



'''simple docstring'''



def 					UpperCAmelCase   (						A  :				list[int]					,		A  :				list[int]				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			len(A				)
							print('''The following activities are selected:'''				)

							# The first activity is always selected
							SCREAMING_SNAKE_CASE					:      str     					=			0
							print(A					,		end=''','''				)

							# Consider rest of the activities
							for j in range(A				):
														# If this activity has start time greater than
														# or equal to the finish time of previously
														# selected activity, then select it
														if start[j] >= finish[i]:
																					print(A					,		end=''','''				)
																					SCREAMING_SNAKE_CASE					:      int     					=			j


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							lowerCAmelCase_     :       Tuple								=						[1, 3, 0, 5, 8, 5]
							lowerCAmelCase_     :       Any								=						[2, 4, 6, 7, 9, 9]
							print_max_activities(start, finish)









'''simple docstring'''



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class 	lowerCamelCase_							:







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			parent
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			batch_size
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image_size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			num_channels
													SCREAMING_SNAKE_CASE					:      str     					=			embeddings_size
													SCREAMING_SNAKE_CASE					:      str     					=			hidden_sizes
													SCREAMING_SNAKE_CASE					:      Tuple     					=			depths
													SCREAMING_SNAKE_CASE					:      Dict     					=			is_training
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			use_labels
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      str     					=			num_labels
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			scope
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			len(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			None
													if self.use_labels:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			ids_tensor([self.batch_size]				,    self.num_labels				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.get_config()
													return config, pixel_values, labels







						def 					__lowercase    (  self   :      Any				):

													"""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				,    )







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			TFRegNetModel(config=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			model(lowerCAmelCase__				,    training=lowerCAmelCase__				)
													# expected last hidden states: B, C, H // 32, W // 32
													self.parent.assertEqual(
													    result.last_hidden_state.shape				,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,    )







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      Any     					=			TFRegNetForImageClassification(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			model(lowerCAmelCase__				,    labels=lowerCAmelCase__				,    training=lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      Tuple     					=			{'''pixel_values''': pixel_values}
													return config, inputs_dict


@require_tf
class 	lowerCamelCase_							(					snake_case_    ,				snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   str	     =				(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
						_lowerCAmelCase     :   str	     =				(
						    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						_lowerCAmelCase     :   Tuple	     =				False
						_lowerCAmelCase     :   List[Any]	     =				False
						_lowerCAmelCase     :   List[str]	     =				False
						_lowerCAmelCase     :   str	     =				False
						_lowerCAmelCase     :   str	     =				False







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			TFRegNetModelTester(self				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			ConfigTester(self				,    config_class=lowerCAmelCase__				,    has_text_modality=lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													return







						@unittest.skip(reason='''RegNet does not use inputs_embeds'''				)
						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													pass







						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''				)				) == 0				,    reason='''TF does not support backprop for grouped convolutions on CPU.'''				,    )
						@slow
						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													super().test_keras_fit()







						@unittest.skip(reason='''RegNet does not support input and output embeddings'''				)
						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													pass







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													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					:      int     					=			model_class(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Any     					=			inspect.signature(model.call				)
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			[*signature.parameters.keys()]

																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			['''pixel_values''']
																				self.assertListEqual(arg_names[:1]				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													def check_hidden_states_output(lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Tuple				):
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			model_class(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Dict     					=			model(**self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)				,    training=lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      int     					=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																				SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.num_stages
																				self.assertEqual(len(lowerCAmelCase__				)				,    expected_num_stages + 1				)

																				# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
																				self.assertListEqual(
																				    list(hidden_states[0].shape[-2:]				)				,    [self.model_tester.image_size // 2, self.model_tester.image_size // 2]				,    )

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			['''basic''', '''bottleneck''']
													for model_class in self.all_model_classes:
																				for layer_type in layers_type:
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			layer_type
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			True
																											check_hidden_states_output(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)

																											# check that output_hidden_states also work using config
																											del inputs_dict["output_hidden_states"]
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			True

																											check_hidden_states_output(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

													def check_equivalence(lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      str={}				):
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(lowerCAmelCase__				,    return_dict=lowerCAmelCase__				,    **lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Any     					=			model(lowerCAmelCase__				,    return_dict=lowerCAmelCase__				,    **lowerCAmelCase__				).to_tuple()

																				def recursive_check(lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      str				):
																											if isinstance(lowerCAmelCase__				,    (List, Tuple)				):
																																		for tuple_iterable_value, dict_iterable_value in zip(lowerCAmelCase__				,    lowerCAmelCase__				):
																																									recursive_check(lowerCAmelCase__				,    lowerCAmelCase__				)
																											elif tuple_object is None:
																																		return
																											else:
																																		self.assertTrue(
																																		    all(tf.equal(lowerCAmelCase__				,    lowerCAmelCase__				)				)				,    msg=(
																																		        '''Tuple and dict output are not equal. Difference:'''
																																		        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object				)				)}"""
																																		    )				,    )

																											recursive_check(lowerCAmelCase__				,    lowerCAmelCase__				)

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			model_class(lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      int     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)
																				check_equivalence(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				,    return_labels=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      str     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				,    return_labels=lowerCAmelCase__				)
																				check_equivalence(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      Tuple     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Dict     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)
																				check_equivalence(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    {'''output_hidden_states''': True}				)

																				SCREAMING_SNAKE_CASE					:      Any     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				,    return_labels=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				,    return_labels=lowerCAmelCase__				)
																				check_equivalence(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    {'''output_hidden_states''': True}				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__				)







						@slow
						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			TFRegNetModel.from_pretrained(lowerCAmelCase__				)
																				self.assertIsNotNone(lowerCAmelCase__				)


def 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
							return image






@require_tf
@require_vision
class 	lowerCamelCase_							(					unittest.TestCase			):







						@cached_property
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													return (
													    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
													    if is_vision_available()
													    else None
													)







						@slow
						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			self.default_image_processor
													SCREAMING_SNAKE_CASE					:      int     					=			prepare_img()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			image_processor(images=lowerCAmelCase__				,    return_tensors='''tf'''				)

													# forward pass
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(**lowerCAmelCase__				,    training=lowerCAmelCase__				)

													# verify the logits
													SCREAMING_SNAKE_CASE					:      List[str]     					=			tf.TensorShape((1, 10_00)				)
													self.assertEqual(outputs.logits.shape				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tf.constant([-0.4180, -1.5051, -3.4836]				)

													tf.debugging.assert_near(outputs.logits[0, :3]				,    lowerCAmelCase__				,    atol=1e-4				)




