


import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
	from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor





class       A    :


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase=1_3      ,				lowerCAmelCase=3_2      ,				lowerCAmelCase=3      ,				lowerCAmelCase=4      ,				lowerCAmelCase=[1_0, 2_0, 3_0, 4_0]      ,				lowerCAmelCase=[2, 2, 3, 2]      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				lowerCAmelCase=3_7      ,				lowerCAmelCase="gelu"      ,				lowerCAmelCase=1_0      ,				lowerCAmelCase=0.02      ,				lowerCAmelCase=["stage2", "stage3", "stage4"]      ,				lowerCAmelCase=[2, 3, 4]      ,				lowerCAmelCase=None      ,				):
									__lowercase=			parent
									__lowercase=			batch_size
									__lowercase=			image_size
									__lowercase=			num_channels
									__lowercase=			num_stages
									__lowercase=			hidden_sizes
									__lowercase=			depths
									__lowercase=			is_training
									__lowercase=			use_labels
									__lowercase=			intermediate_size
									__lowercase=			hidden_act
									__lowercase=			num_labels
									__lowercase=			initializer_range
									__lowercase=			out_features
									__lowercase=			out_indices
									__lowercase=			scope


							def 			_A       (self					):
									__lowercase=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

									__lowercase=			None
									if self.use_labels:
											__lowercase=			ids_tensor([self.batch_size]      ,				self.num_labels					)

									__lowercase=			self.get_config()
									return config, pixel_values, labels


							def 			_A       (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      ,				out_features=self.out_features      ,				out_indices=self.out_indices      ,				num_labels=self.num_labels      ,				)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									__lowercase=			ConvNextModel(config=lowerCAmelCase					)
									model.to(lowerCAmelCase					)
									model.eval()
									__lowercase=			model(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 // 3_2, self.image_size // 3_2)      ,				)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									__lowercase=			ConvNextForImageClassification(lowerCAmelCase					)
									model.to(lowerCAmelCase					)
									model.eval()
									__lowercase=			model(lowerCAmelCase      ,				labels=lowerCAmelCase					)
									self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels)					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									__lowercase=			ConvNextBackbone(config=lowerCAmelCase					)
									model.to(lowerCAmelCase					)
									model.eval()
									__lowercase=			model(lowerCAmelCase					)

									# verify hidden states
									self.parent.assertEqual(len(result.feature_maps					)      ,				len(config.out_features					)					)
									self.parent.assertListEqual(list(result.feature_maps[0].shape					)      ,				[self.batch_size, self.hidden_sizes[1], 4, 4]					)

									# verify channels
									self.parent.assertEqual(len(model.channels					)      ,				len(config.out_features					)					)
									self.parent.assertListEqual(model.channels      ,				config.hidden_sizes[1:]					)

									# verify backbone works with out_features=None
									__lowercase=			None
									__lowercase=			ConvNextBackbone(config=lowerCAmelCase					)
									model.to(lowerCAmelCase					)
									model.eval()
									__lowercase=			model(lowerCAmelCase					)

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps					)      ,				1					)
									self.parent.assertListEqual(list(result.feature_maps[0].shape					)      ,				[self.batch_size, self.hidden_sizes[-1], 1, 1]					)

									# verify channels
									self.parent.assertEqual(len(model.channels					)      ,				1					)
									self.parent.assertListEqual(model.channels      ,				[config.hidden_sizes[-1]]					)







							def 			_A       (self					):
									__lowercase=			self.prepare_config_and_inputs()
									__lowercase,							__lowercase,							__lowercase=			config_and_inputs
									__lowercase=			{'pixel_values': pixel_values}
									return config, inputs_dict





@require_torch
class       A    (   A_					,				A_					,				unittest.TestCase ):
							UpperCamelCase_   :							str       =(
							    (
							        ConvNextModel,
							        ConvNextForImageClassification,
							        ConvNextBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							UpperCamelCase_   :							Union[str, Any]       =(
							    {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
							    if is_torch_available()
							    else {}
							)

							UpperCamelCase_   :							List[str]       =True
							UpperCamelCase_   :							Dict       =False
							UpperCamelCase_   :							Tuple       =False
							UpperCamelCase_   :							Any       =False
							UpperCamelCase_   :							Optional[Any]       =False


							def 			_A       (self					):
									__lowercase=			ConvNextModelTester(self					)
									__lowercase=			ConfigTester(self      ,				config_class=lowerCAmelCase      ,				has_text_modality=lowerCAmelCase      ,				hidden_size=3_7					)


							def 			_A       (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 			_A       (self					):
									return


