


'''simple docstring'''


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      List[str]      	=  (PNDMScheduler,)
							A_							:      Any      	=  (("""num_inference_steps""", 50),)






							def UpperCAmelCase_	(    self    ,					**__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Dict     		=      {
											    """num_train_timesteps""": 1000,
											    """beta_start""": 0.0001,
											    """beta_end""": 0.02,
											    """beta_schedule""": """linear""",
											}

											config.update(**__snake_case  )
											return config






							def UpperCAmelCase_	(    self    ,					__snake_case=0    ,					**__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      dict(self.forward_default_kwargs  )
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      kwargs.pop("""num_inference_steps"""    ,					__snake_case  )
											_SCREAMING_SNAKE_CASE :       List[str]     		=      self.dummy_sample
											_SCREAMING_SNAKE_CASE :       List[str]     		=      0.1 * sample
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
															_SCREAMING_SNAKE_CASE :       Any     		=      self.get_scheduler_config(**__snake_case  )
															_SCREAMING_SNAKE_CASE :       int     		=      scheduler_class(**__snake_case  )
															scheduler.set_timesteps(__snake_case  )
															# copy over dummy past residuals
															_SCREAMING_SNAKE_CASE :       Any     		=      dummy_past_residuals[:]

															with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(__snake_case  )
																			_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      scheduler_class.from_pretrained(__snake_case  )
																			new_scheduler.set_timesteps(__snake_case  )
																			# copy over dummy past residuals
																			_SCREAMING_SNAKE_CASE :       Tuple     		=      dummy_past_residuals[:]

															_SCREAMING_SNAKE_CASE :       Dict     		=      scheduler.step_prk(__snake_case    ,					__snake_case    ,					__snake_case    ,					**__snake_case  ).prev_sample
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      new_scheduler.step_prk(__snake_case    ,					__snake_case    ,					__snake_case    ,					**__snake_case  ).prev_sample

															assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

															_SCREAMING_SNAKE_CASE :       Dict     		=      scheduler.step_plms(__snake_case    ,					__snake_case    ,					__snake_case    ,					**__snake_case  ).prev_sample
															_SCREAMING_SNAKE_CASE :       List[str]     		=      new_scheduler.step_plms(__snake_case    ,					__snake_case    ,					__snake_case    ,					**__snake_case  ).prev_sample

															assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"






							def UpperCAmelCase_	(    self  ):
											pass






							def UpperCAmelCase_	(    self    ,					__snake_case=0    ,					**__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Dict     		=      dict(self.forward_default_kwargs  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      kwargs.pop("""num_inference_steps"""    ,					__snake_case  )
											_SCREAMING_SNAKE_CASE :       Any     		=      self.dummy_sample
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      0.1 * sample
											_SCREAMING_SNAKE_CASE :       Tuple     		=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
															_SCREAMING_SNAKE_CASE :       int     		=      self.get_scheduler_config()
															_SCREAMING_SNAKE_CASE :       Dict     		=      scheduler_class(**__snake_case  )
															scheduler.set_timesteps(__snake_case  )

															# copy over dummy past residuals (must be after setting timesteps)
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      dummy_past_residuals[:]

															with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(__snake_case  )
																			_SCREAMING_SNAKE_CASE :       str     		=      scheduler_class.from_pretrained(__snake_case  )
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(__snake_case  )

																			# copy over dummy past residual (must be after setting timesteps)
																			_SCREAMING_SNAKE_CASE :       Tuple     		=      dummy_past_residuals[:]

															_SCREAMING_SNAKE_CASE :       Tuple     		=      scheduler.step_prk(__snake_case    ,					__snake_case    ,					__snake_case    ,					**__snake_case  ).prev_sample
															_SCREAMING_SNAKE_CASE :       Any     		=      new_scheduler.step_prk(__snake_case    ,					__snake_case    ,					__snake_case    ,					**__snake_case  ).prev_sample

															assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

															_SCREAMING_SNAKE_CASE :       List[str]     		=      scheduler.step_plms(__snake_case    ,					__snake_case    ,					__snake_case    ,					**__snake_case  ).prev_sample
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      new_scheduler.step_plms(__snake_case    ,					__snake_case    ,					__snake_case    ,					**__snake_case  ).prev_sample

															assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"






							def UpperCAmelCase_	(    self    ,					**__snake_case  ):
											_SCREAMING_SNAKE_CASE :       int     		=      self.scheduler_classes[0]
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.get_scheduler_config(**__snake_case  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      scheduler_class(**__snake_case  )

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      10
											_SCREAMING_SNAKE_CASE :       Dict     		=      self.dummy_model()
											_SCREAMING_SNAKE_CASE :       int     		=      self.dummy_sample_deter
											scheduler.set_timesteps(__snake_case  )

											for i, t in enumerate(scheduler.prk_timesteps  ):
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      model(__snake_case    ,					__snake_case  )
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      scheduler.step_prk(__snake_case    ,					__snake_case    ,					__snake_case  ).prev_sample

											for i, t in enumerate(scheduler.plms_timesteps  ):
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      model(__snake_case    ,					__snake_case  )
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      scheduler.step_plms(__snake_case    ,					__snake_case    ,					__snake_case  ).prev_sample

											return sample






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      dict(self.forward_default_kwargs  )

