






import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_MAPPING,
		    SegformerForImageClassification,
		    SegformerForSemanticSegmentation,
		    SegformerModel,
		)
		from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import SegformerImageProcessor





class 	snake_case__ (							_lowerCAmelCase				):
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       Optional[int]       						= self.config_class(**self.inputs_dict			)
							self.parent.assertTrue(hasattr(lowerCAmelCase__      ,       """hidden_sizes"""			)			)
							self.parent.assertTrue(hasattr(lowerCAmelCase__      ,       """num_attention_heads"""			)			)
							self.parent.assertTrue(hasattr(lowerCAmelCase__      ,       """num_encoder_blocks"""			)			)





class 	snake_case__ :
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=13      ,       lowerCAmelCase__=64      ,       lowerCAmelCase__=3      ,       lowerCAmelCase__=4      ,       lowerCAmelCase__=[2, 2, 2, 2]      ,       lowerCAmelCase__=[8, 4, 2, 1]      ,       lowerCAmelCase__=[16, 32, 64, 1_28]      ,       lowerCAmelCase__=[1, 4, 8, 16]      ,       lowerCAmelCase__=[1, 2, 4, 8]      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__="gelu"      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=0.0_2      ,       lowerCAmelCase__=3      ,       lowerCAmelCase__=None      ,       )							->				str:
							__magic_name__      :       Union[str, Any]       						= parent
							__magic_name__      :       List[Any]       						= batch_size
							__magic_name__      :       Optional[Any]       						= image_size
							__magic_name__      :       str       						= num_channels
							__magic_name__      :       Tuple       						= num_encoder_blocks
							__magic_name__      :       Dict       						= sr_ratios
							__magic_name__      :       Union[str, Any]       						= depths
							__magic_name__      :       Tuple       						= hidden_sizes
							__magic_name__      :       Dict       						= downsampling_rates
							__magic_name__      :       Dict       						= num_attention_heads
							__magic_name__      :       str       						= is_training
							__magic_name__      :       List[str]       						= use_labels
							__magic_name__      :       Tuple       						= hidden_act
							__magic_name__      :       Optional[Any]       						= hidden_dropout_prob
							__magic_name__      :       List[str]       						= attention_probs_dropout_prob
							__magic_name__      :       Any       						= initializer_range
							__magic_name__      :       Dict       						= num_labels
							__magic_name__      :       Union[str, Any]       						= scope
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       Optional[int]       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

							__magic_name__      :       Union[str, Any]       						= None
							if self.use_labels:
													__magic_name__      :       Dict       						= ids_tensor([self.batch_size, self.image_size, self.image_size]      ,       self.num_labels			)

							__magic_name__      :       Any       						= self.get_config()
							return config, pixel_values, labels
	def       __magic_name__    (    self			)							->				str:
							return SegformerConfig(
							    image_size=self.image_size      ,       num_channels=self.num_channels      ,       num_encoder_blocks=self.num_encoder_blocks      ,       depths=self.depths      ,       hidden_sizes=self.hidden_sizes      ,       num_attention_heads=self.num_attention_heads      ,       hidden_act=self.hidden_act      ,       hidden_dropout_prob=self.hidden_dropout_prob      ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,       initializer_range=self.initializer_range      ,       )
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Dict:
							__magic_name__      :       str       						= SegformerModel(config=lowerCAmelCase__			)
							model.to(lowerCAmelCase__			)
							model.eval()
							__magic_name__      :       List[str]       						= model(lowerCAmelCase__			)
							__magic_name__      :       Any       						= self.image_size // (self.downsampling_rates[-1] * 2)
							self.parent.assertEqual(
							    result.last_hidden_state.shape      ,       (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Optional[Any]:
							__magic_name__      :       List[Any]       						= self.num_labels
							__magic_name__      :       Any       						= SegformerForSemanticSegmentation(lowerCAmelCase__			)
							model.to(lowerCAmelCase__			)
							model.eval()
							__magic_name__      :       Optional[int]       						= model(lowerCAmelCase__			)
							self.parent.assertEqual(
							    result.logits.shape      ,       (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)			)
							__magic_name__      :       int       						= model(lowerCAmelCase__      ,       labels=lowerCAmelCase__			)
							self.parent.assertEqual(
							    result.logits.shape      ,       (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)			)
							self.parent.assertGreater(result.loss      ,       0.0			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Tuple       						= 1
							__magic_name__      :       Union[str, Any]       						= SegformerForSemanticSegmentation(config=lowerCAmelCase__			)
							model.to(lowerCAmelCase__			)
							model.eval()
							__magic_name__      :       Optional[Any]       						= torch.randint(0      ,       1      ,       (self.batch_size, self.image_size, self.image_size)			).to(lowerCAmelCase__			)
							__magic_name__      :       Union[str, Any]       						= model(lowerCAmelCase__      ,       labels=lowerCAmelCase__			)
							self.parent.assertGreater(result.loss      ,       0.0			)







