
"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertConfig,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertWithLMHeadModel,
    )





class 				a			:



      def __init__(   self     ,	UpperCamelCase_     ,	):
          UpperCAmelCase__	:      Any     							=					parent
          UpperCAmelCase__	:      Tuple     							=					13
          UpperCAmelCase__	:      str     							=					7
          UpperCAmelCase__	:      List[str]     							=					True
          UpperCAmelCase__	:      Tuple     							=					True
          UpperCAmelCase__	:      Union[str, Any]     							=					True
          UpperCAmelCase__	:      str     							=					True
          UpperCAmelCase__	:      Optional[int]     							=					True
          UpperCAmelCase__	:      List[str]     							=					False
          UpperCAmelCase__	:      int     							=					False
          UpperCAmelCase__	:      Tuple     							=					False
          UpperCAmelCase__	:      Tuple     							=					2
          UpperCAmelCase__	:      Union[str, Any]     							=					99
          UpperCAmelCase__	:      Tuple     							=					0
          UpperCAmelCase__	:      Optional[int]     							=					32
          UpperCAmelCase__	:      Optional[int]     							=					2
          UpperCAmelCase__	:      Optional[Any]     							=					4
          UpperCAmelCase__	:      Optional[int]     							=					0.1
          UpperCAmelCase__	:      List[Any]     							=					0.1
          UpperCAmelCase__	:      str     							=					512
          UpperCAmelCase__	:      str     							=					16
          UpperCAmelCase__	:      Any     							=					2
          UpperCAmelCase__	:      Optional[int]     							=					0.02
          UpperCAmelCase__	:      Optional[Any]     							=					3
          UpperCAmelCase__	:      Optional[Any]     							=					4
          UpperCAmelCase__	:      Any     							=					'last'
          UpperCAmelCase__	:      str     							=					True
          UpperCAmelCase__	:      List[str]     							=					None
          UpperCAmelCase__	:      Union[str, Any]     							=					0



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size      )
          UpperCAmelCase__	:      List[Any]     							=					random_attention_mask([self.batch_size, self.seq_length]     ,	dtype=tf.floataa      )

          UpperCAmelCase__	:      int     							=					None
          if self.use_input_lengths:
              UpperCAmelCase__	:      Dict     							=					(
                  ids_tensor([self.batch_size]     ,	vocab_size=2      ) + self.seq_length - 2
              )  # small variation of seq_length

          UpperCAmelCase__	:      Optional[int]     							=					None
          if self.use_token_type_ids:
              UpperCAmelCase__	:      Union[str, Any]     							=					ids_tensor([self.batch_size, self.seq_length]     ,	self.n_langs      )

          UpperCAmelCase__	:      int     							=					None
          UpperCAmelCase__	:      str     							=					None
          UpperCAmelCase__	:      Union[str, Any]     							=					None
          if self.use_labels:
              UpperCAmelCase__	:      List[Any]     							=					ids_tensor([self.batch_size]     ,	self.type_sequence_label_size      )
              UpperCAmelCase__	:      Optional[Any]     							=					ids_tensor([self.batch_size, self.seq_length]     ,	self.num_labels      )
              UpperCAmelCase__	:      List[str]     							=					ids_tensor([self.batch_size]     ,	2     ,	dtype=tf.floataa      )
              UpperCAmelCase__	:      str     							=					ids_tensor([self.batch_size]     ,	self.num_choices      )

          UpperCAmelCase__	:      int     							=					FlaubertConfig(
              vocab_size=self.vocab_size     ,	n_special=self.n_special     ,	emb_dim=self.hidden_size     ,	n_layers=self.num_hidden_layers     ,	n_heads=self.num_attention_heads     ,	dropout=self.hidden_dropout_prob     ,	attention_dropout=self.attention_probs_dropout_prob     ,	gelu_activation=self.gelu_activation     ,	sinusoidal_embeddings=self.sinusoidal_embeddings     ,	asm=self.asm     ,	causal=self.causal     ,	n_langs=self.n_langs     ,	max_position_embeddings=self.max_position_embeddings     ,	initializer_range=self.initializer_range     ,	summary_type=self.summary_type     ,	use_proj=self.use_proj     ,	bos_token_id=self.bos_token_id     ,	)

          return (
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              choice_labels,
              input_mask,
          )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	):
          UpperCAmelCase__	:      int     							=					TFFlaubertModel(config=UpperCamelCase_      )
          UpperCAmelCase__	:      List[str]     							=					{'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
          UpperCAmelCase__	:      Optional[int]     							=					model(UpperCamelCase_      )

          UpperCAmelCase__	:      int     							=					[input_ids, input_mask]
          UpperCAmelCase__	:      Tuple     							=					model(UpperCamelCase_      )
          self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	):
          UpperCAmelCase__	:      Dict     							=					TFFlaubertWithLMHeadModel(UpperCamelCase_      )

