


"""simple docstring"""






def 			A__		(					_UpperCAmelCase    :      str	)							->	list:

	'''simple docstring'''


	snake_case__  :						Any		=      []
	snake_case__  :						List[Any]		=      1

	for i in range(len(_UpperCAmelCase	)	):
		if i + 1 < len(_UpperCAmelCase	) and text[i] == text[i + 1]:
			count += 1
		else:
			encoded.append((text[i], count)	)
			snake_case__  :						Dict		=      1

	return encoded



def 			A__		(					_UpperCAmelCase    :      list	)							->	str:

	'''simple docstring'''


	return "".join(char * length for char, length in encoded	)


if __name__ == "__main__":
					from doctest import testmod

					testmod(name="""run_length_encode""", verbose=True)
					testmod(name="""run_length_decode""", verbose=True)












from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
						from PIL import Image

						from transformers import ConvNextImageProcessor

class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=13						,	_lowerCAmelCase=32						,	_lowerCAmelCase=3						,	_lowerCAmelCase=4						,	_lowerCAmelCase=[10, 20, 30, 40]						,	_lowerCAmelCase=[2, 2, 3, 2]						,	_lowerCAmelCase=True						,	_lowerCAmelCase=True						,	_lowerCAmelCase=37						,	_lowerCAmelCase="gelu"						,	_lowerCAmelCase=10						,	_lowerCAmelCase=0.02						,	_lowerCAmelCase=3						,	_lowerCAmelCase=None						,	):
										lowerCamelCase__      =							parent
										lowerCamelCase__      =							batch_size
										lowerCamelCase__      =							image_size
										lowerCamelCase__      =							num_channels
										lowerCamelCase__      =							num_stages
										lowerCamelCase__      =							hidden_sizes
										lowerCamelCase__      =							depths
										lowerCamelCase__      =							is_training
										lowerCamelCase__      =							use_labels
										lowerCamelCase__      =							intermediate_size
										lowerCamelCase__      =							hidden_act
										lowerCamelCase__      =							type_sequence_label_size
										lowerCamelCase__      =							initializer_range
										lowerCamelCase__      =							scope





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

										lowerCamelCase__      =							None
										if self.use_labels:
															lowerCamelCase__      =							ids_tensor([self.batch_size]						,	self.type_sequence_label_size						)

										lowerCamelCase__      =							self.get_config()

										return config, pixel_values, labels





					def        __magic_name__			(			self						):
										return ConvNextConfig(
										    num_channels=self.num_channels						,	hidden_sizes=self.hidden_sizes						,	depths=self.depths						,	num_stages=self.num_stages						,	hidden_act=self.hidden_act						,	is_decoder=_lowerCAmelCase						,	initializer_range=self.initializer_range						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							TFConvNextModel(config=_lowerCAmelCase						)
										lowerCamelCase__      =							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        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							self.type_sequence_label_size
										lowerCamelCase__      =							TFConvNextForImageClassification(_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	labels=_lowerCAmelCase						,	training=_lowerCAmelCase						)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)						)






					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.prepare_config_and_inputs()
										lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__      =							config_and_inputs
										lowerCamelCase__      =							{"pixel_values": pixel_values}
										return config, inputs_dict

@require_tf
class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_				,							unittest.TestCase ):





					"""simple docstring"""


					A__								=     (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
					A__								=     (
					    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
					    if is_tf_available()
					    else {}
					)

					A__								=     False
					A__								=     False
					A__								=     False
					A__								=     False
					A__								=     False





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextModelTester(self						)
										lowerCamelCase__      =							ConfigTester(
										    self						,	config_class=_lowerCAmelCase						,	has_text_modality=_lowerCAmelCase						,	hidden_size=37						,	)





					@unittest.skip(reason="ConvNext does not use inputs_embeds"						)
					def        __magic_name__			(			self						):
										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        __magic_name__			(			self						):
										super().test_keras_fit()





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





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

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

															lowerCamelCase__      =							["pixel_values"]
															self.assertListEqual(arg_names[:1]						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCAmelCase						)





					@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."						,	)
					def        __magic_name__			(			self						):
										super().test_dataset_conversion()





					def        __magic_name__			(			self						):
										def check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)

															lowerCamelCase__      =							model(**self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)						)
															lowerCamelCase__      =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															lowerCamelCase__      =							self.model_tester.num_stages
															self.assertEqual(len(_lowerCAmelCase						)						,	expected_num_stages + 1						)

															# ConvNext'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 // 4, self.model_tester.image_size // 4]						,	)

										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							True
															check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															lowerCamelCase__      =							True

															check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										def check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase={}						):
															lowerCamelCase__      =							model(_lowerCAmelCase						,	return_dict=_lowerCAmelCase						,	**_lowerCAmelCase						)
															lowerCamelCase__      =							model(_lowerCAmelCase						,	return_dict=_lowerCAmelCase						,	**_lowerCAmelCase						).to_tuple()

															def recursive_check(_lowerCAmelCase						,	_lowerCAmelCase						):
																				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:
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	{"output_hidden_states": True}						)

															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															check_equivalence(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	{"output_hidden_states": True}						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)






					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"						)
										self.assertIsNotNone(_lowerCAmelCase						)





def  __UpperCamelCase   (     )			->List[str]:
					lowerCamelCase__      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
					return image







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





					"""simple docstring"""


					@cached_property
					def        __magic_name__			(			self						):
										return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"						) if is_vision_available() else None






					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"						)

										lowerCamelCase__      =							self.default_image_processor
										lowerCamelCase__      =							prepare_img()
										lowerCamelCase__      =							image_processor(images=_lowerCAmelCase						,	return_tensors="tf"						)

										# forward pass
										lowerCamelCase__      =							model(**_lowerCAmelCase						)

										# verify the logits
										lowerCamelCase__      =							tf.TensorShape((1, 1000)						)
										self.assertEqual(outputs.logits.shape						,	_lowerCAmelCase						)

										lowerCamelCase__      =							tf.constant([-0.0_260, -0.4_739, 0.1_911]						)

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

