




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'''



    lowerCamelCase_      :	str	     =      MvpTokenizer
    lowerCamelCase_      :	List[Any]	     =      MvpTokenizerFast
    lowerCamelCase_      :	str	     =      True
    lowerCamelCase_      :	str	     =      filter_roberta_detectors







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

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







    def     _lowercase     (    self     ,  **UpperCamelCase__     )     ->      Optional[Any]:
           kwargs.update(self.special_tokens_map     )
           return self.tokenizer_class.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__     )







    def     _lowercase     (    self     ,  **UpperCamelCase__     )     ->      Optional[Any]:
           kwargs.update(self.special_tokens_map     )
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Tuple:
           return "lower newer", "lower newer"







    @cached_property
    def     _lowercase     (    self     )     ->      Tuple:
           return MvpTokenizer.from_pretrained("RUCAIBox/mvp"     )







    @cached_property
    def     _lowercase     (    self     )     ->      Optional[Any]:
           return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"     )







    @require_torch
    def     _lowercase     (    self     )     ->      List[str]:
           lowerCamelCase   :      Optional[Any]			 =					["A long paragraph for summarization.", "Another paragraph for summarization."]
           lowerCamelCase   :      Dict			 =					[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  lowerCamelCase   :      Union[str, Any]			 =					tokenizer(UpperCamelCase__     ,  max_length=len(UpperCamelCase__     )     ,  padding=UpperCamelCase__     ,  return_tensors="pt"     )
                  self.assertIsInstance(UpperCamelCase__     ,  UpperCamelCase__     )

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







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







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







    @require_torch
    def     _lowercase     (    self     )     ->      Dict:
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  lowerCamelCase   :      Union[str, Any]			 =					tokenizer(
                      ["I am a small frog" * 1024, "I am a small frog"]     ,  padding=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  return_tensors="pt"     )
                  self.assertIsInstance(UpperCamelCase__     ,  UpperCamelCase__     )
                  self.assertEqual(batch.input_ids.shape     ,  (2, 1024)     )







    @require_torch
    def     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      str			 =					["A long paragraph for summarization."]
           lowerCamelCase   :      List[str]			 =					[
               "Summary of the text.",
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  lowerCamelCase   :      str			 =					tokenizer(UpperCamelCase__     ,  text_target=UpperCamelCase__     ,  return_tensors="pt"     )
                  lowerCamelCase   :      Union[str, Any]			 =					inputs["input_ids"]
                  lowerCamelCase   :      List[str]			 =					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     _lowercase     (    self     )     ->      Optional[Any]:
           pass







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

                         # 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   :      Dict			 =					tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]     )
                         lowerCamelCase   :      List[str]			 =					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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]     )
                         self.assertSequenceEqual(tokens_r["input_ids"]     ,  [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]     )

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













'''simple docstring'''


lowerCAmelCase					:     str												='''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase					:     int												=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase					:     List[str]												={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}


