






import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
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_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 (
       MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
       MODEL_MAPPING,
       EfficientFormerForImageClassification,
       EfficientFormerForImageClassificationWithTeacher,
       EfficientFormerModel,
   )
   from transformers.models.efficientformer.modeling_efficientformer import (
       EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
   )


if is_vision_available():
   from PIL import Image

   from transformers import EfficientFormerImageProcessor




class        _lowerCAmelCase:





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase						,    _lowerCamelCase = 1_3						,    _lowerCamelCase = 6_4						,    _lowerCamelCase = 2						,    _lowerCamelCase = 3						,    _lowerCamelCase = 3						,    _lowerCamelCase = True						,    _lowerCamelCase = True						,    _lowerCamelCase = 1_2_8						,    _lowerCamelCase=[1_6, 3_2, 6_4, 1_2_8]						,    _lowerCamelCase = 7						,    _lowerCamelCase = 4						,    _lowerCamelCase = 3_7						,    _lowerCamelCase = "gelu"						,    _lowerCamelCase = 0.1						,    _lowerCamelCase = 0.1						,    _lowerCamelCase = 1_0						,    _lowerCamelCase = 0.0_2						,    _lowerCamelCase = 2						,    _lowerCamelCase = 1						,    _lowerCamelCase = 1_2_8						,    _lowerCamelCase = [2, 2, 2, 2]						,    _lowerCamelCase = 2						,    _lowerCamelCase = 2						,    ):
         UpperCamelCase_:      Union[str, Any]					   =       parent
         UpperCamelCase_:      Any					   =       batch_size
         UpperCamelCase_:      Optional[Any]					   =       image_size
         UpperCamelCase_:      str					   =       patch_size
         UpperCamelCase_:      Dict					   =       num_channels
         UpperCamelCase_:      List[Any]					   =       is_training
         UpperCamelCase_:      int					   =       use_labels
         UpperCamelCase_:      str					   =       hidden_size
         UpperCamelCase_:      int					   =       num_hidden_layers
         UpperCamelCase_:      List[str]					   =       num_attention_heads
         UpperCamelCase_:      str					   =       intermediate_size
         UpperCamelCase_:      List[Any]					   =       hidden_act
         UpperCamelCase_:      Union[str, Any]					   =       hidden_dropout_prob
         UpperCamelCase_:      Any					   =       attention_probs_dropout_prob
         UpperCamelCase_:      Any					   =       type_sequence_label_size
         UpperCamelCase_:      List[str]					   =       initializer_range
         UpperCamelCase_:      Union[str, Any]					   =       encoder_stride
         UpperCamelCase_:      Optional[Any]					   =       num_attention_outputs
         UpperCamelCase_:      int					   =       embed_dim
         UpperCamelCase_:      int					   =       embed_dim + 1
         UpperCamelCase_:      str					   =       resolution
         UpperCamelCase_:      Dict					   =       depths
         UpperCamelCase_:      Tuple					   =       hidden_sizes
         UpperCamelCase_:      Optional[Any]					   =       dim
         UpperCamelCase_:      int					   =       mlp_expansion_ratio





       def     _a					(      self							):
         UpperCamelCase_:      Union[str, Any]					   =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         UpperCamelCase_:      Union[str, Any]					   =       None
         if self.use_labels:
           UpperCamelCase_:      Optional[int]					   =       ids_tensor([self.batch_size]						,    self.type_sequence_label_size							)

         UpperCamelCase_:      int					   =       self.get_config()
         return config, pixel_values, labels





       def     _a					(      self							):
         return EfficientFormerConfig(
             image_size=self.image_size						,    patch_size=self.patch_size						,    num_channels=self.num_channels						,    hidden_size=self.hidden_size						,    num_hidden_layers=self.num_hidden_layers						,    num_attention_heads=self.num_attention_heads						,    intermediate_size=self.intermediate_size						,    hidden_act=self.hidden_act						,    hidden_dropout_prob=self.hidden_dropout_prob						,    attention_probs_dropout_prob=self.attention_probs_dropout_prob						,    is_decoder=_lowerCamelCase						,    initializer_range=self.initializer_range						,    encoder_stride=self.encoder_stride						,    resolution=self.resolution						,    depths=self.depths						,    hidden_sizes=self.hidden_sizes						,    dim=self.dim						,    mlp_expansion_ratio=self.mlp_expansion_ratio						,    )





