

import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available






class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						['image_processor', 'tokenizer']
 UpperCAmelCase							    =						'OwlViTImageProcessor'
 UpperCAmelCase							    =						('CLIPTokenizer', 'CLIPTokenizerFast')


 def __init__(						self		: Optional[int]			,						lowerCAmelCase__		: Union[str, Any]=None			,						lowerCAmelCase__		: Dict=None			,						**lowerCAmelCase__		: Union[str, Any]    )    ->      int:
   UpperCAmelCase					      =  None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."			,						lowerCAmelCase__			,						)
     UpperCAmelCase					      =  kwargs.pop("feature_extractor"    )

   UpperCAmelCase					      =  image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("You need to specify an `image_processor`."    )
   if tokenizer is None:
     raise ValueError("You need to specify a `tokenizer`."    )

   super().__init__(lowerCAmelCase__			,						lowerCAmelCase__    )


 def __call__(						self		: Tuple			,						lowerCAmelCase__		: Tuple=None			,						lowerCAmelCase__		: Any=None			,						lowerCAmelCase__		: Tuple=None			,						lowerCAmelCase__		: Union[str, Any]="max_length"			,						lowerCAmelCase__		: int="np"			,						**lowerCAmelCase__		: Tuple    )    ->      Union[str, Any]:







   if text is None and query_images is None and images is None:
     raise ValueError(
         "You have to specify at least one text or query image or image. All three cannot be none."    )

   if text is not None:
     if isinstance(lowerCAmelCase__			,						lowerCAmelCase__    ) or (isinstance(lowerCAmelCase__			,						lowerCAmelCase__    ) and not isinstance(text[0]			,						lowerCAmelCase__    )):
       UpperCAmelCase					      =  [self.tokenizer(lowerCAmelCase__			,						padding=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__    )]

     elif isinstance(lowerCAmelCase__			,						lowerCAmelCase__    ) and isinstance(text[0]			,						lowerCAmelCase__    ):
       UpperCAmelCase					      =  []

       # Maximum number of queries across batch
       UpperCAmelCase					      =  max([len(lowerCAmelCase__    ) for t in text]    )

       # Pad all batch samples to max number of text queries
       for t in text:
         if len(lowerCAmelCase__    ) != max_num_queries:
           UpperCAmelCase					      =  t + [" "] * (max_num_queries - len(lowerCAmelCase__    ))

         UpperCAmelCase					      =  self.tokenizer(lowerCAmelCase__			,						padding=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__    )
         encodings.append(lowerCAmelCase__    )
     else:
       raise TypeError("Input text should be a string, a list of strings or a nested list of strings"    )

     if return_tensors == "np":
       UpperCAmelCase					      =  np.concatenate([encoding["input_ids"] for encoding in encodings]			,						axis=0    )
       UpperCAmelCase					      =  np.concatenate([encoding["attention_mask"] for encoding in encodings]			,						axis=0    )

     elif return_tensors == "jax" and is_flax_available():
       import jax.numpy as jnp

       UpperCAmelCase					      =  jnp.concatenate([encoding["input_ids"] for encoding in encodings]			,						axis=0    )
       UpperCAmelCase					      =  jnp.concatenate([encoding["attention_mask"] for encoding in encodings]			,						axis=0    )

     elif return_tensors == "pt" and is_torch_available():
       import torch

       UpperCAmelCase					      =  torch.cat([encoding["input_ids"] for encoding in encodings]			,						dim=0    )
       UpperCAmelCase					      =  torch.cat([encoding["attention_mask"] for encoding in encodings]			,						dim=0    )

     elif return_tensors == "tf" and is_tf_available():
       import tensorflow as tf

       UpperCAmelCase					      =  tf.stack([encoding["input_ids"] for encoding in encodings]			,						axis=0    )
       UpperCAmelCase					      =  tf.stack([encoding["attention_mask"] for encoding in encodings]			,						axis=0    )

     else:
       raise ValueError("Target return tensor type could not be returned"    )

