




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

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


lowercase__				:      Tuple	   = logging.getLogger(__name__)




class       a__	( UpperCamelCase__				):







     def __init__(     self  ,		A=-1			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				label_idx







     def       lowerCAmelCase_    (     self  ,		A  ,		A			)  ->   List[InputExample]:



          '''simple docstring'''



          if isinstance(A  ,		A			):
               a     =				mode.value
          a     =				os.path.join(A  ,		F'''{mode}.txt'''			)
          a     =				1
          a     =				[]
          with open(A  ,		encoding="utf-8"			) as f:
               a     =				[]
               a     =				[]
               for line in f:
                    if line.startswith("-DOCSTART-"			) or line == "" or line == "\n":
                         if words:
                              examples.append(InputExample(guid=F'''{mode}-{guid_index}'''  ,		words=A  ,		labels=A			)			)
                              guid_index += 1
                              a     =				[]
                              a     =				[]
                    else:
                         a     =				line.split(" "			)
                         words.append(splits[0]			)
                         if len(A			) > 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=A  ,		labels=A			)			)
          return examples







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   str:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A			)  ->   List[str]:



          '''simple docstring'''



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







class       a__	( UpperCamelCase__				):







     def __init__(     self			)  ->   Optional[int]:



          '''simple docstring'''



          super().__init__(label_idx=-2			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   List[str]:



          '''simple docstring'''



          if path:
               with open(A  ,		"r"			) as f:
                    a     =				f.read().splitlines()
               if "O" not in labels:
                    a     =				["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       a__	( UpperCamelCase__				):







     def       lowerCAmelCase_    (     self  ,		A  ,		A			)  ->   List[InputExample]:



          '''simple docstring'''



          if isinstance(A  ,		A			):
               a     =				mode.value
          a     =				os.path.join(A  ,		F'''{mode}.txt'''			)
          a     =				1
          a     =				[]

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







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   Dict:



          '''simple docstring'''



          a     =				0
          for sentence in parse_incr(A			):
               a     =				preds_list[example_id]
               a     =				""
               for token in sentence:
                    out += F'''{token["form"]} ({token["upos"]}|{s_p.pop(0			)}) '''
               out += "\n"
               writer.write(A			)
               example_id += 1







     def       lowerCAmelCase_    (     self  ,		A			)  ->   List[str]:



          '''simple docstring'''



          if path:
               with open(A  ,		"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 functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__				:      Any	   = logging.get_logger(__name__)

lowercase__				:      int	   = {
    "asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}




class       a__	( UpperCamelCase__				):
     a		:      Optional[Any]		 =     """sew-d"""







     def __init__(     self  ,		A=32  ,		A=768  ,		A=12  ,		A=12  ,		A=3072  ,		A=2  ,		A=512  ,		A=256  ,		A=True  ,		A=True  ,		A=("p2c", "c2p")  ,		A="layer_norm"  ,		A="gelu_python"  ,		A=0.1  ,		A=0.1  ,		A=0.1  ,		A=0.0  ,		A=0.1  ,		A=0.0_2  ,		A=1e-7  ,		A=1e-5  ,		A="group"  ,		A="gelu"  ,		A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)  ,		A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)  ,		A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)  ,		A=False  ,		A=128  ,		A=16  ,		A=True  ,		A=0.0_5  ,		A=10  ,		A=2  ,		A=0.0  ,		A=10  ,		A=0  ,		A="mean"  ,		A=False  ,		A=False  ,		A=256  ,		A=0  ,		A=1  ,		A=2  ,		**A  ,		)  ->   Dict:



          '''simple docstring'''



          super().__init__(**A  ,		pad_token_id=A  ,		bos_token_id=A  ,		eos_token_id=A			)
          a     =				hidden_size
          a     =				feat_extract_norm
          a     =				feat_extract_activation
          a     =				list(A			)
          a     =				list(A			)
          a     =				list(A			)
          a     =				conv_bias
          a     =				num_conv_pos_embeddings
          a     =				num_conv_pos_embedding_groups
          a     =				len(self.conv_dim			)
          a     =				num_hidden_layers
          a     =				intermediate_size
          a     =				squeeze_factor
          a     =				max_position_embeddings
          a     =				position_buckets
          a     =				share_att_key
          a     =				relative_attention
          a     =				norm_rel_ebd
          a     =				list(A			)
          a     =				hidden_act
          a     =				num_attention_heads
          a     =				hidden_dropout
          a     =				attention_dropout
          a     =				activation_dropout
          a     =				feat_proj_dropout
          a     =				final_dropout
          a     =				layer_norm_eps
          a     =				feature_layer_norm_eps
          a     =				initializer_range
          a     =				vocab_size

          if (
              (len(self.conv_stride			) != self.num_feat_extract_layers)
              or (len(self.conv_kernel			) != self.num_feat_extract_layers)
              or (len(self.conv_dim			) != self.num_feat_extract_layers)
          ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F'''but is `len(config.conv_dim) = {len(self.conv_dim			)}`, `len(config.conv_stride)'''
                   F'''= {len(self.conv_stride			)}`, `len(config.conv_kernel) = {len(self.conv_kernel			)}`.'''			)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          a     =				apply_spec_augment
          a     =				mask_time_prob
          a     =				mask_time_length
          a     =				mask_time_min_masks
          a     =				mask_feature_prob
          a     =				mask_feature_length
          a     =				mask_feature_min_masks

          # ctc loss
          a     =				ctc_loss_reduction
          a     =				ctc_zero_infinity

          # sequence classification
          a     =				use_weighted_layer_sum
          a     =				classifier_proj_size







     @property
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          return functools.reduce(operator.mul  ,		self.conv_stride  ,		1			)

