






'''simple docstring'''




import collections.abc
from typing import Optional, Tuple, Union

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

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


__lowerCamelCase   :					int   							=					logging.get_logger(__name__)

# General docstring
__lowerCamelCase   :					str   							=					'''PoolFormerConfig'''

# Base docstring
__lowerCamelCase   :					Tuple   							=					'''sail/poolformer_s12'''
__lowerCamelCase   :					int   							=					[1, 512, 7, 7]

# Image classification docstring
__lowerCamelCase   :					Any   							=					'''sail/poolformer_s12'''
__lowerCamelCase   :					Any   							=					'''tabby, tabby cat'''

__lowerCamelCase   :					Any   							=					[
    '''sail/poolformer_s12''',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__ = 0.0     ,__magic_name__ = False						)->    str:



			"""simple docstring"""



			if drop_prob == 0.0 or not training:
						return input
			snake_case_				:					List[Any]   =       1 - drop_prob
			snake_case_				:					Union[str, Any]   =       (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
			snake_case_				:					Any   =       keep_prob + torch.rand(__magic_name__     ,dtype=input.dtype     ,device=input.device						)
			random_tensor.floor_()  # binarize
			snake_case_				:					Dict   =       input.div(__magic_name__						) * random_tensor
			return output







class    A_	(nn.Module							):
							"""simple docstring"""




							def __init__(		self			:List[str]       ,							lowerCAmelCase__			:Optional[float] = None				)				->  None:


										'''simple docstring'''




										super().__init__()
										snake_case_				:					Optional[int]   =       drop_prob

							def 							_A	(		self			:List[str]       ,							lowerCAmelCase__			:torch.Tensor				)				->  torch.Tensor:


										'''simple docstring'''




										return drop_path(lowerCAmelCase__       ,							self.drop_prob       ,							self.training				)






							def 							_A	(		self			:Optional[Any]				)				->  str:


										'''simple docstring'''




										return "p={}".format(self.drop_prob				)







class    A_	(nn.Module							):
							"""simple docstring"""




							def __init__(		self			:int       ,							lowerCAmelCase__			:Dict       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:Dict       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Tuple=None				)				->  Any:


										'''simple docstring'''




										super().__init__()
										snake_case_				:					List[Any]   =       patch_size if isinstance(lowerCAmelCase__       ,							collections.abc.Iterable				) else (patch_size, patch_size)
										snake_case_				:					Union[str, Any]   =       stride if isinstance(lowerCAmelCase__       ,							collections.abc.Iterable				) else (stride, stride)
										snake_case_				:					Dict   =       padding if isinstance(lowerCAmelCase__       ,							collections.abc.Iterable				) else (padding, padding)

										snake_case_				:					Union[str, Any]   =       nn.Convad(lowerCAmelCase__       ,							lowerCAmelCase__       ,							kernel_size=lowerCAmelCase__       ,							stride=lowerCAmelCase__       ,							padding=lowerCAmelCase__				)
										snake_case_				:					Optional[Any]   =       norm_layer(lowerCAmelCase__				) if norm_layer else nn.Identity()






							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:int				)				->  str:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       self.projection(lowerCAmelCase__				)
										snake_case_				:					Dict   =       self.norm(lowerCAmelCase__				)
										return embeddings







class    A_	(nn.GroupNorm							):
							"""simple docstring"""




							def __init__(		self			:Optional[Any]       ,							lowerCAmelCase__			:List[Any]       ,							**lowerCAmelCase__			:Optional[Any]				)				->  str:


										'''simple docstring'''




										super().__init__(1       ,							lowerCAmelCase__       ,							**lowerCAmelCase__				)







class    A_	(nn.Module							):
							"""simple docstring"""




							def __init__(		self			:List[Any]       ,							lowerCAmelCase__			:Dict				)				->  int:


										'''simple docstring'''




										super().__init__()
										snake_case_				:					Any   =       nn.AvgPoolad(lowerCAmelCase__       ,							stride=1       ,							padding=pool_size // 2       ,							count_include_pad=lowerCAmelCase__				)






							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Optional[int]				)				->  Optional[int]:


