

'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_barthez import BarthezTokenizer
else:
      __lowerCamelCase									=     None

__lowerCamelCase									=     logging.get_logger(__name__)

__lowerCamelCase									=     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase									=     {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

__lowerCamelCase									=     {
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

__lowerCamelCase									=     '''▁'''


class        A__   (   _snake_case      ):
    lowercase						       =		VOCAB_FILES_NAMES
    lowercase						       =		PRETRAINED_VOCAB_FILES_MAP
    lowercase						       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase						       =		["input_ids", "attention_mask"]
    lowercase						       =		BarthezTokenizer



    def __init__(       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__="<s>"		,					UpperCamelCase__="</s>"		,					UpperCamelCase__="</s>"		,					UpperCamelCase__="<s>"		,					UpperCamelCase__="<unk>"		,					UpperCamelCase__="<pad>"		,					UpperCamelCase__="<mask>"		,					**UpperCamelCase__		,					)    ->   Any:





           '''simple docstring'''
           # Mask token behave like a normal word, i.e. include the space before it
           A_						       =   AddedToken(UpperCamelCase__		,					lstrip=UpperCamelCase__		,					rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__		,					UpperCamelCase__							) else mask_token

           super().__init__(
               UpperCamelCase__		,					tokenizer_file=UpperCamelCase__		,					bos_token=UpperCamelCase__		,					eos_token=UpperCamelCase__		,					unk_token=UpperCamelCase__		,					sep_token=UpperCamelCase__		,					cls_token=UpperCamelCase__		,					pad_token=UpperCamelCase__		,					mask_token=UpperCamelCase__		,					**UpperCamelCase__		,					)

           A_						       =   vocab_file
           A_						       =   False if not self.vocab_file else True



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__ = None							)    ->   List[int]:





           '''simple docstring'''

           if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           A_						       =   [self.cls_token_id]
           A_						       =   [self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__ = None							)    ->   List[int]:





           '''simple docstring'''
           A_						       =   [self.sep_token_id]
           A_						       =   [self.cls_token_id]

           if token_ids_a is None:
                  return len(cls + token_ids_a + sep							) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]







    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__ = None							)    ->   Tuple[str]:





           '''simple docstring'''
           if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                      """tokenizer."""							)

           if not os.path.isdir(UpperCamelCase__							):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
                  return
           A_						       =   os.path.join(
               UpperCamelCase__		,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

           if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCamelCase__							):
                  copyfile(self.vocab_file		,					UpperCamelCase__							)

           return (out_vocab_file,)



'''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        A__   (   _snake_case      ):
    lowercase						       =		["image_processor", "tokenizer"]
    lowercase						       =		"OwlViTImageProcessor"
    lowercase						       =		("CLIPTokenizer", "CLIPTokenizerFast")



    def __init__(       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					**UpperCamelCase__							)    ->   str:





           '''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."""		,					UpperCamelCase__		,					)
                  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__(UpperCamelCase__		,					UpperCamelCase__							)



    def __call__(       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__="max_length"		,					UpperCamelCase__="np"		,					**UpperCamelCase__							)    ->   int:





           '''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(UpperCamelCase__		,					UpperCamelCase__							) or (isinstance(UpperCamelCase__		,					UpperCamelCase__							) and not isinstance(text[0]		,					UpperCamelCase__							)):
                         A_						       =   [self.tokenizer(UpperCamelCase__		,					padding=UpperCamelCase__		,					return_tensors=UpperCamelCase__		,					**UpperCamelCase__							)]

                  elif isinstance(UpperCamelCase__		,					UpperCamelCase__							) and isinstance(text[0]		,					UpperCamelCase__							):
                         A_						       =   []

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

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

                                A_						       =   self.tokenizer(UpperCamelCase__		,					padding=UpperCamelCase__		,					return_tensors=UpperCamelCase__		,					**UpperCamelCase__							)
                                encodings.append(UpperCamelCase__							)
                  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(
                      UpperCamelCase__		,					return_tensors=UpperCamelCase__		,					**UpperCamelCase__							).pixel_values
                  A_						       =   query_pixel_values

           if images is not None:
                  A_						       =   self.image_processor(UpperCamelCase__		,					return_tensors=UpperCamelCase__		,					**UpperCamelCase__							)

           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(**UpperCamelCase__							)		,					tensor_type=UpperCamelCase__							)



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   int:





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



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   List[Any]:





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



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   Tuple:





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



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   Any:





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



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   List[str]:





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



    @property
    def        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,					UpperCamelCase__		,					)
           return self.image_processor_class







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





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