


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

    if is_vision_available():
        from transformers import MaskaFormerImageProcessor

if is_vision_available():
    from PIL import Image







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			List[str]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			List[str]=2					,      __lowerCamelCase							:			str=True					,      __lowerCamelCase							:			Optional[Any]=False					,      __lowerCamelCase							:			Dict=10					,      __lowerCamelCase							:			List[str]=3					,      __lowerCamelCase							:			Optional[int]=32 * 8					,      __lowerCamelCase							:			Optional[Any]=32 * 8					,      __lowerCamelCase							:			List[str]=4					,      __lowerCamelCase							:			List[str]=64					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  parent
             lowerCamelCase__		:				List[Any]    =  batch_size
             lowerCamelCase__		:				Dict    =  is_training
             lowerCamelCase__		:				List[str]    =  use_auxiliary_loss
             lowerCamelCase__		:				List[str]    =  num_queries
             lowerCamelCase__		:				Union[str, Any]    =  num_channels
             lowerCamelCase__		:				Union[str, Any]    =  min_size
             lowerCamelCase__		:				int    =  max_size
             lowerCamelCase__		:				Optional[Any]    =  num_labels
             lowerCamelCase__		:				Union[str, Any]    =  hidden_dim
             lowerCamelCase__		:				Optional[int]    =  hidden_dim





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
                 __lowerCamelCase       )

             lowerCamelCase__		:				List[str]    =  torch.ones([self.batch_size, self.min_size, self.max_size]					,      device=__lowerCamelCase       )

             lowerCamelCase__		:				List[str]    =  (
                 torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]					,      device=__lowerCamelCase       ) > 0.5
             ).float()
             lowerCamelCase__		:				Dict    =  (torch.rand((self.batch_size, self.num_labels)					,      device=__lowerCamelCase       ) > 0.5).long()

             lowerCamelCase__		:				Optional[Any]    =  self.get_config()
             return config, pixel_values, pixel_mask, mask_labels, class_labels





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  MaskaFormerConfig(
                 hidden_size=self.hidden_dim					,      )
             lowerCamelCase__		:				str    =  self.num_queries
             lowerCamelCase__		:				int    =  self.num_labels

             lowerCamelCase__		:				Tuple    =  [1, 1, 1, 1]
             lowerCamelCase__		:				List[Any]    =  self.num_channels

             lowerCamelCase__		:				Optional[Any]    =  64
             lowerCamelCase__		:				Any    =  128
             lowerCamelCase__		:				int    =  self.hidden_dim
             lowerCamelCase__		:				Optional[Any]    =  self.hidden_dim
             lowerCamelCase__		:				Tuple    =  self.hidden_dim
             return config





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				str    =  self.prepare_config_and_inputs()
             lowerCamelCase__		:				Union[str, Any]    =  {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
             return config, inputs_dict





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  output.encoder_hidden_states
             lowerCamelCase__		:				List[str]    =  output.pixel_decoder_hidden_states
             lowerCamelCase__		:				List[str]    =  output.transformer_decoder_hidden_states

             self.parent.assertTrue(len(__lowerCamelCase       )					,      len(config.backbone_config.depths       )       )
             self.parent.assertTrue(len(__lowerCamelCase       )					,      len(config.backbone_config.depths       )       )
             self.parent.assertTrue(len(__lowerCamelCase       )					,      config.decoder_layers       )





       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Any=False       ):


             '''simple docstring'''



             with torch.no_grad():
                   lowerCamelCase__		:				Dict    =  MaskaFormerModel(config=__lowerCamelCase       )
                   model.to(__lowerCamelCase       )
                   model.eval()

                   lowerCamelCase__		:				int    =  model(pixel_values=__lowerCamelCase					,      pixel_mask=__lowerCamelCase       )
                   lowerCamelCase__		:				str    =  model(__lowerCamelCase					,      output_hidden_states=__lowerCamelCase       )

             self.parent.assertEqual(
                 output.transformer_decoder_last_hidden_state.shape					,      (self.batch_size, self.num_queries, self.hidden_dim)					,      )
             # let's ensure the other two hidden state exists
             self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None       )
             self.parent.assertTrue(output.encoder_last_hidden_state is not None       )

             if output_hidden_states:
                   self.check_output_hidden_state(__lowerCamelCase					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  MaskaFormerForUniversalSegmentation(config=__lowerCamelCase       )
             model.to(__lowerCamelCase       )
             model.eval()