	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Any       						= self.prepare_config_and_inputs()
							__magic_name__						,__magic_name__						,__magic_name__      :       int       						= config_and_inputs
							__magic_name__      :       Dict       						= {"""pixel_values""": pixel_values}
							return config, inputs_dict





@require_torch
class 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					Optional[int]											=   (
	    (
	        SegformerModel,
	        SegformerForSemanticSegmentation,
	        SegformerForImageClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase__						:					Tuple											=   (
	    {
	        '''feature-extraction''': SegformerModel,
	        '''image-classification''': SegformerForImageClassification,
	        '''image-segmentation''': SegformerForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	lowercase__						:					Any											=   True
	lowercase__						:					Any											=   False
	lowercase__						:					Dict											=   False
	lowercase__						:					int											=   False
	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       Optional[Any]       						= SegformerModelTester(self			)
							__magic_name__      :       str       						= SegformerConfigTester(self      ,       config_class=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Any:
							self.config_tester.run_common_tests()
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Dict       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Union[str, Any]       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_binary_image_segmentation(*lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Optional[Any]       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_segmentation(*lowerCAmelCase__			)
	@unittest.skip("""SegFormer does not use inputs_embeds"""			)
	def       __magic_name__    (    self			)							->				List[str]:
							pass
	@unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods"""			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							pass
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__						,__magic_name__      :       List[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													__magic_name__      :       Dict       						= model_class(lowerCAmelCase__			)
													__magic_name__      :       List[Any]       						= inspect.signature(model.forward			)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													__magic_name__      :       Tuple       						= [*signature.parameters.keys()]

													__magic_name__      :       Optional[int]       						= ["""pixel_values"""]
													self.assertListEqual(arg_names[:1]      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				str:
							__magic_name__						,__magic_name__      :       Optional[int]       						= self.model_tester.prepare_config_and_inputs_for_common()
							__magic_name__      :       Dict       						= True

							for model_class in self.all_model_classes:
													__magic_name__      :       Union[str, Any]       						= True
													__magic_name__      :       Optional[int]       						= False
													__magic_name__      :       int       						= True
													__magic_name__      :       List[Any]       						= model_class(lowerCAmelCase__			)
													model.to(lowerCAmelCase__			)
													model.eval()
													with torch.no_grad():
																			__magic_name__      :       List[Any]       						= model(**self._prepare_for_class(lowerCAmelCase__      ,       lowerCAmelCase__			)			)
													__magic_name__      :       List[str]       						= outputs.attentions

