





'''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 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   ["image_processor", "tokenizer"]
     snake_case_						      =   "OwlViTImageProcessor"
     snake_case_						      =   ("CLIPTokenizer", "CLIPTokenizerFast")



     def __init__(   self							:			Optional[int]   ,       __snake_case							:			List[Any]=None   ,       __snake_case							:			str=None   ,       **__snake_case							:			List[str]						)->	Optional[int]:
        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."""   ,       __snake_case   ,       )
           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__(__snake_case   ,       __snake_case						)



     def __call__(   self							:			List[Any]   ,       __snake_case							:			int=None   ,       __snake_case							:			int=None   ,       __snake_case							:			List[Any]=None   ,       __snake_case							:			Any="max_length"   ,       __snake_case							:			Tuple="np"   ,       **__snake_case							:			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(__snake_case   ,       __snake_case						) or (isinstance(__snake_case   ,       __snake_case						) and not isinstance(text[0]   ,       __snake_case						)):
              snake_case 						=   [self.tokenizer(__snake_case   ,       padding=__snake_case   ,       return_tensors=__snake_case   ,       **__snake_case						)]

           elif isinstance(__snake_case   ,       __snake_case						) and isinstance(text[0]   ,       __snake_case						):
              snake_case 						=   []

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

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

                 snake_case 						=   self.tokenizer(__snake_case   ,       padding=__snake_case   ,       return_tensors=__snake_case   ,       **__snake_case						)
                 encodings.append(__snake_case						)
           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(
               __snake_case   ,       return_tensors=__snake_case   ,       **__snake_case						).pixel_values
           snake_case 						=   query_pixel_values

        if images is not None:
           snake_case 						=   self.image_processor(__snake_case   ,       return_tensors=__snake_case   ,       **__snake_case						)

        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(**__snake_case						)   ,       tensor_type=__snake_case						)



     def      lowerCAmelCase			(   self							:			int   ,       *__snake_case							:			List[Any]   ,       **__snake_case							:			Tuple						)->	List[Any]:
        return self.image_processor.post_process(*__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			Optional[int]   ,       *__snake_case							:			Tuple   ,       **__snake_case							:			List[str]						)->	int:
        return self.image_processor.post_process_object_detection(*__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			str   ,       *__snake_case							:			Dict   ,       **__snake_case							:			Optional[Any]						)->	Tuple:
        return self.image_processor.post_process_image_guided_detection(*__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			int   ,       *__snake_case							:			Optional[int]   ,       **__snake_case							:			str						)->	str:
        return self.tokenizer.batch_decode(*__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			int   ,       *__snake_case							:			Optional[int]   ,       **__snake_case							:			Any						)->	Optional[Any]:
        return self.tokenizer.decode(*__snake_case   ,       **__snake_case						)



     @property
     def      lowerCAmelCase			(   self							:			Optional[int]						)->	Tuple:
        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,       __snake_case   ,       )
        return self.image_processor_class



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








'''simple docstring'''




import logging

from transformers.configuration_utils import PretrainedConfig


_SCREAMING_SNAKE_CASE							     =			logging.getLogger(__name__)
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "masked_bert"



     def __init__(   self							:			Any   ,       __snake_case							:			Optional[Any]=3_05_22   ,       __snake_case							:			List[Any]=7_68   ,       __snake_case							:			int=12   ,       __snake_case							:			List[str]=12   ,       __snake_case							:			int=30_72   ,       __snake_case							:			Optional[int]="gelu"   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			Tuple=0.1   ,       __snake_case							:			List[str]=5_12   ,       __snake_case							:			Tuple=2   ,       __snake_case							:			Tuple=0.02   ,       __snake_case							:			Tuple=1e-12   ,       __snake_case							:			Union[str, Any]=0   ,       __snake_case							:			int="topK"   ,       __snake_case							:			int="constant"   ,       __snake_case							:			List[str]=0.0   ,       **__snake_case							:			Any   ,       )->	Union[str, Any]:
        super().__init__(pad_token_id=__snake_case   ,       **__snake_case						)

        snake_case 						=   vocab_size
        snake_case 						=   hidden_size
        snake_case 						=   num_hidden_layers
        snake_case 						=   num_attention_heads
        snake_case 						=   hidden_act
        snake_case 						=   intermediate_size
        snake_case 						=   hidden_dropout_prob
        snake_case 						=   attention_probs_dropout_prob
        snake_case 						=   max_position_embeddings
        snake_case 						=   type_vocab_size
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   pruning_method
        snake_case 						=   mask_init
        snake_case 						=   mask_scale

