


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_				):





     '''simple docstring'''



     __lowercase				:							Union[str, Any]	   =       ['image_processor', 'tokenizer']
     __lowercase				:							Any	   =       'OwlViTImageProcessor'
     __lowercase				:							Optional[Any]	   =       ('CLIPTokenizer', 'CLIPTokenizerFast')

     def __init__(					self:Optional[int]     ,		_a:List[Any]=None     ,		_a:str=None     ,		**_a:Any   ):
       snake_case__						= None
       if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''     ,		_a     ,		)
         snake_case__						= kwargs.pop('''feature_extractor'''   )

       snake_case__						= 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__(_a     ,		_a   )

     def __call__(					self:Dict     ,		_a:Any=None     ,		_a:Optional[Any]=None     ,		_a:Tuple=None     ,		_a:List[str]="max_length"     ,		_a:Dict="np"     ,		**_a: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(_a     ,		_a   ) or (isinstance(_a     ,		_a   ) and not isinstance(text[0]     ,		_a   )):
           snake_case__						= [self.tokenizer(_a     ,		padding=_a     ,		return_tensors=_a     ,		**_a   )]

         elif isinstance(_a     ,		_a   ) and isinstance(text[0]     ,		_a   ):
           snake_case__						= []

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

           # Pad all batch samples to max number of text queries
           for t in text:
             if len(_a   ) != max_num_queries:
               snake_case__						= t + [''' '''] * (max_num_queries - len(_a   ))

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

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

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

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

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

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

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

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

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

         snake_case__						= BatchEncoding()
         snake_case__						= input_ids
         snake_case__						= attention_mask

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

       if images is not None:
         snake_case__						= self.image_processor(_a     ,		return_tensors=_a     ,		**_a   )

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		*_a:Tuple     ,		**_a:Optional[Any]   ):
       return self.image_processor.post_process(*_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		*_a:int     ,		**_a:Optional[Any]   ):
       return self.image_processor.post_process_object_detection(*_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		*_a:Any     ,		**_a:List[Any]   ):
       return self.image_processor.post_process_image_guided_detection(*_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		*_a:Optional[Any]     ,		**_a:List[str]   ):
       return self.tokenizer.batch_decode(*_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		*_a:int     ,		**_a:str   ):
       return self.tokenizer.decode(*_a     ,		**_a   )

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''     ,		_a     ,		)
       return self.image_processor_class







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,		_a     ,		)
       return self.image_processor





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__							:						List[Any]							     =   logging.get_logger(__name__)

lowerCamelCase__							:						Optional[int]							     =   {
    """facebook/data2vec-vision-base-ft""": (
        """https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
    ),
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       'data2vec-vision'

     def __init__(					self:int     ,		_a:Tuple=7_68     ,		_a:int=12     ,		_a:Any=12     ,		_a:Optional[int]=30_72     ,		_a:Optional[int]="gelu"     ,		_a:Any=0.0     ,		_a:Any=0.0     ,		_a:List[str]=0.02     ,		_a:Dict=1e-12     ,		_a:Tuple=2_24     ,		_a:Any=16     ,		_a:str=3     ,		_a:str=False     ,		_a:Union[str, Any]=False     ,		_a:Optional[int]=False     ,		_a:Any=False     ,		_a:Dict=0.1     ,		_a:Dict=0.1     ,		_a:str=True     ,		_a:str=[3, 5, 7, 11]     ,		_a:List[str]=[1, 2, 3, 6]     ,		_a:List[str]=True     ,		_a:Any=0.4     ,		_a:str=2_56     ,		_a:Union[str, Any]=1     ,		_a:int=False     ,		_a:Optional[int]=2_55     ,		**_a:Dict     ,		):
       super().__init__(**_a   )

       snake_case__						= hidden_size
       snake_case__						= num_hidden_layers
       snake_case__						= num_attention_heads
       snake_case__						= intermediate_size
       snake_case__						= hidden_act
       snake_case__						= hidden_dropout_prob
       snake_case__						= attention_probs_dropout_prob
       snake_case__						= initializer_range
       snake_case__						= layer_norm_eps

       snake_case__						= image_size
       snake_case__						= patch_size
       snake_case__						= num_channels
       snake_case__						= use_mask_token
       snake_case__						= use_absolute_position_embeddings
       snake_case__						= use_relative_position_bias
       snake_case__						= use_shared_relative_position_bias
       snake_case__						= layer_scale_init_value
       snake_case__						= drop_path_rate
       snake_case__						= use_mean_pooling
       # decode head attributes (semantic segmentation)
       snake_case__						= out_indices
       snake_case__						= pool_scales
       # auxiliary head attributes (semantic segmentation)
       snake_case__						= use_auxiliary_head
       snake_case__						= auxiliary_loss_weight
       snake_case__						= auxiliary_channels
       snake_case__						= auxiliary_num_convs
       snake_case__						= auxiliary_concat_input
       snake_case__						= semantic_loss_ignore_index




class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Any	   =       version.parse('1.11'				)

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ]   )







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       return 1e-4

