
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	,  )					->   Dict:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




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

								__a    							=					self.get_config()

								return config, pixel_values




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[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        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case       )					->   int:


								'''simple docstring'''




								__a    							=					FlaxResNetModel(config=_snake_case       )
								__a    							=					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        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case       )					->   str:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								__a    							=					self.prepare_config_and_inputs()
								__a							,  __a    							=					config_and_inputs
								__a    							=					{'''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        SCREAMING_SNAKE_CASE_	(  self       )					->   None:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[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        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[int]:


								'''simple docstring'''




								return




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


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




								__a    							=					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        SCREAMING_SNAKE_CASE_	(  self       )					->   Any:


								'''simple docstring'''




								pass




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


								'''simple docstring'''




								pass




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Any:


								'''simple docstring'''




								__a							,  __a    							=					self.model_tester.prepare_config_and_inputs_for_common()

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

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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Union[str, Any]:


								'''simple docstring'''




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

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

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

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




				@unittest.skip(reason='''ResNet does not use feedforward chunking'''       )
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								pass




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[int]:


								'''simple docstring'''




								__a							,  __a    							=					self.model_tester.prepare_config_and_inputs_for_common()

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

																with self.subTest('''JIT Disabled'''       ):
																				with jax.disable_jit():
																								__a    							=					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    (    ) ->						List[Any]:
				__a    							=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
				return image







@require_flax
class     __A(				unittest.TestCase    ):




				@cached_property
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




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




				@slow
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




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

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

								__a    							=					model(**_snake_case       )

								# verify the logits
								__a    							=					(1, 1_000)
								self.assertEqual(outputs.logits.shape	,  _snake_case       )

								__a    							=					jnp.array([-11.1069, -9.7877, -8.3777]       )

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

from itertools import compress, repeat
from math import ceil, sqrt


def 					__lowerCAmelCase    (    a__						) ->						list[int]:

				if num <= 2:
								return []
				if num == 3:
								return [2]

				# Odd sieve for numbers in range [3, num - 1]
				__a    							=					bytearray(B'''\x01'''						) * ((num >> 1) - 1)

				for i in range(3		,					int(sqrt(a__						)						) + 1		,					2						):
								if sieve[(i >> 1) - 1]:
												__a    							=					i**2
												__a    							=					repeat(
												    0		,					ceil((num - i_squared) / (i << 1)						)						)

				return [2] + list(compress(range(3		,					a__		,					2						)		,					a__						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()