




'''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      _a       (		__lowerCAmelCase      ,      unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:					int					       =		BartTokenizer
     SCREAMING_SNAKE_CASE_							:					int					       =		BartTokenizerFast
     SCREAMING_SNAKE_CASE_							:					Optional[Any]					       =		True
     SCREAMING_SNAKE_CASE_							:					List[Any]					       =		filter_roberta_detectors






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

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






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






     def        _lowercase			(				self      ,**_SCREAMING_SNAKE_CASE					)							->    Any:
          kwargs.update(self.special_tokens_map					)
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    Dict:
          return "lower newer", "lower newer"






     @cached_property
     def        _lowercase			(				self					)							->    int:
          return BartTokenizer.from_pretrained("facebook/bart-large"					)






     @cached_property
     def        _lowercase			(				self					)							->    List[Any]:
          return BartTokenizerFast.from_pretrained("facebook/bart-large"					)






     @require_torch
     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   ["A long paragraph for summarization.", "Another paragraph for summarization."]
          _snake_case							=   [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

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






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






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






     @require_torch
     def        _lowercase			(				self					)							->    List[Any]:
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               _snake_case							=   tokenizer(
                   ["I am a small frog" * 1_024, "I am a small frog"]      ,padding=_SCREAMING_SNAKE_CASE      ,truncation=_SCREAMING_SNAKE_CASE      ,return_tensors="pt"					)
               self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)
               self.assertEqual(batch.input_ids.shape      ,(2, 1_024)					)






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






     def        _lowercase			(				self					)							->    Optional[Any]:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
                    _snake_case							=   self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)
                    _snake_case							=   self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)
                    _snake_case							=   "A, <mask> AllenNLP sentence."
                    _snake_case							=   tokenizer_r.encode_plus(_SCREAMING_SNAKE_CASE      ,add_special_tokens=_SCREAMING_SNAKE_CASE      ,return_token_type_ids=_SCREAMING_SNAKE_CASE					)
                    _snake_case							=   tokenizer_p.encode_plus(_SCREAMING_SNAKE_CASE      ,add_special_tokens=_SCREAMING_SNAKE_CASE      ,return_token_type_ids=_SCREAMING_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"]					)      ,)

                    _snake_case							=   tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]					)
                    _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, 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(
                        _SCREAMING_SNAKE_CASE      ,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]					)
                    self.assertSequenceEqual(
                        _SCREAMING_SNAKE_CASE      ,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]					)








'''simple docstring'''





import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.electra.modeling_flax_electra import (
       FlaxElectraForCausalLM,
       FlaxElectraForMaskedLM,
       FlaxElectraForMultipleChoice,
       FlaxElectraForPreTraining,
       FlaxElectraForQuestionAnswering,
       FlaxElectraForSequenceClassification,
       FlaxElectraForTokenClassification,
       FlaxElectraModel,
   )

class      _a       (		unittest.TestCase							):






     def __init__(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE=13      ,_SCREAMING_SNAKE_CASE=7      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=99      ,_SCREAMING_SNAKE_CASE=24      ,_SCREAMING_SNAKE_CASE=32      ,_SCREAMING_SNAKE_CASE=5      ,_SCREAMING_SNAKE_CASE=4      ,_SCREAMING_SNAKE_CASE=37      ,_SCREAMING_SNAKE_CASE="gelu"      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=512      ,_SCREAMING_SNAKE_CASE=16      ,_SCREAMING_SNAKE_CASE=2      ,_SCREAMING_SNAKE_CASE=0.0_2      ,_SCREAMING_SNAKE_CASE=4      ,)							->    List[str]:
          _snake_case							=   parent
          _snake_case							=   batch_size
          _snake_case							=   seq_length
          _snake_case							=   is_training
          _snake_case							=   use_attention_mask
          _snake_case							=   use_token_type_ids
          _snake_case							=   use_labels
          _snake_case							=   vocab_size
          _snake_case							=   hidden_size
          _snake_case							=   embedding_size
          _snake_case							=   num_hidden_layers
          _snake_case							=   num_attention_heads
          _snake_case							=   intermediate_size
          _snake_case							=   hidden_act
          _snake_case							=   hidden_dropout_prob
          _snake_case							=   attention_probs_dropout_prob
          _snake_case							=   max_position_embeddings
          _snake_case							=   type_vocab_size
          _snake_case							=   type_sequence_label_size
          _snake_case							=   initializer_range
          _snake_case							=   num_choices






     def        _lowercase			(				self					)							->    int:
          _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size					)

          _snake_case							=   None
          if self.use_attention_mask:
               _snake_case							=   random_attention_mask([self.batch_size, self.seq_length]					)

          _snake_case							=   None
          if self.use_token_type_ids:
               _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.type_vocab_size					)

          _snake_case							=   ElectraConfig(
              vocab_size=self.vocab_size      ,hidden_size=self.hidden_size      ,embedding_size=self.embedding_size      ,num_hidden_layers=self.num_hidden_layers      ,num_attention_heads=self.num_attention_heads      ,intermediate_size=self.intermediate_size      ,hidden_act=self.hidden_act      ,hidden_dropout_prob=self.hidden_dropout_prob      ,attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,max_position_embeddings=self.max_position_embeddings      ,type_vocab_size=self.type_vocab_size      ,initializer_range=self.initializer_range      ,)

          return config, input_ids, token_type_ids, attention_mask






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   self.prepare_config_and_inputs()
          _snake_case							,      _snake_case							,      _snake_case							,      _snake_case							=   config_and_inputs
          _snake_case							=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict







@require_flax
class      _a       (		__lowerCAmelCase      ,      unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:					List[str]					       =		True

     SCREAMING_SNAKE_CASE_							:					Any					       =		(
         (
             FlaxElectraModel,
             FlaxElectraForCausalLM,
             FlaxElectraForMaskedLM,
             FlaxElectraForPreTraining,
             FlaxElectraForTokenClassification,
             FlaxElectraForQuestionAnswering,
             FlaxElectraForMultipleChoice,
             FlaxElectraForSequenceClassification,
         )
         if is_flax_available()
         else ()
     )






     def        _lowercase			(				self					)							->    List[Any]:
          _snake_case							=   FlaxElectraModelTester(self					)






     @slow
     def        _lowercase			(				self					)							->    List[Any]:
          for model_class_name in self.all_model_classes:
               if model_class_name == FlaxElectraForMaskedLM:
                    _snake_case							=   model_class_name.from_pretrained("google/electra-small-generator"					)
               else:
                    _snake_case							=   model_class_name.from_pretrained("google/electra-small-discriminator"					)
               _snake_case							=   model(np.ones((1, 1)					)					)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)