          UpperCAmelCase__	:      Dict     							=					{'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
          UpperCAmelCase__	:      Optional[Any]     							=					model(UpperCamelCase_      )

          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.vocab_size)      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	):
          UpperCAmelCase__	:      List[Any]     							=					TFFlaubertForQuestionAnsweringSimple(UpperCamelCase_      )

          UpperCAmelCase__	:      Any     							=					{'input_ids': input_ids, 'lengths': input_lengths}

          UpperCAmelCase__	:      str     							=					model(UpperCamelCase_      )

          self.parent.assertEqual(result.start_logits.shape     ,	(self.batch_size, self.seq_length)      )
          self.parent.assertEqual(result.end_logits.shape     ,	(self.batch_size, self.seq_length)      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	):
          UpperCAmelCase__	:      Optional[Any]     							=					TFFlaubertForSequenceClassification(UpperCamelCase_      )

          UpperCAmelCase__	:      Optional[int]     							=					{'input_ids': input_ids, 'lengths': input_lengths}

          UpperCAmelCase__	:      Optional[int]     							=					model(UpperCamelCase_      )

          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	):
          UpperCAmelCase__	:      Optional[Any]     							=					self.num_labels
          UpperCAmelCase__	:      Dict     							=					TFFlaubertForTokenClassification(config=UpperCamelCase_      )
          UpperCAmelCase__	:      List[str]     							=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
          UpperCAmelCase__	:      int     							=					model(UpperCamelCase_      )
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.num_labels)      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	):
          UpperCAmelCase__	:      Tuple     							=					self.num_choices
          UpperCAmelCase__	:      Any     							=					TFFlaubertForMultipleChoice(config=UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					tf.tile(tf.expand_dims(UpperCamelCase_     ,	1      )     ,	(1, self.num_choices, 1)      )
          UpperCAmelCase__	:      str     							=					tf.tile(tf.expand_dims(UpperCamelCase_     ,	1      )     ,	(1, self.num_choices, 1)      )
          UpperCAmelCase__	:      List[Any]     							=					tf.tile(tf.expand_dims(UpperCamelCase_     ,	1      )     ,	(1, self.num_choices, 1)      )
          UpperCAmelCase__	:      List[Any]     							=					{
              'input_ids': multiple_choice_inputs_ids,
              'attention_mask': multiple_choice_input_mask,
              'token_type_ids': multiple_choice_token_type_ids,
          }
          UpperCAmelCase__	:      str     							=					model(UpperCamelCase_      )
          self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_choices)      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase__
          )							,							(
              UpperCAmelCase__
          )							,							(
              UpperCAmelCase__
          )							,							(
              UpperCAmelCase__
          )							,							(
              UpperCAmelCase__
          )							,							(
              UpperCAmelCase__
          )							,							(
              UpperCAmelCase__
          )							,							(
              UpperCAmelCase__
          )							,							(
              UpperCAmelCase__
          )							,							
          )	:      str     							=					config_and_inputs
          UpperCAmelCase__	:      List[str]     							=					{
              'input_ids': input_ids,
              'token_type_ids': token_type_ids,
              'langs': token_type_ids,
              'lengths': input_lengths,
          }
          return config, inputs_dict





@require_tf
class 				a			(					lowercase						,		lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Dict           =     (
          (
              TFFlaubertModel,
              TFFlaubertWithLMHeadModel,
              TFFlaubertForSequenceClassification,
              TFFlaubertForQuestionAnsweringSimple,
              TFFlaubertForTokenClassification,
              TFFlaubertForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      UpperCamelCase							:   Optional[int]           =     (
          (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      UpperCamelCase							:   Dict           =     (
          {
              """feature-extraction""": TFFlaubertModel,
              """fill-mask""": TFFlaubertWithLMHeadModel,
              """question-answering""": TFFlaubertForQuestionAnsweringSimple,
              """text-classification""": TFFlaubertForSequenceClassification,
              """token-classification""": TFFlaubertForTokenClassification,
              """zero-shot""": TFFlaubertForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      UpperCamelCase							:   Optional[Any]           =     False
      UpperCamelCase							:   Optional[Any]           =     False



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          if (
              pipeline_test_casse_name == "QAPipelineTests"
              and tokenizer_name is not None
              and not tokenizer_name.endswith('Fast'      )
          ):
              # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
              # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
              # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
              return True

          return False



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					TFFlaubertModelTester(self      )
          UpperCAmelCase__	:      Tuple     							=					ConfigTester(self     ,	config_class=UpperCamelCase_     ,	emb_dim=37      )



      def  __snake_case							(   self      ):
          self.config_tester.run_common_tests()



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Any     							=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_model(*UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_lm_head(*UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_qa(*UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_sequence_classif(*UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_for_token_classification(*UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_for_multiple_choice(*UpperCamelCase_      )



      @slow
      def  __snake_case							(   self      ):
          for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase__	:      List[Any]     							=					TFFlaubertModel.from_pretrained(UpperCamelCase_      )
              self.assertIsNotNone(UpperCamelCase_      )





@require_tf
@require_sentencepiece
@require_tokenizers
class 				a			(					unittest.TestCase      ):



      @slow
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[Any]     							=					TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased'      )

          UpperCAmelCase__	:      Union[str, Any]     							=					tf.convert_to_tensor(
              [[0, 158, 735, 2_592, 1_424, 6_727, 82, 1]]     ,	dtype=tf.intaa     ,	)  # "J'aime flaubert !"