										'''simple docstring'''




										return self.pool(lowerCAmelCase__				) - hidden_states







class    A_	(nn.Module							):
							"""simple docstring"""




							def __init__(		self			:str       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:Any       ,							lowerCAmelCase__			:Tuple				)				->  Tuple:


										'''simple docstring'''




										super().__init__()
										snake_case_				:					int   =       nn.Convad(lowerCAmelCase__       ,							lowerCAmelCase__       ,							1				)
										snake_case_				:					Any   =       nn.Convad(lowerCAmelCase__       ,							lowerCAmelCase__       ,							1				)
										snake_case_				:					Optional[Any]   =       PoolFormerDropPath(lowerCAmelCase__				)
										if isinstance(config.hidden_act       ,							lowerCAmelCase__				):
													snake_case_				:					List[str]   =       ACTaFN[config.hidden_act]
										else:
													snake_case_				:					int   =       config.hidden_act






							def 							_A	(		self			:Optional[Any]       ,							lowerCAmelCase__			:int				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					Any   =       self.conva(lowerCAmelCase__				)
										snake_case_				:					Dict   =       self.act_fn(lowerCAmelCase__				)
										snake_case_				:					Optional[int]   =       self.drop(lowerCAmelCase__				)
										snake_case_				:					Union[str, Any]   =       self.conva(lowerCAmelCase__				)
										snake_case_				:					int   =       self.drop(lowerCAmelCase__				)

										return hidden_states







class    A_	(nn.Module							):
							"""simple docstring"""




							def __init__(		self			:Any       ,							lowerCAmelCase__			:List[Any]       ,							lowerCAmelCase__			:List[Any]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Dict       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:Union[str, Any]				)				->  List[Any]:


										'''simple docstring'''




										super().__init__()
										snake_case_				:					str   =       PoolFormerPooling(lowerCAmelCase__				)
										snake_case_				:					Dict   =       PoolFormerOutput(lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__				)
										snake_case_				:					Any   =       PoolFormerGroupNorm(lowerCAmelCase__				)
										snake_case_				:					Any   =       PoolFormerGroupNorm(lowerCAmelCase__				)

										# Useful for training neural nets
										snake_case_				:					Optional[int]   =       PoolFormerDropPath(lowerCAmelCase__				) if drop_path > 0.0 else nn.Identity()
										snake_case_				:					Any   =       config.use_layer_scale
										if config.use_layer_scale:
													snake_case_				:					Union[str, Any]   =       nn.Parameter(
													    config.layer_scale_init_value * torch.ones((lowerCAmelCase__)				)       ,							requires_grad=lowerCAmelCase__				)
													snake_case_				:					Optional[int]   =       nn.Parameter(
													    config.layer_scale_init_value * torch.ones((lowerCAmelCase__)				)       ,							requires_grad=lowerCAmelCase__				)






							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:Any				)				->  List[Any]:


										'''simple docstring'''




										if self.use_layer_scale:
													snake_case_				:					str   =       self.pooling(self.before_norm(lowerCAmelCase__				)				)
													snake_case_				:					Tuple   =       self.layer_scale_a.unsqueeze(-1				).unsqueeze(-1				) * pooling_output
													# First residual connection
													snake_case_				:					Tuple   =       hidden_states + self.drop_path(lowerCAmelCase__				)
													snake_case_				:					str   =       ()

													snake_case_				:					Optional[int]   =       self.output(self.after_norm(lowerCAmelCase__				)				)
													snake_case_				:					str   =       self.layer_scale_a.unsqueeze(-1				).unsqueeze(-1				) * layer_output
													# Second residual connection
													snake_case_				:					Dict   =       hidden_states + self.drop_path(lowerCAmelCase__				)

													snake_case_				:					Dict   =       (output,) + outputs
													return outputs

										else:
													snake_case_				:					List[str]   =       self.drop_path(self.pooling(self.before_norm(lowerCAmelCase__				)				)				)
													# First residual connection
													snake_case_				:					Optional[int]   =       pooling_output + hidden_states
													snake_case_				:					Union[str, Any]   =       ()

