


import math
import sys





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	str:


						'''simple docstring'''






						__snake_case				=		""
						try:
												with open(_lowerCAmelCase      ,	"rb" ) as binary_file:
																		__snake_case				=		binary_file.read()
												for dat in data:
																		__snake_case				=		F'''{dat:08b}'''
																		result += curr_byte
												return result
						except OSError:
												print("File not accessible" )
												sys.exit()





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	str:


						'''simple docstring'''






						__snake_case				=		{"0": "0", "1": "1"}
						__snake_case		,					__snake_case				=		"", ""
						__snake_case				=		len(_lowerCAmelCase )

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

												__snake_case				=		lexicon[curr_string]
												result += last_match_id
												__snake_case				=		last_match_id + "0"

												if math.loga(_lowerCAmelCase ).is_integer():
																		__snake_case				=		{}
																		for curr_key in list(_lowerCAmelCase ):
																								__snake_case				=		lexicon.pop(_lowerCAmelCase )
																		__snake_case				=		new_lex

												__snake_case				=		last_match_id + "1"
												index += 1
												__snake_case				=		""
						return result





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	None:


						'''simple docstring'''






						__snake_case				=		8
						try:
												with open(_lowerCAmelCase      ,	"wb" ) as opened_file:
																		__snake_case				=		[
																		    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[:-1]:
																								opened_file.write(int(_lowerCAmelCase      ,	2 ).to_bytes(1      ,	byteorder="big" ) )
						except OSError:
												print("File not accessible" )
												sys.exit()





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	str:


						'''simple docstring'''






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

						__snake_case				=		data_bits[counter:]
						__snake_case				=		data_bits[counter + 1 :]
						return data_bits





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	None:


						'''simple docstring'''






						__snake_case				=		read_file_binary(_lowerCAmelCase )
						__snake_case				=		remove_prefix(_lowerCAmelCase )
						__snake_case				=		decompress_data(_lowerCAmelCase )
						write_file_binary(_lowerCAmelCase      ,	_lowerCAmelCase )


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









import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A							:					int     						=     logging.get_logger(__name__)  # pylint: disable=invalid-name






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict=7_6_8						)			->	int:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE						)
											__snake_case				=		proj_size

											__snake_case				=		CLIPVisionModel(SCREAMING_SNAKE_CASE						)
											__snake_case				=		PaintByExampleMapper(SCREAMING_SNAKE_CASE						)
											__snake_case				=		nn.LayerNorm(config.hidden_size						)
											__snake_case				=		nn.Linear(config.hidden_size     ,    self.proj_size						)

											# uncondition for scaling
											__snake_case				=		nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.model(pixel_values=SCREAMING_SNAKE_CASE						)
											__snake_case				=		clip_output.pooler_output
											__snake_case				=		self.mapper(latent_states[:, None]						)
											__snake_case				=		self.final_layer_norm(SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.proj_out(SCREAMING_SNAKE_CASE						)
											if return_uncond_vector:
																	return latent_states, self.uncond_vector

											return latent_states






class     UpperCamelCase(  nn.Module   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	List[Any]:




											'''simple docstring'''

											super().__init__()
											__snake_case				=		(config.num_hidden_layers + 1) // 5
											__snake_case				=		config.hidden_size
											__snake_case				=		1
											__snake_case				=		nn.ModuleList(
											    [
											        BasicTransformerBlock(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    activation_fn="gelu"     ,    attention_bias=SCREAMING_SNAKE_CASE						)
											        for _ in range(SCREAMING_SNAKE_CASE						)
											    ]						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Optional[int]:




											'''simple docstring'''

											for block in self.blocks:
																	__snake_case				=		block(SCREAMING_SNAKE_CASE						)

											return hidden_states





