


import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
           CTRLForSequenceClassification,
           CTRLLMHeadModel,
           CTRLModel,
       )

class       __magic_name__	:


     def __init__(      self							:     Any							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Union[str, Any]=14							,	lowerCamelCase__							:     int=7							,	lowerCamelCase__							:     str=True							,	lowerCamelCase__							:     str=True							,	lowerCamelCase__							:     Dict=True							,	lowerCamelCase__							:     int=True							,	lowerCamelCase__							:     List[str]=True							,	lowerCamelCase__							:     List[str]=99							,	lowerCamelCase__							:     List[str]=32							,	lowerCamelCase__							:     Union[str, Any]=5							,	lowerCamelCase__							:     Any=4							,	lowerCamelCase__							:     str=37							,	lowerCamelCase__							:     Optional[Any]="gelu"							,	lowerCamelCase__							:     Optional[Any]=0.1							,	lowerCamelCase__							:     str=0.1							,	lowerCamelCase__							:     Union[str, Any]=512							,	lowerCamelCase__							:     Any=16							,	lowerCamelCase__							:     Optional[int]=2							,	lowerCamelCase__							:     int=0.02							,	lowerCamelCase__							:     Tuple=3							,	lowerCamelCase__							:     Tuple=4							,	lowerCamelCase__							:     List[str]=None							,	)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   parent
      UpperCamelCase__					:    Tuple          =   batch_size
      UpperCamelCase__					:    Dict          =   seq_length
      UpperCamelCase__					:    Dict          =   is_training
      UpperCamelCase__					:    List[str]          =   use_token_type_ids
      UpperCamelCase__					:    Union[str, Any]          =   use_input_mask
      UpperCamelCase__					:    Union[str, Any]          =   use_labels
      UpperCamelCase__					:    Union[str, Any]          =   use_mc_token_ids
      UpperCamelCase__					:    Any          =   vocab_size
      UpperCamelCase__					:    int          =   hidden_size
      UpperCamelCase__					:    int          =   num_hidden_layers
      UpperCamelCase__					:    Dict          =   num_attention_heads
      UpperCamelCase__					:    Any          =   intermediate_size
      UpperCamelCase__					:    Union[str, Any]          =   hidden_act
      UpperCamelCase__					:    List[Any]          =   hidden_dropout_prob
      UpperCamelCase__					:    List[Any]          =   attention_probs_dropout_prob
      UpperCamelCase__					:    List[Any]          =   max_position_embeddings
      UpperCamelCase__					:    Optional[int]          =   type_vocab_size
      UpperCamelCase__					:    Tuple          =   type_sequence_label_size
      UpperCamelCase__					:    int          =   initializer_range
      UpperCamelCase__					:    Optional[Any]          =   num_labels
      UpperCamelCase__					:    int          =   num_choices
      UpperCamelCase__					:    Optional[Any]          =   scope
      UpperCamelCase__					:    Dict          =   self.vocab_size - 1


     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size			)

      UpperCamelCase__					:    Optional[int]          =   None
      if self.use_input_mask:
       UpperCamelCase__					:    List[str]          =   random_attention_mask([self.batch_size, self.seq_length]			)

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

      UpperCamelCase__					:    Optional[int]          =   None
      if self.use_mc_token_ids:
       UpperCamelCase__					:    Dict          =   ids_tensor([self.batch_size, self.num_choices]							,	self.seq_length			)

      UpperCamelCase__					:    Dict          =   None
      UpperCamelCase__					:    str          =   None
      UpperCamelCase__					:    List[Any]          =   None
      if self.use_labels:
       UpperCamelCase__					:    str          =   ids_tensor([self.batch_size]							,	self.type_sequence_label_size			)
       UpperCamelCase__					:    List[Any]          =   ids_tensor([self.batch_size, self.seq_length]							,	self.num_labels			)
       UpperCamelCase__					:    Dict          =   ids_tensor([self.batch_size]							,	self.num_choices			)

      UpperCamelCase__					:    Optional[Any]          =   self.get_config()

      UpperCamelCase__					:    Optional[int]          =   ids_tensor([self.num_hidden_layers, self.num_attention_heads]							,	2			)

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
          token_type_ids,
          mc_token_ids,
          sequence_labels,
          token_labels,
          choice_labels,
      )


