



"""simple docstring"""






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
     from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
     snake_case   					=				get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
     from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

snake_case   					=				1_2_8_0_2_2
snake_case   					=				1_2_8_0_2_8



@require_sentencepiece
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Optional[Any]           =  MaMaaaTokenizer
   A__		:	List[Any]           =  False
   A__		:	Any           =  False
   A__		:	str           =  True



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     super().setUp()

     _snake_case   	=      ['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
     _snake_case   	=      dict(zip(__lowerCamelCase							,       range(len(__lowerCamelCase				)				)				)				)
     _snake_case   	=      Path(self.tmpdirname				)
     save_json(__lowerCamelCase							,       save_dir / VOCAB_FILES_NAMES['''vocab_file''']				)
     if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
       copyfile(__lowerCamelCase							,       save_dir / VOCAB_FILES_NAMES['''spm_file''']				)

     _snake_case   	=      MaMaaaTokenizer.from_pretrained(self.tmpdirname				)
     tokenizer.save_pretrained(self.tmpdirname				)



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       **__lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     return MaMaaaTokenizer.from_pretrained(self.tmpdirname							,       **__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     return (
         "This is a test",
         "This is a test",
     )



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      '''</s>'''
     _snake_case   	=      0

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase				)							,       __lowerCamelCase				)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase				)							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.get_tokenizer()
     _snake_case   	=      list(tokenizer.get_vocab().keys()				)

     self.assertEqual(vocab_keys[0]							,       '''</s>'''				)
     self.assertEqual(vocab_keys[1]							,       '''<unk>'''				)
     self.assertEqual(vocab_keys[-1]							,       '''<s>'''				)
     self.assertEqual(len(__lowerCamelCase				)							,       tokenizer.vocab_size + len(tokenizer.get_added_vocab()				)				)



   @unittest.skip('''Skip this test while all models are still to be uploaded.'''				)
   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      self.get_tokenizer()

     _snake_case   	=      tokenizer.tokenize('''This is a test'''				)
     self.assertListEqual(__lowerCamelCase							,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(__lowerCamelCase				)							,       [2, 3, 4, 5, 6]							,       )

     _snake_case   	=      tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]				)
     self.assertListEqual(__lowerCamelCase							,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

     _snake_case   	=      tokenizer.convert_tokens_to_string(__lowerCamelCase				)
     self.assertEqual(__lowerCamelCase							,       '''This is a test'''				)





   @slow
   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     # fmt: off
     _snake_case   	=      {'''input_ids''': [[1_2_8_0_2_2, 1_1_0_1_0_8, 3_9_7, 1_1, 3_8_2_7_2, 2_2_4_7, 1_2_4_8_1_1, 2_8_5, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 3_9_5_3_4, 4_4_2_8, 3_9_7, 1_0_1_9, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 4_1_3_3_7, 1_6_7_8_6, 2_4_1, 7, 2_0_2_1_4, 1_7, 1_2_5_6_9_0, 1_0_3_9_8, 7, 4_4_3_7_8, 5_8_0_6_9, 6_8_3_4_2, 7_7_9_8, 7_3_4_3, 1_1, 2_9_9, 3_3_3_1_0, 4, 1_5_8, 3_7_3_5_0, 9_4_0_7_7, 4_5_6_9, 2_9_9, 3_3_3_1_0, 9_0, 4, 5_2_8_4_0, 2_9_0, 4, 3_1_2_7_0, 1_1_2, 2_9_9, 6_8_2, 4, 5_2_8_4_0, 3_9_9_5_3, 1_4_0_7_9, 1_9_3, 5_2_5_1_9, 9_0_8_9_4, 1_7_8_9_4, 1_2_0_6_9_7, 1_1, 4_0_4_4_5, 5_5_1, 1_7, 1_0_1_9, 5_2_5_1_9, 9_0_8_9_4, 1_7_7_5_6, 9_6_3, 1_1, 4_0_4_4_5, 4_8_0, 1_7, 9_7_9_2, 1_1_2_0, 5_1_7_3, 1_3_9_3, 6_2_4_0, 1_6_7_8_6, 2_4_1, 1_2_0_9_9_6, 2_8, 1_2_4_5, 1_3_9_3, 1_1_8_2_4_0, 1_1_1_2_3, 1_0_1_9, 9_3_6_1_2, 2_6_9_1, 1_0_6_1_8, 9_8_0_5_8, 1_2_0_4_0_9, 1_9_2_8, 2_7_9, 4, 4_0_6_8_3, 3_6_7, 1_7_8, 2_0_7, 1_0_1_9, 1_0_3, 1_0_3_1_2_1, 5_0_6, 6_5_2_9_6, 5, 2], [1_2_8_0_2_2, 2_1_2_1_7, 3_6_7, 1_1_7, 1_2_5_4_5_0, 1_2_8, 7_1_9, 7, 7_3_0_8, 4_0, 9_3_6_1_2, 1_2_6_6_9, 1_1_1_6, 1_6_7_0_4, 7_1, 1_7_7_8_5, 3_6_9_9, 1_5_5_9_2, 3_5, 1_4_4, 9_5_8_4, 2_4_1, 1_1_9_4_3, 7_1_3, 9_5_0, 7_9_9, 2_2_4_7, 8_8_4_2_7, 1_5_0, 1_4_9, 1_1_8_8_1_3, 1_2_0_7_0_6, 1_0_1_9, 1_0_6_9_0_6, 8_1_5_1_8, 2_8, 1_2_2_4, 2_2_7_9_9, 3_9_7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1_2_8_0_2_2, 1_6_5_8, 1_2_3_3_1_1, 5_1_5_5, 5_5_7_8, 4_7_2_2, 2_7_9, 1_4_9_4_7, 2_3_6_6, 1_1_2_0, 1_1_9_7, 1_4, 1_3_4_8, 9_2_3_2, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=__lowerCamelCase							,       model_name='''facebook/m2m100_418M'''							,       revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e'''							,       )



