



'''simple docstring'''

import math
import os
import sys







def 						__UpperCamelCase				(							_lowercase					) ->			str:
	_lowercase					:       Dict       =		''
	try:
		with open(_lowercase,				'rb'					) as binary_file:
			_lowercase					:       List[Any]       =		binary_file.read()
		for dat in data:
			_lowercase					:       Optional[Any]       =		f'''{dat:08b}'''
			result += curr_byte
		return result
	except OSError:
		print('File not accessible'					)
		sys.exit()







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase,				_lowercase					) ->			None:
	lexicon.pop(_lowercase					)
	_lowercase					:       Dict       =		last_match_id

	if math.loga(_lowercase					).is_integer():
		for curr_key in lexicon:
			_lowercase					:       Optional[int]       =		'0' + lexicon[curr_key]

	_lowercase					:       Union[str, Any]       =		bin(_lowercase					)[2:]







def 						__UpperCamelCase				(							_lowercase					) ->			str:
	_lowercase					:       Tuple       =		{'0': '0', '1': '1'}
	_lowercase  ,    _lowercase					:       Any       =		'', ''
	_lowercase					:       Dict       =		len(_lowercase					)

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

		_lowercase					:       List[Any]       =		lexicon[curr_string]
		result += last_match_id
		add_key_to_lexicon(_lowercase,				_lowercase,				_lowercase,				_lowercase					)
		index += 1
		_lowercase					:       Tuple       =		''

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

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

	return result







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			str:
	_lowercase					:       Union[str, Any]       =		os.path.getsize(_lowercase					)
	_lowercase					:       Optional[Any]       =		bin(_lowercase					)[2:]
	_lowercase					:       str       =		len(_lowercase					)

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







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			None:
	_lowercase					:       Dict       =		8
	try:
		with open(_lowercase,				'wb'					) as opened_file:
			_lowercase					:       str       =		[
			    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:
				opened_file.write(int(_lowercase,				2					).to_bytes(1,				byteorder='big'					)					)
	except OSError:
		print('File not accessible'					)
		sys.exit()







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			None:
	_lowercase					:       Dict       =		read_file_binary(_lowercase					)
	_lowercase					:       Optional[Any]       =		compress_data(_lowercase					)
	_lowercase					:       int       =		add_file_length(_lowercase,				_lowercase					)
	write_file_binary(_lowercase,				_lowercase					)


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





'''simple docstring'''

import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roformer.modeling_flax_roformer import (
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		)



class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					def __init__(  self				:	Dict     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	List[str]=13     ,  UpperCamelCase_				:	Union[str, Any]=7     ,  UpperCamelCase_				:	Union[str, Any]=True     ,  UpperCamelCase_				:	Any=True     ,  UpperCamelCase_				:	Dict=True     ,  UpperCamelCase_				:	List[str]=True     ,  UpperCamelCase_				:	int=99     ,  UpperCamelCase_				:	Tuple=32     ,  UpperCamelCase_				:	List[str]=5     ,  UpperCamelCase_				:	Dict=4     ,  UpperCamelCase_				:	Tuple=37     ,  UpperCamelCase_				:	int="gelu"     ,  UpperCamelCase_				:	int=0.1     ,  UpperCamelCase_				:	Dict=0.1     ,  UpperCamelCase_				:	Any=512     ,  UpperCamelCase_				:	List[Any]=16     ,  UpperCamelCase_				:	Optional[int]=2     ,  UpperCamelCase_				:	Tuple=0.02     ,  UpperCamelCase_				:	Union[str, Any]=4     ,  )  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       int       =		parent
						_lowercase					:       str       =		batch_size
						_lowercase					:       List[str]       =		seq_length
						_lowercase					:       Dict       =		is_training
						_lowercase					:       Optional[int]       =		use_attention_mask
						_lowercase					:       List[Any]       =		use_token_type_ids
						_lowercase					:       Union[str, Any]       =		use_labels
						_lowercase					:       Dict       =		vocab_size
						_lowercase					:       List[Any]       =		hidden_size
						_lowercase					:       Any       =		num_hidden_layers
						_lowercase					:       int       =		num_attention_heads
						_lowercase					:       Optional[int]       =		intermediate_size
						_lowercase					:       Any       =		hidden_act
						_lowercase					:       List[str]       =		hidden_dropout_prob
						_lowercase					:       Union[str, Any]       =		attention_probs_dropout_prob
						_lowercase					:       Optional[int]       =		max_position_embeddings
						_lowercase					:       int       =		type_vocab_size
						_lowercase					:       Any       =		type_sequence_label_size
						_lowercase					:       Any       =		initializer_range
						_lowercase					:       str       =		num_choices




					def 						__UpperCAmelCase     (  self				:	str					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

						_lowercase					:       int       =		None
						if self.use_attention_mask:
							_lowercase					:       Optional[int]       =		random_attention_mask([self.batch_size, self.seq_length]					)

						_lowercase					:       Any       =		None
						if self.use_token_type_ids:
							_lowercase					:       Optional[Any]       =		ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size					)

						_lowercase					:       str       =		RoFormerConfig(
						    vocab_size=self.vocab_size     ,  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     ,  max_position_embeddings=self.max_position_embeddings     ,  type_vocab_size=self.type_vocab_size     ,  is_decoder=UpperCamelCase_     ,  initializer_range=self.initializer_range     ,  )

						return config, input_ids, token_type_ids, attention_mask




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



						'''simple docstring'''





						_lowercase					:       Dict       =		self.prepare_config_and_inputs()
						_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Union[str, Any]       =		config_and_inputs
						_lowercase					:       Optional[int]       =		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict






@require_flax
class 		lowerCamelCase__      (						A	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						True

					A_						    =						(
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)




					def 						__UpperCAmelCase     (  self				:	str					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Tuple       =		FlaxRoFormerModelTester(self					)




					@slow
					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Optional[int]:



						'''simple docstring'''





						for model_class_name in self.all_model_classes:
							_lowercase					:       Optional[int]       =		model_class_name.from_pretrained('junnyu/roformer_chinese_small'     ,  from_pt=UpperCamelCase_					)
							_lowercase					:       str       =		model(np.ones((1, 1)					)					)
							self.assertIsNotNone(UpperCamelCase_					)






@require_flax
class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



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



						'''simple docstring'''





						_lowercase					:       Dict       =		FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'					)
						_lowercase					:       Any       =		jnp.array([[0, 1, 2, 3, 4, 5]]					)
						_lowercase					:       int       =		model(UpperCamelCase_					)[0]

						_lowercase					:       Union[str, Any]       =		5_0000

						_lowercase					:       str       =		(1, 6, vocab_size)
						self.assertEqual(output.shape     ,  UpperCamelCase_					)

						_lowercase					:       int       =		jnp.array(
						    [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]					)

						self.assertTrue(jnp.allclose(output[:, :3, :3]     ,  UpperCamelCase_     ,  atol=1E-4					)					)