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      List[str]:





      '''simple docstring'''






      return CTRLConfig(
          vocab_size=self.vocab_size							,	n_embd=self.hidden_size							,	n_layer=self.num_hidden_layers							,	n_head=self.num_attention_heads							,	n_positions=self.max_position_embeddings							,	pad_token_id=self.pad_token_id							,	)


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





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   CTRLModel(config=lowerCamelCase__			)
      model.to(lowerCamelCase__			)
      model.eval()

      model(lowerCamelCase__							,	token_type_ids=lowerCamelCase__							,	head_mask=lowerCamelCase__			)
      model(lowerCamelCase__							,	token_type_ids=lowerCamelCase__			)
      UpperCamelCase__					:    Optional[Any]          =   model(lowerCamelCase__			)
      self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size)			)
      self.parent.assertEqual(len(result.past_key_values			)							,	config.n_layer			)


     def      UpperCAmelCase__	(      self							:     Tuple							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     List[str]							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     List[Any]							,	*lowerCamelCase__							:     Tuple			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   CTRLLMHeadModel(lowerCamelCase__			)
      model.to(lowerCamelCase__			)
      model.eval()

      UpperCamelCase__					:    Tuple          =   model(lowerCamelCase__							,	token_type_ids=lowerCamelCase__							,	labels=lowerCamelCase__			)
      self.parent.assertEqual(result.loss.shape							,	()			)
      self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.seq_length, self.vocab_size)			)


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   self.prepare_config_and_inputs()

      (
          (
          UpperCamelCase__
      )       ,				(
          UpperCamelCase__
      )       ,				(
          UpperCamelCase__
      )       ,				(
          UpperCamelCase__
      )       ,				(
          UpperCamelCase__
      )       ,				(
          UpperCamelCase__
      )       ,				(
          UpperCamelCase__
      )       ,				(
          UpperCamelCase__
      )       ,				(
          UpperCamelCase__
      )       ,				
      )					:    List[Any]          =   config_and_inputs

      UpperCamelCase__					:    Optional[Any]          =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

      return config, inputs_dict


     def      UpperCAmelCase__	(      self							:     List[str]							,	lowerCamelCase__							:     List[str]							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     List[Any]							,	*lowerCamelCase__							:     List[str]			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   self.num_labels
      UpperCamelCase__					:    List[Any]          =   CTRLForSequenceClassification(lowerCamelCase__			)
      model.to(lowerCamelCase__			)
      model.eval()
      UpperCamelCase__					:    Tuple          =   ids_tensor([self.batch_size]							,	self.type_sequence_label_size			)
      UpperCamelCase__					:    int          =   model(lowerCamelCase__							,	token_type_ids=lowerCamelCase__							,	labels=lowerCamelCase__			)
      self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_labels)			)



@require_torch
class       __magic_name__	(     __lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					,				unittest.TestCase):
     A:		Any			=      (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
     A:		str			=      (CTRLLMHeadModel,) if is_torch_available() else ()
     A:		Any			=      (
         {
             "feature-extraction": CTRLModel,
             "text-classification": CTRLForSequenceClassification,
             "text-generation": CTRLLMHeadModel,
             "zero-shot": CTRLForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     A:		Optional[int]			=      True
     A:		List[Any]			=      False
     A:		Optional[Any]			=      False


     def      UpperCAmelCase__	(      self							:     List[str]							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Any							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     Any			)  ->      List[Any]:





      '''simple docstring'''






      if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
       # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
       # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
       # config could not be created.
       return True

      return False


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   CTRLModelTester(self			)
      UpperCamelCase__					:    Tuple          =   ConfigTester(self							,	config_class=lowerCamelCase__							,	n_embd=37			)


     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      int:





      '''simple docstring'''






      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      torch.cuda.empty_cache()


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      str:





      '''simple docstring'''






      self.config_tester.run_common_tests()


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_model(*lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_lm_head_model(*lowerCamelCase__			)


     @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''			)
     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      str:





      '''simple docstring'''






      pass


     @slow
     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      List[str]:





      '''simple docstring'''






      for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCamelCase__					:    Union[str, Any]          =   CTRLModel.from_pretrained(lowerCamelCase__			)
       self.assertIsNotNone(lowerCamelCase__			)