													# Second residual connection inside the PoolFormerOutput block
													snake_case_				:					Tuple   =       self.drop_path(self.output(self.after_norm(lowerCAmelCase__				)				)				)
													snake_case_				:					Optional[int]   =       hidden_states + layer_output

													snake_case_				:					List[Any]   =       (output,) + outputs
													return outputs







class    A_	(nn.Module							):
							"""simple docstring"""




							def __init__(		self			:Union[str, Any]       ,							lowerCAmelCase__			:int				)				->  int:


										'''simple docstring'''




										super().__init__()
										snake_case_				:					int   =       config
										# stochastic depth decay rule
										snake_case_				:					int   =       [x.item() for x in torch.linspace(0       ,							config.drop_path_rate       ,							sum(config.depths				)				)]

										# patch embeddings
										snake_case_				:					Optional[Any]   =       []
										for i in range(config.num_encoder_blocks				):
													embeddings.append(
													    PoolFormerEmbeddings(
													        patch_size=config.patch_sizes[i]       ,							stride=config.strides[i]       ,							padding=config.padding[i]       ,							num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]       ,							hidden_size=config.hidden_sizes[i]       ,							)				)
										snake_case_				:					List[Any]   =       nn.ModuleList(lowerCAmelCase__				)

										# Transformer blocks
										snake_case_				:					Dict   =       []
										snake_case_				:					List[Any]   =       0
										for i in range(config.num_encoder_blocks				):
													# each block consists of layers
													snake_case_				:					Dict   =       []
													if i != 0:
																cur += config.depths[i - 1]
													for j in range(config.depths[i]				):
																layers.append(
																    PoolFormerLayer(
																        lowerCAmelCase__       ,							num_channels=config.hidden_sizes[i]       ,							pool_size=config.pool_size       ,							hidden_size=config.hidden_sizes[i]       ,							intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio				)       ,							drop_path=dpr[cur + j]       ,							)				)
													blocks.append(nn.ModuleList(lowerCAmelCase__				)				)

										snake_case_				:					int   =       nn.ModuleList(lowerCAmelCase__				)






							def 							_A	(		self			:Optional[Any]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Dict=False       ,							lowerCAmelCase__			:Tuple=True				)				->  str:


										'''simple docstring'''




										snake_case_				:					str   =       () if output_hidden_states else None

										snake_case_				:					List[Any]   =       pixel_values
										for idx, layers in enumerate(zip(self.patch_embeddings       ,							self.block				)				):
													snake_case_,	snake_case_				:					str   =       layers
													# Get patch embeddings from hidden_states
													snake_case_				:					Dict   =       embedding_layer(lowerCAmelCase__				)
													# Send the embeddings through the blocks
													for _, blk in enumerate(lowerCAmelCase__				):
																snake_case_				:					List[str]   =       blk(lowerCAmelCase__				)
																snake_case_				:					Any   =       layer_outputs[0]

													if output_hidden_states:
																snake_case_				:					Any   =       all_hidden_states + (hidden_states,)

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

										return BaseModelOutputWithNoAttention(last_hidden_state=lowerCAmelCase__       ,							hidden_states=lowerCAmelCase__				)







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	PoolFormerConfig
							a__   		=	'''poolformer'''
							a__   		=	'''pixel_values'''
							a__   		=	True

							def 							_A	(		self			:List[Any]       ,							lowerCAmelCase__			:List[Any]				)				->  Optional[int]:


										'''simple docstring'''




										if isinstance(lowerCAmelCase__       ,							(nn.Linear, nn.Convad)				):
													module.weight.data.normal_(mean=0.0       ,							std=self.config.initializer_range				)
													if module.bias is not None:
																module.bias.data.zero_()
										elif isinstance(lowerCAmelCase__       ,							nn.LayerNorm				):
													module.bias.data.zero_()
													module.weight.data.fill_(1.0				)






							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:str=False				)				->  str:


										'''simple docstring'''




										if isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				):
													snake_case_				:					List[str]   =       value
__lowerCamelCase   :					str   							=					R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): 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.
'''

__lowerCamelCase   :					Tuple   							=					R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PoolFormerImageProcessor.__call__`] for details.
'''







