import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				if isinstance(lowercase							,			lowercase							):
								__lowercase 			=							np.full((len(lowercase							), sequence_length, 2)							,			lowercase							)
				else:
								__lowercase 			=							np.full((len(lowercase							), sequence_length)							,			lowercase							)

				for i, tensor in enumerate(lowercase							):
								if padding_side == "right":
												if isinstance(lowercase							,			lowercase							):
																__lowercase 			=							tensor[:sequence_length]
												else:
																__lowercase 			=							tensor[:sequence_length]
								else:
												if isinstance(lowercase							,			lowercase							):
																__lowercase 			=							tensor[:sequence_length]
												else:
																__lowercase 			=							tensor[:sequence_length]

				return out_tensor.tolist()





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							ord(lowercase							)
				if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
								return True
				__lowercase 			=							unicodedata.category(lowercase							)
				if cat.startswith('''P'''							):
								return True
				return False




@dataclass
class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : PreTrainedTokenizerBase
			__a  : Union[bool, str, PaddingStrategy]			 =						True
			__a  : Optional[int]			 =						None
			__a  : Optional[int]			 =						None
			__a  : int			 =						-100
			__a  : str			 =						"pt"





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					Dict:

							'''simple docstring'''


							import torch

							__lowercase 			=							'''label''' if '''label''' in features[0].keys() else '''labels'''
							__lowercase 			=							[feature[label_name] for feature in features] if label_name in features[0].keys() else None
							__lowercase 			=							self.tokenizer.pad(
							    lowerCAmelCase__      ,   padding=self.padding      ,   max_length=self.max_length      ,   pad_to_multiple_of=self.pad_to_multiple_of      ,   return_tensors='''pt''' if labels is None else None      ,   )

							if labels is None:
											return batch

							__lowercase 			=							torch.tensor(batch['''entity_ids''']						).shape[1]
							__lowercase 			=							self.tokenizer.padding_side
							if padding_side == "right":
											__lowercase 			=							[
											    list(lowerCAmelCase__						) + [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase__						)) for label in labels
											]
							else:
											__lowercase 			=							[
											    [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase__						)) + list(lowerCAmelCase__						) for label in labels
											]

							__lowercase 			=							[feature['''ner_tags'''] for feature in features]
							__lowercase 			=							padding_tensor(lowerCAmelCase__      ,   -1      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
							__lowercase 			=							[feature['''original_entity_spans'''] for feature in features]
							__lowercase 			=							padding_tensor(lowerCAmelCase__      ,   (-1, -1)      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
							__lowercase 			=							{k: torch.tensor(lowerCAmelCase__      ,   dtype=torch.intaa						) for k, v in batch.items()}

							return batch
from collections import OrderedDict
from typing import Mapping

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


__a  :     Optional[int]       =  logging.get_logger(__name__)

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




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Dict			 =						'''data2vec-text'''





			def __init__(   self      ,   lowerCAmelCase__=3_05_22      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-12      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__="absolute"      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Any:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__      ,   bos_token_id=lowerCAmelCase__      ,   eos_token_id=lowerCAmelCase__      ,   **lowerCAmelCase__						)

							__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      (						_UpperCAmelCase ):


			"""simple docstring"""






			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							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),
							    ]						)