

import math
import os
import sys







def       lowercase					(	__A :					str		)				->			str:





    '''simple docstring'''




    snake_case							:		Dict        =		""""""
    try:
        with open(_lowerCamelCase ,    """rb"""		) as binary_file:
            snake_case							:		Tuple        =		binary_file.read()
        for dat in data:
            snake_case							:		Optional[int]        =		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		)				->			None:





    '''simple docstring'''




    lexicon.pop(_lowerCamelCase		)
    snake_case							:		Union[str, Any]        =		last_match_id

    if math.loga(_lowerCamelCase		).is_integer():
        for curr_key in lexicon:
            snake_case							:		str        =		"""0""" + lexicon[curr_key]

    snake_case							:		List[Any]        =		bin(_lowerCamelCase		)[2:]







def       lowercase					(	__A :					str		)				->			str:





    '''simple docstring'''




    snake_case							:		Union[str, Any]        =		{"""0""": """0""", """1""": """1"""}
    snake_case      ,		snake_case							:		Optional[Any]        =		"""""", """"""
    snake_case							:		Optional[int]        =		len(_lowerCamelCase		)

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

        snake_case							:		Optional[Any]        =		lexicon[curr_string]
        result += last_match_id
        add_key_to_lexicon(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase		)
        index += 1
        snake_case							:		Union[str, Any]        =		""""""

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

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

    return result







def       lowercase					(	__A :					str ,    __A :					str		)				->			str:





    '''simple docstring'''




    snake_case							:		Tuple        =		os.path.getsize(_lowerCamelCase		)
    snake_case							:		Optional[int]        =		bin(_lowerCamelCase		)[2:]
    snake_case							:		Optional[int]        =		len(_lowerCamelCase		)

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







def       lowercase					(	__A :					str ,    __A :					str		)				->			None:





    '''simple docstring'''




    snake_case							:		List[str]        =		8
    try:
        with open(_lowerCamelCase ,    """wb"""		) as opened_file:
            snake_case							:		Dict        =		[
                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					(	__A :					str ,    __A :					str		)				->			None:





    '''simple docstring'''




    snake_case							:		Optional[Any]        =		read_file_binary(_lowerCamelCase		)
    snake_case							:		List[Any]        =		compress_data(_lowerCamelCase		)
    snake_case							:		Dict        =		add_file_length(_lowerCamelCase ,    _lowerCamelCase		)
    write_file_binary(_lowerCamelCase ,    _lowerCamelCase		)


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









'''simple docstring'''



from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

UpperCAmelCase_							:	str	     =		logging.get_logger(__name__)





@add_end_docstrings(__lowerCAmelCase)
class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE	)

            requires_backends(self     ,	'''vision'''	)
            self.check_model_type(
                TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
                if self.framework == '''tf'''
                else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING	)


       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




            return super().__call__(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	{}
            if "candidate_labels" in kwargs:
                 __snake_case            =	kwargs['''candidate_labels''']
            if "hypothesis_template" in kwargs:
                 __snake_case            =	kwargs['''hypothesis_template''']

            return preprocess_params, {}, {}


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE="This is a photo of {}."	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	load_image(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.image_processor(images=[image]     ,	return_tensors=self.framework	)
            __snake_case            =	candidate_labels
            __snake_case            =	[hypothesis_template.format(__SCREAMING_SNAKE_CASE	) for x in candidate_labels]
            __snake_case            =	self.tokenizer(__SCREAMING_SNAKE_CASE     ,	return_tensors=self.framework     ,	padding=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	[text_inputs]
            return inputs


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	model_inputs.pop('''candidate_labels'''	)
            __snake_case            =	model_inputs.pop('''text_inputs'''	)
            if isinstance(text_inputs[0]     ,	__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	text_inputs[0]
            else:
                 # Batching case.
                 __snake_case            =	text_inputs[0][0]

            __snake_case            =	self.model(**__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)

            __snake_case            =	{
                '''candidate_labels''': candidate_labels,
                '''logits''': outputs.logits_per_image,
            }
            return model_outputs




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            __snake_case            =	model_outputs.pop('''candidate_labels'''	)
            __snake_case            =	model_outputs['''logits'''][0]
            if self.framework == "pt":
                 __snake_case            =	logits.softmax(dim=-1	).squeeze(-1	)
                 __snake_case            =	probs.tolist()
                 if not isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                      __snake_case            =	[scores]
            elif self.framework == "tf":
                 __snake_case            =	stable_softmax(__SCREAMING_SNAKE_CASE     ,	axis=-1	)
                 __snake_case            =	probs.numpy().tolist()
            else:
                 raise ValueError(F'''Unsupported framework: {self.framework}'''	)

            __snake_case            =	[
                {'''score''': score, '''label''': candidate_label}
                for score, candidate_label in sorted(zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)     ,	key=lambda __SCREAMING_SNAKE_CASE	: -x[0]	)
            ]
            return result






