

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 			a					(		unittest.TestCase			):


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







									'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


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

									_UpperCAmelCase    :						List[str]							=      self.get_config()

									return config, pixel_values


						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									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  ,   A_  ,   A_		):







									'''simple docstring'''


									_UpperCAmelCase    :						Optional[int]							=      FlaxRegNetModel(config=A_		)
									_UpperCAmelCase    :						List[str]							=      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  ,   A_  ,   A_		):







									'''simple docstring'''


									_UpperCAmelCase    :						Union[str, Any]							=      self.num_labels
									_UpperCAmelCase    :						List[Any]							=      FlaxRegNetForImageClassification(config=A_		)
									_UpperCAmelCase    :						Tuple							=      model(A_		)
									self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.num_labels)		)





						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									_UpperCAmelCase    :						Tuple							=      self.prepare_config_and_inputs()
									_UpperCAmelCase    :						Optional[Any]							=      config_and_inputs
									_UpperCAmelCase    :						Union[str, Any]							=      {"""pixel_values""": pixel_values}
									return config, inputs_dict







@require_flax
class 			a					(		__UpperCamelCase							,	unittest.TestCase			):
						_lowercase        =     (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

						_lowercase        =     False
						_lowercase        =     False
						_lowercase        =     False


						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									_UpperCAmelCase    :						int							=      FlaxRegNetModelTester(self		)
									_UpperCAmelCase    :						Optional[Any]							=      ConfigTester(self  ,   config_class=A_  ,   has_text_modality=A_		)


						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'''


									_UpperCAmelCase    :						int							=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*A_		)


						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									_UpperCAmelCase    :						str							=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*A_		)


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







									'''simple docstring'''


									pass


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







									'''simple docstring'''


									pass


						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									_UpperCAmelCase    :						Optional[Any]							=      self.model_tester.prepare_config_and_inputs_for_common()

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

												_UpperCAmelCase    :						Dict							=      ["""pixel_values"""]
												self.assertListEqual(arg_names[:1]  ,   A_		)


						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									def check_hidden_states_output(A_  ,   A_  ,   A_		):
												_UpperCAmelCase    :						Union[str, Any]							=      model_class(A_		)

												_UpperCAmelCase    :						Tuple							=      model(**self._prepare_for_class(A_  ,   A_		)		)

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

												_UpperCAmelCase    :						Tuple							=      self.model_tester.num_stages
												self.assertEqual(len(A_		)  ,   expected_num_stages + 1		)

									_UpperCAmelCase    :						Tuple							=      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												_UpperCAmelCase    :						List[Any]							=      True
												check_hidden_states_output(A_  ,   A_  ,   A_		)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												_UpperCAmelCase    :						Any							=      True

												check_hidden_states_output(A_  ,   A_  ,   A_		)





						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									_UpperCAmelCase    :						Tuple							=      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__		):
															_UpperCAmelCase    :						Optional[int]							=      self._prepare_for_class(A_  ,   A_		)
															_UpperCAmelCase    :						str							=      model_class(A_		)

															@jax.jit
															def model_jitted(A_  ,   **A_		):
																		return model(pixel_values=A_  ,   **A_		)

															with self.subTest("JIT Enabled"		):
																		_UpperCAmelCase    :						List[str]							=      model_jitted(**A_		).to_tuple()

															with self.subTest("JIT Disabled"		):
																		with jax.disable_jit():
																					_UpperCAmelCase    :						List[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							(			)				->    Any:
			_UpperCAmelCase    :						Any							=      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("facebook/regnet-y-040"		) if is_vision_available() else None





						@slow
						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									_UpperCAmelCase    :						Dict							=      FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"		)

									_UpperCAmelCase    :						int							=      self.default_image_processor
									_UpperCAmelCase    :						str							=      prepare_img()
									_UpperCAmelCase    :						Optional[int]							=      image_processor(images=A_  ,   return_tensors="np"		)

									_UpperCAmelCase    :						int							=      model(**A_		)

									# verify the logits
									_UpperCAmelCase    :						Optional[int]							=      (1, 1000)
									self.assertEqual(outputs.logits.shape  ,   A_		)

									_UpperCAmelCase    :						Union[str, Any]							=      jnp.array([-0.41_80, -1.50_51, -3.48_36]		)

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





from __future__ import annotations

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      order

										# a_{0} ... a_{k}
										_UpperCAmelCase    :						Tuple							=      [1.0] + [0.0] * order
										# b_{0} ... b_{k}
										_UpperCAmelCase    :						int							=      [1.0] + [0.0] * order

										# x[n-1] ... x[n-k]
										_UpperCAmelCase    :						Optional[Any]							=      [0.0] * self.order
										# y[n-1] ... y[n-k]
										_UpperCAmelCase    :						Dict							=      [0.0] * self.order


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if len(A_		) < self.order:
														_UpperCAmelCase    :						List[str]							=      [1.0, *a_coeffs]

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						List[Any]							=      (
														    f'Expected a_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						int							=      (
														    f'Expected b_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      a_coeffs
										_UpperCAmelCase    :						Union[str, Any]							=      b_coeffs





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      0.0

										# Start at index 1 and do index 0 at the end.
										for i in range(1  ,   self.order + 1		):
														result += (
														    self.b_coeffs[i] * self.input_history[i - 1]
														    - self.a_coeffs[i] * self.output_history[i - 1]
														)

										_UpperCAmelCase    :						Dict							=      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

										_UpperCAmelCase    :						Optional[Any]							=      self.input_history[:-1]
										_UpperCAmelCase    :						Optional[int]							=      self.output_history[:-1]

										_UpperCAmelCase    :						Optional[Any]							=      sample
										_UpperCAmelCase    :						str							=      result

										return result


