



"""simple docstring"""







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

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


UpperCamelCase     =							logging.getLogger(__name__)






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=-1		)      ->	Optional[int]:
				# in NER datasets, the last column is usually reserved for NER label
				A__											=					label_idx


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	List[InputExample]:
				if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					A__											=					mode.value
				A__											=					os.path.join(SCREAMING_SNAKE_CASE__						,	f"""{mode}.txt"""		)
				A__											=					1
				A__											=					[]
				with open(SCREAMING_SNAKE_CASE__						,	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=SCREAMING_SNAKE_CASE__						,	labels=SCREAMING_SNAKE_CASE__		)		)
								guid_index += 1
								A__											=					[]
								A__											=					[]
						else:
							A__											=					line.split(" "		)
							words.append(splits[0]		)
							if len(SCREAMING_SNAKE_CASE__		) > 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=SCREAMING_SNAKE_CASE__						,	labels=SCREAMING_SNAKE_CASE__		)		)
				return examples


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	int:
				A__											=					0
				for line in test_input_reader:
					if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
						writer.write(SCREAMING_SNAKE_CASE__		)
						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(SCREAMING_SNAKE_CASE__		)
					else:
						logger.warning("Maximum sequence length exceeded: No prediction for '%s'."						,	line.split()[0]		)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				if path:
					with open(SCREAMING_SNAKE_CASE__						,	"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       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self		)      ->	Tuple:
				# in CONLL2003 dataset chunk column is second-to-last
				super().__init__(label_idx=-2		)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				if path:
					with open(SCREAMING_SNAKE_CASE__						,	"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       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	List[InputExample]:
				if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					A__											=					mode.value
				A__											=					os.path.join(SCREAMING_SNAKE_CASE__						,	f"""{mode}.txt"""		)
				A__											=					1
				A__											=					[]

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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				A__											=					0
				for sentence in parse_incr(SCREAMING_SNAKE_CASE__		):
					A__											=					preds_list[example_id]
					A__											=					""
					for token in sentence:
						out += f"""{token['form']} ({token['upos']}|{s_p.pop(0		)}) """
					out += "\n"
					writer.write(SCREAMING_SNAKE_CASE__		)
					example_id += 1







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				if path:
					with open(SCREAMING_SNAKE_CASE__						,	"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",
					]









"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase     =							{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							["""PerceiverFeatureExtractor"""]
			UpperCamelCase     =							["""PerceiverImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """PerceiverForImageClassificationConvProcessing""",
			    """PerceiverForImageClassificationFourier""",
			    """PerceiverForImageClassificationLearned""",
			    """PerceiverForMaskedLM""",
			    """PerceiverForMultimodalAutoencoding""",
			    """PerceiverForOpticalFlow""",
			    """PerceiverForSequenceClassification""",
			    """PerceiverLayer""",
			    """PerceiverModel""",
			    """PerceiverPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
			from .tokenization_perceiver import PerceiverTokenizer

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_perceiver import PerceiverFeatureExtractor
						from .image_processing_perceiver import PerceiverImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_perceiver import (
						    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PerceiverForImageClassificationConvProcessing,
						    PerceiverForImageClassificationFourier,
						    PerceiverForImageClassificationLearned,
						    PerceiverForMaskedLM,
						    PerceiverForMultimodalAutoencoding,
						    PerceiverForOpticalFlow,
						    PerceiverForSequenceClassification,
						    PerceiverLayer,
						    PerceiverModel,
						    PerceiverPreTrainedModel,
						)

else:
			import sys

			UpperCamelCase     =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




