import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
      from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor

class       snake_case					:




  def __init__(      self     :	List[Any]     , a_     :	List[Any]     , a_     :	Tuple=13     , a_     :	int=32     , a_     :	Optional[Any]=2     , a_     :	List[Any]=3     , a_     :	str=16     , a_     :	List[Any]=[1, 2, 1]     , a_     :	List[str]=[2, 2, 4]     , a_     :	str=2     , a_     :	str=2.0     , a_     :	Any=True     , a_     :	Optional[int]=0.0     , a_     :	Dict=0.0     , a_     :	int=0.1     , a_     :	Optional[Any]="gelu"     , a_     :	Tuple=False     , a_     :	Any=True     , a_     :	int=0.02     , a_     :	List[str]=1e-5     , a_     :	List[str]=True     , a_     :	Optional[Any]=None     , a_     :	str=True     , a_     :	Optional[Any]=10     , a_     :	int=8     , )->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		parent
       SCREAMING_SNAKE_CASE__				:				Tuple			=		batch_size
       SCREAMING_SNAKE_CASE__				:				Any			=		image_size
       SCREAMING_SNAKE_CASE__				:				Any			=		patch_size
       SCREAMING_SNAKE_CASE__				:				Any			=		num_channels
       SCREAMING_SNAKE_CASE__				:				Any			=		embed_dim
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		depths
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		num_heads
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		window_size
       SCREAMING_SNAKE_CASE__				:				str			=		mlp_ratio
       SCREAMING_SNAKE_CASE__				:				Tuple			=		qkv_bias
       SCREAMING_SNAKE_CASE__				:				Tuple			=		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__				:				List[str]			=		drop_path_rate
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		hidden_act
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		use_absolute_embeddings
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		patch_norm
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		layer_norm_eps
       SCREAMING_SNAKE_CASE__				:				str			=		initializer_range
       SCREAMING_SNAKE_CASE__				:				Dict			=		is_training
       SCREAMING_SNAKE_CASE__				:				str			=		scope
       SCREAMING_SNAKE_CASE__				:				str			=		use_labels
       SCREAMING_SNAKE_CASE__				:				Tuple			=		type_sequence_label_size
       SCREAMING_SNAKE_CASE__				:				Any			=		encoder_stride




  def 	__lowercase(      self     :	Any				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

       SCREAMING_SNAKE_CASE__				:				Any			=		None
       if self.use_labels:
            SCREAMING_SNAKE_CASE__				:				int			=		ids_tensor([self.batch_size]     , self.type_sequence_label_size				)

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

       return config, pixel_values, labels




  def 	__lowercase(      self     :	int				)->       Union[str, Any]:




       """simple docstring"""
       return SwinvaConfig(
           image_size=self.image_size     , patch_size=self.patch_size     , num_channels=self.num_channels     , embed_dim=self.embed_dim     , depths=self.depths     , num_heads=self.num_heads     , window_size=self.window_size     , mlp_ratio=self.mlp_ratio     , qkv_bias=self.qkv_bias     , hidden_dropout_prob=self.hidden_dropout_prob     , attention_probs_dropout_prob=self.attention_probs_dropout_prob     , drop_path_rate=self.drop_path_rate     , hidden_act=self.hidden_act     , use_absolute_embeddings=self.use_absolute_embeddings     , path_norm=self.patch_norm     , layer_norm_eps=self.layer_norm_eps     , initializer_range=self.initializer_range     , encoder_stride=self.encoder_stride     , )




  def 	__lowercase(      self     :	List[str]     , a_     :	Optional[int]     , a_     :	List[Any]     , a_     :	List[Any]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		SwinvaModel(config=a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		model(a_				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

       self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, expected_seq_len, expected_dim)				)




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	Optional[int]     , a_     :	Union[str, Any]     , a_     :	str				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		SwinvaForMaskedImageModeling(config=a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				Tuple			=		model(a_				)
       self.parent.assertEqual(
           result.logits.shape     , (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

       # test greyscale images
       SCREAMING_SNAKE_CASE__				:				Dict			=		1
       SCREAMING_SNAKE_CASE__				:				Dict			=		SwinvaForMaskedImageModeling(a_				)
       model.to(a_				)
       model.eval()

       SCREAMING_SNAKE_CASE__				:				Dict			=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model(a_				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, 1, self.image_size, self.image_size)				)




  def 	__lowercase(      self     :	int     , a_     :	Union[str, Any]     , a_     :	Union[str, Any]     , a_     :	List[str]				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.type_sequence_label_size
       SCREAMING_SNAKE_CASE__				:				int			=		SwinvaForImageClassification(a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				int			=		model(a_     , labels=a_				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.type_sequence_label_size)				)




  def 	__lowercase(      self     :	List[Any]				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		config_and_inputs
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{'pixel_values': pixel_values}
       return config, inputs_dict