											_SCREAMING_SNAKE_CASE :       str     		=      kwargs.pop("""num_inference_steps"""    ,					__snake_case  )

											for scheduler_class in self.scheduler_classes:
															_SCREAMING_SNAKE_CASE :       Any     		=      self.get_scheduler_config()
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      scheduler_class(**__snake_case  )

															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.dummy_sample
															_SCREAMING_SNAKE_CASE :       int     		=      0.1 * sample

															if num_inference_steps is not None and hasattr(__snake_case    ,					"""set_timesteps"""  ):
																			scheduler.set_timesteps(__snake_case  )
															elif num_inference_steps is not None and not hasattr(__snake_case    ,					"""set_timesteps"""  ):
																			_SCREAMING_SNAKE_CASE :       Any     		=      num_inference_steps

															# copy over dummy past residuals (must be done after set_timesteps)
															_SCREAMING_SNAKE_CASE :       List[str]     		=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
															_SCREAMING_SNAKE_CASE :       Any     		=      dummy_past_residuals[:]

															_SCREAMING_SNAKE_CASE :       Any     		=      scheduler.step_prk(__snake_case    ,					0    ,					__snake_case    ,					**__snake_case  ).prev_sample
															_SCREAMING_SNAKE_CASE :       Any     		=      scheduler.step_prk(__snake_case    ,					1    ,					__snake_case    ,					**__snake_case  ).prev_sample

															self.assertEqual(output_a.shape    ,					sample.shape  )
															self.assertEqual(output_a.shape    ,					output_a.shape  )

															_SCREAMING_SNAKE_CASE :       List[Any]     		=      scheduler.step_plms(__snake_case    ,					0    ,					__snake_case    ,					**__snake_case  ).prev_sample
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      scheduler.step_plms(__snake_case    ,					1    ,					__snake_case    ,					**__snake_case  ).prev_sample

															self.assertEqual(output_a.shape    ,					sample.shape  )
															self.assertEqual(output_a.shape    ,					output_a.shape  )






							def UpperCAmelCase_	(    self  ):
											for timesteps in [100, 1000]:
															self.check_over_configs(num_train_timesteps=__snake_case  )






							def UpperCAmelCase_	(    self  ):
											for steps_offset in [0, 1]:
															self.check_over_configs(steps_offset=__snake_case  )

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.scheduler_classes[0]
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.get_scheduler_config(steps_offset=1  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      scheduler_class(**__snake_case  )
											scheduler.set_timesteps(10  )
											assert torch.equal(
											    scheduler.timesteps    ,					torch.LongTensor(
											        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]  )    ,					)






							def UpperCAmelCase_	(    self  ):
											for beta_start, beta_end in zip([0.0001, 0.001]    ,					[0.002, 0.02]  ):
															self.check_over_configs(beta_start=__snake_case    ,					beta_end=__snake_case  )






							def UpperCAmelCase_	(    self  ):
											for schedule in ["linear", "squaredcos_cap_v2"]:
															self.check_over_configs(beta_schedule=__snake_case  )






							def UpperCAmelCase_	(    self  ):
											for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=__snake_case  )






							def UpperCAmelCase_	(    self  ):
											for t in [1, 5, 10]:
															self.check_over_forward(time_step=__snake_case  )






							def UpperCAmelCase_	(    self  ):
											for t, num_inference_steps in zip([1, 5, 10]    ,					[10, 50, 100]  ):
															self.check_over_forward(num_inference_steps=__snake_case  )






							def UpperCAmelCase_	(    self  ):
											# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
											_SCREAMING_SNAKE_CASE :       Any     		=      27

											for scheduler_class in self.scheduler_classes:
															_SCREAMING_SNAKE_CASE :       str     		=      self.dummy_sample
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      0.1 * sample

															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.get_scheduler_config()
															_SCREAMING_SNAKE_CASE :       Any     		=      scheduler_class(**__snake_case  )

															scheduler.set_timesteps(__snake_case  )

															# before power of 3 fix, would error on first step, so we only need to do two
															for i, t in enumerate(scheduler.prk_timesteps[:2]  ):
																			_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      scheduler.step_prk(__snake_case    ,					__snake_case    ,					__snake_case  ).prev_sample






							def UpperCAmelCase_	(    self  ):
											with self.assertRaises(__snake_case  ):
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.scheduler_classes[0]
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.get_scheduler_config()
															_SCREAMING_SNAKE_CASE :       Any     		=      scheduler_class(**__snake_case  )

															scheduler.step_plms(self.dummy_sample    ,					1    ,					self.dummy_sample  ).prev_sample






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.full_loop()
											_SCREAMING_SNAKE_CASE :       List[str]     		=      torch.sum(torch.abs(__snake_case  )  )
											_SCREAMING_SNAKE_CASE :       str     		=      torch.mean(torch.abs(__snake_case  )  )

											assert abs(result_sum.item() - 198.1318  ) < 1e-2
											assert abs(result_mean.item() - 0.2580  ) < 1e-3






