


import inspect
import unittest

from transformers import BitConfig
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 torch import nn

  from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
  from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image



class    UpperCAmelCase_	:
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=32 ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=10 ,					__SCREAMING_SNAKE_CASE=[8, 16, 32, 64] ,					__SCREAMING_SNAKE_CASE=[1, 1, 2, 1] ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE="relu" ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=["stage2", "stage3", "stage4"] ,					__SCREAMING_SNAKE_CASE=[2, 3, 4] ,					__SCREAMING_SNAKE_CASE=1 ,					):




              """simple docstring"""




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






       def  _lowercase   (     self						):




              """simple docstring"""




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

              UpperCamelCase    :	Optional[int]   	=		None
              if self.use_labels:
                     UpperCamelCase    :	List[str]   	=		ids_tensor([self.batch_size] ,					self.num_labels						)

              UpperCamelCase    :	Optional[int]   	=		self.get_config()

              return config, pixel_values, labels






       def  _lowercase   (     self						):




              """simple docstring"""




              return BitConfig(
                  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 ,					out_features=self.out_features ,					out_indices=self.out_indices ,					num_groups=self.num_groups ,					)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		BitModel(config=__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()
              UpperCamelCase    :	str   	=		model(__SCREAMING_SNAKE_CASE						)
              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 ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		self.num_labels
              UpperCamelCase    :	Any   	=		BitForImageClassification(__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()
              UpperCamelCase    :	Optional[int]   	=		model(__SCREAMING_SNAKE_CASE ,					labels=__SCREAMING_SNAKE_CASE						)
              self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		BitBackbone(config=__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()
              UpperCamelCase    :	int   	=		model(__SCREAMING_SNAKE_CASE						)

              # verify feature maps
              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
              UpperCamelCase    :	List[str]   	=		None
              UpperCamelCase    :	str   	=		BitBackbone(config=__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()
              UpperCamelCase    :	Any   	=		model(__SCREAMING_SNAKE_CASE						)

              # 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  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		self.prepare_config_and_inputs()
              UpperCamelCase	, UpperCamelCase	, UpperCamelCase    :	List[str]   	=		config_and_inputs
              UpperCamelCase    :	Dict   	=		{'''pixel_values''': pixel_values}
              return config, inputs_dict




@require_torch
class    UpperCAmelCase_	(							_a,     _a,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     Any							=					(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
       __UpperCamelCase      :     Optional[int]							=					(
           {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
           if is_torch_available()
           else {}
       )

       __UpperCamelCase      :     int							=					False
       __UpperCamelCase      :     Dict							=					False
       __UpperCamelCase      :     List[Any]							=					False
       __UpperCamelCase      :     List[str]							=					False
       __UpperCamelCase      :     List[str]							=					False






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		BitModelTester(self						)
              UpperCamelCase    :	Tuple   	=		ConfigTester(self ,					config_class=__SCREAMING_SNAKE_CASE ,					has_text_modality=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     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  _lowercase   (     self						):




              """simple docstring"""




              return






       @unittest.skip(reason='''Bit does not output attentions'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       @unittest.skip(reason='''Bit does not use inputs_embeds'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       @unittest.skip(reason='''Bit does not support input and output embeddings'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	Tuple   	=		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase    :	Any   	=		model_class(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Optional[int]   	=		inspect.signature(model.forward						)
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     UpperCamelCase    :	Dict   	=		[*signature.parameters.keys()]

                     UpperCamelCase    :	int   	=		['''pixel_values''']
                     self.assertListEqual(arg_names[:1] ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	Any   	=		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase    :	List[str]   	=		model_class(config=__SCREAMING_SNAKE_CASE						)
                     for name, module in model.named_modules():
                            if isinstance(__SCREAMING_SNAKE_CASE ,					(nn.BatchNormad, nn.GroupNorm)						):
                                   self.assertTrue(
                                       torch.all(module.weight == 1						) ,					msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,					)
                                   self.assertTrue(
                                       torch.all(module.bias == 0						) ,					msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,					)






       def  _lowercase   (     self						):




