






from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


__magic_name__:					List[str]							=     logging.get_logger(__name__)

# General docstring
__magic_name__:					List[str]							=     '''ResNetConfig'''

# Base docstring
__magic_name__:					Any							=     '''microsoft/resnet-50'''
__magic_name__:					List[Any]							=     [1, 2_048, 7, 7]

# Image classification docstring
__magic_name__:					Union[str, Any]							=     '''microsoft/resnet-50'''
__magic_name__:					Tuple							=     '''tiger cat'''

__magic_name__:					Optional[int]							=     [
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]





class 	snake_case__ (							nn.Module				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = 3      ,       lowerCAmelCase__ = 1      ,       lowerCAmelCase__ = "relu"			)							->				str:
							super().__init__()
							__magic_name__      :       int       						= nn.Convad(
							    _SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE      ,       kernel_size=_SCREAMING_SNAKE_CASE      ,       stride=_SCREAMING_SNAKE_CASE      ,       padding=kernel_size // 2      ,       bias=_SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Union[str, Any]       						= nn.BatchNormad(_SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Dict       						= ACTaFN[activation] if activation is not None else nn.Identity()







	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Tensor:
							__magic_name__      :       List[str]       						= self.convolution(_SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Optional[Any]       						= self.normalization(_SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Dict       						= self.activation(_SCREAMING_SNAKE_CASE			)
							return hidden_state





class 	snake_case__ (							nn.Module				):
	def __init__(    self      ,       lowerCAmelCase__			)							->				Optional[Any]:
							super().__init__()
							__magic_name__      :       Dict       						= ResNetConvLayer(
							    config.num_channels      ,       config.embedding_size      ,       kernel_size=7      ,       stride=2      ,       activation=config.hidden_act			)
							__magic_name__      :       str       						= nn.MaxPoolad(kernel_size=3      ,       stride=2      ,       padding=1			)
							__magic_name__      :       List[str]       						= config.num_channels







	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Tensor:
							__magic_name__      :       Optional[Any]       						= pixel_values.shape[1]
							if num_channels != self.num_channels:
													raise ValueError(
													    """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""			)
							__magic_name__      :       Dict       						= self.embedder(_SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Dict       						= self.pooler(_SCREAMING_SNAKE_CASE			)
							return embedding





class 	snake_case__ (							nn.Module				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = 2			)							->				List[Any]:
							super().__init__()
							__magic_name__      :       Union[str, Any]       						= nn.Convad(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE      ,       kernel_size=1      ,       stride=_SCREAMING_SNAKE_CASE      ,       bias=_SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Union[str, Any]       						= nn.BatchNormad(_SCREAMING_SNAKE_CASE			)







	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Tensor:
							__magic_name__      :       List[str]       						= self.convolution(_SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Dict       						= self.normalization(_SCREAMING_SNAKE_CASE			)
							return hidden_state





class 	snake_case__ (							nn.Module				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = 1      ,       lowerCAmelCase__ = "relu"			)							->				Tuple:
							super().__init__()
							__magic_name__      :       Optional[Any]       						= in_channels != out_channels or stride != 1
							__magic_name__      :       Tuple       						= (
							    ResNetShortCut(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE      ,       stride=_SCREAMING_SNAKE_CASE			) if should_apply_shortcut else nn.Identity()
							)
							__magic_name__      :       Optional[int]       						= nn.Sequential(
							    ResNetConvLayer(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE      ,       stride=_SCREAMING_SNAKE_CASE			)      ,       ResNetConvLayer(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE      ,       activation=_SCREAMING_SNAKE_CASE			)      ,       )
							__magic_name__      :       Any       						= ACTaFN[activation]