             def comm_check_on_output(__lowerCamelCase							:			int       ):
                   # let's still check that all the required stuff is there
                   self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None       )
                   self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None       )
                   self.parent.assertTrue(result.encoder_last_hidden_state is not None       )
                   # okay, now we need to check the logits shape
                   # due to the encoder compression, masks have a //4 spatial size
                   self.parent.assertEqual(
                       result.masks_queries_logits.shape					,      (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)					,      )
                   # + 1 for null class
                   self.parent.assertEqual(
                       result.class_queries_logits.shape					,      (self.batch_size, self.num_queries, self.num_labels + 1)       )

             with torch.no_grad():
                   lowerCamelCase__		:				Tuple    =  model(pixel_values=__lowerCamelCase					,      pixel_mask=__lowerCamelCase       )
                   lowerCamelCase__		:				Union[str, Any]    =  model(__lowerCamelCase       )

                   comm_check_on_output(__lowerCamelCase       )

                   lowerCamelCase__		:				Union[str, Any]    =  model(
                       pixel_values=__lowerCamelCase					,      pixel_mask=__lowerCamelCase					,      mask_labels=__lowerCamelCase					,      class_labels=__lowerCamelCase       )

             comm_check_on_output(__lowerCamelCase       )

             self.parent.assertTrue(result.loss is not None       )
             self.parent.assertEqual(result.loss.shape					,      torch.Size([1]       )       )







@require_torch
class 			_lowercase (							lowercase__							,  lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
       A__											=			{"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

       A__											=			False
       A__											=			False
       A__											=			False
       A__											=			False





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  MaskaFormerModelTester(self       )
             lowerCamelCase__		:				Optional[Any]    =  ConfigTester(self					,      config_class=__lowerCamelCase					,      has_text_modality=__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				Optional[Any]    =  self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.create_and_check_maskaformer_model(__lowerCamelCase					,      **__lowerCamelCase					,      output_hidden_states=__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__lowerCamelCase       )





       @unittest.skip(reason="Mask2Former does not use inputs_embeds"       )
       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             pass





       @unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"       )
       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             pass





       @unittest.skip(reason="Mask2Former is not a generative model"       )
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             pass





       @unittest.skip(reason="Mask2Former does not use token embeddings"       )
       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             pass





       @require_torch_multi_gpu
       @unittest.skip(
           reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"       )
       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             pass





       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				str    =  self.model_tester.prepare_config_and_inputs_for_common()

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

                   lowerCamelCase__		:				Dict    =  ["pixel_values"]
                   self.assertListEqual(arg_names[:1]					,      __lowerCamelCase       )





       @slow
       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
                   lowerCamelCase__		:				Dict    =  MaskaFormerModel.from_pretrained(__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  (self.model_tester.min_size,) * 2
             lowerCamelCase__		:				Optional[int]    =  {
                 "pixel_values": torch.randn((2, 3, *size)					,      device=__lowerCamelCase       ),
                 "mask_labels": torch.randn((2, 10, *size)					,      device=__lowerCamelCase       ),
                 "class_labels": torch.zeros(2					,      10					,      device=__lowerCamelCase       ).long(),
             }
             lowerCamelCase__		:				Union[str, Any]    =  self.model_tester.get_config()

             lowerCamelCase__		:				int    =  MaskaFormerForUniversalSegmentation(__lowerCamelCase       ).to(__lowerCamelCase       )
             lowerCamelCase__		:				Tuple    =  model(**__lowerCamelCase       )
             self.assertTrue(outputs.loss is not None       )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				Dict    =  self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.create_and_check_maskaformer_model(__lowerCamelCase					,      **__lowerCamelCase					,      output_hidden_states=__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



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

             for model_class in self.all_model_classes:
                   lowerCamelCase__		:				Union[str, Any]    =  model_class(__lowerCamelCase       ).to(__lowerCamelCase       )
                   lowerCamelCase__		:				Any    =  model(**__lowerCamelCase					,      output_attentions=__lowerCamelCase       )
                   self.assertTrue(outputs.attentions is not None       )





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             if not self.model_tester.is_training:
                   return

             lowerCamelCase__		:				str    =  self.all_model_classes[1]
             lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				str    =  self.model_tester.prepare_config_and_inputs()

             lowerCamelCase__		:				List[Any]    =  model_class(__lowerCamelCase       )
             model.to(__lowerCamelCase       )
             model.train()

             lowerCamelCase__		:				List[Any]    =  model(__lowerCamelCase					,      mask_labels=__lowerCamelCase					,      class_labels=__lowerCamelCase       ).loss
             loss.backward()





