


'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

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


if is_sentencepiece_available():
 from .tokenization_ta import TaTokenizer
else:
 UpperCAmelCase_ :     Tuple								=      None


UpperCAmelCase_ :     Any								=      logging.get_logger(__name__)

UpperCAmelCase_ :     int								=      {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_ :     Optional[Any]								=      {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_ :     Dict								=      {
    't5-small': 5_1_2,
    't5-base': 5_1_2,
    't5-large': 5_1_2,
    't5-3b': 5_1_2,
    't5-11b': 5_1_2,
}

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Tuple    							=						VOCAB_FILES_NAMES
 __lowercase				:		Optional[Any]    							=						PRETRAINED_VOCAB_FILES_MAP
 __lowercase				:		int    							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase				:		Optional[int]    							=						['''input_ids''', '''attention_mask''']
 __lowercase				:		Optional[int]    							=						TaTokenizer

 __lowercase				:		List[int]    							=						[]


 def __init__(      self      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase="</s>"      ,      __lowercase="<unk>"      ,      __lowercase="<pad>"      ,      __lowercase=100      ,      __lowercase=None      ,      **__lowercase      ,      ):

  """simple docstring"""







  if extra_ids > 0 and additional_special_tokens is None:
   __A  :		List[Any]       =			[F"""<extra_id_{i}>""" for i in range(__lowercase						)]
  elif extra_ids > 0 and additional_special_tokens is not None:
   # Check that we have the right number of extra special tokens
   __A  :		Union[str, Any]       =			len(set(filter(lambda __lowercase						: bool('extra_id_' in str(__lowercase						)						)      ,      __lowercase						)						)						)
   if extra_tokens != extra_ids:
    raise ValueError(
        F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
        ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
        ' tokens'						)

  super().__init__(
      __lowercase      ,      tokenizer_file=__lowercase      ,      eos_token=__lowercase      ,      unk_token=__lowercase      ,      pad_token=__lowercase      ,      extra_ids=__lowercase      ,      additional_special_tokens=__lowercase      ,      **__lowercase      ,      )

  __A  :		List[str]       =			vocab_file
  __A  :		Any       =			False if not self.vocab_file else True
  __A  :		List[Any]       =			extra_ids


 @staticmethod
 def  snake_case__   (      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
   __A  :		str       =			TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
   if init_max_model_length is not None and init_max_model_length != max_model_length:
    return init_max_model_length
   elif init_max_model_length is None:
    warnings.warn(
        'This tokenizer was incorrectly instantiated with a model max length of'
        F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
        ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
        ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
        F""" {pretrained_model_name_or_path} automatically truncating your input to"""
        F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
        F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
        ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
        ' instantiate this tokenizer with `model_max_length` set to your preferred value.'      ,      __lowercase      ,      )

  return max_model_length


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """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(__lowercase						):
   logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
   return
  __A  :		Any       =			os.path.join(
      __lowercase      ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

  if os.path.abspath(self.vocab_file						) != os.path.abspath(__lowercase						):
   copyfile(self.vocab_file      ,      __lowercase						)
   logger.info(F"""Copy vocab file to {out_vocab_file}"""						)

  return (out_vocab_file,)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  __A  :		Optional[Any]       =			token_ids_a + [self.eos_token_id]
  if token_ids_a is None:
   return self.prefix_tokens + token_ids_a
  else:
   __A  :		List[str]       =			token_ids_a + [self.eos_token_id]
   return self.prefix_tokens + token_ids_a + token_ids_a


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  __A  :		List[Any]       =			[self.eos_token_id]

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


 def  snake_case__   (      self						):

  """simple docstring"""







  return list(
      set(filter(lambda __lowercase						: bool(re.search(R'<extra_id_\d+>'      ,      __lowercase						)						) is not None      ,      self.additional_special_tokens						)						)						)







 def  snake_case__   (      self						):

  """simple docstring"""







  return [self.convert_tokens_to_ids(__lowercase						) for token in self.get_sentinel_tokens()]










'''simple docstring'''
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			tempfile.mkdtemp()