													__magic_name__      :       str       						= sum(self.model_tester.depths			)
													self.assertEqual(len(lowerCAmelCase__			)      ,       lowerCAmelCase__			)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													__magic_name__      :       List[Any]       						= True
													__magic_name__      :       Dict       						= model_class(lowerCAmelCase__			)
													model.to(lowerCAmelCase__			)
													model.eval()
													with torch.no_grad():
																			__magic_name__      :       Any       						= model(**self._prepare_for_class(lowerCAmelCase__      ,       lowerCAmelCase__			)			)
													__magic_name__      :       List[Any]       						= outputs.attentions

													self.assertEqual(len(lowerCAmelCase__			)      ,       lowerCAmelCase__			)

													# verify the first attentions (first block, first layer)
													__magic_name__      :       str       						= (self.model_tester.image_size // 4) ** 2
													__magic_name__      :       List[Any]       						= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(attentions[0].shape[-3:]			)      ,       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]      ,       )

													# verify the last attentions (last block, last layer)
													__magic_name__      :       str       						= (self.model_tester.image_size // 32) ** 2
													__magic_name__      :       int       						= (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
													self.assertListEqual(
													    list(attentions[-1].shape[-3:]			)      ,       [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]      ,       )
													__magic_name__      :       Tuple       						= len(lowerCAmelCase__			)

													# Check attention is always last and order is fine
													__magic_name__      :       List[Any]       						= True
													__magic_name__      :       Optional[int]       						= True
													__magic_name__      :       Union[str, Any]       						= model_class(lowerCAmelCase__			)
													model.to(lowerCAmelCase__			)
													model.eval()
													with torch.no_grad():
																			__magic_name__      :       Optional[Any]       						= model(**self._prepare_for_class(lowerCAmelCase__      ,       lowerCAmelCase__			)			)

													self.assertEqual(out_len + 1      ,       len(lowerCAmelCase__			)			)

													__magic_name__      :       Dict       						= outputs.attentions

													self.assertEqual(len(lowerCAmelCase__			)      ,       lowerCAmelCase__			)
													# verify the first attentions (first block, first layer)
													__magic_name__      :       Tuple       						= (self.model_tester.image_size // 4) ** 2
													__magic_name__      :       Dict       						= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]			)      ,       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]      ,       )
	def       __magic_name__    (    self			)							->				Dict:
							def check_hidden_states_output(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			):
													__magic_name__      :       str       						= model_class(lowerCAmelCase__			)
													model.to(lowerCAmelCase__			)
													model.eval()

													with torch.no_grad():
																			__magic_name__      :       Union[str, Any]       						= model(**self._prepare_for_class(lowerCAmelCase__      ,       lowerCAmelCase__			)			)

													__magic_name__      :       List[str]       						= outputs.hidden_states

													__magic_name__      :       Tuple       						= self.model_tester.num_encoder_blocks
													self.assertEqual(len(lowerCAmelCase__			)      ,       lowerCAmelCase__			)

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]			)      ,       [
													        self.model_tester.hidden_sizes[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ]      ,       )

							__magic_name__						,__magic_name__      :       Dict       						= self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													__magic_name__      :       Optional[int]       						= True
													check_hidden_states_output(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													__magic_name__      :       str       						= True

													check_hidden_states_output(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							if not self.model_tester.is_training:
													return

							__magic_name__						,__magic_name__      :       Optional[int]       						= self.model_tester.prepare_config_and_inputs_for_common()
							__magic_name__      :       List[Any]       						= True

							for model_class in self.all_model_classes:
													if model_class in get_values(lowerCAmelCase__			):
																			continue

													__magic_name__      :       int       						= model_class(lowerCAmelCase__			)
													model.to(lowerCAmelCase__			)
													model.train()
													__magic_name__      :       int       						= self._prepare_for_class(lowerCAmelCase__      ,       lowerCAmelCase__      ,       return_labels=lowerCAmelCase__			)
													__magic_name__      :       Union[str, Any]       						= model(**lowerCAmelCase__			).loss
													loss.backward()
	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
	def       __magic_name__    (    self			)							->				Any:
							pass







