


'''simple docstring'''
import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class     _lowerCamelCase       (     snake_case_     ,		unittest.TestCase					):




 '''simple docstring'''
 __lowercase					:		int     				=    LongformerTokenizer
 __lowercase					:		Optional[Any]     				=    True
 __lowercase					:		int     				=    LongformerTokenizerFast
 __lowercase					:		Union[str, Any]     				=    True


 def 			snake_case__ (   self    ):

  """simple docstring"""







  super().setUp()

  # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  __A	:		Tuple	  =      [
      'l',
      'o',
      'w',
      'e',
      'r',
      's',
      't',
      'i',
      'd',
      'n',
      '\u0120',
      '\u0120l',
      '\u0120n',
      '\u0120lo',
      '\u0120low',
      'er',
      '\u0120lowest',
      '\u0120newer',
      '\u0120wider',
      '<unk>',
  ]
  __A	:		Optional[Any]	  =      dict(zip(__lowercase   ,   range(len(__lowercase    )    )    )    )
  __A	:		Union[str, Any]	  =      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
  __A	:		str	  =      {'unk_token': '<unk>'}

  __A	:		Union[str, Any]	  =      os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES['vocab_file']    )
  __A	:		Dict	  =      os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES['merges_file']    )
  with open(self.vocab_file   ,   'w'   ,   encoding='utf-8'    ) as fp:
   fp.write(json.dumps(__lowercase    ) + '\n'    )
  with open(self.merges_file   ,   'w'   ,   encoding='utf-8'    ) as fp:
   fp.write('\n'.join(__lowercase    )    )


 def 			snake_case__ (   self   ,   **__lowercase    ):

  """simple docstring"""







  kwargs.update(self.special_tokens_map    )
  return self.tokenizer_class.from_pretrained(self.tmpdirname   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   **__lowercase    ):

  """simple docstring"""







  kwargs.update(self.special_tokens_map    )
  return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		str	  =      'lower newer'
  __A	:		Union[str, Any]	  =      'lower newer'
  return input_text, output_text


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      self.tokenizer_class(self.vocab_file   ,   self.merges_file   ,   **self.special_tokens_map    )
  __A	:		Tuple	  =      'lower newer'
  __A	:		Dict	  =      ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
  __A	:		Any	  =      tokenizer.tokenize(__lowercase    )  # , add_prefix_space=True)
  self.assertListEqual(__lowercase   ,   __lowercase    )

  __A	:		Tuple	  =      tokens + [tokenizer.unk_token]
  __A	:		int	  =      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase    )   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







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

  self.assertListEqual(tokenizer.encode('Hello world!'   ,   add_special_tokens=__lowercase    )   ,   [0, 31_414, 232, 328, 2]    )
  self.assertListEqual(
      tokenizer.encode('Hello world! cécé herlolip 418'   ,   add_special_tokens=__lowercase    )   ,   [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]   ,   )


 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[int]	  =      self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'    )

  __A	:		Union[str, Any]	  =      tokenizer.encode('sequence builders'   ,   add_special_tokens=__lowercase    )
  __A	:		Any	  =      tokenizer.encode('multi-sequence build'   ,   add_special_tokens=__lowercase    )

  __A	:		List[str]	  =      tokenizer.encode(
      'sequence builders'   ,   add_special_tokens=__lowercase   ,   add_prefix_space=__lowercase    )
  __A	:		str	  =      tokenizer.encode(
      'sequence builders'   ,   'multi-sequence build'   ,   add_special_tokens=__lowercase   ,   add_prefix_space=__lowercase    )

  __A	:		List[Any]	  =      tokenizer.build_inputs_with_special_tokens(__lowercase    )
  __A	:		Optional[int]	  =      tokenizer.build_inputs_with_special_tokens(__lowercase   ,   __lowercase    )

  assert encoded_sentence == encoded_text_from_decode
  assert encoded_pair == encoded_pair_from_decode


 def 			snake_case__ (   self    ):

  """simple docstring"""







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

  __A	:		Tuple	  =      'Encode this sequence.'
  __A	:		List[Any]	  =      tokenizer.byte_encoder[' '.encode('utf-8'    )[0]]

  # Testing encoder arguments
  __A	:		Tuple	  =      tokenizer.encode(__lowercase   ,   add_special_tokens=__lowercase   ,   add_prefix_space=__lowercase    )
  __A	:		List[str]	  =      tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
  self.assertNotEqual(__lowercase   ,   __lowercase    )

  __A	:		Union[str, Any]	  =      tokenizer.encode(__lowercase   ,   add_special_tokens=__lowercase   ,   add_prefix_space=__lowercase    )
  __A	:		Tuple	  =      tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
  self.assertEqual(__lowercase   ,   __lowercase    )

  tokenizer.add_special_tokens({'bos_token': '<s>'}    )
  __A	:		Tuple	  =      tokenizer.encode(__lowercase   ,   add_special_tokens=__lowercase    )
  __A	:		Any	  =      tokenizer.convert_ids_to_tokens(encoded[1]    )[0]
  self.assertNotEqual(__lowercase   ,   __lowercase    )

