





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     SCREAMING_SNAKE_CASE__ :



 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=["stage2", "stage3", "stage4"],__lowerCamelCase=[2, 3, 4],__lowerCamelCase=None,):
        A__											=						parent
        A__											=						batch_size
        A__											=						image_size
        A__											=						num_channels
        A__											=						num_stages
        A__											=						hidden_sizes
        A__											=						depths
        A__											=						is_training
        A__											=						use_labels
        A__											=						intermediate_size
        A__											=						hidden_act
        A__											=						num_labels
        A__											=						initializer_range
        A__											=						out_features
        A__											=						out_indices
        A__											=						scope



 def 				UpperCamelCase			(							self ):
        A__											=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

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



 def 				UpperCamelCase			(							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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						ConvNextModel(config=__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()
        A__											=						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 // 32, self.image_size // 32),)



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						ConvNextForImageClassification(__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()
        A__											=						model(__lowerCamelCase,labels=__lowerCamelCase )
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels) )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						ConvNextBackbone(config=__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()
        A__											=						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
        A__											=						None
        A__											=						ConvNextBackbone(config=__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()
        A__											=						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 				UpperCamelCase			(							self ):
        A__											=						self.prepare_config_and_inputs()
        A__					,	A__					,	A__											=						config_and_inputs
        A__											=						{'''pixel_values''': pixel_values}
        return config, inputs_dict




@require_torch
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	(
     (
         ConvNextModel,
         ConvNextForImageClassification,
         ConvNextBackbone,
     )
     if is_torch_available()
     else ()
 )
 __SCREAMING_SNAKE_CASE							       =	(
     {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
     if is_torch_available()
     else {}
 )

 __SCREAMING_SNAKE_CASE							       =	True
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False



 def 				UpperCamelCase			(							self ):
        A__											=						ConvNextModelTester(self )
        A__											=						ConfigTester(self,config_class=__lowerCamelCase,has_text_modality=__lowerCamelCase,hidden_size=37 )



 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



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



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



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



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

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

               A__											=						['''pixel_values''']
               self.assertListEqual(arg_names[:1],__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        def check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
               A__											=						model_class(__lowerCamelCase )
               model.to(__lowerCamelCase )
               model.eval()

               with torch.no_grad():
                      A__											=						model(**self._prepare_for_class(__lowerCamelCase,__lowerCamelCase ) )

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

               A__											=						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],)

        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               A__											=						True
               check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )

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

               check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )




 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A__											=						ConvNextModel.from_pretrained(__lowerCamelCase )
               self.assertIsNotNone(__lowerCamelCase )


def 	UpperCamelCase__(       )->Optional[Any]:
       A__											=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
       return image




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



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




 @slow
 def 				UpperCamelCase			(							self ):
        A__											=						ConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224''' ).to(__lowerCamelCase )

        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(images=__lowerCamelCase,return_tensors='''pt''' ).to(__lowerCamelCase )

        # forward pass
        with torch.no_grad():
               A__											=						model(**__lowerCamelCase )

        # verify the logits
        A__											=						torch.Size((1, 1000) )
        self.assertEqual(outputs.logits.shape,__lowerCamelCase )

        A__											=						torch.tensor([-0.0260, -0.4739, 0.1911] ).to(__lowerCamelCase )

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




@require_torch
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase				,					UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(ConvNextBackbone,) if is_torch_available() else ()
 __SCREAMING_SNAKE_CASE							       =	ConvNextConfig

 __SCREAMING_SNAKE_CASE							       =	False



 def 				UpperCamelCase			(							self ):
        A__											=						ConvNextModelTester(self )












def 	UpperCamelCase__(       UpperCamelCase__			:       Dict  )->Dict:
       # if the collection is empty, returns empty
       if collection == []:
              return []

       # get some information about the collection
       A__											=						len(UpperCamelCase__  )
       A__											=						max(UpperCamelCase__  )
       A__											=						min(UpperCamelCase__  )

       # create the counting array
       A__											=						coll_max + 1 - coll_min
       A__											=						[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 ,     UpperCamelCase__  ):
              A__											=						counting_arr[i] + counting_arr[i - 1]

       # create the output collection
       A__											=						[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 ,     UpperCamelCase__  )  ):
              A__											=						collection[i]
              counting_arr[collection[i] - coll_min] -= 1

       return ordered


def 	UpperCamelCase__(       UpperCamelCase__			:       Optional[int]  )->Tuple:
       return "".join([chr(UpperCamelCase__  ) for i in counting_sort([ord(UpperCamelCase__  ) for c in string]  )]  )


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

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





