from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, 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 tensorflow as tf

		from transformers import (
		    TFConvBertForMaskedLM,
		    TFConvBertForMultipleChoice,
		    TFConvBertForQuestionAnswering,
		    TFConvBertForSequenceClassification,
		    TFConvBertForTokenClassification,
		    TFConvBertModel,
		)






class 		a__						:

					def __init__(    self      :       Optional[int],_A      :       Dict,_A      :       List[str]=13,_A      :       List[str]=7,_A      :       int=True,_A      :       str=True,_A      :       Union[str, Any]=True,_A      :       Tuple=True,_A      :       Dict=99,_A      :       Tuple=32,_A      :       Tuple=2,_A      :       Tuple=4,_A      :       Optional[Any]=37,_A      :       str="gelu",_A      :       Dict=0.1,_A      :       List[Any]=0.1,_A      :       List[str]=512,_A      :       str=16,_A      :       int=2,_A      :       Dict=0.02,_A      :       List[Any]=3,_A      :       Optional[Any]=4,_A      :       Optional[int]=None,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    parent
							SCREAMING_SNAKE_CASE_  :		Any				      =    13
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    7
							SCREAMING_SNAKE_CASE_  :		Dict				      =    True
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    True
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    True
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    True
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    99
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    384
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    2
							SCREAMING_SNAKE_CASE_  :		Any				      =    4
							SCREAMING_SNAKE_CASE_  :		str				      =    37
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    "gelu"
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    0.1
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    0.1
							SCREAMING_SNAKE_CASE_  :		Dict				      =    512
							SCREAMING_SNAKE_CASE_  :		int				      =    16
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    2
							SCREAMING_SNAKE_CASE_  :		Any				      =    0.02
							SCREAMING_SNAKE_CASE_  :		str				      =    3
							SCREAMING_SNAKE_CASE_  :		int				      =    4
							SCREAMING_SNAKE_CASE_  :		Dict				      =    128
							SCREAMING_SNAKE_CASE_  :		Any				      =    2
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    9
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    1
							SCREAMING_SNAKE_CASE_  :		Any				      =    None

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    ids_tensor([self.batch_size, self.seq_length],self.vocab_size    )

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

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

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

							SCREAMING_SNAKE_CASE_  :		Any				      =    ConvBertConfig(
							    vocab_size=self.vocab_size,hidden_size=self.hidden_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_dict=_A,)

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       List[Any],_A      :       int,_A      :       Tuple,_A      :       Optional[int],_A      :       Union[str, Any],_A      :       Union[str, Any],_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    TFConvBertModel(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

							SCREAMING_SNAKE_CASE_  :		str				      =    [input_ids, input_mask]
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    model(_A    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    model(_A    )

							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)    )

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Dict,_A      :       int,_A      :       Union[str, Any],_A      :       List[Any],_A      :       int,_A      :       str,_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    TFConvBertForMaskedLM(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    model(_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)    )

					def 				__UpperCamelCase   (    self      :       Any,_A      :       Optional[int],_A      :       List[Any],_A      :       Union[str, Any],_A      :       List[Any],_A      :       Union[str, Any],_A      :       Optional[int],_A      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.num_labels
							SCREAMING_SNAKE_CASE_  :		Any				      =    TFConvBertForSequenceClassification(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    model(_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       int,_A      :       Dict,_A      :       List[str],_A      :       Tuple,_A      :       Dict,_A      :       Optional[int],_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.num_choices
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    TFConvBertForMultipleChoice(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    tf.tile(tf.expand_dims(_A,1    ),(1, self.num_choices, 1)    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    tf.tile(tf.expand_dims(_A,1    ),(1, self.num_choices, 1)    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tf.tile(tf.expand_dims(_A,1    ),(1, self.num_choices, 1)    )
							SCREAMING_SNAKE_CASE_  :		int				      =    {
							    "input_ids": multiple_choice_inputs_ids,
							    "attention_mask": multiple_choice_input_mask,
							    "token_type_ids": multiple_choice_token_type_ids,
							}
							SCREAMING_SNAKE_CASE_  :		int				      =    model(_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)    )

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       Union[str, Any],_A      :       int,_A      :       Optional[int],_A      :       str,_A      :       str,_A      :       Tuple,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.num_labels
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    TFConvBertForTokenClassification(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							SCREAMING_SNAKE_CASE_  :		str				      =    model(_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)    )