@add_start_docstrings(
    '''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.'''						,					a_						,					)
class    A_	(a_							):
							"""simple docstring"""




							def __init__(		self			:Optional[Any]       ,							lowerCAmelCase__			:str				)				->  List[Any]:


										'''simple docstring'''




										super().__init__(lowerCAmelCase__				)
										snake_case_				:					Optional[Any]   =       config

										snake_case_				:					int   =       PoolFormerEncoder(lowerCAmelCase__				)

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

							def 							_A	(		self			:List[str]				)				->  Tuple:


										'''simple docstring'''




										return self.embeddings.patch_embeddings






							@add_start_docstrings_to_model_forward(lowerCAmelCase__				)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC       ,							output_type=lowerCAmelCase__       ,							config_class=_CONFIG_FOR_DOC       ,							modality="vision"       ,							expected_output=_EXPECTED_OUTPUT_SHAPE       ,							)
							def 							_A	(		self			:str       ,							lowerCAmelCase__			:Optional[torch.FloatTensor] = None       ,							lowerCAmelCase__			:Optional[bool] = None       ,							lowerCAmelCase__			:Optional[bool] = None       ,							)				->  Union[Tuple, BaseModelOutputWithNoAttention]:


										'''simple docstring'''




										snake_case_				:					str   =       (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										snake_case_				:					Optional[Any]   =       return_dict if return_dict is not None else self.config.use_return_dict

										if pixel_values is None:
													raise ValueError("You have to specify pixel_values"				)

										snake_case_				:					int   =       self.encoder(
										    lowerCAmelCase__       ,							output_hidden_states=lowerCAmelCase__       ,							return_dict=lowerCAmelCase__       ,							)
										snake_case_				:					List[str]   =       encoder_outputs[0]

										if not return_dict:
													return (sequence_output, None) + encoder_outputs[1:]

										return BaseModelOutputWithNoAttention(
										    last_hidden_state=lowerCAmelCase__       ,							hidden_states=encoder_outputs.hidden_states       ,							)







class    A_	(nn.Module							):
							"""simple docstring"""




							def __init__(		self			:Dict       ,							lowerCAmelCase__			:Dict				)				->  Optional[Any]:


										'''simple docstring'''




										super().__init__()
										snake_case_				:					Optional[Any]   =       nn.Linear(config.hidden_size       ,							config.hidden_size				)






							def 							_A	(		self			:str       ,							lowerCAmelCase__			:Dict				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					str   =       self.dense(lowerCAmelCase__				)
										return output







@add_start_docstrings(
    '''
    PoolFormer Model transformer with an image classification head on top
    '''						,					a_						,					)
class    A_	(a_							):
							"""simple docstring"""




							def __init__(		self			:Dict       ,							lowerCAmelCase__			:List[Any]				)				->  str:


										'''simple docstring'''




										super().__init__(lowerCAmelCase__				)
										snake_case_				:					Union[str, Any]   =       config.num_labels
										snake_case_				:					int   =       PoolFormerModel(lowerCAmelCase__				)

										# Final norm
										snake_case_				:					Optional[int]   =       PoolFormerGroupNorm(config.hidden_sizes[-1]				)
										# Classifier head
										snake_case_				:					Optional[int]   =       (
										    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(lowerCAmelCase__				)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT       ,							output_type=lowerCAmelCase__       ,							config_class=_CONFIG_FOR_DOC       ,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,							)
							def 							_A	(		self			:Dict       ,							lowerCAmelCase__			:Optional[torch.FloatTensor] = None       ,							lowerCAmelCase__			:Optional[torch.LongTensor] = None       ,							lowerCAmelCase__			:Optional[bool] = None       ,							lowerCAmelCase__			:Optional[bool] = None       ,							)				->  Union[Tuple, ImageClassifierOutputWithNoAttention]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       return_dict if return_dict is not None else self.config.use_return_dict

										snake_case_				:					Tuple   =       self.poolformer(
										    lowerCAmelCase__       ,							output_hidden_states=lowerCAmelCase__       ,							return_dict=lowerCAmelCase__       ,							)

										snake_case_				:					Dict   =       outputs[0]

