

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		:						List[str]      ,  __lowerCamelCase		:						str      ,  __lowerCamelCase		:						Union[str, Any]=3      ,  __lowerCamelCase		:						Any=32      ,  __lowerCamelCase		:						List[str]=3      ,  __lowerCamelCase		:						List[Any]=10      ,  __lowerCamelCase		:						Union[str, Any]=[8, 16, 32, 64]      ,  __lowerCamelCase		:						Tuple=[1, 1, 2, 1]      ,  __lowerCamelCase		:						str=True      ,  __lowerCamelCase		:						Tuple=True      ,  __lowerCamelCase		:						Tuple="relu"      ,  __lowerCamelCase		:						str=3      ,  __lowerCamelCase		:						int=None      ,  __lowerCamelCase		:						Optional[Any]=["stage2", "stage3", "stage4"]      ,  __lowerCamelCase		:						List[str]=[2, 3, 4]      ,  __lowerCamelCase		:						Any=1      ,  )						->				List[str]:
       SCREAMING_SNAKE_CASE__       =       parent
       SCREAMING_SNAKE_CASE__       =       batch_size
       SCREAMING_SNAKE_CASE__       =       image_size
       SCREAMING_SNAKE_CASE__       =       num_channels
       SCREAMING_SNAKE_CASE__       =       embeddings_size
       SCREAMING_SNAKE_CASE__       =       hidden_sizes
       SCREAMING_SNAKE_CASE__       =       depths
       SCREAMING_SNAKE_CASE__       =       is_training
       SCREAMING_SNAKE_CASE__       =       use_labels
       SCREAMING_SNAKE_CASE__       =       hidden_act
       SCREAMING_SNAKE_CASE__       =       num_labels
       SCREAMING_SNAKE_CASE__       =       scope
       SCREAMING_SNAKE_CASE__       =       len(__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       out_features
       SCREAMING_SNAKE_CASE__       =       out_indices
       SCREAMING_SNAKE_CASE__       =       num_groups






  def  lowercase_						(				self		:						Optional[int]   )						->				Optional[int]:
       SCREAMING_SNAKE_CASE__       =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

       SCREAMING_SNAKE_CASE__       =       self.get_config()

       return config, pixel_values, labels






  def  lowercase_						(				self		:						int   )						->				str:
       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		:						Optional[int]      ,  __lowerCamelCase		:						str      ,  __lowerCamelCase		:						str      ,  __lowerCamelCase		:						Dict   )						->				str:
       SCREAMING_SNAKE_CASE__       =       BitModel(config=__lowerCamelCase   )
       model.to(__lowerCamelCase   )
       model.eval()
       SCREAMING_SNAKE_CASE__       =       model(__lowerCamelCase   )
       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		:						Optional[Any]      ,  __lowerCamelCase		:						Tuple      ,  __lowerCamelCase		:						Tuple      ,  __lowerCamelCase		:						Optional[int]   )						->				List[str]:
       SCREAMING_SNAKE_CASE__       =       self.num_labels
       SCREAMING_SNAKE_CASE__       =       BitForImageClassification(__lowerCamelCase   )
       model.to(__lowerCamelCase   )
       model.eval()
       SCREAMING_SNAKE_CASE__       =       model(__lowerCamelCase      ,  labels=__lowerCamelCase   )
       self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_labels)   )






  def  lowercase_						(				self		:						Any      ,  __lowerCamelCase		:						str      ,  __lowerCamelCase		:						Tuple      ,  __lowerCamelCase		:						Dict   )						->				List[str]:
       SCREAMING_SNAKE_CASE__       =       BitBackbone(config=__lowerCamelCase   )
       model.to(__lowerCamelCase   )
       model.eval()
       SCREAMING_SNAKE_CASE__       =       model(__lowerCamelCase   )

       # 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
       SCREAMING_SNAKE_CASE__       =       None
       SCREAMING_SNAKE_CASE__       =       BitBackbone(config=__lowerCamelCase   )
       model.to(__lowerCamelCase   )
       model.eval()
       SCREAMING_SNAKE_CASE__       =       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  lowercase_						(				self		:						str   )						->				Optional[Any]:
       SCREAMING_SNAKE_CASE__       =       self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__       =       config_and_inputs
       SCREAMING_SNAKE_CASE__       =       {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class 			UpperCAmelCase__						(			__lowerCamelCase		,	__lowerCamelCase		,	unittest.TestCase			):






  """simple docstring"""


  a 	=   (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
  a 	=   (
      {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
      if is_torch_available()
      else {}
  )

  a 	=   False
  a 	=   False
  a 	=   False
  a 	=   False
  a 	=   False






  def  lowercase_						(				self		:						List[Any]   )						->				List[Any]:
       SCREAMING_SNAKE_CASE__       =       BitModelTester(self   )
       SCREAMING_SNAKE_CASE__       =       ConfigTester(self      ,  config_class=__lowerCamelCase      ,  has_text_modality=__lowerCamelCase   )






  def  lowercase_						(				self		:						Union[str, Any]   )						->				List[str]:
       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		:						Union[str, Any]   )						->				List[str]:
       return






