





import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


UpperCamelCase_		:				Tuple				=						logging.getLogger(__name__)


class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Dict ,				lowerCamelCase_			:		Dict=-1					):





										'''simple docstring'''







										_snake_case :			str  						=   label_idx







						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		Union[Split, str]					):





										'''simple docstring'''







										if isinstance(lowerCamelCase_ ,				lowerCamelCase_					):
														_snake_case :			Union[str, Any]  						=   mode.value
										_snake_case :			Optional[int]  						=   os.path.join(lowerCamelCase_ ,				f'''{mode}.txt'''					)
										_snake_case :			Dict  						=   1
										_snake_case :			List[str]  						=   []
										with open(lowerCamelCase_ ,				encoding='utf-8'					) as f:
														_snake_case :			List[Any]  						=   []
														_snake_case :			int  						=   []
														for line in f:
																		if line.startswith('-DOCSTART-'					) or line == "" or line == "\n":
																						if words:
																										examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
																										guid_index += 1
																										_snake_case :			Optional[Any]  						=   []
																										_snake_case :			Union[str, Any]  						=   []
																		else:
																						_snake_case :			Tuple  						=   line.split(' '					)
																						words.append(splits[0]					)
																						if len(lowerCamelCase_					) > 1:
																										labels.append(splits[self.label_idx].replace('\n' ,				''					)					)
																						else:
																										# Examples could have no label for mode = "test"
																										labels.append('O'					)
														if words:
																		examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
										return examples







						def 		__UpperCAmelCase       (    self			:		Union[str, Any] ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		List					):





										'''simple docstring'''







										_snake_case :			str  						=   0
										for line in test_input_reader:
														if line.startswith('-DOCSTART-'					) or line == "" or line == "\n":
																		writer.write(lowerCamelCase_					)
																		if not preds_list[example_id]:
																						example_id += 1
														elif preds_list[example_id]:
																		_snake_case :			List[str]  						=   line.split()[0] + ' ' + preds_list[example_id].pop(0					) + '\n'
																		writer.write(lowerCamelCase_					)
														else:
																		logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.' ,				line.split()[0]					)







						def 		__UpperCAmelCase       (    self			:		List[str] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		_snake_case :			Optional[int]  						=   f.read().splitlines()
														if "O" not in labels:
																		_snake_case :			Optional[int]  						=   ['O'] + labels
														return labels
										else:
														return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Optional[int]					):





										'''simple docstring'''







										super().__init__(label_idx=-2					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		_snake_case :			str  						=   f.read().splitlines()
														if "O" not in labels:
																		_snake_case :			Union[str, Any]  						=   ['O'] + labels
														return labels
										else:
														return [
														    "O",
														    "B-ADVP",
														    "B-INTJ",
														    "B-LST",
														    "B-PRT",
														    "B-NP",
														    "B-SBAR",
														    "B-VP",
														    "B-ADJP",
														    "B-CONJP",
														    "B-PP",
														    "I-ADVP",
														    "I-INTJ",
														    "I-LST",
														    "I-PRT",
														    "I-NP",
														    "I-SBAR",
														    "I-VP",
														    "I-ADJP",
														    "I-CONJP",
														    "I-PP",
														]



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		Union[Split, str]					):





										'''simple docstring'''







										if isinstance(lowerCamelCase_ ,				lowerCamelCase_					):
														_snake_case :			str  						=   mode.value
										_snake_case :			List[str]  						=   os.path.join(lowerCamelCase_ ,				f'''{mode}.txt'''					)
										_snake_case :			Tuple  						=   1
										_snake_case :			List[str]  						=   []

										with open(lowerCamelCase_ ,				encoding='utf-8'					) as f:
														for sentence in parse_incr(lowerCamelCase_					):
																		_snake_case :			List[str]  						=   []
																		_snake_case :			str  						=   []
																		for token in sentence:
																						words.append(token['form']					)
																						labels.append(token['upos']					)
																		assert len(lowerCamelCase_					) == len(lowerCamelCase_					)
																		if words:
																						examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
																						guid_index += 1
										return examples







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		List					):





										'''simple docstring'''







										_snake_case :			Dict  						=   0
										for sentence in parse_incr(lowerCamelCase_					):
														_snake_case :			Optional[int]  						=   preds_list[example_id]
														_snake_case :			List[Any]  						=   ''
														for token in sentence:
																		out += f'''{token['form']} ({token['upos']}|{s_p.pop(0					)}) '''
														out += "\n"
														writer.write(lowerCamelCase_					)
														example_id += 1







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		return f.read().splitlines()
										else:
														return [
														    "ADJ",
														    "ADP",
														    "ADV",
														    "AUX",
														    "CCONJ",
														    "DET",
														    "INTJ",
														    "NOUN",
														    "NUM",
														    "PART",
														    "PRON",
														    "PROPN",
														    "PUNCT",
														    "SCONJ",
														    "SYM",
														    "VERB",
														    "X",
														]













import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 			lowercase				(							a_		,			a_		,			a_     ):



						"""simple docstring"""

						@register_to_config
						def __init__(    self			:		Any ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		float ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		bool = False ,				):





										'''simple docstring'''







										super().__init__()

										_snake_case :			Optional[int]  						=   nn.Embedding(lowerCamelCase_ ,				lowerCamelCase_					)

										_snake_case :			List[str]  						=   nn.Embedding(lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case :			Union[str, Any]  						=   False

										_snake_case :			Tuple  						=   nn.Dropout(p=lowerCamelCase_					)

										_snake_case :			Union[str, Any]  						=   TaConfig(
										    vocab_size=lowerCamelCase_ ,				d_model=lowerCamelCase_ ,				num_heads=lowerCamelCase_ ,				d_kv=lowerCamelCase_ ,				d_ff=lowerCamelCase_ ,				dropout_rate=lowerCamelCase_ ,				feed_forward_proj=lowerCamelCase_ ,				is_decoder=lowerCamelCase_ ,				is_encoder_decoder=lowerCamelCase_ ,				)

										_snake_case :			Union[str, Any]  						=   nn.ModuleList()
										for lyr_num in range(lowerCamelCase_					):
														_snake_case :			Any  						=   TaBlock(lowerCamelCase_					)
														self.encoders.append(lowerCamelCase_					)

										_snake_case :			Tuple  						=   TaLayerNorm(lowerCamelCase_					)
										_snake_case :			List[str]  						=   nn.Dropout(p=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Any  						=   self.token_embedder(lowerCamelCase_					)

										_snake_case :			List[Any]  						=   encoder_input_tokens.shape[1]
										_snake_case :			Any  						=   torch.arange(lowerCamelCase_ ,				device=encoder_input_tokens.device					)
										x += self.position_encoding(lowerCamelCase_					)

										_snake_case :			Tuple  						=   self.dropout_pre(lowerCamelCase_					)

										# inverted the attention mask
										_snake_case :			Dict  						=   encoder_input_tokens.size()
										_snake_case :			Optional[int]  						=   self.get_extended_attention_mask(lowerCamelCase_ ,				lowerCamelCase_					)

										for lyr in self.encoders:
														_snake_case :			str  						=   lyr(lowerCamelCase_ ,				lowerCamelCase_					)[0]
										_snake_case :			Any  						=   self.layer_norm(lowerCamelCase_					)

										return self.dropout_post(lowerCamelCase_					), encoder_inputs_mask