@require_torch
@require_sentencepiece
@require_tokenizers
class     UpperCAmelCase    (	unittest.TestCase  ):
   A__		:	Tuple           =  '''facebook/m2m100_418M'''
   A__		:	str           =  [
       '''In my opinion, there are two levels of response from the French government.''',
       '''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
   ]
   A__		:	Union[str, Any]           =  [
       '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
       '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
   ]

   # fmt: off
   A__		:	Any           =  [EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2]



   @classmethod
   def    __UpperCAmelCase    (       cls    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      MaMaaaTokenizer.from_pretrained(
         cls.checkpoint_name							,       src_lang='''en'''							,       tgt_lang='''fr'''				)
     _snake_case   	=      1
     return cls



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     self.assertEqual(self.tokenizer.get_lang_id('''ar'''				)							,       1_2_8_0_0_6				)
     self.assertEqual(self.tokenizer.get_lang_id('''en'''				)							,       1_2_8_0_2_2				)
     self.assertEqual(self.tokenizer.get_lang_id('''ro'''				)							,       1_2_8_0_7_6				)
     self.assertEqual(self.tokenizer.get_lang_id('''mr'''				)							,       1_2_8_0_6_3				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      self.tokenizer.get_vocab()
     self.assertEqual(len(__lowerCamelCase				)							,       self.tokenizer.vocab_size				)
     self.assertEqual(vocab['''<unk>''']							,       3				)
     self.assertIn(self.tokenizer.get_lang_token('''en'''				)							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      '''en'''
     _snake_case   	=      self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
     self.assertListEqual(self.expected_src_tokens							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     self.assertIn(__lowerCamelCase							,       self.tokenizer.all_special_ids				)
     # fmt: off
     _snake_case   	=      [FR_CODE, 5_3_6_4, 8_2, 8_6_4_2, 4, 2_9_4, 4_7, 8, 1_4_0_2_8, 1_3_6, 3_2_8_6, 9_7_0_6, 6, 9_0_7_9_7, 6, 1_4_4_0_1_2, 1_6_2, 8_8_1_2_8, 3_0_0_6_1, 5, 2]
     # fmt: on
     _snake_case   	=      self.tokenizer.decode(__lowerCamelCase							,       skip_special_tokens=__lowerCamelCase				)
     _snake_case   	=      self.tokenizer.decode(generated_ids[1:]							,       skip_special_tokens=__lowerCamelCase				)
     self.assertEqual(__lowerCamelCase							,       __lowerCamelCase				)
     self.assertNotIn(self.tokenizer.eos_token							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     _snake_case   	=      tempfile.mkdtemp()
     _snake_case   	=      self.tokenizer.lang_token_to_id
     self.tokenizer.save_pretrained(__lowerCamelCase				)
     _snake_case   	=      MaMaaaTokenizer.from_pretrained(__lowerCamelCase				)
     self.assertDictEqual(new_tok.lang_token_to_id							,       __lowerCamelCase				)



   @require_torch
   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      '''en'''
     _snake_case   	=      '''fr'''

