


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







import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


__A								=       {
    "<": operator.lt,
    "<=": operator.le,
    "==": operator.eq,
    "!=": operator.ne,
    ">=": operator.ge,
    ">": operator.gt,
}







def      lowerCamelCase_						(			UpperCamelCase__							: List[str]					,	UpperCamelCase__							: Dict					,	UpperCamelCase__							: Dict					,	UpperCamelCase__							: Any					,	UpperCamelCase__							: Dict					,	UpperCamelCase__							: int )	->			List[Any]:





				"""simple docstring"""


				if got_ver is None or want_ver is None:
								raise ValueError(
								    F"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
								    F""" reinstalling {pkg}.""" )
				if not ops[op](version.parse(UpperCamelCase__ )					,	version.parse(UpperCamelCase__ ) ):
								raise ImportError(
								    F"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: Optional[str] = None )	->			None:





				"""simple docstring"""


				__lowerCamelCase									=       F"""\n{hint}""" if hint is not None else ''

				# non-versioned check
				if re.match(R'^[\w_\-\d]+$'					,	UpperCamelCase__ ):
								__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase									=       requirement, None, None
				else:
								__lowerCamelCase									=       re.findall(R'^([^!=<>\s]+)([\s!=<>]{1,2}.+)'					,	UpperCamelCase__ )
								if not match:
												raise ValueError(
												    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
												    F""" got {requirement}""" )
								__lowerCamelCase			,		__lowerCamelCase									=       match[0]
								__lowerCamelCase									=       want_full.split(',' )  # there could be multiple requirements
								__lowerCamelCase									=       {}
								for w in want_range:
												__lowerCamelCase									=       re.findall(R'^([\s!=<>]{1,2})(.+)'					,	UpperCamelCase__ )
												if not match:
																raise ValueError(
																    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
																    F""" but got {requirement}""" )
												__lowerCamelCase			,		__lowerCamelCase									=       match[0]
												__lowerCamelCase									=       want_ver
												if op not in ops:
																raise ValueError(F"""{requirement}: need one of {list(ops.keys() )}, but got {op}""" )

    # special case
				if pkg == "python":
								__lowerCamelCase									=       '.'.join([str(UpperCamelCase__ ) for x in sys.version_info[:3]] )
								for op, want_ver in wanted.items():
												_compare_versions(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__ )
								return

				# check if any version is installed
				try:
								__lowerCamelCase									=       importlib.metadata.version(UpperCamelCase__ )
				except importlib.metadata.PackageNotFoundError:
								raise importlib.metadata.PackageNotFoundError(
								    F"""The '{requirement}' distribution was not found and is required by this application. {hint}""" )

				# check that the right version is installed if version number or a range was provided
				if want_ver is not None:
								for op, want_ver in wanted.items():
												_compare_versions(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__ )







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[int] )	->			Optional[int]:





				"""simple docstring"""


				__lowerCamelCase									=       'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
				return require_version(UpperCamelCase__					,	UpperCamelCase__ )