										snake_case_				:					Optional[int]   =       self.classifier(self.norm(lowerCAmelCase__				).mean([-2, -1]				)				)

										snake_case_				:					Optional[int]   =       None
										if labels is not None:
													if self.config.problem_type is None:
																if self.num_labels == 1:
																			snake_case_				:					int   =       "regression"
																elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																			snake_case_				:					Tuple   =       "single_label_classification"
																else:
																			snake_case_				:					Union[str, Any]   =       "multi_label_classification"

													if self.config.problem_type == "regression":
																snake_case_				:					Optional[Any]   =       MSELoss()
																if self.num_labels == 1:
																			snake_case_				:					List[Any]   =       loss_fct(logits.squeeze()       ,							labels.squeeze()				)
																else:
																			snake_case_				:					Union[str, Any]   =       loss_fct(lowerCAmelCase__       ,							lowerCAmelCase__				)
													elif self.config.problem_type == "single_label_classification":
																snake_case_				:					List[Any]   =       CrossEntropyLoss()
																snake_case_				:					List[str]   =       loss_fct(logits.view(-1       ,							self.num_labels				)       ,							labels.view(-1				)				)
													elif self.config.problem_type == "multi_label_classification":
																snake_case_				:					Any   =       BCEWithLogitsLoss()
																snake_case_				:					Dict   =       loss_fct(lowerCAmelCase__       ,							lowerCAmelCase__				)

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

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












'''simple docstring'''




import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


__lowerCamelCase   :					List[str]   							=					re.compile(R'''\s+''')

def 	__UpperCAmelCase       ( __magic_name__						)->    Union[str, Any]:



			"""simple docstring"""



			return {"hash": hashlib.mda(re.sub(__magic_name__     ,""     ,example["content"]						).encode("utf-8"						)						).hexdigest()}

def 	__UpperCAmelCase       ( __magic_name__						)->    str:



			"""simple docstring"""



			snake_case_				:					Optional[Any]   =       [len(__magic_name__						) for line in example["content"].splitlines()]
			return {"line_mean": np.mean(__magic_name__						), "line_max": max(__magic_name__						)}

def 	__UpperCAmelCase       ( __magic_name__						)->    int:



			"""simple docstring"""



			snake_case_				:					Optional[int]   =       np.mean([c.isalnum() for c in example["content"]]						)
			return {"alpha_frac": alpha_frac}

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Tuple:



			"""simple docstring"""



			if example["hash"] in uniques:
						uniques.remove(example["hash"]						)
						return True
			else:
						return False

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=5						)->    Tuple:



			"""simple docstring"""



			snake_case_				:					List[str]   =       ["auto-generated", "autogenerated", "automatically generated"]
			snake_case_				:					Optional[Any]   =       example["content"].splitlines()
			for _, line in zip(range(__magic_name__						)     ,__magic_name__						):
						for keyword in keywords:
									if keyword in line.lower():
												return {"autogenerated": True}
			else:
						return {"autogenerated": False}

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=5     ,__magic_name__=0.05						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					str   =       ["unit tests", "test file", "configuration file"]
			snake_case_				:					int   =       example["content"].splitlines()
			snake_case_				:					Optional[Any]   =       0
			snake_case_				:					Any   =       0
			# first test
			for _, line in zip(range(__magic_name__						)     ,__magic_name__						):
						for keyword in keywords:
									if keyword in line.lower():
												return {"config_or_test": True}
    # second test
			snake_case_				:					Tuple   =       example["content"].count("\n"						)
			snake_case_				:					int   =       int(coeff * nlines						)
			for line in lines:
						count_config += line.lower().count("config"						)
						count_test += line.lower().count("test"						)
						if count_config > threshold or count_test > threshold:
									return {"config_or_test": True}
			return {"config_or_test": False}

def 	__UpperCAmelCase       ( __magic_name__						)->    str:



			"""simple docstring"""



			snake_case_				:					List[Any]   =       ["def ", "class ", "for ", "while "]
			snake_case_				:					Optional[Any]   =       example["content"].splitlines()
			for line in lines:
						for keyword in keywords:
									if keyword in line.lower():
												return {"has_no_keywords": False}
			return {"has_no_keywords": True}