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							):
         UpperCamelCase_:      List[Any]					   =       EfficientFormerModel(config=_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()
         UpperCamelCase_:      Optional[Any]					   =       model(_lowerCamelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							):
         UpperCamelCase_:      List[str]					   =       self.type_sequence_label_size
         UpperCamelCase_:      Optional[Any]					   =       EfficientFormerForImageClassification(_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()
         UpperCamelCase_:      Optional[int]					   =       model(_lowerCamelCase						,    labels=_lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.type_sequence_label_size)							)

         # test greyscale images
         UpperCamelCase_:      Tuple					   =       1
         UpperCamelCase_:      Any					   =       EfficientFormerForImageClassification(_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()

         UpperCamelCase_:      List[Any]					   =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
         UpperCamelCase_:      List[str]					   =       model(_lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.type_sequence_label_size)							)





       def     _a					(      self							):
         UpperCamelCase_:      Dict					   =       self.prepare_config_and_inputs()
         (
             (
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,(
             UpperCamelCase_
         )   ,
         ):      Tuple					   =       config_and_inputs
         UpperCamelCase_:      Any					   =       {'pixel_values': pixel_values}
         return config, inputs_dict







@require_torch
class        _lowerCAmelCase( UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase      ):





       """simple docstring"""







       a       :	List[Any]								=(
           (
               EfficientFormerModel,
               EfficientFormerForImageClassificationWithTeacher,
               EfficientFormerForImageClassification,
           )
           if is_torch_available()
           else ()
       )
       a       :	Any								=(
           {
               '''feature-extraction''': EfficientFormerModel,
               '''image-classification''': (
                   EfficientFormerForImageClassification,
                   EfficientFormerForImageClassificationWithTeacher,
               ),
           }
           if is_torch_available()
           else {}
       )
       a       :	Tuple								=False

       a       :	Any								=False
       a       :	Dict								=False
       a       :	str								=False





       def     _a					(      self							):
         UpperCamelCase_:      int					   =       EfficientFormerModelTester(self							)
         UpperCamelCase_:      Optional[Any]					   =       ConfigTester(
             self						,    config_class=_lowerCamelCase						,    has_text_modality=_lowerCamelCase						,    hidden_size=3_7							)





       def     _a					(      self							):
         self.config_tester.run_common_tests()





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





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





       def     _a					(      self							):
         UpperCamelCase_   ,UpperCamelCase_:      List[Any]					   =       self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase_:      str					   =       ['pixel_values']
           self.assertListEqual(arg_names[:1]						,    _lowerCamelCase							)





       def     _a					(      self							):
         def check_hidden_states_output(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							):
           UpperCamelCase_:      Tuple					   =       model_class(_lowerCamelCase							)
           model.to(_lowerCamelCase							)
           model.eval()

           with torch.no_grad():
             UpperCamelCase_:      Any					   =       model(**self._prepare_for_class(_lowerCamelCase						,    _lowerCamelCase							)							)

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

           UpperCamelCase_:      Dict					   =       getattr(
               self.model_tester						,    'expected_num_hidden_layers'						,    self.model_tester.num_hidden_layers + 1							)
           self.assertEqual(len(_lowerCamelCase							)						,    _lowerCamelCase							)

           if hasattr(self.model_tester						,    'encoder_seq_length'							):
             UpperCamelCase_:      Optional[Any]					   =       self.model_tester.encoder_seq_length
             if hasattr(self.model_tester						,    'chunk_length'							) and self.model_tester.chunk_length > 1:
               UpperCamelCase_:      Tuple					   =       seq_length * self.model_tester.chunk_length
           else:
             UpperCamelCase_:      Optional[int]					   =       self.model_tester.seq_length

