





'''simple docstring'''






import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 				UpperCAmelCase  (    lowerCAmelCase__							,	unittest.TestCase     ):
     _lowercase:    Any		  =     BartTokenizer
     _lowercase:    str		  =     BartTokenizerFast
     _lowercase:    Any		  =     True
     _lowercase:    Optional[int]		  =     filter_roberta_detectors




     def        lowercase__					(    self    :				Tuple						)       ->   Dict:
           super().setUp()
           _lowerCAmelCase							  =							[
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """\u0120""",
               """\u0120l""",
               """\u0120n""",
               """\u0120lo""",
               """\u0120low""",
               """er""",
               """\u0120lowest""",
               """\u0120newer""",
               """\u0120wider""",
               """<unk>""",
           ]
           _lowerCAmelCase							  =							dict(zip(lowerCAmelCase_    ,    range(len(lowerCAmelCase_						)						)						)						)
           _lowerCAmelCase							  =							["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
           _lowerCAmelCase							  =							{"""unk_token""": """<unk>"""}

           _lowerCAmelCase							  =							os.path.join(self.tmpdirname    ,    VOCAB_FILES_NAMES["""vocab_file"""]						)
           _lowerCAmelCase							  =							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(lowerCAmelCase_						) + """\n"""						)
           with open(self.merges_file    ,    """w"""    ,    encoding="""utf-8"""						) as fp:
                 fp.write("""\n""".join(lowerCAmelCase_						)						)




     def        lowercase__					(    self    :				List[str]    ,    **__snake_case    :				Optional[int]						)       ->   Union[str, Any]:
           kwargs.update(self.special_tokens_map						)
           return self.tokenizer_class.from_pretrained(self.tmpdirname    ,    **lowerCAmelCase_						)




     def        lowercase__					(    self    :				Any    ,    **__snake_case    :				Optional[int]						)       ->   int:
           kwargs.update(self.special_tokens_map						)
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,    **lowerCAmelCase_						)




     def        lowercase__					(    self    :				List[str]    ,    __snake_case    :				List[str]						)       ->   int:
           return "lower newer", "lower newer"




     @cached_property
     def        lowercase__					(    self    :				List[str]						)       ->   List[str]:
           return BartTokenizer.from_pretrained("""facebook/bart-large"""						)




     @cached_property
     def        lowercase__					(    self    :				str						)       ->   Dict:
           return BartTokenizerFast.from_pretrained("""facebook/bart-large"""						)




     @require_torch
     def        lowercase__					(    self    :				Optional[int]						)       ->   Optional[int]:
           _lowerCAmelCase							  =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           _lowerCAmelCase							  =							[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 _lowerCAmelCase							  =							tokenizer(lowerCAmelCase_    ,    max_length=len(lowerCAmelCase_						)    ,    padding=lowerCAmelCase_    ,    return_tensors="""pt"""						)
                 self.assertIsInstance(lowerCAmelCase_    ,    lowerCAmelCase_						)

                 self.assertEqual((2, 9)    ,    batch.input_ids.shape						)
                 self.assertEqual((2, 9)    ,    batch.attention_mask.shape						)
                 _lowerCAmelCase							  =							batch.input_ids.tolist()[0]
                 self.assertListEqual(lowerCAmelCase_    ,    lowerCAmelCase_						)
                 # Test that special tokens are reset




     @require_torch
     def        lowercase__					(    self    :				Dict						)       ->   List[Any]:
           _lowerCAmelCase							  =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 _lowerCAmelCase							  =							tokenizer(lowerCAmelCase_    ,    padding=lowerCAmelCase_    ,    return_tensors="""pt"""						)
                 # check if input_ids are returned and no labels
                 self.assertIn("""input_ids"""    ,    lowerCAmelCase_						)
                 self.assertIn("""attention_mask"""    ,    lowerCAmelCase_						)
                 self.assertNotIn("""labels"""    ,    lowerCAmelCase_						)
                 self.assertNotIn("""decoder_attention_mask"""    ,    lowerCAmelCase_						)




     @require_torch
     def        lowercase__					(    self    :				List[Any]						)       ->   Dict:
           _lowerCAmelCase							  =							[
               """Summary of the text.""",
               """Another summary.""",
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 _lowerCAmelCase							  =							tokenizer(text_target=lowerCAmelCase_    ,    max_length=32    ,    padding="""max_length"""    ,    return_tensors="""pt"""						)
                 self.assertEqual(32    ,    targets["""input_ids"""].shape[1]						)




     @require_torch
     def        lowercase__					(    self    :				Optional[int]						)       ->   List[str]:






           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 _lowerCAmelCase							  =							tokenizer(
                     ["""I am a small frog""" * 10_24, """I am a small frog"""]    ,    padding=lowerCAmelCase_    ,    truncation=lowerCAmelCase_    ,    return_tensors="""pt"""						)
                 self.assertIsInstance(lowerCAmelCase_    ,    lowerCAmelCase_						)
                 self.assertEqual(batch.input_ids.shape    ,    (2, 10_24)						)




     @require_torch
     def        lowercase__					(    self    :				Any						)       ->   Union[str, Any]:
           _lowerCAmelCase							  =							["""A long paragraph for summarization."""]
           _lowerCAmelCase							  =							[
               """Summary of the text.""",
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 _lowerCAmelCase							  =							tokenizer(lowerCAmelCase_    ,    return_tensors="""pt"""						)
                 _lowerCAmelCase							  =							tokenizer(text_target=lowerCAmelCase_    ,    return_tensors="""pt"""						)
                 _lowerCAmelCase							  =							inputs["""input_ids"""]
                 _lowerCAmelCase							  =							targets["""input_ids"""]
                 self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()						)
                 self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()						)
                 self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()						)
                 self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()						)




     def        lowercase__					(    self    :				List[Any]						)       ->   Union[str, Any]:
           pass




     def        lowercase__					(    self    :				List[str]						)       ->   int:






           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"						):
                       _lowerCAmelCase							  =							self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_    ,    **lowerCAmelCase_						)
                       _lowerCAmelCase							  =							self.tokenizer_class.from_pretrained(lowerCAmelCase_    ,    **lowerCAmelCase_						)
                       _lowerCAmelCase							  =							"""A, <mask> AllenNLP sentence."""
                       _lowerCAmelCase							  =							tokenizer_r.encode_plus(lowerCAmelCase_    ,    add_special_tokens=lowerCAmelCase_    ,    return_token_type_ids=lowerCAmelCase_						)
                       _lowerCAmelCase							  =							tokenizer_p.encode_plus(lowerCAmelCase_    ,    add_special_tokens=lowerCAmelCase_    ,    return_token_type_ids=lowerCAmelCase_						)

                       # 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"""]						)    ,    )

                       _lowerCAmelCase							  =							tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]						)
                       _lowerCAmelCase							  =							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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]						)
                       self.assertSequenceEqual(tokens_r["""input_ids"""]    ,    [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]						)

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













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__       :     Tuple          ={
    '''configuration_oneformer''': ['''ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OneFormerConfig'''],
    '''processing_oneformer''': ['''OneFormerProcessor'''],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[str]          =['''OneFormerImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Dict          =[
          '''ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''OneFormerForUniversalSegmentation''',
          '''OneFormerModel''',
          '''OneFormerPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
      from .processing_oneformer import OneFormerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_oneformer import OneFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_oneformer import (
                ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                OneFormerForUniversalSegmentation,
                OneFormerModel,
                OneFormerPreTrainedModel,
            )


else:
      import sys

      A__       :     Optional[Any]          =_LazyModule(__name__, globals()['''__file__'''], _import_structure)