     _snake_case   	=      self.tokenizer(self.src_text							,       text_target=self.tgt_text							,       padding=__lowerCamelCase							,       return_tensors='''pt'''				)

     _snake_case   	=      shift_tokens_right(
         batch['''labels''']							,       self.tokenizer.pad_token_id							,       self.tokenizer.eos_token_id				)

     for k in batch:
       _snake_case   	=      batch[k].tolist()
     # batch = {k: v.tolist() for k,v in batch.items()}
     # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
     # batch.decoder_inputs_ids[0][0] ==
     assert batch.input_ids[1][0] == EN_CODE
     assert batch.input_ids[1][-1] == 2
     assert batch.labels[1][0] == FR_CODE
     assert batch.labels[1][-1] == 2
     assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]



   @require_torch
   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      '''mr'''
     self.assertListEqual(self.tokenizer.prefix_tokens							,       [self.tokenizer.get_lang_id('''mr'''				)]				)
     self.assertListEqual(self.tokenizer.suffix_tokens							,       [self.tokenizer.eos_token_id]				)

     _snake_case   	=      '''zh'''
     self.assertListEqual(self.tokenizer.prefix_tokens							,       [self.tokenizer.get_lang_id('''zh'''				)]				)
     self.assertListEqual(self.tokenizer.suffix_tokens							,       [self.tokenizer.eos_token_id]				)



   @require_torch
   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      '''mr'''
     self.tokenizer._switch_to_target_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens							,       [self.tokenizer.get_lang_id('''mr'''				)]				)
     self.assertListEqual(self.tokenizer.suffix_tokens							,       [self.tokenizer.eos_token_id]				)
     self.tokenizer._switch_to_input_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens							,       [self.tokenizer.get_lang_id(self.tokenizer.src_lang				)]				)

     _snake_case   	=      '''zh'''
     self.tokenizer._switch_to_target_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens							,       [self.tokenizer.get_lang_id('''zh'''				)]				)
     self.assertListEqual(self.tokenizer.suffix_tokens							,       [self.tokenizer.eos_token_id]				)
     self.tokenizer._switch_to_input_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens							,       [self.tokenizer.get_lang_id(self.tokenizer.src_lang				)]				)





   @require_torch
   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.tokenizer._build_translation_inputs('''A test'''							,       return_tensors='''pt'''							,       src_lang='''en'''							,       tgt_lang='''ar'''				)

     self.assertEqual(
         nested_simplify(__lowerCamelCase				)							,       {
             # en_XX, A, test, EOS
             '''input_ids''': [[1_2_8_0_2_2, 5_8, 4_1_8_3, 2]],
             '''attention_mask''': [[1, 1, 1, 1]],
             # ar_AR
             '''forced_bos_token_id''': 1_2_8_0_0_6,
         }							,       )










from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

a_      :      str    =     logging.get_logger(__name__)
@add_end_docstrings(_lowercase				)
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  super().__init__(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  self.check_model_type(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__							,			a__												= {}, {}
  if padding is not None:
   a__												= padding
  if truncation is not None:
   a__												= truncation
  if top_k is not None:
   a__												= top_k
  return preprocess_params, {}, postprocess_params

 def __call__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  if isinstance(SCREAMING_SNAKE_CASE		,				(Image.Image, str)      ) and isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
   a__												= {'''image''': image, '''question''': question}
  else:
   a__												= image
  a__												= super().__call__(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  return results

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=False      ) ->			Dict:
  a__												= load_image(inputs['''image''']      )
  a__												= self.tokenizer(
      inputs['''question''']		,				return_tensors=self.framework		,				padding=SCREAMING_SNAKE_CASE		,				truncation=SCREAMING_SNAKE_CASE      )
  a__												= self.image_processor(images=SCREAMING_SNAKE_CASE		,				return_tensors=self.framework      )
  model_inputs.update(SCREAMING_SNAKE_CASE      )
  return model_inputs

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__												= self.model(**SCREAMING_SNAKE_CASE      )
  return model_outputs



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=5      ) ->			Optional[Any]:
  if top_k > self.model.config.num_labels:
   a__												= self.model.config.num_labels

  if self.framework == "pt":
   a__												= model_outputs.logits.sigmoid()[0]
   a__							,			a__												= probs.topk(SCREAMING_SNAKE_CASE      )
  else:
   raise ValueError(f"Unsupported framework: {self.framework}"      )

  a__												= scores.tolist()
  a__												= ids.tolist()
  return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )]





