

"""simple docstring"""





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 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					def __init__(       self			:				str   ,lowercase_			:				List[str]   ,lowercase_			:				Optional[int]=1_4   ,lowercase_			:				Any=7   ,lowercase_			:				List[Any]=True   ,lowercase_			:				Union[str, Any]=True   ,lowercase_			:				Any=True   ,lowercase_			:				Optional[int]=True   ,lowercase_			:				Union[str, Any]=True   ,lowercase_			:				Union[str, Any]=9_9   ,lowercase_			:				Any=3_2   ,lowercase_			:				str=5   ,lowercase_			:				int=4   ,lowercase_			:				str=3_7   ,lowercase_			:				Optional[Any]="gelu"   ,lowercase_			:				Any=0.1   ,lowercase_			:				Any=0.1   ,lowercase_			:				Tuple=5_1_2   ,lowercase_			:				str=1_6   ,lowercase_			:				str=2   ,lowercase_			:				List[Any]=0.02   ,lowercase_			:				int=3   ,lowercase_			:				List[str]=4   ,lowercase_			:				int=None   ,):
						lowerCAmelCase__	:						Union[str, Any]  				=	parent
						lowerCAmelCase__	:						Union[str, Any]  				=	batch_size
						lowerCAmelCase__	:						Optional[Any]  				=	seq_length
						lowerCAmelCase__	:						Any  				=	is_training
						lowerCAmelCase__	:						int  				=	use_token_type_ids
						lowerCAmelCase__	:						Optional[int]  				=	use_input_mask
						lowerCAmelCase__	:						List[str]  				=	use_labels
						lowerCAmelCase__	:						Optional[Any]  				=	use_mc_token_ids
						lowerCAmelCase__	:						List[str]  				=	vocab_size
						lowerCAmelCase__	:						int  				=	hidden_size
						lowerCAmelCase__	:						Optional[int]  				=	num_hidden_layers
						lowerCAmelCase__	:						Tuple  				=	num_attention_heads
						lowerCAmelCase__	:						Tuple  				=	intermediate_size
						lowerCAmelCase__	:						List[str]  				=	hidden_act
						lowerCAmelCase__	:						Optional[Any]  				=	hidden_dropout_prob
						lowerCAmelCase__	:						int  				=	attention_probs_dropout_prob
						lowerCAmelCase__	:						List[Any]  				=	max_position_embeddings
						lowerCAmelCase__	:						Optional[int]  				=	type_vocab_size
						lowerCAmelCase__	:						int  				=	type_sequence_label_size
						lowerCAmelCase__	:						List[str]  				=	initializer_range
						lowerCAmelCase__	:						Optional[Any]  				=	num_labels
						lowerCAmelCase__	:						Optional[int]  				=	num_choices
						lowerCAmelCase__	:						Dict  				=	scope
						lowerCAmelCase__	:						List[Any]  				=	self.vocab_size - 1







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						List[Any]  				=	ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size		)

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

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

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

						lowerCAmelCase__	:						Tuple  				=	None
						lowerCAmelCase__	:						Optional[Any]  				=	None
						lowerCAmelCase__	:						int  				=	None
						if self.use_labels:
							lowerCAmelCase__	:						List[str]  				=	ids_tensor([self.batch_size]   ,self.type_sequence_label_size		)
							lowerCAmelCase__	:						Tuple  				=	ids_tensor([self.batch_size, self.seq_length]   ,self.num_labels		)
							lowerCAmelCase__	:						Tuple  				=	ids_tensor([self.batch_size]   ,self.num_choices		)

						lowerCAmelCase__	:						Optional[int]  				=	self.get_config()

						lowerCAmelCase__	:						Dict  				=	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        __lowerCAmelCase (       self			:				int		):
						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        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				Optional[int]   ,lowercase_			:				Dict   ,lowercase_			:				List[Any]   ,lowercase_			:				Any   ,lowercase_			:				str   ,*lowercase_			:				str		):
						lowerCAmelCase__	:						Tuple  				=	CTRLModel(config=lowercase_		)
						model.to(lowercase_		)
						model.eval()

						model(lowercase_   ,token_type_ids=lowercase_   ,head_mask=lowercase_		)
						model(lowercase_   ,token_type_ids=lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	model(lowercase_		)
						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        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				Dict   ,lowercase_			:				Tuple   ,lowercase_			:				Dict   ,lowercase_			:				List[str]   ,lowercase_			:				Any   ,*lowercase_			:				Optional[Any]		):
						lowerCAmelCase__	:						List[str]  				=	CTRLLMHeadModel(lowercase_		)
						model.to(lowercase_		)
						model.eval()

						lowerCAmelCase__	:						Optional[Any]  				=	model(lowercase_   ,token_type_ids=lowercase_   ,labels=lowercase_		)
						self.parent.assertEqual(result.loss.shape   ,()		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.vocab_size)		)