  # Testing spaces after special tokens
  __A	:		List[str]	  =      '<mask>'
  tokenizer.add_special_tokens(
      {'mask_token': AddedToken(__lowercase   ,   lstrip=__lowercase   ,   rstrip=__lowercase    )}    )  # mask token has a left space
  __A	:		Dict	  =      tokenizer.convert_tokens_to_ids(__lowercase    )

  __A	:		Tuple	  =      'Encode <mask> sequence'
  __A	:		List[str]	  =      'Encode <mask>sequence'

  __A	:		List[str]	  =      tokenizer.encode(__lowercase    )
  __A	:		Any	  =      encoded.index(__lowercase    )
  __A	:		Union[str, Any]	  =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
  self.assertEqual(__lowercase   ,   __lowercase    )

  __A	:		Dict	  =      tokenizer.encode(__lowercase    )
  __A	:		str	  =      encoded.index(__lowercase    )
  __A	:		Optional[Any]	  =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
  self.assertNotEqual(__lowercase   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  pass


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
   with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
    __A	:		Optional[Any]	  =      self.rust_tokenizer_class.from_pretrained(__lowercase   ,   **__lowercase    )
    __A	:		Union[str, Any]	  =      self.tokenizer_class.from_pretrained(__lowercase   ,   **__lowercase    )
    __A	:		List[str]	  =      'A, <mask> AllenNLP sentence.'
    __A	:		List[Any]	  =      tokenizer_r.encode_plus(__lowercase   ,   add_special_tokens=__lowercase   ,   return_token_type_ids=__lowercase    )
    __A	:		List[str]	  =      tokenizer_p.encode_plus(__lowercase   ,   add_special_tokens=__lowercase   ,   return_token_type_ids=__lowercase    )

    # token_type_ids should put 0 everywhere
    self.assertEqual(sum(tokens_r['token_type_ids']    )   ,   sum(tokens_p['token_type_ids']    )    )

    # attention_mask should put 1 everywhere, so sum over length should be 1
    self.assertEqual(
        sum(tokens_r['attention_mask']    ) / len(tokens_r['attention_mask']    )   ,   sum(tokens_p['attention_mask']    ) / len(tokens_p['attention_mask']    )   ,   )

    __A	:		int	  =      tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']    )
    __A	:		Tuple	  =      tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']    )

    # Rust correctly handles the space before the mask while python doesnt
    self.assertSequenceEqual(tokens_p['input_ids']   ,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]    )
    self.assertSequenceEqual(tokens_r['input_ids']   ,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]    )

    self.assertSequenceEqual(
        __lowercase   ,   ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']    )
    self.assertSequenceEqual(
        __lowercase   ,   ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for trim_offsets, add_prefix_space in itertools.product([True, False]   ,   repeat=2    ):
   __A	:		str	  =      self.rust_tokenizer_class.from_pretrained(
       self.tmpdirname   ,   use_fast=__lowercase   ,   add_prefix_space=__lowercase   ,   trim_offsets=__lowercase    )

   __A	:		Any	  =      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()    )
   __A	:		Any	  =      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()    )

   self.assertEqual(pre_tokenizer_state['add_prefix_space']   ,   __lowercase    )

   self.assertEqual(post_processor_state['add_prefix_space']   ,   __lowercase    )
   self.assertEqual(post_processor_state['trim_offsets']   ,   __lowercase    )







 def 			snake_case__ (   self    ):

  """simple docstring"""







  for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
   with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
    __A	:		List[str]	  =      'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
    __A	:		Optional[Any]	  =      F"""{text_of_1_token} {text_of_1_token}"""

    __A	:		Optional[Any]	  =      self.rust_tokenizer_class.from_pretrained(
        __lowercase   ,   use_fast=__lowercase   ,   add_prefix_space=__lowercase   ,   trim_offsets=__lowercase    )
    __A	:		Any	  =      tokenizer_r(__lowercase   ,   return_offsets_mapping=__lowercase   ,   add_special_tokens=__lowercase    )
    self.assertEqual(encoding.offset_mapping[0]   ,   (0, len(__lowercase    ))    )
    self.assertEqual(
        encoding.offset_mapping[1]   ,   (len(__lowercase    ) + 1, len(__lowercase    ) + 1 + len(__lowercase    ))   ,   )

    __A	:		List[str]	  =      self.rust_tokenizer_class.from_pretrained(
        __lowercase   ,   use_fast=__lowercase   ,   add_prefix_space=__lowercase   ,   trim_offsets=__lowercase    )
    __A	:		List[Any]	  =      tokenizer_r(__lowercase   ,   return_offsets_mapping=__lowercase   ,   add_special_tokens=__lowercase    )
    self.assertEqual(encoding.offset_mapping[0]   ,   (0, len(__lowercase    ))    )
    self.assertEqual(
        encoding.offset_mapping[1]   ,   (len(__lowercase    ) + 1, len(__lowercase    ) + 1 + len(__lowercase    ))   ,   )