	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				int:
							__magic_name__      :       Union[str, Any]       						= hidden_state
							__magic_name__      :       Union[str, Any]       						= self.layer(_SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Dict       						= self.shortcut(_SCREAMING_SNAKE_CASE			)
							hidden_state += residual
							__magic_name__      :       Dict       						= self.activation(_SCREAMING_SNAKE_CASE			)
							return hidden_state





class 	snake_case__ (							nn.Module				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = 1      ,       lowerCAmelCase__ = "relu"      ,       lowerCAmelCase__ = 4			)							->				Dict:
							super().__init__()
							__magic_name__      :       Optional[Any]       						= in_channels != out_channels or stride != 1
							__magic_name__      :       Tuple       						= out_channels // reduction
							__magic_name__      :       Tuple       						= (
							    ResNetShortCut(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE      ,       stride=_SCREAMING_SNAKE_CASE			) if should_apply_shortcut else nn.Identity()
							)
							__magic_name__      :       List[str]       						= nn.Sequential(
							    ResNetConvLayer(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE      ,       kernel_size=1			)      ,       ResNetConvLayer(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE      ,       stride=_SCREAMING_SNAKE_CASE			)      ,       ResNetConvLayer(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE      ,       kernel_size=1      ,       activation=_SCREAMING_SNAKE_CASE			)      ,       )
							__magic_name__      :       str       						= ACTaFN[activation]







	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				int:
							__magic_name__      :       int       						= hidden_state
							__magic_name__      :       Optional[Any]       						= self.layer(_SCREAMING_SNAKE_CASE			)
							__magic_name__      :       List[str]       						= self.shortcut(_SCREAMING_SNAKE_CASE			)
							hidden_state += residual
							__magic_name__      :       Dict       						= self.activation(_SCREAMING_SNAKE_CASE			)
							return hidden_state





class 	snake_case__ (							nn.Module				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = 2      ,       lowerCAmelCase__ = 2      ,       )							->				Tuple:
							super().__init__()

							__magic_name__      :       List[str]       						= ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer

							__magic_name__      :       List[Any]       						= nn.Sequential(
							    # downsampling is done in the first layer with stride of 2
							    layer(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE      ,       stride=_SCREAMING_SNAKE_CASE      ,       activation=config.hidden_act			)      ,       *[layer(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE      ,       activation=config.hidden_act			) for _ in range(depth - 1			)]      ,       )







	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Tensor:
							__magic_name__      :       str       						= input
							for layer in self.layers:
													__magic_name__      :       Optional[Any]       						= layer(_SCREAMING_SNAKE_CASE			)
							return hidden_state





class 	snake_case__ (							nn.Module				):
	def __init__(    self      ,       lowerCAmelCase__			)							->				Dict:
							super().__init__()
							__magic_name__      :       Optional[Any]       						= nn.ModuleList([]			)
							# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
							self.stages.append(
							    ResNetStage(
							        _SCREAMING_SNAKE_CASE      ,       config.embedding_size      ,       config.hidden_sizes[0]      ,       stride=2 if config.downsample_in_first_stage else 1      ,       depth=config.depths[0]      ,       )			)
							__magic_name__      :       str       						= zip(config.hidden_sizes      ,       config.hidden_sizes[1:]			)
							for (in_channels, out_channels), depth in zip(_SCREAMING_SNAKE_CASE      ,       config.depths[1:]			):
													self.stages.append(ResNetStage(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE      ,       depth=_SCREAMING_SNAKE_CASE			)			)







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = True			)							->				BaseModelOutputWithNoAttention:
							__magic_name__      :       int       						= () if output_hidden_states else None

							for stage_module in self.stages:
													if output_hidden_states:
																			__magic_name__      :       Tuple       						= hidden_states + (hidden_state,)

													__magic_name__      :       Optional[Any]       						= stage_module(_SCREAMING_SNAKE_CASE			)

							if output_hidden_states:
													__magic_name__      :       Optional[int]       						= hidden_states + (hidden_state,)

							if not return_dict:
													return tuple(v for v in [hidden_state, hidden_states] if v is not None			)

							return BaseModelOutputWithNoAttention(
							    last_hidden_state=_SCREAMING_SNAKE_CASE      ,       hidden_states=_SCREAMING_SNAKE_CASE      ,       )