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       int,_A      :       List[str],_A      :       List[Any],_A      :       Any,_A      :       Optional[int],_A      :       List[str],_A      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    TFConvBertForQuestionAnswering(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							SCREAMING_SNAKE_CASE_  :		Any				      =    model(_A    )
							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 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    self.prepare_config_and_inputs()
							(
							    (
							    SCREAMING_SNAKE_CASE_
							)      ,    (
							    SCREAMING_SNAKE_CASE_
							)      ,    (
							    SCREAMING_SNAKE_CASE_
							)      ,    (
							    SCREAMING_SNAKE_CASE_
							)      ,    (
							    SCREAMING_SNAKE_CASE_
							)      ,    (
							    SCREAMING_SNAKE_CASE_
							)      ,    (
							    SCREAMING_SNAKE_CASE_
							)      ,    
							)  :		List[Any]				      =    config_and_inputs
							SCREAMING_SNAKE_CASE_  :		Any				      =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict







@require_tf
class 		a__						(					A__    ,		A__    ,		unittest.TestCase					):
					A	       =							(
					    (
					        TFConvBertModel,
					        TFConvBertForMaskedLM,
					        TFConvBertForQuestionAnswering,
					        TFConvBertForSequenceClassification,
					        TFConvBertForTokenClassification,
					        TFConvBertForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					A	       =							(
					    {
					        'feature-extraction': TFConvBertModel,
					        'fill-mask': TFConvBertForMaskedLM,
					        'question-answering': TFConvBertForQuestionAnswering,
					        'text-classification': TFConvBertForSequenceClassification,
					        'token-classification': TFConvBertForTokenClassification,
					        'zero-shot': TFConvBertForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					A	       =							False
					A	       =							False
					A	       =							False

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    TFConvBertModelTester(self    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    ConfigTester(self,config_class=_A,hidden_size=37    )

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							self.config_tester.run_common_tests()

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A    )

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_A    )

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*_A    )

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_A    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_A    )

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_A    )

					@slow
					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.model_tester.prepare_config_and_inputs_for_common()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    True
							SCREAMING_SNAKE_CASE_  :		Any				      =    True

							if hasattr(_A,"use_cache"    ):
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    True

							SCREAMING_SNAKE_CASE_  :		int				      =    getattr(self.model_tester,"encoder_seq_length",self.model_tester.seq_length    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    getattr(self.model_tester,"key_length",_A    )

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    self._prepare_for_class(_A,_A    )
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    model_class(_A    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    len(model(_A    )    )

									with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(_A,saved_model=_A    )
											SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    os.path.join(_A,"saved_model","1"    )
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    tf.keras.models.load_model(_A    )
											SCREAMING_SNAKE_CASE_  :		str				      =    model(_A    )

											if self.is_encoder_decoder:
													SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    outputs["encoder_hidden_states"]
													SCREAMING_SNAKE_CASE_  :		str				      =    outputs["encoder_attentions"]
											else:
													SCREAMING_SNAKE_CASE_  :		Any				      =    outputs["hidden_states"]
													SCREAMING_SNAKE_CASE_  :		List[str]				      =    outputs["attentions"]

											self.assertEqual(len(_A    ),_A    )

											SCREAMING_SNAKE_CASE_  :		Any				      =    getattr(
											    self.model_tester,"expected_num_hidden_layers",self.model_tester.num_hidden_layers + 1    )

											self.assertEqual(len(_A    ),_A    )
											self.assertListEqual(
											    list(output_hidden_states[0].shape[-2:]    ),[self.model_tester.seq_length, self.model_tester.hidden_size],)

											self.assertEqual(len(_A    ),self.model_tester.num_hidden_layers    )
											self.assertListEqual(
											    list(output_attentions[0].shape[-3:]    ),[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length],)

					@slow
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    TFConvBertModel.from_pretrained("YituTech/conv-bert-base"    )
							self.assertIsNotNone(_A    )