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  self.all_model_classes[1]
             lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  self.model_tester.prepare_config_and_inputs()
             lowerCamelCase__		:				Dict    =  True
             lowerCamelCase__		:				Optional[Any]    =  True

             lowerCamelCase__		:				Tuple    =  model_class(__lowerCamelCase       ).to(__lowerCamelCase       )
             model.train()

             lowerCamelCase__		:				List[Any]    =  model(__lowerCamelCase					,      mask_labels=__lowerCamelCase					,      class_labels=__lowerCamelCase       )

             lowerCamelCase__		:				Any    =  outputs.encoder_hidden_states[0]
             encoder_hidden_states.retain_grad()

             lowerCamelCase__		:				int    =  outputs.pixel_decoder_hidden_states[0]
             pixel_decoder_hidden_states.retain_grad()

             lowerCamelCase__		:				int    =  outputs.transformer_decoder_hidden_states[0]
             transformer_decoder_hidden_states.retain_grad()

             lowerCamelCase__		:				Optional[Any]    =  outputs.attentions[0]
             attentions.retain_grad()

             outputs.loss.backward(retain_graph=__lowerCamelCase       )

             self.assertIsNotNone(encoder_hidden_states.grad       )
             self.assertIsNotNone(pixel_decoder_hidden_states.grad       )
             self.assertIsNotNone(transformer_decoder_hidden_states.grad       )
             self.assertIsNotNone(attentions.grad       )



A      :    Any		=					1E-4

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
      return image







@require_vision
@slow
class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       @cached_property
       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             return "facebook/mask2former-swin-small-coco-instance"





       @cached_property
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints       ) if is_vision_available() else None





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(__lowerCamelCase       )
             lowerCamelCase__		:				Any    =  self.default_image_processor
             lowerCamelCase__		:				List[str]    =  prepare_img()
             lowerCamelCase__		:				Dict    =  image_processor(__lowerCamelCase					,      return_tensors="pt"       ).to(__lowerCamelCase       )
             lowerCamelCase__		:				List[str]    =  inputs["pixel_values"].shape
             # check size is divisible by 32
             self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
             # check size
             self.assertEqual(__lowerCamelCase					,      (1, 3, 384, 384)       )

             with torch.no_grad():
                   lowerCamelCase__		:				List[str]    =  model(**__lowerCamelCase       )

             lowerCamelCase__		:				str    =  torch.tensor(
                 [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]       ).to(__lowerCamelCase       )
             self.assertTrue(
                 torch.allclose(
                     outputs.encoder_last_hidden_state[0, 0, :3, :3]					,      __lowerCamelCase					,      atol=__lowerCamelCase       )       )

             lowerCamelCase__		:				Union[str, Any]    =  torch.tensor(
                 [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]       ).to(__lowerCamelCase       )
             self.assertTrue(
                 torch.allclose(
                     outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]					,      __lowerCamelCase					,      atol=__lowerCamelCase       )       )

             lowerCamelCase__		:				List[str]    =  torch.tensor(
                 [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]       ).to(__lowerCamelCase       )
             self.assertTrue(
                 torch.allclose(
                     outputs.transformer_decoder_last_hidden_state[0, :3, :3]					,      __lowerCamelCase					,      atol=__lowerCamelCase       )       )





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(__lowerCamelCase       ).eval()
             lowerCamelCase__		:				Dict    =  self.default_image_processor
             lowerCamelCase__		:				int    =  prepare_img()
             lowerCamelCase__		:				str    =  image_processor(__lowerCamelCase					,      return_tensors="pt"       ).to(__lowerCamelCase       )
             lowerCamelCase__		:				Dict    =  inputs["pixel_values"].shape
             # check size is divisible by 32
             self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
             # check size
             self.assertEqual(__lowerCamelCase					,      (1, 3, 384, 384)       )