	@slow
	def       __magic_name__    (    self			)							->				List[Any]:
							for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__magic_name__      :       int       						= SegformerModel.from_pretrained(lowerCAmelCase__			)
													self.assertIsNotNone(lowerCAmelCase__			)



def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       int       						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
						return image





@require_torch
class 	snake_case__ (							unittest.TestCase				):
	@slow
	def       __magic_name__    (    self			)							->				Any:
							# only resize + normalize
							__magic_name__      :       Tuple       						= SegformerImageProcessor(
							    image_scale=(5_12, 5_12)      ,       keep_ratio=lowerCAmelCase__      ,       align=lowerCAmelCase__      ,       do_random_crop=lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""			).to(
							    lowerCAmelCase__			)

							__magic_name__      :       List[str]       						= prepare_img()
							__magic_name__      :       Any       						= image_processor(images=lowerCAmelCase__      ,       return_tensors="""pt"""			)
							__magic_name__      :       Tuple       						= encoded_inputs.pixel_values.to(lowerCAmelCase__			)

							with torch.no_grad():
													__magic_name__      :       List[Any]       						= model(lowerCAmelCase__			)

							__magic_name__      :       int       						= torch.Size((1, model.config.num_labels, 1_28, 1_28)			)
							self.assertEqual(outputs.logits.shape      ,       lowerCAmelCase__			)

							__magic_name__      :       List[str]       						= torch.tensor(
							    [
							        [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
							        [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
							        [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
							    ]			).to(lowerCAmelCase__			)
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]      ,       lowerCAmelCase__      ,       atol=1e-4			)			)
	@slow
	def       __magic_name__    (    self			)							->				Optional[Any]:
							# only resize + normalize
							__magic_name__      :       List[str]       						= SegformerImageProcessor(
							    image_scale=(5_12, 5_12)      ,       keep_ratio=lowerCAmelCase__      ,       align=lowerCAmelCase__      ,       do_random_crop=lowerCAmelCase__			)
							__magic_name__      :       Any       						= SegformerForSemanticSegmentation.from_pretrained(
							    """nvidia/segformer-b1-finetuned-cityscapes-1024-1024"""			).to(lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= prepare_img()
							__magic_name__      :       Tuple       						= image_processor(images=lowerCAmelCase__      ,       return_tensors="""pt"""			)
							__magic_name__      :       int       						= encoded_inputs.pixel_values.to(lowerCAmelCase__			)

							with torch.no_grad():
													__magic_name__      :       Tuple       						= model(lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= torch.Size((1, model.config.num_labels, 1_28, 1_28)			)
							self.assertEqual(outputs.logits.shape      ,       lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= torch.tensor(
							    [
							        [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
							        [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
							        [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
							    ]			).to(lowerCAmelCase__			)
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]      ,       lowerCAmelCase__      ,       atol=1e-1			)			)







	@slow
	def       __magic_name__    (    self			)							->				List[Any]:
							# only resize + normalize
							__magic_name__      :       Optional[Any]       						= SegformerImageProcessor(
							    image_scale=(5_12, 5_12)      ,       keep_ratio=lowerCAmelCase__      ,       align=lowerCAmelCase__      ,       do_random_crop=lowerCAmelCase__			)
							__magic_name__      :       List[Any]       						= SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""			).to(
							    lowerCAmelCase__			)

							__magic_name__      :       Any       						= prepare_img()
							__magic_name__      :       List[str]       						= image_processor(images=lowerCAmelCase__      ,       return_tensors="""pt"""			)
							__magic_name__      :       Union[str, Any]       						= encoded_inputs.pixel_values.to(lowerCAmelCase__			)

							with torch.no_grad():
													__magic_name__      :       List[str]       						= model(lowerCAmelCase__			)