              """simple docstring"""




              def check_hidden_states_output(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	Union[str, Any]   	=		model_class(__SCREAMING_SNAKE_CASE						)
                     model.to(__SCREAMING_SNAKE_CASE						)
                     model.eval()

                     with torch.no_grad():
                            UpperCamelCase    :	Any   	=		model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)						)

                     UpperCamelCase    :	Dict   	=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                     UpperCamelCase    :	List[str]   	=		self.model_tester.num_stages
                     self.assertEqual(len(__SCREAMING_SNAKE_CASE						) ,					expected_num_stages + 1						)

                     # Bit'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] ,					)

              UpperCamelCase	, UpperCamelCase    :	Tuple   	=		self.model_tester.prepare_config_and_inputs_for_common()
              UpperCamelCase    :	List[Any]   	=		['''preactivation''', '''bottleneck''']
              for model_class in self.all_model_classes:
                     for layer_type in layers_type:
                            UpperCamelCase    :	List[Any]   	=		layer_type
                            UpperCamelCase    :	Tuple   	=		True
                            check_hidden_states_output(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

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

                            check_hidden_states_output(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)






       @unittest.skip(reason='''Bit does not use feedforward chunking'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCamelCase    :	str   	=		BitModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
                     self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)




def      a	(							):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
       return image



@require_torch
@require_vision
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       @cached_property
       def  _lowercase   (     self						):




              """simple docstring"""




              return (
                  BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						) if is_vision_available() else None
              )






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		self.default_image_processor
              UpperCamelCase    :	str   	=		prepare_img()
              UpperCamelCase    :	Optional[int]   	=		image_processor(images=__SCREAMING_SNAKE_CASE ,					return_tensors='''pt'''						).to(__SCREAMING_SNAKE_CASE						)

              # forward pass
              with torch.no_grad():
                     UpperCamelCase    :	List[str]   	=		model(**__SCREAMING_SNAKE_CASE						)

              # verify the logits
              UpperCamelCase    :	Dict   	=		torch.Size((1, 1_000)						)
              self.assertEqual(outputs.logits.shape ,					__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	int   	=		torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]						).to(__SCREAMING_SNAKE_CASE						)

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




@require_torch
class    UpperCAmelCase_	(							_a,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     List[str]							=					(BitBackbone,) if is_torch_available() else ()
       __UpperCamelCase      :     Optional[Any]							=					BitConfig

       __UpperCamelCase      :     Optional[Any]							=					False






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		BitModelTester(self						)





from __future__ import annotations


def      a	(							SCREAMING_SNAKE_CASE_      :		list[int]       ):






       """simple docstring"""





       if len(SCREAMING_SNAKE_CASE_       ) == 0:
              return array

       UpperCamelCase	, UpperCamelCase    :	Union[str, Any]   	=		min(SCREAMING_SNAKE_CASE_       ), max(SCREAMING_SNAKE_CASE_       )

       # Compute the variables
       UpperCamelCase    :	Union[str, Any]   	=		_max - _min + 1
       UpperCamelCase	, UpperCamelCase    :	Optional[Any]   	=		[0] * holes_range, [0] * holes_range

       # Make the sorting.
       for i in array:
              UpperCamelCase    :	Optional[int]   	=		i - _min
              UpperCamelCase    :	Any   	=		i
              holes_repeat[index] += 1

       # Makes the array back by replacing the numbers.
       UpperCamelCase    :	str   	=		0
       for i in range(SCREAMING_SNAKE_CASE_       ):
              while holes_repeat[i] > 0:
                     UpperCamelCase    :	List[Any]   	=		holes[i]
                     index += 1
                     holes_repeat[i] -= 1

    # Returns the sorted array.
       return array


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  __UpperCAmelCase : Any 			=				input("Enter numbers separated by comma:\n")
  __UpperCAmelCase : int 			=				[int(x) for x in user_input.split(",")]
  print(pigeon_sort(unsorted))