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.full_loop(prediction_type="""v_prediction"""  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      torch.sum(torch.abs(__snake_case  )  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      torch.mean(torch.abs(__snake_case  )  )

											assert abs(result_sum.item() - 67.3986  ) < 1e-2
											assert abs(result_mean.item() - 0.0878  ) < 1e-3






							def UpperCAmelCase_	(    self  ):
											# We specify different beta, so that the first alpha is 0.99
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.full_loop(set_alpha_to_one=__snake_case    ,					beta_start=0.01  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      torch.sum(torch.abs(__snake_case  )  )
											_SCREAMING_SNAKE_CASE :       str     		=      torch.mean(torch.abs(__snake_case  )  )

											assert abs(result_sum.item() - 230.0399  ) < 1e-2
											assert abs(result_mean.item() - 0.2995  ) < 1e-3






							def UpperCAmelCase_	(    self  ):
											# We specify different beta, so that the first alpha is 0.99
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.full_loop(set_alpha_to_one=__snake_case    ,					beta_start=0.01  )
											_SCREAMING_SNAKE_CASE :       Dict     		=      torch.sum(torch.abs(__snake_case  )  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      torch.mean(torch.abs(__snake_case  )  )

											assert abs(result_sum.item() - 186.9482  ) < 1e-2
											assert abs(result_mean.item() - 0.2434  ) < 1e-3








'''simple docstring'''


import inspect
import unittest

from transformers import RegNetConfig, 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.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor





class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


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






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

											_SCREAMING_SNAKE_CASE :       Any     		=      self.get_config()

											return config, pixel_values






							def UpperCAmelCase_	(    self  ):
											return RegNetConfig(
											    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  ):
											_SCREAMING_SNAKE_CASE :       List[str]     		=      FlaxRegNetModel(config=__snake_case  )
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      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  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.num_labels
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      FlaxRegNetForImageClassification(config=__snake_case  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      model(__snake_case  )
											self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_labels)  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      config_and_inputs
											_SCREAMING_SNAKE_CASE :       Dict     		=      {"""pixel_values""": pixel_values}
											return config, inputs_dict





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





							'''simple docstring'''


							A_							:      Dict      	=  (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

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






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      FlaxRegNetModelTester(self  )
											_SCREAMING_SNAKE_CASE :       Any     		=      ConfigTester(self    ,					config_class=__snake_case    ,					has_text_modality=__snake_case  )






							def UpperCAmelCase_	(    self  ):
											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  ):
											return






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       int     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__snake_case  )






							@unittest.skip(reason="""RegNet does not use inputs_embeds"""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip(reason="""RegNet does not support input and output embeddings"""  )
							def UpperCAmelCase_	(    self  ):
											pass






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      model_class(__snake_case  )
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      inspect.signature(model.__call__  )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															_SCREAMING_SNAKE_CASE :       int     		=      [*signature.parameters.keys()]

															_SCREAMING_SNAKE_CASE :       Tuple     		=      ["""pixel_values"""]
															self.assertListEqual(arg_names[:1]    ,					__snake_case  )






							def UpperCAmelCase_	(    self  ):
											def check_hidden_states_output(__snake_case    ,					__snake_case    ,					__snake_case  ):
															_SCREAMING_SNAKE_CASE :       Any     		=      model_class(__snake_case  )

															_SCREAMING_SNAKE_CASE :       int     		=      model(**self._prepare_for_class(__snake_case    ,					__snake_case  )  )

															_SCREAMING_SNAKE_CASE :       Dict     		=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.model_tester.num_stages
															self.assertEqual(len(__snake_case  )    ,					expected_num_stages + 1  )

											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      True
															check_hidden_states_output(__snake_case    ,					__snake_case    ,					__snake_case  )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      True

															check_hidden_states_output(__snake_case    ,					__snake_case    ,					__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       str     		=      self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__  ):
																			_SCREAMING_SNAKE_CASE :       List[Any]     		=      self._prepare_for_class(__snake_case    ,					__snake_case  )
																			_SCREAMING_SNAKE_CASE :       str     		=      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"""  ):
																							_SCREAMING_SNAKE_CASE :       Any     		=      model_jitted(**__snake_case  ).to_tuple()

																			with self.subTest("""JIT Disabled"""  ):
																							with jax.disable_jit():
																											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      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 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       List[str]     		=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
				return image







@require_flax
class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							@cached_property
							def UpperCAmelCase_	(    self  ):
											return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040"""  ) if is_vision_available() else None






							@slow
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""  )

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.default_image_processor
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      prepare_img()
											_SCREAMING_SNAKE_CASE :       Tuple     		=      image_processor(images=__snake_case    ,					return_tensors="""np"""  )

											_SCREAMING_SNAKE_CASE :       str     		=      model(**__snake_case  )

											# verify the logits
											_SCREAMING_SNAKE_CASE :       Tuple     		=      (1, 1000)
											self.assertEqual(outputs.logits.shape    ,					__snake_case  )

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      jnp.array([-0.4180, -1.5051, -3.4836]  )

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