							__magic_name__      :       Optional[int]       						= outputs.logits.detach().cpu()

							__magic_name__      :       Union[str, Any]       						= image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__      ,       target_sizes=[(5_00, 3_00)]			)
							__magic_name__      :       Tuple       						= torch.Size((5_00, 3_00)			)
							self.assertEqual(segmentation[0].shape      ,       lowerCAmelCase__			)

							__magic_name__      :       Optional[int]       						= image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= torch.Size((1_28, 1_28)			)
							self.assertEqual(segmentation[0].shape      ,       lowerCAmelCase__			)








import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
__magic_name__:					str							=     logging.getLogger(__name__)

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       int       						= argparse.ArgumentParser(
						    description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."""			)
						parser.add_argument("""--file_path""",   type=_A,   default="""data/dump.txt""",   help="""The path to the data."""			)
						parser.add_argument("""--tokenizer_type""",   type=_A,   default="""bert""",   choices=["""bert""", """roberta""", """gpt2"""]			)
						parser.add_argument("""--tokenizer_name""",   type=_A,   default="""bert-base-uncased""",   help="""The tokenizer to use."""			)
						parser.add_argument("""--dump_file""",   type=_A,   default="""data/dump""",   help="""The dump file prefix."""			)
						__magic_name__      :       Dict       						= parser.parse_args()

						logger.info(f'Loading Tokenizer ({args.tokenizer_name})'			)
						if args.tokenizer_type == "bert":
												__magic_name__      :       Tuple       						= BertTokenizer.from_pretrained(args.tokenizer_name			)
												__magic_name__      :       List[Any]       						= tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
												__magic_name__      :       Optional[int]       						= tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
						elif args.tokenizer_type == "roberta":
												__magic_name__      :       Optional[int]       						= RobertaTokenizer.from_pretrained(args.tokenizer_name			)
												__magic_name__      :       List[Any]       						= tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
												__magic_name__      :       Any       						= tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
						elif args.tokenizer_type == "gpt2":
												__magic_name__      :       Any       						= GPTaTokenizer.from_pretrained(args.tokenizer_name			)
												__magic_name__      :       int       						= tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
												__magic_name__      :       Optional[int]       						= tokenizer.special_tokens_map["""eos_token"""]  # `<|endoftext|>`

						logger.info(f'Loading text from {args.file_path}'			)
						with open(args.file_path,   """r""",   encoding="""utf8"""			) as fp:
												__magic_name__      :       Tuple       						= fp.readlines()

						logger.info("""Start encoding"""			)
						logger.info(f'{len(_A			)} examples to process.'			)

						__magic_name__      :       List[Any]       						= []
						__magic_name__      :       str       						= 0
						__magic_name__      :       str       						= 10000
						__magic_name__      :       Dict       						= time.time()
						for text in data:
												__magic_name__      :       Tuple       						= f'{bos} {text.strip()} {sep}'
												__magic_name__      :       Optional[int]       						= tokenizer.encode(_A,   add_special_tokens=_A			)
												rslt.append(_A			)

												iter += 1
												if iter % interval == 0:
																		__magic_name__      :       Union[str, Any]       						= time.time()
																		logger.info(f'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'			)
																		__magic_name__      :       Any       						= time.time()
						logger.info("""Finished binarization"""			)
						logger.info(f'{len(_A			)} examples processed.'			)

						__magic_name__      :       Tuple       						= f'{args.dump_file}.{args.tokenizer_name}.pickle'
						__magic_name__      :       Tuple       						= tokenizer.vocab_size
						if vocab_size < (1 << 16):
												__magic_name__      :       Optional[int]       						= [np.uintaa(_A			) for d in rslt]
						else:
												__magic_name__      :       str       						= [np.intaa(_A			) for d in rslt]
						random.shuffle(rslt_			)
						logger.info(f'Dump to {dp_file}'			)
						with open(_A,   """wb"""			) as handle:
												pickle.dump(rslt_,   _A,   protocol=pickle.HIGHEST_PROTOCOL			)


if __name__ == "__main__":
		main()