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=4						)->    Optional[int]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       example["content"].splitlines()
			snake_case_				:					Tuple   =       0
			for line in lines:
						counter += line.lower().count("="						)
						if counter > minimum:
									return {"has_few_assignments": False}
			return {"has_few_assignments": True}

def 	__UpperCAmelCase       ( __magic_name__						)->    List[Any]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       tokenizer(example["content"]     ,truncation=__magic_name__						)["input_ids"]
			snake_case_				:					int   =       len(example["content"]						) / len(__magic_name__						)
			return {"ratio": ratio}

def 	__UpperCAmelCase       ( __magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       {}
			results.update(get_hash(__magic_name__						)						)
			results.update(line_stats(__magic_name__						)						)
			results.update(alpha_stats(__magic_name__						)						)
			results.update(char_token_ratio(__magic_name__						)						)
			results.update(is_autogenerated(__magic_name__						)						)
			results.update(is_config_or_test(__magic_name__						)						)
			results.update(has_no_keywords(__magic_name__						)						)
			results.update(has_few_assignments(__magic_name__						)						)
			return results

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Tuple:



			"""simple docstring"""



			if not check_uniques(__magic_name__     ,__magic_name__						):
						return False
			elif example["autogenerated"]:
						return False
			elif example["line_max"] > args.line_max:
						return False
			elif example["line_mean"] > args.line_mean:
						return False
			elif example["alpha_frac"] < args.alpha_frac:
						return False
			elif example["ratio"] < args.min_token_ratio:
						return False
			elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
						return False
			elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
						return False
			elif example["has_few_assignments"]:
						return False
			else:
						return True

def 	__UpperCAmelCase       ( __magic_name__						)->    Dict:



			"""simple docstring"""



			with open(__magic_name__     ,"rb"						) as f_in:
						with gzip.open(str(__magic_name__						) + ".gz"     ,"wb"     ,compresslevel=6						) as f_out:
									shutil.copyfileobj(__magic_name__     ,__magic_name__						)
			os.unlink(__magic_name__						)


# Settings
__lowerCamelCase   :					List[Any]   							=					HfArgumentParser(PreprocessingArguments)
__lowerCamelCase   :					str   							=					parser.parse_args()
if args.num_workers is None:
		__lowerCamelCase   :					List[Any]   							=					multiprocessing.cpu_count()
__lowerCamelCase   :					Optional[int]   							=					AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
__lowerCamelCase   :					Any   							=					time.time()
__lowerCamelCase   :					str   							=					load_dataset(args.dataset_name, split='''train''')
print(f'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
__lowerCamelCase   :					List[str]   							=					time.time()
__lowerCamelCase   :					Any   							=					ds.map(preprocess, num_proc=args.num_workers)
print(f'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
__lowerCamelCase   :					Any   							=					set(ds.unique('''hash'''))
__lowerCamelCase   :					Optional[int]   							=					len(uniques) / len(ds)
print(f'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
__lowerCamelCase   :					List[str]   							=					time.time()
__lowerCamelCase   :					Tuple   							=					ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(f'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(f'''Size of filtered dataset: {len(ds_filter)}''')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
		__lowerCamelCase   :					List[str]   							=					time.time()
		__lowerCamelCase							,      __lowerCamelCase   :					Tuple   							=					deduplicate_dataset(ds_filter, args.jaccard_threshold)
		print(f'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
		print(f'''Size of deduplicate dataset: {len(ds_filter)}''')

# Save data in batches of samples_per_file
__lowerCamelCase   :					List[Any]   							=					Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
		with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
				json.dump(duplicate_clusters, f)

__lowerCamelCase   :					List[str]   							=					output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

__lowerCamelCase   :					int   							=					time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
		__lowerCamelCase   :					Union[str, Any]   							=					str(data_dir / f'''file-{file_number+1:012}.json''')
		__lowerCamelCase   :					List[Any]   							=					min(len(ds_filter), index + args.samples_per_file)
		ds_filter.select(list(range(index, end_index))).to_json(file_path)
		compress_file(file_path)






print(f'''Time to save dataset: {time.time()-t_start:.2f}''')