    __A	:		Any	  =      self.rust_tokenizer_class.from_pretrained(
        __lowercase   ,   use_fast=__lowercase   ,   add_prefix_space=__lowercase   ,   trim_offsets=__lowercase    )
    __A	:		Optional[Any]	  =      tokenizer_r(__lowercase   ,   return_offsets_mapping=__lowercase   ,   add_special_tokens=__lowercase    )
    self.assertEqual(encoding.offset_mapping[0]   ,   (0, len(__lowercase    ))    )
    self.assertEqual(
        encoding.offset_mapping[1]   ,   (len(__lowercase    ), len(__lowercase    ) + 1 + len(__lowercase    ))   ,   )

    __A	:		List[str]	  =      self.rust_tokenizer_class.from_pretrained(
        __lowercase   ,   use_fast=__lowercase   ,   add_prefix_space=__lowercase   ,   trim_offsets=__lowercase    )
    __A	:		List[str]	  =      tokenizer_r(__lowercase   ,   return_offsets_mapping=__lowercase   ,   add_special_tokens=__lowercase    )
    self.assertEqual(encoding.offset_mapping[0]   ,   (0, len(__lowercase    ))    )
    self.assertEqual(
        encoding.offset_mapping[1]   ,   (len(__lowercase    ), len(__lowercase    ) + 1 + len(__lowercase    ))   ,   )

    __A	:		Optional[Any]	  =      F""" {text}"""

    # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
    #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
    # )
    # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
    # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
    # self.assertEqual(
    #     encoding.offset_mapping[1],
    #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
    # )

    __A	:		Optional[Any]	  =      self.rust_tokenizer_class.from_pretrained(
        __lowercase   ,   use_fast=__lowercase   ,   add_prefix_space=__lowercase   ,   trim_offsets=__lowercase    )
    __A	:		Union[str, Any]	  =      tokenizer_r(__lowercase   ,   return_offsets_mapping=__lowercase   ,   add_special_tokens=__lowercase    )
    self.assertEqual(encoding.offset_mapping[0]   ,   (1, 1 + len(__lowercase    ))    )
    self.assertEqual(
        encoding.offset_mapping[1]   ,   (1 + len(__lowercase    ) + 1, 1 + len(__lowercase    ) + 1 + len(__lowercase    ))   ,   )

    __A	:		Dict	  =      self.rust_tokenizer_class.from_pretrained(
        __lowercase   ,   use_fast=__lowercase   ,   add_prefix_space=__lowercase   ,   trim_offsets=__lowercase    )
    __A	:		List[Any]	  =      tokenizer_r(__lowercase   ,   return_offsets_mapping=__lowercase   ,   add_special_tokens=__lowercase    )
    self.assertEqual(encoding.offset_mapping[0]   ,   (0, 1 + len(__lowercase    ))    )
    self.assertEqual(
        encoding.offset_mapping[1]   ,   (1 + len(__lowercase    ), 1 + len(__lowercase    ) + 1 + len(__lowercase    ))   ,   )

    __A	:		Optional[Any]	  =      self.rust_tokenizer_class.from_pretrained(
        __lowercase   ,   use_fast=__lowercase   ,   add_prefix_space=__lowercase   ,   trim_offsets=__lowercase    )
    __A	:		Union[str, Any]	  =      tokenizer_r(__lowercase   ,   return_offsets_mapping=__lowercase   ,   add_special_tokens=__lowercase    )
    self.assertEqual(encoding.offset_mapping[0]   ,   (0, 1 + len(__lowercase    ))    )
    self.assertEqual(
        encoding.offset_mapping[1]   ,   (1 + len(__lowercase    ), 1 + len(__lowercase    ) + 1 + len(__lowercase    ))   ,   )










'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
 from PIL import Image

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		int     				=    '''dandelin/vilt-b32-finetuned-vqa'''
 __lowercase					:		int     				=    (
     '''This is a tool that answers a question about an image. It takes an input named `image` which should be the '''
     '''image containing the information, as well as a `question` which should be the question in English. It '''
     '''returns a text that is the answer to the question.'''
 )
 __lowercase					:		Union[str, Any]     				=    '''image_qa'''
 __lowercase					:		Optional[Any]     				=    AutoProcessor
 __lowercase					:		Tuple     				=    AutoModelForVisualQuestionAnswering

 __lowercase					:		Tuple     				=    ['''image''', '''text''']
 __lowercase					:		List[str]     				=    ['''text''']


 def __init__(   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  requires_backends(self   ,   ['vision']    )
  super().__init__(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  return self.pre_processor(__lowercase   ,   __lowercase   ,   return_tensors='pt'    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  with torch.no_grad():
   return self.model(**__lowercase    ).logits







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      outputs.argmax(-1    ).item()
  return self.model.config.idalabel[idx]






