

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

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


__A			:					str						=			logging.getLogger(__name__)





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Dict     , __lowerCamelCase					:			List[str]=-1			):
       # in NER datasets, the last column is usually reserved for NER label
       SCREAMING_SNAKE_CASE										=   label_idx





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Union[Split, str]			):
       if isinstance(__lowerCamelCase     , __lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   mode.value
       SCREAMING_SNAKE_CASE										=   os.path.join(__lowerCamelCase     , f"{mode}.txt"			)
       SCREAMING_SNAKE_CASE										=   1
       SCREAMING_SNAKE_CASE										=   []
       with open(__lowerCamelCase     , encoding="utf-8"			) as f:
           SCREAMING_SNAKE_CASE										=   []
           SCREAMING_SNAKE_CASE										=   []
           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
                       SCREAMING_SNAKE_CASE										=   []
                       SCREAMING_SNAKE_CASE										=   []
               else:
                   SCREAMING_SNAKE_CASE										=   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      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			TextIO     , __lowerCamelCase					:			TextIO     , __lowerCamelCase					:			List			):
       SCREAMING_SNAKE_CASE										=   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]:
               SCREAMING_SNAKE_CASE										=   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      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			str			):
       if path:
           with open(__lowerCamelCase     , "r"			) as f:
               SCREAMING_SNAKE_CASE										=   f.read().splitlines()
           if "O" not in labels:
               SCREAMING_SNAKE_CASE										=   ["O"] + labels
           return labels
       else:
           return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Optional[Any]			):
       # in CONLL2003 dataset chunk column is second-to-last
       super().__init__(label_idx=-2			)





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			str			):
       if path:
           with open(__lowerCamelCase     , "r"			) as f:
               SCREAMING_SNAKE_CASE										=   f.read().splitlines()
           if "O" not in labels:
               SCREAMING_SNAKE_CASE										=   ["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 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Union[Split, str]			):
       if isinstance(__lowerCamelCase     , __lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   mode.value
       SCREAMING_SNAKE_CASE										=   os.path.join(__lowerCamelCase     , f"{mode}.txt"			)
       SCREAMING_SNAKE_CASE										=   1
       SCREAMING_SNAKE_CASE										=   []

       with open(__lowerCamelCase     , encoding="utf-8"			) as f:
           for sentence in parse_incr(__lowerCamelCase			):
               SCREAMING_SNAKE_CASE										=   []
               SCREAMING_SNAKE_CASE										=   []
               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      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			TextIO     , __lowerCamelCase					:			TextIO     , __lowerCamelCase					:			List			):
       SCREAMING_SNAKE_CASE										=   0
       for sentence in parse_incr(__lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   preds_list[example_id]
           SCREAMING_SNAKE_CASE										=   ""
           for token in sentence:
               out += f"{token['form']} ({token['upos']}|{s_p.pop(0			)}) "
           out += "\n"
           writer.write(__lowerCamelCase			)
           example_id += 1





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			str			):
       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",
           ]


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
    import tensorflow as tf


__A			:					Dict						=			logging.get_logger(__name__)





@dataclass
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      [
       "no_inference",
       "no_cuda",
       "no_tpu",
       "no_speed",
       "no_memory",
       "no_env_print",
       "no_multi_process",
   ]





   def __init__(       self					:			List[Any]     , **__lowerCamelCase					:			Any			):
       for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
               SCREAMING_SNAKE_CASE										=   deprecated_arg[3:]
               SCREAMING_SNAKE_CASE										=   not kwargs.pop(__lowerCamelCase			)
               logger.warning(
                   f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
                   f" {positive_arg}={kwargs[positive_arg]}"			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("tpu_name"     , self.tpu_name			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("device_idx"     , self.device_idx			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("eager_mode"     , self.eager_mode			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("use_xla"     , self.use_xla			)
       super().__init__(**__lowerCamelCase			)

   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Name of TPU"}					,       )
   lowerCamelCase__				=      field(
       default=0					,       metadata={"help": "CPU / GPU device index. Defaults to 0."}					,       )
   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Benchmark models in eager model."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
       }					,       )





   @cached_property
   def      _snake_case		(       self					:			Optional[int]			):
       requires_backends(self     , ["tf"]			)
       SCREAMING_SNAKE_CASE										=   None
       if self.tpu:
           try:
               if self.tpu_name:
                   SCREAMING_SNAKE_CASE										=   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name			)
               else:
                   SCREAMING_SNAKE_CASE										=   tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
               SCREAMING_SNAKE_CASE										=   None
       return tpu





   @cached_property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       if self.is_tpu:
           tf.config.experimental_connect_to_cluster(self._setup_tpu			)
           tf.tpu.experimental.initialize_tpu_system(self._setup_tpu			)

           SCREAMING_SNAKE_CASE										=   tf.distribute.TPUStrategy(self._setup_tpu			)
       else:
           # currently no multi gpu is allowed
           if self.is_gpu:
               # TODO: Currently only single GPU is supported
               tf.config.set_visible_devices(self.gpu_list[self.device_idx]     , "GPU"			)
               SCREAMING_SNAKE_CASE										=   tf.distribute.OneDeviceStrategy(device=f"/gpu:{self.device_idx}"			)
           else:
               tf.config.set_visible_devices([]     , "GPU"			)  # disable GPU
               SCREAMING_SNAKE_CASE										=   tf.distribute.OneDeviceStrategy(device=f"/cpu:{self.device_idx}"			)

       return strategy





   @property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       return self._setup_tpu is not None





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       requires_backends(self     , ["tf"]			)
       return self._setup_strategy





   @property
   def      _snake_case		(       self					:			List[str]			):
       requires_backends(self     , ["tf"]			)
       return tf.config.list_physical_devices("GPU"			)





   @property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       if self.cuda:
           return len(self.gpu_list			)
       return 0





   @property
   def      _snake_case		(       self					:			Dict			):
       return self.n_gpu > 0