  @unittest.skip(reason='''Bit does not output attentions'''   )
  def  lowercase_						(				self		:						int   )						->				Union[str, Any]:
       pass






  @unittest.skip(reason='''Bit does not use inputs_embeds'''   )
  def  lowercase_						(				self		:						List[str]   )						->				Tuple:
       pass






  @unittest.skip(reason='''Bit does not support input and output embeddings'''   )
  def  lowercase_						(				self		:						Dict   )						->				Union[str, Any]:
       pass






  def  lowercase_						(				self		:						Any   )						->				List[Any]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__       =       self.model_tester.prepare_config_and_inputs_for_common()

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

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






  def  lowercase_						(				self		:						Any   )						->				List[str]:
       SCREAMING_SNAKE_CASE__       =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase   )






  def  lowercase_						(				self		:						Optional[Any]   )						->				Dict:
       SCREAMING_SNAKE_CASE__       =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*__lowerCamelCase   )






  def  lowercase_						(				self		:						Dict   )						->				Optional[int]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__       =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__       =       model_class(config=__lowerCamelCase   )
            for name, module in model.named_modules():
                 if isinstance(__lowerCamelCase      ,  (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		:						Optional[int]   )						->				str:






       def check_hidden_states_output(__lowerCamelCase		:						int      ,  __lowerCamelCase		:						Optional[Any]      ,  __lowerCamelCase		:						Optional[int]   ):
            SCREAMING_SNAKE_CASE__       =       model_class(__lowerCamelCase   )
            model.to(__lowerCamelCase   )
            model.eval()

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

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

            SCREAMING_SNAKE_CASE__       =       self.model_tester.num_stages
            self.assertEqual(len(__lowerCamelCase   )      ,  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]      ,  )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__       =       self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__       =       ['''preactivation''', '''bottleneck''']
       for model_class in self.all_model_classes:
            for layer_type in layers_type:
                 SCREAMING_SNAKE_CASE__       =       layer_type
                 SCREAMING_SNAKE_CASE__       =       True
                 check_hidden_states_output(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase   )

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

                 check_hidden_states_output(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase   )






  @unittest.skip(reason='''Bit does not use feedforward chunking'''   )
  def  lowercase_						(				self		:						Union[str, Any]   )						->				Optional[int]:
       pass






  def  lowercase_						(				self		:						Dict   )						->				str:
       SCREAMING_SNAKE_CASE__       =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase   )







  @slow
  def  lowercase_						(				self		:						Tuple   )						->				List[str]:






       for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__       =       BitModel.from_pretrained(__lowerCamelCase   )
            self.assertIsNotNone(__lowerCamelCase   )



def 							UpperCAmelCase_  (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__       =       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		:						List[Any]   )						->				List[Any]:
       return (
           BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ) if is_vision_available() else None
       )







  @slow
  def  lowercase_						(				self		:						Dict   )						->				Dict:
       SCREAMING_SNAKE_CASE__       =       BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(__lowerCamelCase   )

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

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

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

       SCREAMING_SNAKE_CASE__       =       torch.tensor([[-0.6526, -0.5263, -1.4398]]   ).to(__lowerCamelCase   )

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






@require_torch
class 			UpperCAmelCase__						(			__lowerCamelCase		,	unittest.TestCase			):






  """simple docstring"""


  a 	=   (BitBackbone,) if is_torch_available() else ()
  a 	=   BitConfig

  a 	=   False






  def  lowercase_						(				self		:						List[Any]   )						->				Any:
       SCREAMING_SNAKE_CASE__       =       BitModelTester(self   )




# Function to print upper half of diamond (pyramid)


def    a      ( a	)     ->Optional[Any]:




			'''simple docstring'''


			for i in range(0		,						a	):
						for _ in range(0		,						n - i - 1	):  # printing spaces
									print(''' '''		,						end=''''''	)
						for _ in range(0		,						i + 1	):  # printing stars
									print('''* '''		,						end=''''''	)
						print()


def    a      ( a	)     ->Union[str, Any]:




			'''simple docstring'''


			for i in range(a		,						0		,						-1	):
						for _ in range(a		,						0		,						-1	):  # printing stars
									print('''* '''		,						end=''''''	)
						print()
						for _ in range(n - i + 1		,						0		,						-1	):  # printing spaces
									print(''' '''		,						end=''''''	)


def    a      ( a	)     ->Optional[int]:




			'''simple docstring'''


			if n <= 0:
						print('''       ...       ....        nothing printing :('''	)
						return
			floyd(a	)  # upper half
			reverse_floyd(a	)  # lower half


if __name__ == "__main__":
							print(R'| /\ | |- |  |-  |--| |\  /| |-')
							print(R'|/  \| |- |_ |_  |__| | \/ | |_')
							__lowerCAmelCase					   =       1
							while K:
														__lowerCAmelCase					   =       int(input('enter the number and , and see the magic : '))
														print()
														pretty_print(user_number)
														__lowerCAmelCase					   =       int(input('press 0 to exit... and 1 to continue...'))

							print('Good Bye...')