             with torch.no_grad():
                   lowerCamelCase__		:				Dict    =  model(**__lowerCamelCase       )
             # masks_queries_logits
             lowerCamelCase__		:				Dict    =  outputs.masks_queries_logits
             self.assertEqual(
                 masks_queries_logits.shape					,      (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       )
             lowerCamelCase__		:				Union[str, Any]    =  [
                 [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
                 [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
                 [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
             ]
             lowerCamelCase__		:				List[Any]    =  torch.tensor(__lowerCamelCase       ).to(__lowerCamelCase       )
             self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]					,      __lowerCamelCase					,      atol=__lowerCamelCase       )       )
             # class_queries_logits
             lowerCamelCase__		:				List[Any]    =  outputs.class_queries_logits
             self.assertEqual(class_queries_logits.shape					,      (1, model.config.num_queries, model.config.num_labels + 1)       )
             lowerCamelCase__		:				List[str]    =  torch.tensor(
                 [
                     [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
                     [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
                     [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
                 ]       ).to(__lowerCamelCase       )
             self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]					,      __lowerCamelCase					,      atol=__lowerCamelCase       )       )





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(__lowerCamelCase       ).eval()
             lowerCamelCase__		:				Union[str, Any]    =  self.default_image_processor

             lowerCamelCase__		:				Union[str, Any]    =  image_processor(
                 [np.zeros((3, 800, 1333)       ), np.zeros((3, 800, 1333)       )]					,      segmentation_maps=[np.zeros((384, 384)       ).astype(np.floataa       ), np.zeros((384, 384)       ).astype(np.floataa       )]					,      return_tensors="pt"					,      )

             lowerCamelCase__		:				List[Any]    =  inputs["pixel_values"].to(__lowerCamelCase       )
             lowerCamelCase__		:				Dict    =  [el.to(__lowerCamelCase       ) for el in inputs["mask_labels"]]
             lowerCamelCase__		:				Optional[int]    =  [el.to(__lowerCamelCase       ) for el in inputs["class_labels"]]

             with torch.no_grad():
                   lowerCamelCase__		:				List[Any]    =  model(**__lowerCamelCase       )

             self.assertTrue(outputs.loss is not None       )






import math
import os
import sys

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				str    =  ""
      try:
            with open(_A    ,					"rb"						) as binary_file:
                  lowerCamelCase__		:				Union[str, Any]    =  binary_file.read()
            for dat in data:
                  lowerCamelCase__		:				Optional[Any]    =  F"{dat:08b}"
                  result += curr_byte
            return result
      except OSError:
            print("File not accessible"						)
            sys.exit()

def       lowercase_		(		_A			:    dict[str, str]    ,					_A			:    str    ,					_A			:    int    ,					_A			:    str						):

      """simple docstring"""




      lexicon.pop(_A						)
      lowerCamelCase__		:				List[Any]    =  last_match_id

      if math.loga(_A						).is_integer():
            for curr_key in lexicon:
                  lowerCamelCase__		:				Any    =  "0" + lexicon[curr_key]

      lowerCamelCase__		:				Tuple    =  bin(_A						)[2:]

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  {"0": "0", "1": "1"}
      lowerCamelCase__    ,   lowerCamelCase__		:				int    =  "", ""
      lowerCamelCase__		:				int    =  len(_A						)

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

            lowerCamelCase__		:				Optional[Any]    =  lexicon[curr_string]
            result += last_match_id
            add_key_to_lexicon(_A    ,					_A    ,					_A    ,					_A						)
            index += 1
            lowerCamelCase__		:				Dict    =  ""

      while curr_string != "" and curr_string not in lexicon:
            curr_string += "0"

      if curr_string != "":
            lowerCamelCase__		:				int    =  lexicon[curr_string]
            result += last_match_id

      return result

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  os.path.getsize(_A						)
      lowerCamelCase__		:				Dict    =  bin(_A						)[2:]
      lowerCamelCase__		:				List[Any]    =  len(_A						)

      return "0" * (length_length - 1) + file_length_binary + compressed

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  8
      try:
            with open(_A    ,					"wb"						) as opened_file:
                  lowerCamelCase__		:				Any    =  [
                      to_write[i : i + byte_length]
                      for i in range(0    ,					len(_A						)    ,					_A						)
                  ]

                  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:
                        opened_file.write(int(_A    ,					2						).to_bytes(1    ,					byteorder="big"						)						)
      except OSError:
            print("File not accessible"						)
            sys.exit()

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  read_file_binary(_A						)
      lowerCamelCase__		:				List[Any]    =  compress_data(_A						)
      lowerCamelCase__		:				str    =  add_file_length(_A    ,					_A						)
      write_file_binary(_A    ,					_A						)


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


