



'''simple docstring'''





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class 		_lowercase		(       __lowercase	):
   _UpperCAmelCase     	=			['image_processor', 'tokenizer']
   _UpperCAmelCase     	=			'BlipImageProcessor'
   _UpperCAmelCase     	=			'AutoTokenizer'

   def __init__(      self			:       Dict							,   __lowerCAmelCase			:       Tuple							,   __lowerCAmelCase			:       List[str]							,   __lowerCAmelCase			:       Union[str, Any]							)       ->				Optional[Any]:


    """simple docstring"""






    super().__init__(__A							,   __A							)

    # add QFormer tokenizer
    a						=		qformer_tokenizer

   def __call__(      self			:       int							,   __lowerCAmelCase			:       List[str] = None							,   __lowerCAmelCase			:       Any = None							,   __lowerCAmelCase			:       Dict = True							,   __lowerCAmelCase			:       List[str] = False							,   __lowerCAmelCase			:       int = None							,   __lowerCAmelCase			:       Optional[Any] = None							,   __lowerCAmelCase			:       Optional[Any] = 0							,   __lowerCAmelCase			:       Dict = None							,   __lowerCAmelCase			:       List[Any] = None							,   __lowerCAmelCase			:       Any = False							,   __lowerCAmelCase			:       Optional[int] = False							,   __lowerCAmelCase			:       Union[str, Any] = False							,   __lowerCAmelCase			:       Optional[int] = False							,   __lowerCAmelCase			:       Dict = False							,   __lowerCAmelCase			:       int = True							,   __lowerCAmelCase			:       Any = None							,   **__lowerCAmelCase			:       Union[str, Any]							,   )       ->				BatchFeature:


    """simple docstring"""






    if images is None and text is None:
     raise ValueError("You have to specify at least images or text."							)

    a						=		BatchFeature()

    if text is not None:
     a						=		self.tokenizer(
         text=__A							,   add_special_tokens=__A							,   padding=__A							,   truncation=__A							,   max_length=__A							,   stride=__A							,   pad_to_multiple_of=__A							,   return_attention_mask=__A							,   return_overflowing_tokens=__A							,   return_special_tokens_mask=__A							,   return_offsets_mapping=__A							,   return_token_type_ids=__A							,   return_length=__A							,   verbose=__A							,   return_tensors=__A							,   **__A							,   )
     encoding.update(__A							)
     a						=		self.qformer_tokenizer(
         text=__A							,   add_special_tokens=__A							,   padding=__A							,   truncation=__A							,   max_length=__A							,   stride=__A							,   pad_to_multiple_of=__A							,   return_attention_mask=__A							,   return_overflowing_tokens=__A							,   return_special_tokens_mask=__A							,   return_offsets_mapping=__A							,   return_token_type_ids=__A							,   return_length=__A							,   verbose=__A							,   return_tensors=__A							,   **__A							,   )
     a						=		qformer_text_encoding.pop("input_ids"							)
     a						=		qformer_text_encoding.pop("attention_mask"							)

    if images is not None:
     a						=		self.image_processor(__A							,   return_tensors=__A							)
     encoding.update(__A							)

    return encoding

   def 		A							(      self			:       Dict							,   *__lowerCAmelCase			:       Dict							,   **__lowerCAmelCase			:       List[Any]							)       ->				Any:


    """simple docstring"""






    return self.tokenizer.batch_decode(*__A							,   **__A							)

   def 		A							(      self			:       List[str]							,   *__lowerCAmelCase			:       Tuple							,   **__lowerCAmelCase			:       Dict							)       ->				Tuple:


    """simple docstring"""






    return self.tokenizer.decode(*__A							,   **__A							)

   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 		A							(      self			:       Optional[int]							)       ->				str:


    """simple docstring"""






    a						=		self.tokenizer.model_input_names
    a						=		self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       List[Any]							,   **__lowerCAmelCase			:       str							)       ->				List[str]:


    """simple docstring"""






    if os.path.isfile(__A							):
     raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""							)
    os.makedirs(__A							,   exist_ok=__A							)
    a						=		os.path.join(__A							,   "qformer_tokenizer"							)
    self.qformer_tokenizer.save_pretrained(__A							)
    return super().save_pretrained(__A							,   **__A							)




   @classmethod
   def 		A							(      cls			:       Dict							,   __lowerCAmelCase			:       Tuple							,   **__lowerCAmelCase			:       Optional[Any]							)       ->				int:


    """simple docstring"""






    a						=		AutoTokenizer.from_pretrained(__A							,   subfolder="qformer_tokenizer"							)
    a						=		cls._get_arguments_from_pretrained(__A							,   **__A							)
    args.append(__A							)
    return cls(*__A							)






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt



class 		_lowercase		:

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


    """simple docstring"""






    a						=		""
    a						=		""
    a						=		[]
    a						=		0
    a						=		256
    a						=		0
    a						=		0
    a						=		0
    a						=		0

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       Any							)       ->				int:


    """simple docstring"""






    a						=		cva.imread(__lowerCAmelCase							,   0							)
    a						=		copy.deepcopy(self.img							)
    a		,  a		,  a						=		plt.hist(self.img.ravel()							,   256							,   [0, 256]							,   label="x"							)
    a						=		np.sum(__lowerCAmelCase							)
    for i in range(len(__lowerCAmelCase							)							):
     a						=		x[i] / self.k
     self.sk += prk
     a						=		(self.L - 1) * self.sk
     if self.rem != 0:
      a						=		int(last % last							)
     a						=		int(last + 1 if self.rem >= 0.5 else last							)
     self.last_list.append(__lowerCAmelCase							)
     a						=		int(np.ma.count(self.img							) / self.img[1].size							)
     a						=		self.img[1].size
    for i in range(self.number_of_cols							):
     for j in range(self.number_of_rows							):
      a						=		self.img[j][i]
      if num != self.last_list[num]:
       a						=		self.last_list[num]
    cva.imwrite("output_data/output.jpg"							,   self.img							)

   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    plt.hist(self.img.ravel()							,   256							,   [0, 256]							)




   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    cva.imshow("Output-Image"							,   self.img							)
    cva.imshow("Input-Image"							,   self.original_image							)
    cva.waitKey(5000							)
    cva.destroyAllWindows()


if __name__ == "__main__":
     A_		:     List[Any]        =     os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
     A_		:     int        =     ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()

