




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

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


__A						:							List[Any]							=  logging.getLogger(__name__)




class 			UpperCAmelCase_      (   _a       ):




							'''simple docstring'''



							def __init__(					self       :	str       ,     a       :	Optional[Any]=-1			)			->					Any:
										# in NER datasets, the last column is usually reserved for NER label
										SCREAMING_SNAKE_CASE           =					label_idx





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	List[str]       ,     a       :	Union[str, Any]			)			->					str:
										if isinstance(_A       ,     _A			):
													SCREAMING_SNAKE_CASE           =					mode.value
										SCREAMING_SNAKE_CASE           =					os.path.join(_A       ,     f"""{mode}.txt"""			)
										SCREAMING_SNAKE_CASE           =					1
										SCREAMING_SNAKE_CASE           =					[]
										with open(_A       ,     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=_A       ,     labels=_A			)			)
																						guid_index += 1
																						SCREAMING_SNAKE_CASE           =					[]
																						SCREAMING_SNAKE_CASE           =					[]
																else:
																			SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	List[str]       ,     a       :	Optional[int]       ,     a       :	Any			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					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]:
																SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Any       ,     a       :	List[str]			)			->					List[str]:
										if path:
													with open(_A       ,     """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 			UpperCAmelCase_      (   _a       ):




							'''simple docstring'''



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







							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	int			)			->					Optional[int]:
										if path:
													with open(_A       ,     """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 			UpperCAmelCase_      (   _a       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	List[str]       ,     a       :	List[str]			)			->					Tuple:
										if isinstance(_A       ,     _A			):
													SCREAMING_SNAKE_CASE           =					mode.value
										SCREAMING_SNAKE_CASE           =					os.path.join(_A       ,     f"""{mode}.txt"""			)
										SCREAMING_SNAKE_CASE           =					1
										SCREAMING_SNAKE_CASE           =					[]

										with open(_A       ,     encoding="""utf-8"""			) as f:
													for sentence in parse_incr(_A			):
																SCREAMING_SNAKE_CASE           =					[]
																SCREAMING_SNAKE_CASE           =					[]
																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    _UpperCAmelCase     (					self       :	str       ,     a       :	int       ,     a       :	Union[str, Any]       ,     a       :	Tuple			)			->					int:
										SCREAMING_SNAKE_CASE           =					0
										for sentence in parse_incr(_A			):
													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(_A			)
													example_id += 1







							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	List[Any]			)			->					Union[str, Any]:
										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 os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
		import onnxruntime as ort


__A						:							str							=  logging.get_logger(__name__)

__A						:							str							=  {
    """tensor(bool)""": np.bool_,
    """tensor(int8)""": np.inta,
    """tensor(uint8)""": np.uinta,
    """tensor(int16)""": np.intaa,
    """tensor(uint16)""": np.uintaa,
    """tensor(int32)""": np.intaa,
    """tensor(uint32)""": np.uintaa,
    """tensor(int64)""": np.intaa,
    """tensor(uint64)""": np.uintaa,
    """tensor(float16)""": np.floataa,
    """tensor(float)""": np.floataa,
    """tensor(double)""": np.floataa,
}




class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	List[str]       ,     a       :	Tuple=None       ,     **a       :	Any			)			->					List[str]:
										logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""			)
										SCREAMING_SNAKE_CASE           =					model
										SCREAMING_SNAKE_CASE           =					kwargs.get("""model_save_dir"""       ,     a			)
										SCREAMING_SNAKE_CASE           =					kwargs.get("""latest_model_name"""       ,     a			)





							def __call__(					self       :	List[str]       ,     **a       :	Any			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					{k: np.array(a			) for k, v in kwargs.items()}
										return self.model.run(a       ,     a			)





							@staticmethod
							def    _UpperCAmelCase     (					a       :	Union[str, Path]       ,     a       :	Any=None       ,     a       :	Optional[int]=None			)			->					Optional[Any]:
										if provider is None:
													logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""			)
													SCREAMING_SNAKE_CASE           =					"""CPUExecutionProvider"""

										return ort.InferenceSession(a       ,     providers=[provider]       ,     sess_options=a			)





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	Union[str, Path]       ,     a       :	Optional[str] = None       ,     **a       :	str			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					file_name if file_name is not None else ONNX_WEIGHTS_NAME

										SCREAMING_SNAKE_CASE           =					self.model_save_dir.joinpath(self.latest_model_name			)
										SCREAMING_SNAKE_CASE           =					Path(a			).joinpath(a			)
										try:
													shutil.copyfile(a       ,     a			)
										except shutil.SameFileError:
													pass

										# copy external weights (for models >2GB)
										SCREAMING_SNAKE_CASE           =					self.model_save_dir.joinpath(a			)
										if src_path.exists():
													SCREAMING_SNAKE_CASE           =					Path(a			).joinpath(a			)
													try:
																shutil.copyfile(a       ,     a			)
													except shutil.SameFileError:
																pass





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	Union[str, os.PathLike]       ,     **a       :	Tuple       ,     )			->					str:
										if os.path.isfile(a			):
													logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""			)
													return

										os.makedirs(a       ,     exist_ok=a			)

										# saving model weights/files
										self._save_pretrained(a       ,     **a			)





							@classmethod
							def    _UpperCAmelCase     (					cls       :	List[Any]       ,     a       :	Union[str, Path]       ,     a       :	Optional[Union[bool, str, None]] = None       ,     a       :	Optional[Union[str, None]] = None       ,     a       :	bool = False       ,     a       :	Optional[str] = None       ,     a       :	Optional[str] = None       ,     a       :	Optional[str] = None       ,     a       :	Optional["ort.SessionOptions"] = None       ,     **a       :	Union[str, Any]       ,     )			->					List[str]:
										SCREAMING_SNAKE_CASE           =					file_name if file_name is not None else ONNX_WEIGHTS_NAME
										# load model from local directory
										if os.path.isdir(a			):
													SCREAMING_SNAKE_CASE           =					OnnxRuntimeModel.load_model(
													    os.path.join(a       ,     a			)       ,     provider=a       ,     sess_options=a			)
													SCREAMING_SNAKE_CASE           =					Path(a			)
										# load model from hub
										else:
													# download model
													SCREAMING_SNAKE_CASE           =					hf_hub_download(
													    repo_id=a       ,     filename=a       ,     use_auth_token=a       ,     revision=a       ,     cache_dir=a       ,     force_download=a       ,     )
													SCREAMING_SNAKE_CASE           =					Path(a			).parent
													SCREAMING_SNAKE_CASE           =					Path(a			).name
													SCREAMING_SNAKE_CASE           =					OnnxRuntimeModel.load_model(a       ,     provider=a       ,     sess_options=a			)
										return cls(model=a       ,     **a			)







							@classmethod
							def    _UpperCAmelCase     (					cls       :	List[Any]       ,     a       :	Union[str, Path]       ,     a       :	bool = True       ,     a       :	Optional[str] = None       ,     a       :	Optional[str] = None       ,     **a       :	Union[str, Any]       ,     )			->					Any:
										SCREAMING_SNAKE_CASE           =					None
										if len(str(a			).split("""@"""			)			) == 2:
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					model_id.split("""@"""			)

										return cls._from_pretrained(
										    model_id=a       ,     revision=a       ,     cache_dir=a       ,     force_download=a       ,     use_auth_token=a       ,     **a       ,     )