							@unittest.skip(reason='ConvNext does not use inputs_embeds'					)
							def 			_A       (self					):
									pass


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


							@unittest.skip(reason='ConvNext does not use feedforward chunking'					)
							def 			_A       (self					):
									pass


							def 			_A       (self					):
									__lowercase,							__lowercase=			self.model_tester.prepare_config_and_inputs_for_common()

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

											__lowercase=			['pixel_values']
											self.assertListEqual(arg_names[:1]      ,				lowerCAmelCase					)


							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCAmelCase					)


							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*lowerCAmelCase					)


							def 			_A       (self					):
									def check_hidden_states_output(lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
											__lowercase=			model_class(lowerCAmelCase					)
											model.to(lowerCAmelCase					)
											model.eval()

											with torch.no_grad():
													__lowercase=			model(**self._prepare_for_class(lowerCAmelCase      ,				lowerCAmelCase					)					)

											__lowercase=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											__lowercase=			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]      ,				)

									__lowercase,							__lowercase=			self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											__lowercase=			True
											check_hidden_states_output(lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					)

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

											check_hidden_states_output(lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					)


							def 			_A       (self					):
									__lowercase=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase					)







							@slow
							def 			_A       (self					):
									for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowercase=			ConvNextModel.from_pretrained(lowerCAmelCase					)
											self.assertIsNotNone(lowerCAmelCase					)







def        _lowerCamelCase(	)						->	Optional[Any]:

		'''simple docstring'''







		__lowercase=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
		return image





@require_torch
@require_vision
class       A    (   unittest.TestCase ):


							@cached_property
							def 			_A       (self					):
									return AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224'					) if is_vision_available() else None







							@slow
							def 			_A       (self					):
									__lowercase=			ConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'					).to(lowerCAmelCase					)

									__lowercase=			self.default_image_processor
									__lowercase=			prepare_img()
									__lowercase=			image_processor(images=lowerCAmelCase      ,				return_tensors='pt'					).to(lowerCAmelCase					)

									# forward pass
									with torch.no_grad():
											__lowercase=			model(**lowerCAmelCase					)

									# verify the logits
									__lowercase=			torch.Size((1, 1_0_0_0)					)
									self.assertEqual(outputs.logits.shape      ,				lowerCAmelCase					)

									__lowercase=			torch.tensor([-0.02_60, -0.47_39, 0.19_11]					).to(lowerCAmelCase					)

									self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,				lowerCAmelCase      ,				atol=1E-4					)					)





@require_torch
class       A    (   unittest.TestCase					,				A_ ):
							UpperCamelCase_   :							Optional[Any]       =(ConvNextBackbone,) if is_torch_available() else ()
							UpperCamelCase_   :							List[str]       =ConvNextConfig

							UpperCamelCase_   :							int       =False


							def 			_A       (self					):
									__lowercase=			ConvNextModelTester(self					)










def        _lowerCamelCase(	lowercase__	)						->	int:

		'''simple docstring'''







		if collection == []:
				return []

		# get some information about the collection
		__lowercase=			len(lowercase__	)
		__lowercase=			max(lowercase__	)
		__lowercase=			min(lowercase__	)

		# create the counting array
		__lowercase=			coll_max + 1 - coll_min
		__lowercase=			[0] * counting_arr_length

		# count how much a number appears in the collection
		for number in collection:
				counting_arr[number - coll_min] += 1

		# sum each position with it's predecessors. now, counting_arr[i] tells
		# us how many elements <= i has in the collection
		for i in range(1   , lowercase__	):
				__lowercase=			counting_arr[i] + counting_arr[i - 1]

		# create the output collection
		__lowercase=			[0] * coll_len

		# place the elements in the output, respecting the original order (stable
		# sort) from end to begin, updating counting_arr
		for i in reversed(range(0   , lowercase__	)	):
				__lowercase=			collection[i]
				counting_arr[collection[i] - coll_min] -= 1

		return ordered







def        _lowerCamelCase(	lowercase__	)						->	List[str]:

		'''simple docstring'''







		return "".join([chr(lowercase__	) for i in counting_sort([ord(lowercase__	) for c in string]	)]	)


if __name__ == "__main__":
	# Test string sort
	assert counting_sort_string('''thisisthestring''') == "eghhiiinrsssttt"

	lowerCAmelCase           =		input('''Enter numbers separated by a comma:\n''').strip()
	lowerCAmelCase           =		[int(item) for item in user_input.split(''',''')]
	print(counting_sort(unsorted))