     UpperCAmelCase					      =  BatchEncoding()
     UpperCAmelCase					      =  input_ids
     UpperCAmelCase					      =  attention_mask

   if query_images is not None:
     UpperCAmelCase					      =  BatchEncoding()
     UpperCAmelCase					      =  self.image_processor(
         lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__    ).pixel_values
     UpperCAmelCase					      =  query_pixel_values

   if images is not None:
     UpperCAmelCase					      =  self.image_processor(lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__    )

   if text is not None and images is not None:
     UpperCAmelCase					      =  image_features.pixel_values
     return encoding
   elif query_images is not None and images is not None:
     UpperCAmelCase					      =  image_features.pixel_values
     return encoding
   elif text is not None or query_images is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**lowerCAmelCase__    )			,						tensor_type=lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Any			,						*lowerCAmelCase__		: Optional[int]			,						**lowerCAmelCase__		: List[Any]    )    ->      List[str]:
   return self.image_processor.post_process(*lowerCAmelCase__			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Any			,						*lowerCAmelCase__		: Optional[Any]			,						**lowerCAmelCase__		: Tuple    )    ->      Optional[Any]:
   return self.image_processor.post_process_object_detection(*lowerCAmelCase__			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Any			,						*lowerCAmelCase__		: List[str]			,						**lowerCAmelCase__		: Any    )    ->      int:
   return self.image_processor.post_process_image_guided_detection(*lowerCAmelCase__			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Dict			,						*lowerCAmelCase__		: List[Any]			,						**lowerCAmelCase__		: Union[str, Any]    )    ->      Union[str, Any]:
   return self.tokenizer.batch_decode(*lowerCAmelCase__			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Optional[int]			,						*lowerCAmelCase__		: Optional[int]			,						**lowerCAmelCase__		: Tuple    )    ->      Optional[Any]:
   return self.tokenizer.decode(*lowerCAmelCase__			,						**lowerCAmelCase__    )


 @property
 def _UpperCamelCase	(						self		: Any    )    ->      str:
   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,						lowerCAmelCase__			,						)
   return self.image_processor_class


 @property
 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Tuple:
   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,						lowerCAmelCase__			,						)
   return self.image_processor






import copy
import os

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






class 						__magic_name__    :


 def __init__(						self		: Optional[int]    )    ->      Optional[Any]:
   UpperCAmelCase					      =  ""
   UpperCAmelCase					      =  ""
   UpperCAmelCase					      =  []
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  2_5_6
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  0


 def _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: Optional[Any]    )    ->      List[str]:
   UpperCAmelCase					      =  cva.imread(lowerCAmelCase__			,						0    )
   UpperCAmelCase					      =  copy.deepcopy(self.img    )
   UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  plt.hist(self.img.ravel()			,						2_5_6			,						[0, 2_5_6]			,						label="x"    )
   UpperCAmelCase					      =  np.sum(lowerCAmelCase__    )
   for i in range(len(lowerCAmelCase__    )    ):
     UpperCAmelCase					      =  x[i] / self.k
     self.sk += prk
     UpperCAmelCase					      =  (self.L - 1) * self.sk
     if self.rem != 0:
       UpperCAmelCase					      =  int(last % last    )
     UpperCAmelCase					      =  int(last + 1 if self.rem >= 0.5 else last    )
     self.last_list.append(lowerCAmelCase__    )
     UpperCAmelCase					      =  int(np.ma.count(self.img    ) / self.img[1].size    )
     UpperCAmelCase					      =  self.img[1].size
   for i in range(self.number_of_cols    ):
     for j in range(self.number_of_rows    ):
       UpperCAmelCase					      =  self.img[j][i]
       if num != self.last_list[num]:
         UpperCAmelCase					      =  self.last_list[num]
   cva.imwrite("output_data/output.jpg"			,						self.img    )


 def _UpperCamelCase	(						self		: str    )    ->      int:
   plt.hist(self.img.ravel()			,						2_5_6			,						[0, 2_5_6]    )


 def _UpperCamelCase	(						self		: Dict    )    ->      Optional[Any]:
   cva.imshow("Output-Image"			,						self.img    )
   cva.imshow("Input-Image"			,						self.original_image    )
   cva.waitKey(5_0_0_0    )
   cva.destroyAllWindows()


if __name__ == "__main__":
      lowerCAmelCase__       = os.path.join(os.path.basename(__file__), "image_data/input.jpg")
      lowerCAmelCase__       = ConstantStretch()
      stretcher.stretch(file_path)
      stretcher.plot_histogram()
      stretcher.show_image()



