






from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

A__	:			List[str]			   = _symbol_database.Default()


A__	:			int			   = _descriptor_pool.Default().AddSerializedFile(
    b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)

A__	:			Any			   = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
							A__	:			List[Any]			   = None
							A__	:			int			   = b'H\003'
							# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
							# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
							# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
							# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
							# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
							A__	:			Any			   = 45
							A__	:			List[str]			   = 15_81
							A__	:			Optional[Any]			   = 15_17
							A__	:			Any			   = 15_70
							A__	:			List[Any]			   = 15_84
							A__	:			Dict			   = 17_93
							A__	:			int			   = 17_95
							A__	:			int			   = 19_16
							A__	:			List[Any]			   = 18_64
							A__	:			Optional[Any]			   = 19_05
							A__	:			List[str]			   = 19_19
							A__	:			int			   = 24_29
							A__	:			Union[str, Any]			   = 22_08
							A__	:			int			   = 24_18
							A__	:			List[Any]			   = 23_23
							A__	:			str			   = 24_07
# @@protoc_insertion_point(module_scope)












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       _UpperCAmelCase					:

				"""simple docstring"""




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

								'''simple docstring'''
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			seq_length
								lowercase__    =			is_training
								lowercase__    =			use_token_type_ids
								lowercase__    =			use_input_mask
								lowercase__    =			use_labels
								lowercase__    =			use_mc_token_ids
								lowercase__    =			vocab_size
								lowercase__    =			hidden_size
								lowercase__    =			num_hidden_layers
								lowercase__    =			num_attention_heads
								lowercase__    =			intermediate_size
								lowercase__    =			hidden_act
								lowercase__    =			hidden_dropout_prob
								lowercase__    =			attention_probs_dropout_prob
								lowercase__    =			max_position_embeddings
								lowercase__    =			type_vocab_size
								lowercase__    =			type_sequence_label_size
								lowercase__    =			initializer_range
								lowercase__    =			num_labels
								lowercase__    =			num_choices
								lowercase__    =			scope
								lowercase__    =			self.vocab_size - 1




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )

								lowercase__    =			None
								if self.use_input_mask:
												lowercase__    =			random_attention_mask([self.batch_size, self.seq_length]  )

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

								lowercase__    =			None
								if self.use_mc_token_ids:
												lowercase__    =			ids_tensor([self.batch_size, self.num_choices],  self.seq_length  )

								lowercase__    =			None
								lowercase__    =			None
								lowercase__    =			None
								if self.use_labels:
												lowercase__    =			ids_tensor([self.batch_size],  self.type_sequence_label_size  )
												lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.num_labels  )
												lowercase__    =			ids_tensor([self.batch_size],  self.num_choices  )

								lowercase__    =			self.get_config()

								lowercase__    =			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 				lowercase__   (     self						:    Optional[int]  ):

								'''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 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    Any,  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Any,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    Union[str, Any],  *lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			CTRLModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								model(lowerCamelCase,  token_type_ids=lowerCamelCase,  head_mask=lowerCamelCase  )
								model(lowerCamelCase,  token_type_ids=lowerCamelCase  )
								lowercase__    =			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 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Optional[int],  *lowerCamelCase						:    Any  ):

								'''simple docstring'''
								lowercase__    =			CTRLLMHeadModel(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								lowercase__    =			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 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			self.prepare_config_and_inputs()

								(
								    (
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						
								)    =			config_and_inputs

								lowercase__    =			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

								return config, inputs_dict




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    int,  lowerCamelCase						:    Any,  *lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.num_labels
								lowercase__    =			CTRLForSequenceClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			ids_tensor([self.batch_size],  self.type_sequence_label_size  )
								lowercase__    =			model(lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)  )



@require_torch
class       _UpperCAmelCase					(       A__		,A__		,A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
				lowercase__											=       (CTRLLMHeadModel,) if is_torch_available() else ()
				lowercase__											=       (
				    {
				        """feature-extraction""": CTRLModel,
				        """text-classification""": CTRLForSequenceClassification,
				        """text-generation""": CTRLLMHeadModel,
				        """zero-shot""": CTRLForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowercase__											=       True
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str,  lowerCamelCase						:    Any,  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[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 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			CTRLModelTester(self  )
								lowercase__    =			ConfigTester(self,  config_class=lowerCamelCase,  n_embd=37  )




				def 				lowercase__   (     self						:    Tuple  ):

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




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_model(*lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								lowercase__    =			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 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								pass




				@slow
				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__    =			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 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								pass



@require_torch
class       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    Any  ):

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




				@slow
				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			CTRLLMHeadModel.from_pretrained('''ctrl'''  )
								model.to(lowerCamelCase  )
								lowercase__    =			torch.tensor(
								    [[11_859, 0, 1_611, 8]],  dtype=torch.long,  device=lowerCamelCase  )  # Legal the president is
								lowercase__    =			[
								    11_859,
								    0,
								    1_611,
								    8,
								    5,
								    150,
								    26_449,
								    2,
								    19,
								    348,
								    469,
								    3,
								    2_595,
								    48,
								    20_740,
								    246_533,
								    246_533,
								    19,
								    30,
								    5,
								]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

								lowercase__    =			model.generate(lowerCamelCase,  do_sample=lowerCamelCase  )
								self.assertListEqual(output_ids[0].tolist(),  lowerCamelCase  )