@require_torch
class       snake_case					(      UpperCamelCase_   ,						UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     (
      (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
  )
  lowercase_			=     (
      {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
      if is_torch_available()
      else {}
  )

  lowercase_			=     False
  lowercase_			=     False
  lowercase_			=     False
  lowercase_			=     False




  def 	__lowercase(      self     :	Optional[Any]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		SwinvaModelTester(self				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		ConfigTester(self     , config_class=a_     , embed_dim=37				)




  def 	__lowercase(      self     :	Optional[Any]				)->       str:




       """simple docstring"""
       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     :	List[Any]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a_				)




  @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'				)
  def 	__lowercase(      self     :	Union[str, Any]				)->       Optional[Any]:




       """simple docstring"""
       pass




  @unittest.skip(reason='Swinv2 does not use inputs_embeds'				)
  def 	__lowercase(      self     :	str				)->       List[str]:




       """simple docstring"""
       pass




  def 	__lowercase(      self     :	Any				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__				:				Dict			=		model_class(a_				)
            self.assertIsInstance(model.get_input_embeddings()     , (nn.Module)				)
            SCREAMING_SNAKE_CASE__				:				Any			=		model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(a_     , nn.Linear				)				)




  def 	__lowercase(      self     :	Tuple				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		['pixel_values']
            self.assertListEqual(arg_names[:1]     , a_				)




  def 	__lowercase(      self     :	Any				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__				:				str			=		True

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__				:				str			=		True
            SCREAMING_SNAKE_CASE__				:				Dict			=		False
            SCREAMING_SNAKE_CASE__				:				int			=		True
            SCREAMING_SNAKE_CASE__				:				Any			=		model_class(a_				)
            model.to(a_				)
            model.eval()
            with torch.no_grad():
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		model(**self._prepare_for_class(a_     , a_				)				)
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		outputs.attentions
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		len(self.model_tester.depths				)
            self.assertEqual(len(a_				)     , a_				)

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            SCREAMING_SNAKE_CASE__				:				str			=		True
            SCREAMING_SNAKE_CASE__				:				Tuple			=		config.window_size**2
            SCREAMING_SNAKE_CASE__				:				int			=		model_class(a_				)
            model.to(a_				)
            model.eval()
            with torch.no_grad():
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		model(**self._prepare_for_class(a_     , a_				)				)
            SCREAMING_SNAKE_CASE__				:				Tuple			=		outputs.attentions
            self.assertEqual(len(a_				)     , a_				)

            self.assertListEqual(
                list(attentions[0].shape[-3:]				)     , [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     , )
            SCREAMING_SNAKE_CASE__				:				str			=		len(a_				)

            # Check attention is always last and order is fine
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		True
            SCREAMING_SNAKE_CASE__				:				str			=		True
            SCREAMING_SNAKE_CASE__				:				Any			=		model_class(a_				)
            model.to(a_				)
            model.eval()
            with torch.no_grad():
                 SCREAMING_SNAKE_CASE__				:				int			=		model(**self._prepare_for_class(a_     , a_				)				)

            if hasattr(self.model_tester     , 'num_hidden_states_types'				):
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.model_tester.num_hidden_states_types
            else:
                 # also another +1 for reshaped_hidden_states
                 SCREAMING_SNAKE_CASE__				:				Dict			=		2
            self.assertEqual(out_len + added_hidden_states     , len(a_				)				)

            SCREAMING_SNAKE_CASE__				:				int			=		outputs.attentions

            self.assertEqual(len(a_				)     , a_				)

            self.assertListEqual(
                list(self_attentions[0].shape[-3:]				)     , [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     , )




  def 	__lowercase(      self     :	str     , a_     :	Tuple     , a_     :	List[str]     , a_     :	Dict     , a_     :	Optional[Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		model_class(a_				)
       model.to(a_				)
       model.eval()

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

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		outputs.hidden_states

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		getattr(
           self.model_tester     , 'expected_num_hidden_layers'     , len(self.model_tester.depths				) + 1				)
       self.assertEqual(len(a_				)     , a_				)

       # Swinv2 has a different seq_length
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		(
           config.patch_size
           if isinstance(config.patch_size     , collections.abc.Iterable				)
           else (config.patch_size, config.patch_size)
       )

       SCREAMING_SNAKE_CASE__				:				List[str]			=		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]				)     , [num_patches, self.model_tester.embed_dim]     , )

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		outputs.reshaped_hidden_states
       self.assertEqual(len(a_				)     , a_				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		reshaped_hidden_states[0].shape
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		(
           reshaped_hidden_states[0].view(a_     , a_     , height * width				).permute(0     , 2     , 1				)
       )
       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:]				)     , [num_patches, self.model_tester.embed_dim]     , )




  def 	__lowercase(      self     :	Tuple				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		(
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size     , collections.abc.Iterable				)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		True
            self.check_hidden_states_output(a_     , a_     , a_     , a_				)