class 	snake_case__ (							SCREAMING_SNAKE_CASE__				):
	lowercase__						:					Tuple											=   ResNetConfig
	lowercase__						:					List[Any]											=   'resnet'
	lowercase__						:					Dict											=   'pixel_values'
	lowercase__						:					Optional[int]											=   True
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Optional[int]:
							if isinstance(_SCREAMING_SNAKE_CASE      ,       nn.Convad			):
													nn.init.kaiming_normal_(module.weight      ,       mode="""fan_out"""      ,       nonlinearity="""relu"""			)
							elif isinstance(_SCREAMING_SNAKE_CASE      ,       (nn.BatchNormad, nn.GroupNorm)			):
													nn.init.constant_(module.weight      ,       1			)
													nn.init.constant_(module.bias      ,       0			)







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=False			)							->				Optional[int]:
							if isinstance(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE			):
													__magic_name__      :       List[Any]       						= value



__magic_name__:					Optional[int]							=     r'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

__magic_name__:					Any							=     r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''





@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''						,  SCREAMING_SNAKE_CASE__						,  )
class 	snake_case__ (							SCREAMING_SNAKE_CASE__				):
	def __init__(    self      ,       lowerCAmelCase__			)							->				Optional[Any]:
							super().__init__(_SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Tuple       						= config
							__magic_name__      :       Optional[int]       						= ResNetEmbeddings(_SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Optional[int]       						= ResNetEncoder(_SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Optional[Any]       						= nn.AdaptiveAvgPoolad((1, 1)			)
							# Initialize weights and apply final processing
							self.post_init()







	@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE			)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC      ,       output_type=_SCREAMING_SNAKE_CASE      ,       config_class=_CONFIG_FOR_DOC      ,       modality="""vision"""      ,       expected_output=_EXPECTED_OUTPUT_SHAPE      ,       )
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None			)							->				BaseModelOutputWithPoolingAndNoAttention:
							__magic_name__      :       Optional[Any]       						= (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							__magic_name__      :       Tuple       						= return_dict if return_dict is not None else self.config.use_return_dict

							__magic_name__      :       Any       						= self.embedder(_SCREAMING_SNAKE_CASE			)

							__magic_name__      :       Tuple       						= self.encoder(
							    _SCREAMING_SNAKE_CASE      ,       output_hidden_states=_SCREAMING_SNAKE_CASE      ,       return_dict=_SCREAMING_SNAKE_CASE			)

							__magic_name__      :       Any       						= encoder_outputs[0]

							__magic_name__      :       str       						= self.pooler(_SCREAMING_SNAKE_CASE			)

							if not return_dict:
													return (last_hidden_state, pooled_output) + encoder_outputs[1:]

							return BaseModelOutputWithPoolingAndNoAttention(
							    last_hidden_state=_SCREAMING_SNAKE_CASE      ,       pooler_output=_SCREAMING_SNAKE_CASE      ,       hidden_states=encoder_outputs.hidden_states      ,       )





@add_start_docstrings(
    '''\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '''						,  SCREAMING_SNAKE_CASE__						,  )
class 	snake_case__ (							SCREAMING_SNAKE_CASE__				):
	def __init__(    self      ,       lowerCAmelCase__			)							->				Dict:
							super().__init__(_SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Union[str, Any]       						= config.num_labels
							__magic_name__      :       Union[str, Any]       						= ResNetModel(_SCREAMING_SNAKE_CASE			)
							# classification head
							__magic_name__      :       Any       						= nn.Sequential(
							    nn.Flatten()      ,       nn.Linear(config.hidden_sizes[-1]      ,       config.num_labels			) if config.num_labels > 0 else nn.Identity()      ,       )
							# initialize weights and apply final processing
							self.post_init()







	@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE			)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT      ,       output_type=_SCREAMING_SNAKE_CASE      ,       config_class=_CONFIG_FOR_DOC      ,       expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,       )
	def       __magic_name__    (    self      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None      ,       )							->				ImageClassifierOutputWithNoAttention:
							__magic_name__      :       Tuple       						= return_dict if return_dict is not None else self.config.use_return_dict

