


"""simple docstring"""


import math
import sys


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     {"0": "0", "1": "1"}
			__lowerCAmelCase       ,      __lowerCAmelCase							       =     "", ""
			__lowerCAmelCase							       =     len(_UpperCamelCase )

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

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

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

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


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




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

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


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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

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


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     read_file_binary(_UpperCamelCase )
			__lowerCAmelCase							       =     remove_prefix(_UpperCamelCase )
			__lowerCAmelCase							       =     decompress_data(_UpperCamelCase )
			write_file_binary(_UpperCamelCase       ,					_UpperCamelCase )


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








"""simple docstring"""


import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
	from PIL import Image
else:
	class     _UpperCamelCase    :
								'''simple docstring'''







								@staticmethod
								def      snake_case							(	*__a       ,  **__a      ):
											pass
@is_pipeline_test
@require_torch
@require_vision
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     pipeline("visual-question-answering"       ,  model="hf-internal-testing/tiny-vilt-random-vqa"      )
										__lowerCAmelCase							       =     [
										    {
										        "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      ),
										        "question": "How many cats are there?",
										    },
										    {
										        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
										        "question": "How many cats are there?",
										    },
										]
										return vqa_pipeline, examples

							def      snake_case							(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     vqa_pipeline(__a       ,  top_k=1      )
										self.assertEqual(
										    __a       ,  [
										        [{"score": ANY(__a      ), "answer": ANY(__a      )}],
										        [{"score": ANY(__a      ), "answer": ANY(__a      )}],
										    ]       ,  )

							@require_torch
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     pipeline("visual-question-answering"       ,  model="hf-internal-testing/tiny-vilt-random-vqa"      )
										__lowerCAmelCase							       =     "./tests/fixtures/tests_samples/COCO/000000039769.png"
										__lowerCAmelCase							       =     "How many cats are there?"

										__lowerCAmelCase							       =     vqa_pipeline(image=__a       ,  question="How many cats are there?"       ,  top_k=2      )
										self.assertEqual(
										    __a       ,  [{"score": ANY(__a      ), "answer": ANY(__a      )}, {"score": ANY(__a      ), "answer": ANY(__a      )}]      )

										__lowerCAmelCase							       =     vqa_pipeline({"image": image, "question": question}       ,  top_k=2      )
										self.assertEqual(
										    __a       ,  [{"score": ANY(__a      ), "answer": ANY(__a      )}, {"score": ANY(__a      ), "answer": ANY(__a      )}]      )

							@slow
							@require_torch
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     pipeline("visual-question-answering"       ,  model="dandelin/vilt-b32-finetuned-vqa"      )
										__lowerCAmelCase							       =     "./tests/fixtures/tests_samples/COCO/000000039769.png"
										__lowerCAmelCase							       =     "How many cats are there?"

										__lowerCAmelCase							       =     vqa_pipeline(image=__a       ,  question=__a       ,  top_k=2      )
										self.assertEqual(
										    nested_simplify(__a       ,  decimals=4      )       ,  [{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]      )

										__lowerCAmelCase							       =     vqa_pipeline({"image": image, "question": question}       ,  top_k=2      )
										self.assertEqual(
										    nested_simplify(__a       ,  decimals=4      )       ,  [{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]      )

										__lowerCAmelCase							       =     vqa_pipeline(
										    [{"image": image, "question": question}, {"image": image, "question": question}]       ,  top_k=2      )
										self.assertEqual(
										    nested_simplify(__a       ,  decimals=4      )       ,  [[{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]] * 2       ,  )



							@require_tf
							@unittest.skip("Visual question answering not implemented in TF"      )
							def      snake_case							(	self      ):
										pass