           self.assertListEqual(
               list(hidden_states[-1].shape[-2:]							)						,    [seq_length, self.model_tester.hidden_size]						,    )

           if config.is_encoder_decoder:
             UpperCamelCase_:      Optional[Any]					   =       outputs.decoder_hidden_states

             self.assertIsInstance(_lowerCamelCase						,    (list, tuple)							)
             self.assertEqual(len(_lowerCamelCase							)						,    _lowerCamelCase							)
             UpperCamelCase_:      List[Any]					   =       getattr(self.model_tester						,    'seq_length'						,    _lowerCamelCase							)
             UpperCamelCase_:      Union[str, Any]					   =       getattr(self.model_tester						,    'decoder_seq_length'						,    _lowerCamelCase							)

             self.assertListEqual(
                 list(hidden_states[-1].shape[-2:]							)						,    [decoder_seq_length, self.model_tester.hidden_size]						,    )

         UpperCamelCase_   ,UpperCamelCase_:      int					   =       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           UpperCamelCase_:      Any					   =       True
           check_hidden_states_output(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							)

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

           check_hidden_states_output(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase=False							):
         UpperCamelCase_:      Any					   =       super()._prepare_for_class(_lowerCamelCase						,    _lowerCamelCase						,    return_labels=_lowerCamelCase							)

         if return_labels:
           if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
             del inputs_dict["labels"]

         return inputs_dict





       def     _a					(      self							):
         UpperCamelCase_:      List[Any]					   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCamelCase							)





       @unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'							)
       def     _a					(      self							):
         UpperCamelCase_:      Any					   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCamelCase							)





       def     _a					(      self							):
         UpperCamelCase_:      str					   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase							)





       def     _a					(      self							):
         if not self.model_tester.is_training:
           return

         UpperCamelCase_   ,UpperCamelCase_:      int					   =       self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase_:      Dict					   =       True

         for model_class in self.all_model_classes:
           # EfficientFormerForImageClassificationWithTeacher supports inference-only
           if (
               model_class in get_values(_lowerCamelCase							)
               or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
           ):
             continue
           UpperCamelCase_:      List[str]					   =       model_class(_lowerCamelCase							)
           model.to(_lowerCamelCase							)
           model.train()
           UpperCamelCase_:      str					   =       self._prepare_for_class(_lowerCamelCase						,    _lowerCamelCase						,    return_labels=_lowerCamelCase							)
           UpperCamelCase_:      List[str]					   =       model(**_lowerCamelCase							).loss
           loss.backward()





       def     _a					(      self							):
         UpperCamelCase_   ,UpperCamelCase_:      Any					   =       self.model_tester.prepare_config_and_inputs_for_common()

         UpperCamelCase_:      Optional[int]					   =       [
             {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
             {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
             {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
         ]

         for model_class in self.all_model_classes:
           if (
               model_class
               not in [
                   *get_values(_lowerCamelCase							),
               ]
               or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
           ):
             continue

           for problem_type in problem_types:
             with self.subTest(msg=f'''Testing {model_class} with {problem_type['title']}'''							):
               UpperCamelCase_:      Any					   =       problem_type['title']
               UpperCamelCase_:      List[Any]					   =       problem_type['num_labels']

               UpperCamelCase_:      Tuple					   =       model_class(_lowerCamelCase							)
               model.to(_lowerCamelCase							)
               model.train()

               UpperCamelCase_:      List[str]					   =       self._prepare_for_class(_lowerCamelCase						,    _lowerCamelCase						,    return_labels=_lowerCamelCase							)

               if problem_type["num_labels"] > 1:
                 UpperCamelCase_:      str					   =       inputs['labels'].unsqueeze(1							).repeat(1						,    problem_type['num_labels']							)

               UpperCamelCase_:      Dict					   =       inputs['labels'].to(problem_type['dtype']							)

               # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
               # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
               # they have the same size." which is a symptom something in wrong for the regression problem.
               # See https://github.com/huggingface/transformers/issues/11780
               with warnings.catch_warnings(record=_lowerCamelCase							) as warning_list:
                 UpperCamelCase_:      Optional[Any]					   =       model(**_lowerCamelCase							).loss
               for w in warning_list:
                 if "Using a target size that is different to the input size" in str(w.message							):
                   raise ValueError(
                       f'''Something is going wrong in the regression problem: intercepted {w.message}'''							)

               loss.backward()





       @slow
       def     _a					(      self							):
         for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase_:      Union[str, Any]					   =       EfficientFormerModel.from_pretrained(_lowerCamelCase							)
           self.assertIsNotNone(_lowerCamelCase							)





       def     _a					(      self							):
         UpperCamelCase_   ,UpperCamelCase_:      int					   =       self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase_:      str					   =       True

         UpperCamelCase_:      int					   =       getattr(self.model_tester						,    'seq_length'						,    _lowerCamelCase							)
         UpperCamelCase_:      Optional[int]					   =       getattr(self.model_tester						,    'encoder_seq_length'						,    _lowerCamelCase							)
         UpperCamelCase_:      Optional[int]					   =       getattr(self.model_tester						,    'key_length'						,    _lowerCamelCase							)
         UpperCamelCase_:      int					   =       getattr(self.model_tester						,    'chunk_length'						,    _lowerCamelCase							)

         if chunk_length is not None and hasattr(self.model_tester						,    'num_hashes'							):
           UpperCamelCase_:      Union[str, Any]					   =       encoder_seq_length * self.model_tester.num_hashes

         for model_class in self.all_model_classes:
           UpperCamelCase_:      Optional[Any]					   =       True
           UpperCamelCase_:      Union[str, Any]					   =       False
           UpperCamelCase_:      List[str]					   =       True
           UpperCamelCase_:      Dict					   =       model_class(_lowerCamelCase							)
           model.to(_lowerCamelCase							)
           model.eval()
           with torch.no_grad():
             UpperCamelCase_:      Optional[int]					   =       model(**self._prepare_for_class(_lowerCamelCase						,    _lowerCamelCase							)							)
           UpperCamelCase_:      int					   =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
           self.assertEqual(len(_lowerCamelCase							)						,    self.model_tester.num_attention_outputs							)

           # check that output_attentions also work using config
           del inputs_dict["output_attentions"]
           UpperCamelCase_:      Optional[int]					   =       True
           UpperCamelCase_:      Any					   =       model_class(_lowerCamelCase							)
           model.to(_lowerCamelCase							)
           model.eval()
           with torch.no_grad():
             UpperCamelCase_:      int					   =       model(**self._prepare_for_class(_lowerCamelCase						,    _lowerCamelCase							)							)
           UpperCamelCase_:      Dict					   =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
           self.assertEqual(len(_lowerCamelCase							)						,    self.model_tester.num_attention_outputs							)

           if chunk_length is not None:
             self.assertListEqual(
                 list(attentions[0].shape[-4:]							)						,    [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]						,    )
           else:
             self.assertListEqual(
                 list(attentions[0].shape[-3:]							)						,    [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]						,    )







def 						snake_case      () ->       str:
  UpperCamelCase_:      Any					   =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
  return image




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





       """simple docstring"""







       @cached_property
       def     _a					(      self							):
         return (
             EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'							)
             if is_vision_available()
             else None
         )





       @slow
       def     _a					(      self							):
         UpperCamelCase_:      List[Any]					   =       EfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'							).to(
             _lowerCamelCase							)

         UpperCamelCase_:      Optional[int]					   =       self.default_image_processor
         UpperCamelCase_:      Optional[int]					   =       prepare_img()
         UpperCamelCase_:      List[str]					   =       image_processor(images=_lowerCamelCase						,    return_tensors='pt'							).to(_lowerCamelCase							)