							__magic_name__      :       Any       						= self.resnet(_SCREAMING_SNAKE_CASE      ,       output_hidden_states=_SCREAMING_SNAKE_CASE      ,       return_dict=_SCREAMING_SNAKE_CASE			)

							__magic_name__      :       Dict       						= outputs.pooler_output if return_dict else outputs[1]

							__magic_name__      :       str       						= self.classifier(_SCREAMING_SNAKE_CASE			)

							__magic_name__      :       Dict       						= None

							if labels is not None:
													if self.config.problem_type is None:
																			if self.num_labels == 1:
																									__magic_name__      :       Tuple       						= "regression"
																			elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																									__magic_name__      :       Tuple       						= "single_label_classification"
																			else:
																									__magic_name__      :       int       						= "multi_label_classification"
													if self.config.problem_type == "regression":
																			__magic_name__      :       str       						= MSELoss()
																			if self.num_labels == 1:
																									__magic_name__      :       Union[str, Any]       						= loss_fct(logits.squeeze()      ,       labels.squeeze()			)
																			else:
																									__magic_name__      :       int       						= loss_fct(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE			)
													elif self.config.problem_type == "single_label_classification":
																			__magic_name__      :       Optional[Any]       						= CrossEntropyLoss()
																			__magic_name__      :       int       						= loss_fct(logits.view(-1      ,       self.num_labels			)      ,       labels.view(-1			)			)
													elif self.config.problem_type == "multi_label_classification":
																			__magic_name__      :       Union[str, Any]       						= BCEWithLogitsLoss()
																			__magic_name__      :       Tuple       						= loss_fct(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE			)

							if not return_dict:
													__magic_name__      :       List[str]       						= (logits,) + outputs[2:]
													return (loss,) + output if loss is not None else output

							return ImageClassifierOutputWithNoAttention(loss=_SCREAMING_SNAKE_CASE      ,       logits=_SCREAMING_SNAKE_CASE      ,       hidden_states=outputs.hidden_states			)





@add_start_docstrings(
    '''\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    '''						,  SCREAMING_SNAKE_CASE__						,  )
class 	snake_case__ (							SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__				):
	def __init__(    self      ,       lowerCAmelCase__			)							->				int:
							super().__init__(_SCREAMING_SNAKE_CASE			)
							super()._init_backbone(_SCREAMING_SNAKE_CASE			)

							__magic_name__      :       str       						= [config.embedding_size] + config.hidden_sizes
							__magic_name__      :       List[Any]       						= ResNetEmbeddings(_SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Dict       						= ResNetEncoder(_SCREAMING_SNAKE_CASE			)

							# initialize weights and apply final processing
							self.post_init()







	@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE			)
	@replace_return_docstrings(output_type=_SCREAMING_SNAKE_CASE      ,       config_class=_CONFIG_FOR_DOC			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None			)							->				BackboneOutput:
							__magic_name__      :       List[str]       						= return_dict if return_dict is not None else self.config.use_return_dict
							__magic_name__      :       Dict       						= (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)

							__magic_name__      :       Union[str, Any]       						= self.embedder(_SCREAMING_SNAKE_CASE			)

							__magic_name__      :       List[Any]       						= self.encoder(_SCREAMING_SNAKE_CASE      ,       output_hidden_states=_SCREAMING_SNAKE_CASE      ,       return_dict=_SCREAMING_SNAKE_CASE			)

							__magic_name__      :       List[Any]       						= outputs.hidden_states

							__magic_name__      :       Any       						= ()
							for idx, stage in enumerate(self.stage_names			):
													if stage in self.out_features:
																			feature_maps += (hidden_states[idx],)

							if not return_dict:
													__magic_name__      :       Union[str, Any]       						= (feature_maps,)
													if output_hidden_states:
																			output += (outputs.hidden_states,)
													return output

							return BackboneOutput(
							    feature_maps=_SCREAMING_SNAKE_CASE      ,       hidden_states=outputs.hidden_states if output_hidden_states else None      ,       attentions=_SCREAMING_SNAKE_CASE      ,       )








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()