          UpperCAmelCase__	:      Any     							=					model(UpperCamelCase_      )[0]
          UpperCAmelCase__	:      Union[str, Any]     							=					tf.TensorShape((1, 8, 512)      )
          self.assertEqual(output.shape     ,	UpperCamelCase_      )
          # compare the actual values for a slice.
          UpperCAmelCase__	:      Any     							=					tf.convert_to_tensor(
              [
                  [
                      [-1.8768773, -1.566555, 0.27072418],
                      [-1.6920038, -0.5873505, 1.9329599],
                      [-2.9563985, -1.6993835, 1.7972052],
                  ]
              ]     ,	dtype=tf.floataa     ,	)

          self.assertTrue(np.allclose(output[:, :3, :3].numpy()     ,	expected_slice.numpy()     ,	atol=1E-4      )      )



"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCamelCase__          =	logging.get_logger(__name__)

UpperCamelCase__          =	{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__          =	{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

UpperCamelCase__          =	{
    'junnyu/roformer_chinese_small': 15_36,
    'junnyu/roformer_chinese_base': 15_36,
    'junnyu/roformer_chinese_char_small': 5_12,
    'junnyu/roformer_chinese_char_base': 5_12,
    'junnyu/roformer_small_discriminator': 1_28,
    'junnyu/roformer_small_generator': 1_28,
}


UpperCamelCase__          =	{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}





class 				a			(					lowercase      ):
      UpperCamelCase							:   int           =     VOCAB_FILES_NAMES
      UpperCamelCase							:   Dict           =     PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase							:   Union[str, Any]           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase							:   Tuple           =     PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase							:   Optional[int]           =     RoFormerTokenizer



      def __init__(   self     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	UpperCamelCase_=True     ,	UpperCamelCase_="[UNK]"     ,	UpperCamelCase_="[SEP]"     ,	UpperCamelCase_="[PAD]"     ,	UpperCamelCase_="[CLS]"     ,	UpperCamelCase_="[MASK]"     ,	UpperCamelCase_=True     ,	UpperCamelCase_=None     ,	**UpperCamelCase_     ,	):
          super().__init__(
              UpperCamelCase_     ,	tokenizer_file=UpperCamelCase_     ,	do_lower_case=UpperCamelCase_     ,	unk_token=UpperCamelCase_     ,	sep_token=UpperCamelCase_     ,	pad_token=UpperCamelCase_     ,	cls_token=UpperCamelCase_     ,	mask_token=UpperCamelCase_     ,	tokenize_chinese_chars=UpperCamelCase_     ,	strip_accents=UpperCamelCase_     ,	**UpperCamelCase_     ,	)

          UpperCAmelCase__	:      Union[str, Any]     							=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
          if (
              pre_tok_state.get('lowercase'     ,	UpperCamelCase_      ) != do_lower_case
              or pre_tok_state.get('strip_accents'     ,	UpperCamelCase_      ) != strip_accents
          ):
              UpperCAmelCase__	:      Any     							=					getattr(UpperCamelCase_     ,	pre_tok_state.pop('type'      )      )
              UpperCAmelCase__	:      str     							=					do_lower_case
              UpperCAmelCase__	:      Union[str, Any]     							=					strip_accents
              UpperCAmelCase__	:      Dict     							=					pre_tok_class(**UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					do_lower_case



      def __getstate__(   self      ):
          UpperCAmelCase__	:      int     							=					self.__dict__.copy()
          UpperCAmelCase__	:      int     							=					BertPreTokenizer()
          return state



      def __setstate__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					d
          UpperCAmelCase__	:      List[str]     							=					self.__dict__['_tokenizer'].get_vocab()
          UpperCAmelCase__	:      List[Any]     							=					PreTokenizer.custom(JiebaPreTokenizer(UpperCamelCase_      )      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=None      ):
          UpperCAmelCase__	:      List[Any]     							=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

          return output



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          UpperCAmelCase__	:      int     							=					[self.sep_token_id]
          UpperCAmelCase__	:      Dict     							=					[self.cls_token_id]
          if token_ids_a is None:
              return len(cls + token_ids_a + sep      ) * [0]
          return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          UpperCAmelCase__	:      Any     							=					self._tokenizer.model.save(UpperCamelCase_     ,	name=UpperCamelCase_      )
          return tuple(UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	UpperCamelCase_=False     ,	**UpperCamelCase_     ,	):
          UpperCAmelCase__	:      int     							=					BertPreTokenizer()
          return super().save_pretrained(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      )

