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



           '''simple docstring'''







           lowercase							:					Tuple	     =				parent
           lowercase							:					int	     =				batch_size
           lowercase							:					int	     =				image_size
           lowercase							:					Union[str, Any]	     =				num_channels
           lowercase							:					str	     =				embeddings_size
           lowercase							:					List[str]	     =				hidden_sizes
           lowercase							:					Union[str, Any]	     =				depths
           lowercase							:					Tuple	     =				is_training
           lowercase							:					Union[str, Any]	     =				use_labels
           lowercase							:					int	     =				hidden_act
           lowercase							:					List[Any]	     =				num_labels
           lowercase							:					str	     =				scope
           lowercase							:					int	     =				len(snake_case				)
           lowercase							:					Tuple	     =				out_features
           lowercase							:					Union[str, Any]	     =				out_indices
           lowercase							:					Optional[Any]	     =				num_groups
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

           lowercase							:					Tuple	     =				self.get_config()

           return config, pixel_values, labels
    def 				_SCREAMING_SNAKE_CASE	(  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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				BitModel(config=snake_case				)
           model.to(snake_case				)
           model.eval()
           lowercase							:					Optional[int]	     =				model(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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					int	     =				self.num_labels
           lowercase							:					str	     =				BitForImageClassification(snake_case				)
           model.to(snake_case				)
           model.eval()
           lowercase							:					Tuple	     =				model(snake_case		,labels=snake_case				)
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				BitBackbone(config=snake_case				)
           model.to(snake_case				)
           model.eval()
           lowercase							:					int	     =				model(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
           lowercase							:					List[str]	     =				None
           lowercase							:					Dict	     =				BitBackbone(config=snake_case				)
           model.to(snake_case				)
           model.eval()
           lowercase							:					str	     =				model(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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				self.prepare_config_and_inputs()
           lowercase							:					int	     =				config_and_inputs
           lowercase							:					str	     =				{"""pixel_values""": pixel_values}
           return config, inputs_dict

@require_torch
class __snake_case		(				lowerCAmelCase ,     lowerCAmelCase ,     unittest.TestCase					):
    _a		:						str=    (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
    _a		:						Optional[Any]=    (
        {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
        if is_torch_available()
        else {}
    )

    _a		:						Optional[int]=    False
    _a		:						Dict=    False
    _a		:						Optional[int]=    False
    _a		:						int=    False
    _a		:						Union[str, Any]=    False
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				BitModelTester(self				)
           lowercase							:					str	     =				ConfigTester(self		,config_class=snake_case		,has_text_modality=snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return
    @unittest.skip(reason="""Bit does not output attentions"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    @unittest.skip(reason="""Bit does not use inputs_embeds"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				self.model_tester.prepare_config_and_inputs_for_common()

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

                  lowercase							:					Dict	     =				["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  lowercase							:					List[str]	     =				model_class(config=snake_case				)
                  for name, module in model.named_modules():
                         if isinstance(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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           def check_hidden_states_output(snake_case		,snake_case		,snake_case				):
                  lowercase							:					List[Any]	     =				model_class(snake_case				)
                  model.to(snake_case				)
                  model.eval()

                  with torch.no_grad():
                         lowercase							:					List[Any]	     =				model(**self._prepare_for_class(snake_case		,snake_case				)				)

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

                  lowercase							:					Tuple	     =				self.model_tester.num_stages
                  self.assertEqual(len(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]		,)

           lowercase							:					List[Any]	     =				self.model_tester.prepare_config_and_inputs_for_common()
           lowercase							:					Optional[int]	     =				["""preactivation""", """bottleneck"""]
           for model_class in self.all_model_classes:
                  for layer_type in layers_type:
                         lowercase							:					str	     =				layer_type
                         lowercase							:					str	     =				True
                         check_hidden_states_output(snake_case		,snake_case		,snake_case				)

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

                         check_hidden_states_output(snake_case		,snake_case		,snake_case				)
    @unittest.skip(reason="""Bit does not use feedforward chunking"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*snake_case				)
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase							:					List[str]	     =				BitModel.from_pretrained(snake_case				)
                  self.assertIsNotNone(snake_case				)







def 						_snake_case(  )			->   str:
       lowercase							:					Tuple	     =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
       return image

@require_torch
@require_vision
class __snake_case		(				unittest.TestCase					):
    @cached_property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return (
               BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				) if is_vision_available() else None
           )
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(snake_case				)

           lowercase							:					Any	     =				self.default_image_processor
           lowercase							:					Union[str, Any]	     =				prepare_img()
           lowercase							:					str	     =				image_processor(images=snake_case		,return_tensors="""pt"""				).to(snake_case				)

           # forward pass
           with torch.no_grad():
                  lowercase							:					Any	     =				model(**snake_case				)

           # verify the logits
           lowercase							:					Optional[int]	     =				torch.Size((1, 1000)				)
           self.assertEqual(outputs.logits.shape		,snake_case				)

           lowercase							:					List[Any]	     =				torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]				).to(snake_case				)

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

@require_torch
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    _a		:						List[str]=    (BitBackbone,) if is_torch_available() else ()
    _a		:						List[Any]=    BitConfig

    _a		:						Tuple=    False
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				BitModelTester(self				)


from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat







def 						_snake_case(  )			->   tuple[list[int], int]:
       lowercase							:					List[Any]	     =				[randint(-1_000 ,     1_000    ) for i in range(10    )]
       lowercase							:					Tuple	     =				randint(-5_000 ,     5_000    )
       return (arr, r)


lowercase  : List[Any]						   =							make_dataset()







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple[int, ...]:
       for triplet in permutations(SCREAMING_SNAKE_CASE__ ,     3    ):
              if sum(SCREAMING_SNAKE_CASE__    ) == target:
                     return tuple(sorted(SCREAMING_SNAKE_CASE__    )    )
       return (0, 0, 0)







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple[int, int, int]:
       arr.sort()
       lowercase							:					Optional[int]	     =				len(SCREAMING_SNAKE_CASE__    )
       for i in range(n - 1    ):
              lowercase     ,      lowercase							:					Optional[Any]	     =				i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)







def 						_snake_case(  )			->   tuple[float, float]:
       lowercase							:					Dict	     =				"""
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
       lowercase							:					Tuple	     =				"""
triplet_sum1(*dataset)
"""
       lowercase							:					int	     =				"""
triplet_sum2(*dataset)
"""
       lowercase							:					str	     =				repeat(setup=SCREAMING_SNAKE_CASE__ ,     stmt=SCREAMING_SNAKE_CASE__ ,     repeat=5 ,     number=10_000    )
       lowercase							:					Dict	     =				repeat(setup=SCREAMING_SNAKE_CASE__ ,     stmt=SCREAMING_SNAKE_CASE__ ,     repeat=5 ,     number=10_000    )
       return (min(SCREAMING_SNAKE_CASE__    ), min(SCREAMING_SNAKE_CASE__    ))


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

  testmod()
  lowercase  : Union[str, Any]						   =							solution_times()
  print(F'''The time for naive implementation is {times[0]}.''')
  print(F'''The time for optimized implementation is {times[1]}.''')
