





'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger

class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	Any							,			A__      :	Any							,			A__      :	Any				)					-> Dict:
						lowercase         = params

						lowercase         = np.array(A__				)
						lowercase         = np.array([len(A__				) for t in data]				)

						self.check()
						self.remove_long_sequences()
						self.remove_empty_sequences()
						self.remove_unknown_sequences()
						self.check()
						self.print_statistics()




		def __getitem__(self      :	Tuple							,			A__      :	List[Any]				)					-> Dict:
						return (self.token_ids[index], self.lengths[index])




		def __len__(self      :	List[str]				)					-> Dict:
						return len(self.lengths				)




		def 				UpperCAmelCase__   (self      :	Tuple				)					-> int:
						assert len(self.token_ids				) == len(self.lengths				)
						assert all(self.lengths[i] == len(self.token_ids[i]				) for i in range(len(self.lengths				)				)				)




		def 				UpperCAmelCase__   (self      :	int				)					-> Any:
						lowercase         = self.params.max_model_input_size
						lowercase         = self.lengths > max_len
						logger.info(f'Splitting {sum(A__				)} too long sequences.'				)

						def divide_chunks(A__      :	int							,			A__      :	Dict				):
										return [l[i : i + n] for i in range(0							,			len(A__				)							,			A__				)]

						lowercase         = []
						lowercase         = []
						if self.params.mlm:
										lowercase   ,							lowercase         = self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
						else:
										lowercase   ,							lowercase         = self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

						for seq_, len_ in zip(self.token_ids							,			self.lengths				):
										assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
										if len_ <= max_len:
														new_tok_ids.append(seq_				)
														new_lengths.append(len_				)
										else:
														lowercase         = []
														for sub_s in divide_chunks(seq_							,			max_len - 2				):
																		if sub_s[0] != cls_id:
																						lowercase         = np.insert(A__							,			0							,			A__				)
																		if sub_s[-1] != sep_id:
																						lowercase         = np.insert(A__							,			len(A__				)							,			A__				)
																		assert len(A__				) <= max_len
																		assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																		sub_seqs.append(A__				)

														new_tok_ids.extend(A__				)
														new_lengths.extend([len(A__				) for l in sub_seqs]				)

						lowercase         = np.array(A__				)
						lowercase         = np.array(A__				)




		def 				UpperCAmelCase__   (self      :	Dict				)					-> List[Any]:
						lowercase         = len(self				)
						lowercase         = self.lengths > 1_1
						lowercase         = self.token_ids[indices]
						lowercase         = self.lengths[indices]
						lowercase         = len(self				)
						logger.info(f'Remove {init_size - new_size} too short (<=11 tokens) sequences.'				)




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> List[Any]:
						if "unk_token" not in self.params.special_tok_ids:
										return
						else:
										lowercase         = self.params.special_tok_ids["unk_token"]
						lowercase         = len(self				)
						lowercase         = np.array([np.count_nonzero(a == unk_token_id				) for a in self.token_ids]				)
						lowercase         = (unk_occs / self.lengths) < 0.5
						lowercase         = self.token_ids[indices]
						lowercase         = self.lengths[indices]
						lowercase         = len(self				)
						logger.info(f'Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'				)




		def 				UpperCAmelCase__   (self      :	Dict				)					-> Tuple:
						if not self.params.is_master:
										return
						logger.info(f'{len(self				)} sequences'				)
						# data_len = sum(self.lengths)
						# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
						# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

						# unk_idx = self.params.special_tok_ids['unk_token']
						# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
						# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')





		def 				UpperCAmelCase__   (self      :	Union[str, Any]							,			A__      :	List[str]				)					-> Dict:
						lowercase         = [t[0] for t in batch]
						lowercase         = [t[1] for t in batch]
						assert len(A__				) == len(A__				)

						# Max for paddings
						lowercase         = max(A__				)

						# Pad token ids
						if self.params.mlm:
										lowercase         = self.params.special_tok_ids["pad_token"]
						else:
										lowercase         = self.params.special_tok_ids["unk_token"]
						lowercase         = [list(t.astype(A__				)				) + [pad_idx] * (max_seq_len_ - len(A__				)) for t in token_ids]
						assert len(tk_				) == len(A__				)
						assert all(len(A__				) == max_seq_len_ for t in tk_				)

						lowercase         = torch.tensor(tk_				)  # (bs, max_seq_len_)
						lowercase         = torch.tensor(A__				)  # (bs)
						return tk_t, lg_t







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

__lowerCamelCase : str											=			{
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	List[str] 			=		'''data2vec-text'''




		def __init__(self      :	List[str]							,			A__      :	str=3_0_5_2_2							,			A__      :	Tuple=7_6_8							,			A__      :	Any=1_2							,			A__      :	Optional[int]=1_2							,			A__      :	str=3_0_7_2							,			A__      :	List[str]="gelu"							,			A__      :	List[Any]=0.1							,			A__      :	Optional[int]=0.1							,			A__      :	Union[str, Any]=5_1_2							,			A__      :	Any=2							,			A__      :	str=0.0_2							,			A__      :	int=1e-12							,			A__      :	Union[str, Any]=1							,			A__      :	Optional[int]=0							,			A__      :	Union[str, Any]=2							,			A__      :	Optional[int]="absolute"							,			A__      :	Tuple=True							,			A__      :	int=None							,			**A__      :	Any							,			)					-> Any:
						super().__init__(pad_token_id=A__							,			bos_token_id=A__							,			eos_token_id=A__							,			**A__				)

						lowercase         = vocab_size
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = hidden_act
						lowercase         = intermediate_size
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = max_position_embeddings
						lowercase         = type_vocab_size
						lowercase         = initializer_range
						lowercase         = layer_norm_eps
						lowercase         = position_embedding_type
						lowercase         = use_cache
						lowercase         = classifier_dropout




class       UpperCAmelCase (						_lowercase  ):




		@property
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
										lowercase         = {0: "batch", 1: "choice", 2: "sequence"}
						else:
										lowercase         = {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]				)