     @unittest.skip('''The model doesn\'t support left padding'''			)  # and it's not used enough to be worth fixing :)
     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Tuple:





      '''simple docstring'''






      pass



@require_torch
class       __magic_name__	(     unittest.TestCase):


     def      UpperCAmelCase__	(      self							:     str			)  ->      List[Any]:





      '''simple docstring'''






      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      torch.cuda.empty_cache()


     @slow
     def      UpperCAmelCase__	(      self							:     int			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   CTRLLMHeadModel.from_pretrained('''ctrl'''			)
      model.to(lowerCamelCase__			)
      UpperCamelCase__					:    List[str]          =   torch.tensor(
          [[11859, 0, 1611, 8]]							,	dtype=torch.long							,	device=lowerCamelCase__			)  # Legal the president is
      UpperCamelCase__					:    List[Any]          =   [
          11859,
          0,
          1611,
          8,
          5,
          150,
          26449,
          2,
          19,
          348,
          469,
          3,
          2595,
          48,
          20740,
          246533,
          246533,
          19,
          30,
          5,
      ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

      UpperCamelCase__					:    Tuple          =   model.generate(lowerCamelCase__							,	do_sample=lowerCamelCase__			)
      self.assertListEqual(output_ids[0].tolist()							,	lowerCamelCase__			)






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class       __magic_name__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		Optional[int]			=      BarthezTokenizer
     A:		Optional[int]			=      BarthezTokenizerFast
     A:		str			=      True
     A:		Optional[int]			=      True


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      str:





      '''simple docstring'''






      super().setUp()

      UpperCamelCase__					:    Optional[Any]          =   BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''			)
      tokenizer.save_pretrained(self.tmpdirname			)
      tokenizer.save_pretrained(self.tmpdirname							,	legacy_format=lowerCamelCase__			)
      UpperCamelCase__					:    int          =   tokenizer


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





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   '''<pad>'''
      UpperCamelCase__					:    List[str]          =   1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__			)							,	lowerCamelCase__			)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__			)							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     int			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   list(self.get_tokenizer().get_vocab().keys()			)

      self.assertEqual(vocab_keys[0]							,	'''<s>'''			)
      self.assertEqual(vocab_keys[1]							,	'''<pad>'''			)
      self.assertEqual(vocab_keys[-1]							,	'''<mask>'''			)
      self.assertEqual(len(lowerCamelCase__			)							,	101122			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      str:





      '''simple docstring'''






      self.assertEqual(self.get_tokenizer().vocab_size							,	101122			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      UpperCamelCase__					:    Any          =   [0, 57, 3018, 70307, 91, 2]

      UpperCamelCase__					:    Optional[int]          =   self.tokenizer(
          lowerCamelCase__							,	max_length=len(lowerCamelCase__			)							,	padding=lowerCamelCase__							,	truncation=lowerCamelCase__							,	return_tensors='''pt'''			)
      self.assertIsInstance(lowerCamelCase__							,	lowerCamelCase__			)

      self.assertEqual((2, 6)							,	batch.input_ids.shape			)
      self.assertEqual((2, 6)							,	batch.attention_mask.shape			)
      UpperCamelCase__					:    List[Any]          =   batch.input_ids.tolist()[0]
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      List[Any]:





      '''simple docstring'''






      if not self.test_rust_tokenizer:
       return

      UpperCamelCase__					:    List[Any]          =   self.get_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   self.get_rust_tokenizer()

      UpperCamelCase__					:    Any          =   '''I was born in 92000, and this is falsé.'''

      UpperCamelCase__					:    List[str]          =   tokenizer.tokenize(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   rust_tokenizer.tokenize(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   self.get_rust_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   tokenizer.encode(lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     @slow
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   {'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
      # fmt: on

      # moussaKam/mbarthez is a french model. So we also use french texts.
      UpperCamelCase__					:    int          =   [
          '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
          '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
          '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
          '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
          '''telles que la traduction et la synthèse de texte.''',
      ]

      self.tokenizer_integration_test_util(
          expected_encoding=lowerCamelCase__							,	model_name='''moussaKam/mbarthez'''							,	revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''							,	sequences=lowerCamelCase__							,	)