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

            self.check_hidden_states_output(a_     , a_     , a_     , a_				)




  def 	__lowercase(      self     :	str				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		3

       SCREAMING_SNAKE_CASE__				:				Dict			=		(
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size     , collections.abc.Iterable				)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		(
           config.patch_size
           if isinstance(config.patch_size     , collections.abc.Iterable				)
           else (config.patch_size, config.patch_size)
       )

       SCREAMING_SNAKE_CASE__				:				int			=		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       SCREAMING_SNAKE_CASE__				:				Any			=		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__				:				Any			=		True
            self.check_hidden_states_output(a_     , a_     , a_     , (padded_height, padded_width)				)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		True
            self.check_hidden_states_output(a_     , a_     , a_     , (padded_height, padded_width)				)




  def 	__lowercase(      self     :	Dict				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*a_				)




  def 	__lowercase(      self     :	Dict				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a_				)




  @slow
  def 	__lowercase(      self     :	List[Any]				)->       int:




       """simple docstring"""
       for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		SwinvaModel.from_pretrained(a_				)
            self.assertIsNotNone(a_				)




  def 	__lowercase(      self     :	Dict				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		_config_zero_init(a_				)
       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model_class(config=a_				)
            for name, param in model.named_parameters():
                 if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                      self.assertIn(
                          ((param.data.mean() * 1e9).round() / 1e9).item()     , [0.0, 1.0]     , msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''     , )




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




  @cached_property
  def 	__lowercase(      self     :	Optional[int]				)->       str:




       """simple docstring"""
       return (
           AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'				)
           if is_vision_available()
           else None
       )




  @slow
  def 	__lowercase(      self     :	Optional[Any]				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'				).to(
           a_				)
       SCREAMING_SNAKE_CASE__				:				int			=		self.default_image_processor

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(images=a_     , return_tensors='pt'				).to(a_				)

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model(**a_				)

       # verify the logits
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.Size((1, 1000)				)
       self.assertEqual(outputs.logits.shape     , a_				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor([-0.3947, -0.4306, 0.0026]				).to(a_				)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]     , a_     , atol=1e-4				)				)




import math
import sys







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		''
     try:
          with open(lowercase__     ,  'rb' ) as binary_file:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		binary_file.read()
          for dat in data:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		f'''{dat:08b}'''
               result += curr_byte
          return result
     except OSError:
          print('File not accessible' )
          sys.exit()







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[str]			=		{'0': '0', '1': '1'}
     SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				int			=		'', ''
     SCREAMING_SNAKE_CASE__				:				Tuple			=		len(lowercase__ )

     for i in range(len(lowercase__ ) ):
          curr_string += data_bits[i]
          if curr_string not in lexicon:
               continue

          SCREAMING_SNAKE_CASE__				:				int			=		lexicon[curr_string]
          result += last_match_id
          SCREAMING_SNAKE_CASE__				:				str			=		last_match_id + '0'

          if math.loga(lowercase__ ).is_integer():
               SCREAMING_SNAKE_CASE__				:				List[str]			=		{}
               for curr_key in list(lowercase__ ):
                    SCREAMING_SNAKE_CASE__				:				Optional[int]			=		lexicon.pop(lowercase__ )
               SCREAMING_SNAKE_CASE__				:				Optional[int]			=		new_lex

          SCREAMING_SNAKE_CASE__				:				Any			=		last_match_id + '1'
          index += 1
          SCREAMING_SNAKE_CASE__				:				Tuple			=		''
     return result







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		8
     try:
          with open(lowercase__     ,  'wb' ) as opened_file:
               SCREAMING_SNAKE_CASE__				:				Dict			=		[
                   to_write[i : i + byte_length]
                   for i in range(0     ,  len(lowercase__ )     ,  lowercase__ )
               ]

               if len(result_byte_array[-1] ) % byte_length == 0:
                    result_byte_array.append('10000000' )
               else:
                    result_byte_array[-1] += "1" + "0" * (
                        byte_length - len(result_byte_array[-1] ) - 1
                    )

               for elem in result_byte_array[:-1]:
                    opened_file.write(int(lowercase__     ,  2 ).to_bytes(1     ,  byteorder='big' ) )
     except OSError:
          print('File not accessible' )
          sys.exit()







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		0
     for letter in data_bits:
          if letter == "1":
               break
          counter += 1

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		data_bits[counter:]
     SCREAMING_SNAKE_CASE__				:				int			=		data_bits[counter + 1 :]
     return data_bits







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		read_file_binary(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Dict			=		remove_prefix(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Tuple			=		decompress_data(lowercase__ )
     write_file_binary(lowercase__     ,  lowercase__ )


if __name__ == "__main__":
      compress(sys.argv[1], sys.argv[2])



