





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

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


A   : List[str]       =				logging.getLogger(__name__)


class   A      (UpperCAmelCase__   ):
			'''simple docstring'''




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







					"""simple docstring"""






					A__  				=       label_idx






			def        a_	(      self       :   Tuple							,     __lowerCAmelCase       :   List[Any]							,     __lowerCAmelCase       :   Union[str, Any]       )     ->     List[InputExample]:







					"""simple docstring"""






					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        a_	(      self       :   int							,     __lowerCAmelCase       :   str							,     __lowerCAmelCase       :   str							,     __lowerCAmelCase       :   List[str]       )     ->     Optional[int]:







					"""simple docstring"""






					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        a_	(      self       :   List[str]							,     __lowerCAmelCase       :   str       )     ->     List[str]:







					"""simple docstring"""






					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   A      (UpperCAmelCase__   ):
			'''simple docstring'''




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







					"""simple docstring"""






					super().__init__(label_idx=-2       )







			def        a_	(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   List[Any]       )     ->     List[str]:







					"""simple docstring"""






					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   A      (UpperCAmelCase__   ):
			'''simple docstring'''




			def        a_	(      self       :   Dict							,     __lowerCAmelCase       :   str							,     __lowerCAmelCase       :   Any       )     ->     List[InputExample]:







					"""simple docstring"""






					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        a_	(      self       :   List[Any]							,     __lowerCAmelCase       :   List[str]							,     __lowerCAmelCase       :   Optional[int]							,     __lowerCAmelCase       :   int       )     ->     Any:







					"""simple docstring"""






					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        a_	(      self       :   List[Any]							,     __lowerCAmelCase       :   List[str]       )     ->     List[str]:







					"""simple docstring"""






					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",
							]










# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce







def        __lowerCamelCase (							__a       :List[str]   )		->    Tuple:






		"""simple docstring"""

		return (torch.arange(state.num_processes   ) + 1.0 + (state.num_processes * state.process_index)).to(state.device   )







def        __lowerCamelCase (							__a       :int   )		->    Optional[int]:






		"""simple docstring"""

		A__  				=       create_tensor(__a   )
		A__  				=       gather(__a   )
		assert gathered_tensor.tolist() == list(range(1   ,					state.num_processes**2 + 1   )   )







def        __lowerCamelCase (							__a       :Any   )		->    Any:






		"""simple docstring"""

		A__  				=       [state.process_index]
		A__  				=       gather_object(__a   )
		assert len(__a   ) == state.num_processes, F'{gathered_obj}, {len(__a   )} != {state.num_processes}'
		assert gathered_obj == list(range(state.num_processes   )   ), F'{gathered_obj} != {list(range(state.num_processes   )   )}'







def        __lowerCamelCase (							__a       :List[str]   )		->    List[str]:






		"""simple docstring"""

		A__  				=       create_tensor(__a   )
		A__  				=       broadcast(__a   )
		assert broadcasted_tensor.shape == torch.Size([state.num_processes]   )
		assert broadcasted_tensor.tolist() == list(range(1   ,					state.num_processes + 1   )   )







def        __lowerCamelCase (							__a       :Any   )		->    Any:






		"""simple docstring"""

		if state.is_main_process:
				A__  				=       torch.arange(state.num_processes + 1   ).to(state.device   )
		else:
				A__  				=       torch.arange(state.num_processes   ).to(state.device   )
		A__  				=       pad_across_processes(__a   )
		assert padded_tensor.shape == torch.Size([state.num_processes + 1]   )
		if not state.is_main_process:
				assert padded_tensor.tolist() == list(range(0   ,					state.num_processes   )   ) + [0]







def        __lowerCamelCase (							__a       :Union[str, Any]   )		->    str:






		"""simple docstring"""

		if state.num_processes != 2:
				return
		A__  				=       create_tensor(__a   )
		A__  				=       reduce(__a   ,					"""sum"""   )
		A__  				=       torch.tensor([4.0, 6]   ).to(state.device   )
		assert torch.allclose(__a   ,					__a   ), F'{reduced_tensor} != {truth_tensor}'







def        __lowerCamelCase (							__a       :List[Any]   )		->    List[str]:






		"""simple docstring"""

		if state.num_processes != 2:
				return
		A__  				=       create_tensor(__a   )
		A__  				=       reduce(__a   ,					"""mean"""   )
		A__  				=       torch.tensor([2.0, 3]   ).to(state.device   )
		assert torch.allclose(__a   ,					__a   ), F'{reduced_tensor} != {truth_tensor}'







def        __lowerCamelCase (							__a       :str   )		->    Optional[int]:






		"""simple docstring"""

		main()







def        __lowerCamelCase (							)		->    str:






		"""simple docstring"""

		A__  				=       PartialState()
		state.print(F'State: {state}'   )
		state.print("""testing gather"""   )
		test_gather(__a   )
		state.print("""testing gather_object"""   )
		test_gather_object(__a   )
		state.print("""testing broadcast"""   )
		test_broadcast(__a   )
		state.print("""testing pad_across_processes"""   )
		test_pad_across_processes(__a   )
		state.print("""testing reduce_sum"""   )
		test_reduce_sum(__a   )
		state.print("""testing reduce_mean"""   )
		test_reduce_mean(__a   )


if __name__ == "__main__":
					main()



