





import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
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       ):



     '''simple docstring'''

     __snake_case   :     Any		     =      MvpTokenizer
     __snake_case   :     List[Any]		     =      MvpTokenizerFast
     __snake_case   :     Optional[Any]		     =      True
     __snake_case   :     List[str]		     =      filter_roberta_detectors






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		Tuple:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE		=						os.path.join(self.tmpdirname						,VOCAB_FILES_NAMES["""vocab_file"""]	)
           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   str						,**lowerCamelCase__						:   int	)					->		Dict:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]						,**lowerCamelCase__						:   Any	)					->		Dict:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   str	)					->		Union[str, Any]:




           '''simple docstring'''


           return "lower newer", "lower newer"






     @cached_property
     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		List[str]:




           '''simple docstring'''


           return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""	)






     @cached_property
     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		int:




           '''simple docstring'''


           return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""	)






     @require_torch
     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           SCREAMING_SNAKE_CASE		=						[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 SCREAMING_SNAKE_CASE		=						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	)
                 SCREAMING_SNAKE_CASE		=						batch.input_ids.tolist()[0]
                 self.assertListEqual(_lowerCAmelCase						,_lowerCAmelCase	)
                 # Test that special tokens are reset






     @require_torch
     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[
               """Summary of the text.""",
               """Another summary.""",
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 SCREAMING_SNAKE_CASE		=						tokenizer(text_target=_lowerCAmelCase						,max_length=32						,padding="""max_length"""						,return_tensors="""pt"""	)
                 self.assertEqual(32						,targets["""input_ids"""].shape[1]	)






     @require_torch
     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		int:




           '''simple docstring'''


           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 SCREAMING_SNAKE_CASE		=						tokenizer(
                     ["""I am a small frog""" * 1024, """I am a small frog"""]						,padding=_lowerCAmelCase						,truncation=_lowerCAmelCase						,return_tensors="""pt"""	)
                 self.assertIsInstance(_lowerCAmelCase						,_lowerCAmelCase	)
                 self.assertEqual(batch.input_ids.shape						,(2, 1024)	)






     @require_torch
     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						["""A long paragraph for summarization."""]
           SCREAMING_SNAKE_CASE		=						[
               """Summary of the text.""",
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 SCREAMING_SNAKE_CASE		=						tokenizer(_lowerCAmelCase						,text_target=_lowerCAmelCase						,return_tensors="""pt"""	)
                 SCREAMING_SNAKE_CASE		=						inputs["""input_ids"""]
                 SCREAMING_SNAKE_CASE		=						inputs["""labels"""]
                 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     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		str:




           '''simple docstring'''


           pass






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		int:




           '''simple docstring'''


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

                       SCREAMING_SNAKE_CASE		=						tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]	)
                       SCREAMING_SNAKE_CASE		=						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, 50264, 3823, 487, 21992, 3645, 4, 2]	)
                       self.assertSequenceEqual(tokens_r["""input_ids"""]						,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)

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









from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_						       =	{
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""MvpTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MvpForCausalLM""",
          """MvpForConditionalGeneration""",
          """MvpForQuestionAnswering""",
          """MvpForSequenceClassification""",
          """MvpModel""",
          """MvpPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
      from .tokenization_mvp import MvpTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mvp_fast import MvpTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mvp import (
                MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                MvpForCausalLM,
                MvpForConditionalGeneration,
                MvpForQuestionAnswering,
                MvpForSequenceClassification,
                MvpModel,
                MvpPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE_						       =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


