





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 SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






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







							"""simple docstring"""


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






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







							"""simple docstring"""


							snake_case_		:      Dict           =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

							snake_case_		:      str           =						self.get_config()

							return config, pixel_values






						def UpperCAmelCase_					(   self   :   Any						)				->     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 UpperCAmelCase_					(   self   :   str		, _A   :   Union[str, Any]		, _A   :   Optional[int]						)				->     List[Any]:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						FlaxResNetModel(config=_A						)
							snake_case_		:      Dict           =						model(_A						)

							# 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   :   Dict		, _A   :   Dict		, _A   :   str						)				->     Union[str, Any]:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						self.num_labels
							snake_case_		:      Tuple           =						FlaxResNetForImageClassification(config=_A						)
							snake_case_		:      Tuple           =						model(_A						)
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_labels)						)






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







							"""simple docstring"""


							snake_case_		:      str           =						self.prepare_config_and_inputs()
							snake_case_    ,snake_case_		:      Tuple           =						config_and_inputs
							snake_case_		:      str           =						{'pixel_values': pixel_values}
							return config, inputs_dict
@require_flax
class SCREAMING_SNAKE_CASE_    (       snake_case_							,					unittest.TestCase       ):
						__magic_name__:      Dict			   =							(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

						__magic_name__:      Union[str, Any]			   =							False
						__magic_name__:      List[str]			   =							False
						__magic_name__:      Union[str, Any]			   =							False






						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     None:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						FlaxResNetModelTester(self						)
							snake_case_		:      Optional[Any]           =						ConfigTester(self		, config_class=_A		, has_text_modality=_A						)






						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     str:







							"""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   :   Any						)				->     List[Any]:







							"""simple docstring"""


							return






						def UpperCAmelCase_					(   self   :   Dict						)				->     List[Any]:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A						)






						def UpperCAmelCase_					(   self   :   Dict						)				->     Any:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_A						)






						@unittest.skip(reason='ResNet does not use inputs_embeds'						)
						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     Optional[Any]:







							"""simple docstring"""


							pass






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







							"""simple docstring"""


							pass






						def UpperCAmelCase_					(   self   :   List[str]						)				->     Optional[Any]:







							"""simple docstring"""


							snake_case_    ,snake_case_		:      int           =						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_		:      Dict           =						model_class(_A						)
								snake_case_		:      List[str]           =						inspect.signature(model.__call__						)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								snake_case_		:      Optional[Any]           =						[*signature.parameters.keys()]

								snake_case_		:      str           =						['pixel_values']
								self.assertListEqual(arg_names[:1]		, _A						)






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







							"""simple docstring"""


							def check_hidden_states_output(_A   :   List[str]		, _A   :   List[str]		, _A   :   Union[str, Any]						):
								snake_case_		:      List[str]           =						model_class(_A						)

								snake_case_		:      int           =						model(**self._prepare_for_class(_A		, _A						)						)

								snake_case_		:      Optional[Any]           =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

								snake_case_		:      Any           =						self.model_tester.num_stages
								self.assertEqual(len(_A						)		, expected_num_stages + 1						)






						@unittest.skip(reason='ResNet does not use feedforward chunking'						)
						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     List[str]:







							"""simple docstring"""


							pass






						def UpperCAmelCase_					(   self   :   Tuple						)				->     str:







							"""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_		:      Union[str, Any]           =						self._prepare_for_class(_A		, _A						)
									snake_case_		:      Any           =						model_class(_A						)

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

									with self.subTest('JIT Enabled'						):
										snake_case_		:      Optional[Any]           =						model_jitted(**_A						).to_tuple()

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

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


def 				SCREAMING_SNAKE_CASE__    (     ):
	snake_case_		:      List[str]           =						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
	return image


@require_flax
class SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






						@cached_property
						def UpperCAmelCase_					(   self   :   str						)				->     str:







							"""simple docstring"""


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






						@slow
						def UpperCAmelCase_					(   self   :   List[str]						)				->     List[str]:







							"""simple docstring"""


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

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

							snake_case_		:      Any           =						model(**_A						)

							# verify the logits
							snake_case_		:      Any           =						(1, 1000)
							self.assertEqual(outputs.logits.shape		, _A						)

							snake_case_		:      List[str]           =						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]		, _A		, atol=1E-4						)						)



'''simple docstring'''





import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

a__		: Tuple  							=							[(1,)]





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







      """simple docstring"""
      __A													=      bool(os.getenv('RUN_SLOW'   ,	0						)						)
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"





def 							snake_case				(		)->      Tuple:







      """simple docstring"""
      __A													=      bool(os.getenv('RUN_SLOW'   ,	0						)						)
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"


@require_torch
class 			UpperCamelCase__	(     unittest.TestCase):






 @slow
 @require_torch_gpu
 def 						lowercase_    (      self	:Dict      )					->      List[Any]:







       '''simple docstring'''



       check_slow_torch_cuda()






 @require_torch_gpu
 @slow
 def 						lowercase_    (      self	:int      )					->      int:







       '''simple docstring'''



       check_slow_torch_cuda()






 @parameterized.expand(_A      )
 @slow
 def 						lowercase_    (      self	:Dict						,					_A	:Union[str, Any]=None      )					->      Dict:







       '''simple docstring'''



       check_slow()





@slow
@require_torch_gpu
def 							snake_case				(		)->      Optional[int]:







      """simple docstring"""
      check_slow_torch_cuda()





@require_torch_gpu
@slow
def 							snake_case				(		)->      List[str]:







      """simple docstring"""
      check_slow_torch_cuda()





@slow
@pytest.mark.parametrize('param'   ,	[1]						)
def 							snake_case				(		UpperCAmelCase						)->      int:







      """simple docstring"""
      check_slow()





@pytest.mark.parametrize('param'   ,	[1]						)
@slow
def 							snake_case				(		UpperCAmelCase						)->      Any:







      """simple docstring"""
      check_slow()