         # forward pass
         with torch.no_grad():
           UpperCamelCase_:      Dict					   =       model(**_lowerCamelCase							)

         # verify the logits
         UpperCamelCase_:      str					   =       (1, 1_0_0_0)
         self.assertEqual(outputs.logits.shape						,    _lowerCamelCase							)

         UpperCamelCase_:      List[str]					   =       torch.tensor([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]							).to(_lowerCamelCase							)
         self.assertTrue(torch.allclose(outputs.logits[0][:3]						,    _lowerCamelCase						,    atol=1e-4							)							)





       @slow
       def     _a					(      self							):
         UpperCamelCase_:      str					   =       EfficientFormerForImageClassificationWithTeacher.from_pretrained(
             'snap-research/efficientformer-l1-300'							).to(_lowerCamelCase							)

         UpperCamelCase_:      List[str]					   =       self.default_image_processor
         UpperCamelCase_:      str					   =       prepare_img()
         UpperCamelCase_:      Tuple					   =       image_processor(images=_lowerCamelCase						,    return_tensors='pt'							).to(_lowerCamelCase							)

         # forward pass
         with torch.no_grad():
           UpperCamelCase_:      Optional[int]					   =       model(**_lowerCamelCase							)

         # verify the logits
         UpperCamelCase_:      List[Any]					   =       (1, 1_0_0_0)
         self.assertEqual(outputs.logits.shape						,    _lowerCamelCase							)

         UpperCamelCase_:      str					   =       torch.tensor([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]							).to(_lowerCamelCase							)
         self.assertTrue(torch.allclose(outputs.logits[0][:3]						,    _lowerCamelCase						,    atol=1e-4							)							)







# Lint as: python3

import hashlib
from typing import Union

def 						snake_case      (UpperCAmelCase__						) ->       bytes:
  if isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    # Data already in bytes, returns as it as
    return hash_data
  elif isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    # We keep the data as it as for it ot be later encoded to UTF-8
    # However replace `\\` with `/` for Windows compatibility
    UpperCamelCase_:      List[str]					   =       hash_data.replace('\\'			,					'/'						)
  elif isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    UpperCamelCase_:      Union[str, Any]					   =       str(UpperCAmelCase__						)
  else:
    # If data is not of the required type, raise error
    raise InvalidKeyError(UpperCAmelCase__						)

  return hash_data.encode('utf-8'						)




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      int					   =       '\nFAILURE TO GENERATE DATASET: Invalid key type detected'
         UpperCamelCase_:      Dict					   =       f'''\nFound Key {hash_data} of type {type(_lowerCamelCase							)}'''
         UpperCamelCase_:      Any					   =       '\nKeys should be either str, int or bytes type'
         super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)







class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase=""							):
         UpperCamelCase_:      int					   =       key
         UpperCamelCase_:      Dict					   =       duplicate_key_indices
         UpperCamelCase_:      int					   =       fix_msg
         UpperCamelCase_:      List[Any]					   =       'Found multiple examples generated with the same key'
         if len(_lowerCamelCase							) <= 2_0:
           UpperCamelCase_:      Tuple					   =       f'''\nThe examples at index {', '.join(_lowerCamelCase							)} have the key {key}'''
         else:
           UpperCamelCase_:      Optional[int]					   =       f'''\nThe examples at index {', '.join(duplicate_key_indices[:2_0]							)}... ({len(_lowerCamelCase							) - 2_0} more) have the key {key}'''
         UpperCamelCase_:      List[Any]					   =       '\n' + fix_msg if fix_msg else ''
         super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)







class        _lowerCAmelCase:





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      int					   =       hashlib.mda(_as_bytes(_lowerCamelCase							)							)





       def     _a					(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      Optional[int]					   =       self._split_mda.copy()
         UpperCamelCase_:      str					   =       _as_bytes(_lowerCamelCase							)
         mda.update(_lowerCamelCase							)
         # Convert to integer with hexadecimal conversion
         return int(mda.hexdigest()						,    1_6							)