    with self.subTest('JIT Disabled'			):
     with jax.disable_jit():
      lowerCAmelCase  							=      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 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
 return image







@require_flax
class 	a				(					unittest.TestCase   ):



 @cached_property
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'			)

  lowerCAmelCase  							=      self.default_image_processor
  lowerCAmelCase  							=      prepare_img()
  lowerCAmelCase  							=      image_processor(images=_snake_case							,    return_tensors='np'			)

  lowerCAmelCase  							=      model(**_snake_case			)

  # verify the logits
  lowerCAmelCase  							=      (1, 10_00)
  self.assertEqual(outputs.logits.shape							,    _snake_case			)

  lowerCAmelCase  							=      jnp.array([-11.1_069, -9.7_877, -8.3_777]			)

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








'''simple docstring'''
from __future__ import annotations


def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> set[int]:


	"""simple docstring"""





	UpperCAmelCase_			:	Dict      =   {1}
	UpperCAmelCase_			:	int      =   [1]
	for _ in range(2     ,							depth + 1							):
		UpperCAmelCase_			:	Dict      =   [*previous_coefficients, 0]
		UpperCAmelCase_			:	Tuple      =   [0, *previous_coefficients]
		UpperCAmelCase_			:	Dict      =   []
		for x, y in zip(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							):
			coefficients.add(x + y							)
			previous_coefficients.append(x + y							)
	return coefficients


def       a__    (  _SCREAMING_SNAKE_CASE					:    set[int]							)			-> set[int]:


	"""simple docstring"""





	UpperCAmelCase_			:	List[Any]      =   set()
	for number in unique_coefficients:
		UpperCAmelCase_			:	Tuple      =   2
		UpperCAmelCase_			:	Tuple      =   number
		while divisor**2 <= copy_number:
			UpperCAmelCase_			:	List[Any]      =   0
			while copy_number % divisor == 0:
				copy_number //= divisor
				multiplicity += 1
			if multiplicity >= 2:
				non_squarefrees.add(_SCREAMING_SNAKE_CASE							)
				break
			divisor += 1

	return unique_coefficients.difference(_SCREAMING_SNAKE_CASE							)


def       a__    (  _SCREAMING_SNAKE_CASE					:    int = 51							)			-> int:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[Any]      =   get_pascal_triangle_unique_coefficients(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	Union[str, Any]      =   get_squarefrees(_SCREAMING_SNAKE_CASE							)
	return sum(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
					print(f"""{solution() = }""")