  __A  :		List[Any]       =			BlipImageProcessor()
  __A  :		List[str]       =			BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'						)

  __A  :		Union[str, Any]       =			BlipProcessor(__lowercase      ,      __lowercase						)

  processor.save_pretrained(self.tmpdirname						)


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return AutoProcessor.from_pretrained(self.tmpdirname      ,      **__lowercase						).tokenizer


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return AutoProcessor.from_pretrained(self.tmpdirname      ,      **__lowercase						).image_processor


 def  snake_case__   (      self						):

  """simple docstring"""







  shutil.rmtree(self.tmpdirname						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			[np.random.randint(255      ,      size=(3, 30, 400)      ,      dtype=np.uinta						)]

  __A  :		Union[str, Any]       =			[Image.fromarray(np.moveaxis(__lowercase      ,      0      ,      -1						)						) for x in image_inputs]

  return image_inputs


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			BlipProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()						)
  processor.save_pretrained(self.tmpdirname						)

  __A  :		Optional[int]       =			self.get_tokenizer(bos_token='(BOS)'      ,      eos_token='(EOS)'						)
  __A  :		Optional[Any]       =			self.get_image_processor(do_normalize=__lowercase      ,      padding_value=1.0						)

  __A  :		List[str]       =			BlipProcessor.from_pretrained(
      self.tmpdirname      ,      bos_token='(BOS)'      ,      eos_token='(EOS)'      ,      do_normalize=__lowercase      ,      padding_value=1.0						)

  self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer_add_kwargs.get_vocab()						)
  self.assertIsInstance(processor.tokenizer      ,      __lowercase						)

  self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor_add_kwargs.to_json_string()						)
  self.assertIsInstance(processor.image_processor      ,      __lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			self.get_image_processor()
  __A  :		Union[str, Any]       =			self.get_tokenizer()

  __A  :		Dict       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Dict       =			self.prepare_image_inputs()

  __A  :		str       =			image_processor(__lowercase      ,      return_tensors='np'						)
  __A  :		List[Any]       =			processor(images=__lowercase      ,      return_tensors='np'						)

  for key in input_feat_extract.keys():
   self.assertAlmostEqual(input_feat_extract[key].sum()      ,      input_processor[key].sum()      ,      delta=1E-2						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.get_image_processor()
  __A  :		List[str]       =			self.get_tokenizer()

  __A  :		str       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		List[Any]       =			'lower newer'

  __A  :		Any       =			processor(text=__lowercase						)

  __A  :		int       =			tokenizer(__lowercase      ,      return_token_type_ids=__lowercase						)

  for key in encoded_tok.keys():
   self.assertListEqual(encoded_tok[key]      ,      encoded_processor[key]						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			self.get_image_processor()
  __A  :		Dict       =			self.get_tokenizer()

  __A  :		Any       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		List[Any]       =			'lower newer'
  __A  :		List[str]       =			self.prepare_image_inputs()

  __A  :		Optional[Any]       =			processor(text=__lowercase      ,      images=__lowercase						)

  self.assertListEqual(list(inputs.keys()						)      ,      ['pixel_values', 'input_ids', 'attention_mask']						)

  # test if it raises when no input is passed
  with pytest.raises(__lowercase						):
   processor()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.get_image_processor()
  __A  :		Optional[int]       =			self.get_tokenizer()

  __A  :		List[str]       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		str       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

  __A  :		Tuple       =			processor.batch_decode(__lowercase						)
  __A  :		Tuple       =			tokenizer.batch_decode(__lowercase						)

  self.assertListEqual(__lowercase      ,      __lowercase						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.get_image_processor()
  __A  :		Any       =			self.get_tokenizer()

  __A  :		Any       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Optional[int]       =			'lower newer'
  __A  :		List[str]       =			self.prepare_image_inputs()

  __A  :		int       =			processor(text=__lowercase      ,      images=__lowercase						)

  # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
  self.assertListEqual(list(inputs.keys()						)      ,      ['pixel_values', 'input_ids', 'attention_mask']						)






