


"""simple docstring"""
import math
from collections.abc import Generator







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					List[str]:
							snake_case_     							=		(i for i in range(1					,			(max_n + 1)					))
							for i in (n for n in numbers if n > 1):
														for j in range(2					,			_A					):
																					if (i % j) == 0:
																												break
														else:
																					yield i







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					Optional[int]:
							snake_case_     							=		(i for i in range(1					,			(max_n + 1)					))
							for i in (n for n in numbers if n > 1):
														# only need to check for factors up to sqrt(i)
														snake_case_     							=		int(math.sqrt(_A					)					) + 1
														for j in range(2					,			_A					):
																					if (i % j) == 0:
																												break
														else:
																					yield i







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					Optional[int]:
							snake_case_     							=		(i for i in range(1					,			(max_n + 1)					,			2					))
							# It's useless to test even numbers as they will not be prime
							if max_n > 2:
														yield 2  # Because 2 will not be tested, it's necessary to yield it now
							for i in (n for n in numbers if n > 1):
														snake_case_     							=		int(math.sqrt(_A					)					) + 1
														for j in range(3					,			_A					,			2					):
																					# As we removed the even numbers, we don't need them now
																					if (i % j) == 0:
																												break
														else:
																					yield i







def     _a					(   )     ->					Dict:
							from timeit import timeit

							snake_case_     							=		"from __main__ import slow_primes, primes, fast_primes"
							print(timeit("""slow_primes(1_000_000_000_000)"""					,			setup=_A					,			number=1_000_000					)					)
							print(timeit("""primes(1_000_000_000_000)"""					,			setup=_A					,			number=1_000_000					)					)
							print(timeit("""fast_primes(1_000_000_000_000)"""					,			setup=_A					,			number=1_000_000					)					)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE				:     str		=     int(input('Calculate primes up to:\n>> ').strip())
				for ret in primes(number):
								print(ret)
				benchmark()







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      _lowercase					(				unittest.TestCase):


       """simple docstring"""




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


             '''simple docstring'''



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





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



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

             lowerCamelCase__						:							Any       =    self.get_config()

             return config, pixel_values





       def 		lowerCAmelCase	(  self				:     Optional[int]			):


             '''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 		lowerCAmelCase	(  self				:     str   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    FlaxResNetModel(config=__lowerCamelCase			)
             lowerCamelCase__						:							str       =    model(__lowerCamelCase			)

             # 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 		lowerCAmelCase	(  self				:     int   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    self.num_labels
             lowerCamelCase__						:							int       =    FlaxResNetForImageClassification(config=__lowerCamelCase			)
             lowerCamelCase__						:							Optional[int]       =    model(__lowerCamelCase			)
             self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)			)





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    self.prepare_config_and_inputs()
             lowerCamelCase__       ,  lowerCamelCase__						:							Tuple       =    config_and_inputs
             lowerCamelCase__						:							Optional[int]       =    {"pixel_values": pixel_values}
             return config, inputs_dict







@require_flax
class      _lowercase					(				lowercase__				,    unittest.TestCase):


       """simple docstring"""




       A__   		=     (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

       A__   		=     False
       A__   		=     False
       A__   		=     False





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    FlaxResNetModelTester(self			)
             lowerCamelCase__						:							Dict       =    ConfigTester(self   , config_class=__lowerCamelCase   , has_text_modality=__lowerCamelCase			)





       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				:     Dict			):


             '''simple docstring'''



             return





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





       @unittest.skip(reason="ResNet does not use inputs_embeds"			)
       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             pass





       @unittest.skip(reason="ResNet does not support input and output embeddings"			)
       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



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

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

                   lowerCamelCase__						:							List[Any]       =    ["pixel_values"]
                   self.assertListEqual(arg_names[:1]   , __lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             def check_hidden_states_output(__lowerCamelCase				:     List[str]   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     str			):
                   lowerCamelCase__						:							Any       =    model_class(__lowerCamelCase			)

                   lowerCamelCase__						:							Dict       =    model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase			)			)

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

                   lowerCamelCase__						:							Tuple       =    self.model_tester.num_stages
                   self.assertEqual(len(__lowerCamelCase			)   , expected_num_stages + 1			)





       @unittest.skip(reason="ResNet does not use feedforward chunking"			)
       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__       ,  lowerCamelCase__						:							Dict       =    self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__			):
                         lowerCamelCase__						:							Any       =    self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase			)
                         lowerCamelCase__						:							Optional[int]       =    model_class(__lowerCamelCase			)

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

                         with self.subTest("JIT Enabled"			):
                               lowerCamelCase__						:							Any       =    model_jitted(**__lowerCamelCase			).to_tuple()

                         with self.subTest("JIT Disabled"			):
                               with jax.disable_jit():
                                     lowerCamelCase__						:							Union[str, Any]       =    model_jitted(**__lowerCamelCase			).to_tuple()

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



def  lowercase_						(						):

      """simple docstring"""




      lowerCamelCase__						:							Optional[Any]       =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
      return image







@require_flax
class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       @cached_property
       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



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





       @slow
       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[Any]       =    FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50"			)

             lowerCamelCase__						:							List[str]       =    self.default_image_processor
             lowerCamelCase__						:							Optional[Any]       =    prepare_img()
             lowerCamelCase__						:							str       =    image_processor(images=__lowerCamelCase   , return_tensors="np"			)

             lowerCamelCase__						:							str       =    model(**__lowerCamelCase			)

             # verify the logits
             lowerCamelCase__						:							int       =    (1, 1000)
             self.assertEqual(outputs.logits.shape   , __lowerCamelCase			)

             lowerCamelCase__						:							Optional[int]       =    jnp.array([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]			)

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


