






'''simple docstring'''



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 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =["image_processor", "tokenizer"]
      __a	       ="OwlViTImageProcessor"
      __a	       =("CLIPTokenizer", "CLIPTokenizerFast")




      def __init__(	self			, lowerCamelCase=None			, lowerCamelCase=None			, **lowerCamelCase   )				->Any:



         '''simple docstring'''
         __a					  =     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			, )
            __a					  =     kwargs.pop('feature_extractor'   )

         __a					  =     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			, lowerCamelCase=None			, lowerCamelCase=None			, lowerCamelCase=None			, lowerCamelCase="max_length"			, lowerCamelCase="np"			, **lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''

         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   )):
               __a					  =     [self.tokenizer(lowerCamelCase			, padding=lowerCamelCase			, return_tensors=lowerCamelCase			, **lowerCamelCase   )]

            elif isinstance(lowerCamelCase			, lowerCamelCase   ) and isinstance(text[0]			, lowerCamelCase   ):
               __a					  =     []

               # Maximum number of queries across batch
               __a					  =     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:
                     __a					  =     t + [' '] * (max_num_queries - len(lowerCamelCase   ))

                  __a					  =     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":
               __a					  =     np.concatenate([encoding['input_ids'] for encoding in encodings]			, axis=0   )
               __a					  =     np.concatenate([encoding['attention_mask'] for encoding in encodings]			, axis=0   )

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

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

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

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

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

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

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

            __a					  =     BatchEncoding()
            __a					  =     input_ids
            __a					  =     attention_mask

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

         if images is not None:
            __a					  =     self.image_processor(lowerCamelCase			, return_tensors=lowerCamelCase			, **lowerCamelCase   )

         if text is not None and images is not None:
            __a					  =     image_features.pixel_values
            return encoding
         elif query_images is not None and images is not None:
            __a					  =     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			, *lowerCamelCase			, **lowerCamelCase   )				->Any:



         '''simple docstring'''
         return self.image_processor.post_process(*lowerCamelCase			, **lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, *lowerCamelCase			, **lowerCamelCase   )				->int:



         '''simple docstring'''
         return self.image_processor.post_process_object_detection(*lowerCamelCase			, **lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, *lowerCamelCase			, **lowerCamelCase   )				->Optional[Any]:



         '''simple docstring'''
         return self.image_processor.post_process_image_guided_detection(*lowerCamelCase			, **lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, *lowerCamelCase			, **lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         return self.tokenizer.batch_decode(*lowerCamelCase			, **lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, *lowerCamelCase			, **lowerCamelCase   )				->str:



         '''simple docstring'''
         return self.tokenizer.decode(*lowerCamelCase			, **lowerCamelCase   )




      @property
      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         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   )				->Tuple:



         '''simple docstring'''
         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'			, lowerCamelCase			, )
         return self.image_processor







'''simple docstring'''



import copy
import random

from transformers import CLIPTokenizer
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      def __init__(	self			, *lowerCamelCase			, **lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''
         super().__init__(*lowerCamelCase			, **lowerCamelCase   )
         __a					  =     {}




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )				->Dict:



         '''simple docstring'''
         __a					  =     super().add_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
         if num_added_tokens == 0:
            raise ValueError(
                F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
                ' `placeholder_token` that is not already in the tokenizer.'   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=1			, **lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         __a					  =     []
         if num_vec_per_token == 1:
            self.try_adding_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
            output.append(lowerCamelCase   )
         else:
            __a					  =     []
            for i in range(lowerCamelCase   ):
               __a					  =     placeholder_token + F"""_{i}"""
               self.try_adding_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
               output.append(lowerCamelCase   )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
         for token in self.token_map:
            if token in placeholder_token:
               raise ValueError(
                   F"""The tokenizer already has placeholder token {token} that can get confused with"""
                   F""" {placeholder_token}keep placeholder tokens independent"""   )
         __a					  =     output




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0   )				->int:



         '''simple docstring'''
         if isinstance(lowerCamelCase			, lowerCamelCase   ):
            __a					  =     []
            for i in range(len(lowerCamelCase   )   ):
               output.append(self.replace_placeholder_tokens_in_text(text[i]			, vector_shuffle=lowerCamelCase   )   )
            return output
         for placeholder_token in self.token_map:
            if placeholder_token in text:
               __a					  =     self.token_map[placeholder_token]
               __a					  =     tokens[: 1 + int(len(lowerCamelCase   ) * prop_tokens_to_load   )]
               if vector_shuffle:
                  __a					  =     copy.copy(lowerCamelCase   )
                  random.shuffle(lowerCamelCase   )
               __a					  =     text.replace(lowerCamelCase			, ' '.join(lowerCamelCase   )   )
         return text




      def __call__(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0			, **lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''
         return super().__call__(
             self.replace_placeholder_tokens_in_text(
                 lowerCamelCase			, vector_shuffle=lowerCamelCase			, prop_tokens_to_load=lowerCamelCase   )			, *lowerCamelCase			, **lowerCamelCase			, )






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0			, **lowerCamelCase   )				->List[Any]:



         '''simple docstring'''
         return super().encode(
             self.replace_placeholder_tokens_in_text(
                 lowerCamelCase			, vector_shuffle=lowerCamelCase			, prop_tokens_to_load=lowerCamelCase   )			, *lowerCamelCase			, **lowerCamelCase			, )