					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Any				      =    self.model_tester.prepare_config_and_inputs_for_common()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    True
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    getattr(self.model_tester,"decoder_seq_length",self.model_tester.seq_length    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    getattr(self.model_tester,"encoder_seq_length",self.model_tester.seq_length    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    getattr(self.model_tester,"key_length",_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    getattr(self.model_tester,"key_length",_A    )

							def check_decoder_attentions_output(_A      :       Dict    ):
									SCREAMING_SNAKE_CASE_  :		int				      =    len(_A    )
									self.assertEqual(out_len % 2,0    )
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    outputs.decoder_attentions
									self.assertEqual(len(_A    ),self.model_tester.num_hidden_layers    )
									self.assertListEqual(
									    list(decoder_attentions[0].shape[-3:]    ),[self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length],)

							def check_encoder_attentions_output(_A      :       Tuple    ):
									SCREAMING_SNAKE_CASE_  :		int				      =    [
									    t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
									]
									self.assertEqual(len(_A    ),self.model_tester.num_hidden_layers    )
									self.assertListEqual(
									    list(attentions[0].shape[-3:]    ),[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length],)

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    True
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    False
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    model_class(_A    )
									SCREAMING_SNAKE_CASE_  :		Any				      =    model(self._prepare_for_class(_A,_A    )    )
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    len(_A    )
									self.assertEqual(config.output_hidden_states,_A    )
									check_encoder_attentions_output(_A    )

									if self.is_encoder_decoder:
											SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    model_class(_A    )
											SCREAMING_SNAKE_CASE_  :		int				      =    model(self._prepare_for_class(_A,_A    )    )
											self.assertEqual(config.output_hidden_states,_A    )
											check_decoder_attentions_output(_A    )

									# Check that output attentions can also be changed via the config
									del inputs_dict["output_attentions"]
									SCREAMING_SNAKE_CASE_  :		str				      =    True
									SCREAMING_SNAKE_CASE_  :		int				      =    model_class(_A    )
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    model(self._prepare_for_class(_A,_A    )    )
									self.assertEqual(config.output_hidden_states,_A    )
									check_encoder_attentions_output(_A    )

									# Check attention is always last and order is fine
									SCREAMING_SNAKE_CASE_  :		str				      =    True
									SCREAMING_SNAKE_CASE_  :		int				      =    True
									SCREAMING_SNAKE_CASE_  :		Dict				      =    model_class(_A    )
									SCREAMING_SNAKE_CASE_  :		str				      =    model(self._prepare_for_class(_A,_A    )    )

									self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1),len(_A    )    )
									self.assertEqual(model.config.output_hidden_states,_A    )
									check_encoder_attentions_output(_A    )







@require_tf
class 		a__						(					unittest.TestCase					):

					@slow
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    TFConvBertModel.from_pretrained("YituTech/conv-bert-base"    )
							SCREAMING_SNAKE_CASE_  :		int				      =    tf.constant([[0, 1, 2, 3, 4, 5]]    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    model(_A    )[0]

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [1, 6, 768]
							self.assertEqual(output.shape,_A    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tf.constant(
							    [
							        [
							            [-0.03475493, -0.4686034, -0.30638832],
							            [0.22637248, -0.26988646, -0.7423424],
							            [0.10324868, -0.45013508, -0.58280784],
							        ]
							    ]    )
							tf.debugging.assert_near(output[:, :3, :3],_A,atol=1E-4    )



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    {}
							snake_case_							=    tokenizer(example['content']   ,							truncation=UpperCamelCase__      )['input_ids']
							snake_case_							=    len(example['content']      ) / len(output['input_ids']      )
							return output


_UpperCAmelCase		:   Dict										=       HfArgumentParser(PretokenizationArguments)
_UpperCAmelCase		:   List[Any]										=       parser.parse_args()
if args.num_workers is None:
					_UpperCAmelCase		:   Union[str, Any]										=       multiprocessing.cpu_count()
_UpperCAmelCase		:   int										=       AutoTokenizer.from_pretrained(args.tokenizer_dir)

_UpperCAmelCase		:   Optional[int]										=       time.time()
_UpperCAmelCase		:   List[str]										=       load_dataset(args.dataset_name, split="""train""")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

_UpperCAmelCase		:   Tuple										=       time.time()
_UpperCAmelCase		:   Union[str, Any]										=       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''')

_UpperCAmelCase		:   Dict										=       time.time()
ds.push_to_hub(args.tokenized_data_repo)


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