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						int  				=	self.prepare_config_and_inputs()

						(
						    (
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,
						)	:						Tuple  				=	config_and_inputs

						lowerCAmelCase__	:						Tuple  				=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

						return config, inputs_dict







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				str   ,lowercase_			:				str   ,lowercase_			:				str   ,lowercase_			:				Optional[int]   ,*lowercase_			:				Optional[Any]		):
						lowerCAmelCase__	:						List[str]  				=	self.num_labels
						lowerCAmelCase__	:						Optional[Any]  				=	CTRLForSequenceClassification(lowercase_		)
						model.to(lowercase_		)
						model.eval()
						lowerCAmelCase__	:						Dict  				=	ids_tensor([self.batch_size]   ,self.type_sequence_label_size		)
						lowerCAmelCase__	:						Dict  				=	model(lowercase_   ,token_type_ids=lowercase_   ,labels=lowercase_		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)		)







@require_torch
class 				SCREAMING_SNAKE_CASE					(		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        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				Dict   ,lowercase_			:				Any   ,lowercase_			:				List[str]   ,lowercase_			:				str   ,lowercase_			:				Optional[Any]		):
						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        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						Optional[Any]  				=	CTRLModelTester(self		)
						lowerCAmelCase__	:						Any  				=	ConfigTester(self   ,config_class=lowercase_   ,n_embd=3_7		)







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						super().tearDown()
						# clean-up as much as possible GPU memory occupied by PyTorch
						gc.collect()
						torch.cuda.empty_cache()







					def        __lowerCAmelCase (       self			:				Dict		):
						self.config_tester.run_common_tests()







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_ctrl_model(*lowercase_		)







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Dict  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_lm_head_model(*lowercase_		)







					@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
					def        __lowerCAmelCase (       self			:				List[str]		):
						pass







					@slow
					def        __lowerCAmelCase (       self			:				Any		):
						for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase__	:						int  				=	CTRLModel.from_pretrained(lowercase_		)
							self.assertIsNotNone(lowercase_		)







					@unittest.skip('''The model doesn\'t support left padding'''		)  # and it's not used enough to be worth fixing :)
					def        __lowerCAmelCase (       self			:				Any		):
						pass







@require_torch
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



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







					@slow
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						List[str]  				=	CTRLLMHeadModel.from_pretrained('''ctrl'''		)
						model.to(lowercase_		)
						lowerCAmelCase__	:						str  				=	torch.tensor(
						    [[1_1_8_5_9, 0, 1_6_1_1, 8]]   ,dtype=torch.long   ,device=lowercase_		)  # Legal the president is
						lowerCAmelCase__	:						List[str]  				=	[
						    1_1_8_5_9,
						    0,
						    1_6_1_1,
						    8,
						    5,
						    1_5_0,
						    2_6_4_4_9,
						    2,
						    1_9,
						    3_4_8,
						    4_6_9,
						    3,
						    2_5_9_5,
						    4_8,
						    2_0_7_4_0,
						    2_4_6_5_3_3,
						    2_4_6_5_3_3,
						    1_9,
						    3_0,
						    5,
						]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

						lowerCAmelCase__	:						List[Any]  				=	model.generate(lowercase_   ,do_sample=lowercase_		)
						self.assertListEqual(output_ids[0].tolist()   ,lowercase_		)








'''simple docstring'''




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 	a_					( lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			{}
   lowerCAmelCase     =			tokenizer(example['content']	, truncation=lowerCamelCase				)['input_ids']
   lowerCAmelCase     =			len(example['content']				) / len(output['input_ids']				)
   return output


__snake_case    						=HfArgumentParser(PretokenizationArguments)
__snake_case    						=parser.parse_args()
if args.num_workers is None:
       __snake_case    						=multiprocessing.cpu_count()
__snake_case    						=AutoTokenizer.from_pretrained(args.tokenizer_dir)

__snake_case    						=time.time()
__snake_case    						=load_dataset(args.dataset_name, split="""train""")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__snake_case    						=time.time()
__snake_case    						=ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__snake_case    						=time.time()
ds.push_to_hub(args.tokenized_data_repo)




print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')





