





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   __lowerCAmelCase			(					_UpperCamelCase		,unittest.TestCase  ):
   _UpperCamelCase :						Optional[Any]     		=				MvpTokenizer
   _UpperCamelCase :						Any     		=				MvpTokenizerFast
   _UpperCamelCase :						Dict     		=				True
   _UpperCamelCase :						Union[str, Any]     		=				filter_roberta_detectors





   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





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

      a__	:						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(snake_case      ) + "\n"      )
      with open(self.merges_file					,			"w"					,			encoding="utf-8"      ) as fp:
         fp.write("\n".join(snake_case      )      )





   def 	_snake_case ( self					,			**snake_case      )				->  int:
      """simple docstring"""





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





   def 	_snake_case ( self					,			**snake_case      )				->  List[Any]:
      """simple docstring"""





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





   def 	_snake_case ( self					,			snake_case      )				->  Dict:
      """simple docstring"""





      return "lower newer", "lower newer"





   @cached_property
   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      return MvpTokenizer.from_pretrained("RUCAIBox/mvp"      )





   @cached_property
   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"      )





   @require_torch
   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Optional[Any]      		=  ["A long paragraph for summarization.", "Another paragraph for summarization."]
      a__	:						Dict      		=  [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

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





   @require_torch
   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Dict      		=  ["A long paragraph for summarization.", "Another paragraph for summarization."]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a__	:						Optional[int]      		=  tokenizer(snake_case					,			padding=snake_case					,			return_tensors="pt"      )
         # check if input_ids are returned and no labels
         self.assertIn("input_ids"					,			snake_case      )
         self.assertIn("attention_mask"					,			snake_case      )
         self.assertNotIn("labels"					,			snake_case      )
         self.assertNotIn("decoder_attention_mask"					,			snake_case      )





   @require_torch
   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





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





   @require_torch
   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a__	:						Union[str, Any]      		=  tokenizer(
             ["I am a small frog" * 1_024, "I am a small frog"]					,			padding=snake_case					,			truncation=snake_case					,			return_tensors="pt"      )
         self.assertIsInstance(snake_case					,			snake_case      )
         self.assertEqual(batch.input_ids.shape					,			(2, 1_024)      )





   @require_torch
   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						int      		=  ["A long paragraph for summarization."]
      a__	:						Optional[int]      		=  [
          "Summary of the text.",
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a__	:						List[Any]      		=  tokenizer(snake_case					,			text_target=snake_case					,			return_tensors="pt"      )
         a__	:						int      		=  inputs["input_ids"]
         a__	:						Optional[int]      		=  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 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      pass





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





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

            # 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__	:						Dict      		=  tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]      )
            a__	:						int      		=  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(
                snake_case					,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]      )
            self.assertSequenceEqual(
                snake_case					,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]      )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__       :			Any			      =						{"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						[
      """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """SEWForCTC""",
      """SEWForSequenceClassification""",
      """SEWModel""",
      """SEWPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

