


"""simple docstring"""




import math
import os
import sys

def        lowercase							(		lowerCAmelCase__ :     str			) ->      str:
		__a           =			''''''
		try:
				with open(lowerCAmelCase__    ,					'''rb'''			) as binary_file:
						__a           =			binary_file.read()
				for dat in data:
						__a           =			f'''{dat:08b}'''
						result += curr_byte
				return result
		except OSError:
				print('''File not accessible'''			)
				sys.exit()

def        lowercase							(		lowerCAmelCase__ :     dict[str, str]    ,					lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     str			) ->      None:
		lexicon.pop(lowerCAmelCase__			)
		__a           =			last_match_id

		if math.loga(lowerCAmelCase__			).is_integer():
				for curr_key in lexicon:
						__a           =			'''0''' + lexicon[curr_key]

		__a           =			bin(lowerCAmelCase__			)[2:]

def        lowercase							(		lowerCAmelCase__ :     str			) ->      str:
		__a           =			{'''0''': '''0''', '''1''': '''1'''}
		__a      ,     __a           =			'''''', ''''''
		__a           =			len(lowerCAmelCase__			)

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

				__a           =			lexicon[curr_string]
				result += last_match_id
				add_key_to_lexicon(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
				index += 1
				__a           =			''''''

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

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

		return result

def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     str			) ->      str:
		__a           =			os.path.getsize(lowerCAmelCase__			)
		__a           =			bin(lowerCAmelCase__			)[2:]
		__a           =			len(lowerCAmelCase__			)

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

def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     str			) ->      None:
		__a           =			8
		try:
				with open(lowerCAmelCase__    ,					'''wb'''			) as opened_file:
						__a           =			[
						    to_write[i : i + byte_length]
						    for i in range(0    ,					len(lowerCAmelCase__			)    ,					lowerCAmelCase__			)
						]

						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(lowerCAmelCase__    ,					2			).to_bytes(1    ,					byteorder='''big'''			)			)
		except OSError:
				print('''File not accessible'''			)
				sys.exit()

def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     str			) ->      None:
		__a           =			read_file_binary(lowerCAmelCase__			)
		__a           =			compress_data(lowerCAmelCase__			)
		__a           =			add_file_length(lowerCAmelCase__    ,					lowerCAmelCase__			)
		write_file_binary(lowerCAmelCase__    ,					lowerCAmelCase__			)


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







"""simple docstring"""







import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
       import jax

       from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel







class        UpperCamelCase		(			unittest.TestCase  ):
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase=13     ,__UpperCamelCase=30     ,__UpperCamelCase=2     ,__UpperCamelCase=3     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=32     ,__UpperCamelCase=5     ,__UpperCamelCase=4     ,__UpperCamelCase=37     ,__UpperCamelCase="gelu"     ,__UpperCamelCase=0.1     ,__UpperCamelCase=0.1     ,__UpperCamelCase=10     ,__UpperCamelCase=0.02     ,)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Tuple													=	parent
        lowercase_	:				Union[str, Any]													=	batch_size
        lowercase_	:				int													=	image_size
        lowercase_	:				Tuple													=	patch_size
        lowercase_	:				Optional[int]													=	num_channels
        lowercase_	:				Union[str, Any]													=	is_training
        lowercase_	:				Dict													=	use_labels
        lowercase_	:				Optional[int]													=	hidden_size
        lowercase_	:				List[str]													=	num_hidden_layers
        lowercase_	:				Optional[Any]													=	num_attention_heads
        lowercase_	:				Optional[int]													=	intermediate_size
        lowercase_	:				Tuple													=	hidden_act
        lowercase_	:				int													=	hidden_dropout_prob
        lowercase_	:				str													=	attention_probs_dropout_prob
        lowercase_	:				str													=	type_sequence_label_size
        lowercase_	:				Optional[int]													=	initializer_range

        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowercase_	:				str													=	(image_size // patch_size) ** 2
        lowercase_	:				Optional[int]													=	num_patches + 1
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


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

        lowercase_	:				List[Any]													=	ViTConfig(
            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=__UpperCamelCase     ,initializer_range=self.initializer_range     ,)

        return config, pixel_values
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	FlaxViTModel(config=__UpperCamelCase			)
        lowercase_	:				Dict													=	model(__UpperCamelCase			)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        lowercase_	:				Union[str, Any]													=	(self.image_size, self.image_size)
        lowercase_	:				List[Any]													=	(self.patch_size, self.patch_size)
        lowercase_	:				List[str]													=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, num_patches + 1, self.hidden_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.type_sequence_label_size
        lowercase_	:				str													=	FlaxViTForImageClassification(config=__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)			)

        # test greyscale images
        lowercase_	:				Union[str, Any]													=	1
        lowercase_	:				Optional[int]													=	FlaxViTForImageClassification(__UpperCamelCase			)

        lowercase_	:				List[Any]													=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase_	:				str													=	model(__UpperCamelCase			)







   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.prepare_config_and_inputs()
        (
            (
            lowercase_
        )							,  (
            lowercase_
        )							,  
        )	:				List[Any]													=	config_and_inputs
        lowercase_	:				Tuple													=	{'pixel_values': pixel_values}
        return config, inputs_dict







@require_flax
class        UpperCamelCase		(			lowercase_						,      unittest.TestCase  ):
   lowercase							=  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
   def 			_UpperCAmelCase   (			self			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	FlaxViTModelTester(self			)
        lowercase_	:				Union[str, Any]													=	ConfigTester(self     ,config_class=__UpperCamelCase     ,has_text_modality=__UpperCamelCase     ,hidden_size=37			)
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Any													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_	:				Optional[Any]													=	model_class(__UpperCamelCase			)
             lowercase_	:				Tuple													=	inspect.signature(model.__call__			)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             lowercase_	:				Union[str, Any]													=	[*signature.parameters.keys()]

             lowercase_	:				str													=	['pixel_values']
             self.assertListEqual(arg_names[:1]     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


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

        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__			):
                  lowercase_	:				Optional[Any]													=	self._prepare_for_class(__UpperCamelCase     ,__UpperCamelCase			)
                  lowercase_	:				Dict													=	model_class(__UpperCamelCase			)

                  @jax.jit
                  def model_jitted(__UpperCamelCase     ,**__UpperCamelCase			):
                       return model(pixel_values=__UpperCamelCase     ,**__UpperCamelCase			)

                  with self.subTest('JIT Enabled'			):
                       lowercase_	:				Optional[int]													=	model_jitted(**__UpperCamelCase			).to_tuple()

                  with self.subTest('JIT Disabled'			):
                       with jax.disable_jit():
                            lowercase_	:				List[str]													=	model_jitted(**__UpperCamelCase			).to_tuple()

                  self.assertEqual(len(__UpperCamelCase			)     ,len(__UpperCamelCase			)			)
                  for jitted_output, output in zip(__UpperCamelCase     ,__UpperCamelCase			):
                       self.assertEqual(jitted_output.shape     ,output.shape			)







   @slow
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        for model_class_name in self.all_model_classes:
             lowercase_	:				Optional[int]													=	model_class_name.from_pretrained('google/vit-base-patch16-224'			)
             lowercase_	:				int													=	model(np.ones((1, 3, 224, 224)			)			)
             self.assertIsNotNone(__UpperCamelCase			)

