


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 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


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




      def __init__(    self:	Dict						,				a:	Union[str, Any]=None						,				a:	int=None						,				**a:	List[str]    ):
       __lowerCamelCase		:							Any		 =						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						,				)
        __lowerCamelCase		:							Optional[Any]		 =						kwargs.pop('feature_extractor'    )

       __lowerCamelCase		:							Optional[Any]		 =						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:	Any						,				a:	Tuple=None						,				a:	str=None						,				a:	Any=None						,				a:	int="max_length"						,				a:	List[Any]="np"						,				**a:	int    ):

       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    )):
         __lowerCamelCase		:							Union[str, Any]		 =						[self.tokenizer(a						,				padding=a						,				return_tensors=a						,				**a    )]

        elif isinstance(a						,				a    ) and isinstance(text[0]						,				a    ):
         __lowerCamelCase		:							List[Any]		 =						[]

         # Maximum number of queries across batch
         __lowerCamelCase		:							Optional[Any]		 =						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:
           __lowerCamelCase		:							Optional[int]		 =						t + [' '] * (max_num_queries - len(a    ))

          __lowerCamelCase		:							Dict		 =						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":
         __lowerCamelCase		:							Optional[int]		 =						np.concatenate([encoding['input_ids'] for encoding in encodings]						,				axis=0    )
         __lowerCamelCase		:							List[Any]		 =						np.concatenate([encoding['attention_mask'] for encoding in encodings]						,				axis=0    )

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

         __lowerCamelCase		:							List[Any]		 =						jnp.concatenate([encoding['input_ids'] for encoding in encodings]						,				axis=0    )
         __lowerCamelCase		:							Optional[Any]		 =						jnp.concatenate([encoding['attention_mask'] for encoding in encodings]						,				axis=0    )

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

         __lowerCamelCase		:							List[str]		 =						torch.cat([encoding['input_ids'] for encoding in encodings]						,				dim=0    )
         __lowerCamelCase		:							Union[str, Any]		 =						torch.cat([encoding['attention_mask'] for encoding in encodings]						,				dim=0    )

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

         __lowerCamelCase		:							int		 =						tf.stack([encoding['input_ids'] for encoding in encodings]						,				axis=0    )
         __lowerCamelCase		:							Union[str, Any]		 =						tf.stack([encoding['attention_mask'] for encoding in encodings]						,				axis=0    )

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

        __lowerCamelCase		:							List[str]		 =						BatchEncoding()
        __lowerCamelCase		:							str		 =						input_ids
        __lowerCamelCase		:							int		 =						attention_mask

       if query_images is not None:
        __lowerCamelCase		:							Optional[Any]		 =						BatchEncoding()
        __lowerCamelCase		:							List[Any]		 =						self.image_processor(
            a						,				return_tensors=a						,				**a    ).pixel_values
        __lowerCamelCase		:							int		 =						query_pixel_values

       if images is not None:
        __lowerCamelCase		:							str		 =						self.image_processor(a						,				return_tensors=a						,				**a    )

       if text is not None and images is not None:
        __lowerCamelCase		:							Any		 =						image_features.pixel_values
        return encoding
       elif query_images is not None and images is not None:
        __lowerCamelCase		:							Dict		 =						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 					_snake_case     (    self:	str						,				*a:	int						,				**a:	int    ):
       return self.image_processor.post_process(*a						,				**a    )




      def 					_snake_case     (    self:	Union[str, Any]						,				*a:	Optional[Any]						,				**a:	str    ):
       return self.image_processor.post_process_object_detection(*a						,				**a    )




      def 					_snake_case     (    self:	str						,				*a:	Optional[int]						,				**a:	Dict    ):
       return self.image_processor.post_process_image_guided_detection(*a						,				**a    )




      def 					_snake_case     (    self:	List[str]						,				*a:	Any						,				**a:	Dict    ):
       return self.tokenizer.batch_decode(*a						,				**a    )




      def 					_snake_case     (    self:	int						,				*a:	Any						,				**a:	Dict    ):
       return self.tokenizer.decode(*a						,				**a    )




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





import warnings

from .generation import TFGenerationMixin


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      warnings.warn(
          """Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
          """be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.""" ,				__UpperCamelCase ,				)

