


"""simple docstring"""
import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu





class    __A   (nn.Module):





							'''simple docstring'''

							def __init__( self							:					Union[str, Any]				)    ->int:



														"""simple docstring"""



														super().__init__()
														snake_case_							=						nn.Linear(3						,				4				)
														snake_case_							=						nn.BatchNormad(4				)
														snake_case_							=						nn.Linear(4						,				5				)



							def 						lowerCAmelCase				( self							:					Optional[int]						,				UpperCAmelCase_							:					List[str]				)    ->int:



														"""simple docstring"""



														return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase__				)				)				)





class    __A   (snake_case_):





							'''simple docstring'''

							def 						lowerCAmelCase				( self							:					Any						,				UpperCAmelCase_							:					Any						,				*UpperCAmelCase_							:					List[str]						,				**UpperCAmelCase_							:					Optional[int]				)    ->Optional[int]:



														"""simple docstring"""



														return (args[0] + 1,) + args[1:], kwargs





class    __A   (snake_case_):





							'''simple docstring'''

							def 						lowerCAmelCase				( self							:					Optional[Any]						,				UpperCAmelCase_							:					Tuple						,				UpperCAmelCase_							:					Optional[Any]				)    ->List[str]:



														"""simple docstring"""



														return output + 1





class    __A   (unittest.TestCase):





							'''simple docstring'''

							def 						lowerCAmelCase				( self							:					Dict				)    ->str:



														"""simple docstring"""



														snake_case_							=						ModelForTest()
														snake_case_							=						ModelHook()

														add_hook_to_module(UpperCAmelCase__						,				UpperCAmelCase__				)
														self.assertEqual(test_model._hf_hook						,				UpperCAmelCase__				)
														self.assertTrue(hasattr(UpperCAmelCase__						,				"""_old_forward"""				)				)

														# Check adding the hook did not change the name or the signature
														self.assertEqual(test_model.forward.__name__						,				"""forward"""				)
														self.assertListEqual(list(inspect.signature(test_model.forward				).parameters				)						,				["""x"""]				)

														remove_hook_from_module(UpperCAmelCase__				)
														self.assertFalse(hasattr(UpperCAmelCase__						,				"""_hf_hook"""				)				)
														self.assertFalse(hasattr(UpperCAmelCase__						,				"""_old_forward"""				)				)



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



														"""simple docstring"""



														snake_case_							=						ModelForTest()
														snake_case_							=						ModelHook()

														add_hook_to_module(UpperCAmelCase__						,				UpperCAmelCase__				)
														add_hook_to_module(UpperCAmelCase__						,				UpperCAmelCase__						,				append=UpperCAmelCase__				)

														self.assertEqual(isinstance(test_model._hf_hook						,				UpperCAmelCase__				)						,				UpperCAmelCase__				)
														self.assertEqual(len(test_model._hf_hook.hooks				)						,				2				)
														self.assertTrue(hasattr(UpperCAmelCase__						,				"""_old_forward"""				)				)

														# Check adding the hook did not change the name or the signature
														self.assertEqual(test_model.forward.__name__						,				"""forward"""				)
														self.assertListEqual(list(inspect.signature(test_model.forward				).parameters				)						,				["""x"""]				)

														remove_hook_from_module(UpperCAmelCase__				)
														self.assertFalse(hasattr(UpperCAmelCase__						,				"""_hf_hook"""				)				)
														self.assertFalse(hasattr(UpperCAmelCase__						,				"""_old_forward"""				)				)



							def 						lowerCAmelCase				( self							:					Dict				)    ->int:



														"""simple docstring"""



														snake_case_							=						ModelForTest()
														snake_case_							=						torch.randn(2						,				3				)
														snake_case_							=						test_model(x + 1				)
														snake_case_							=						test_model(x + 2				)

														snake_case_							=						PreForwardHook()
														add_hook_to_module(UpperCAmelCase__						,				UpperCAmelCase__				)
														snake_case_							=						test_model(UpperCAmelCase__				)
														self.assertTrue(torch.allclose(UpperCAmelCase__						,				UpperCAmelCase__						,				atol=1E-5				)				)

														# Attaching a hook to a model when it already has one replaces, does not chain
														snake_case_							=						PreForwardHook()
														add_hook_to_module(UpperCAmelCase__						,				UpperCAmelCase__				)
														snake_case_							=						test_model(UpperCAmelCase__				)
														self.assertTrue(torch.allclose(UpperCAmelCase__						,				UpperCAmelCase__						,				atol=1E-5				)				)

														# You need to use the sequential hook to chain two or more hooks
														snake_case_							=						SequentialHook(PreForwardHook()						,				PreForwardHook()				)
														add_hook_to_module(UpperCAmelCase__						,				UpperCAmelCase__				)

														snake_case_							=						test_model(UpperCAmelCase__				)
														assert torch.allclose(UpperCAmelCase__						,				UpperCAmelCase__						,				atol=1E-5				)



							def 						lowerCAmelCase				( self							:					Tuple				)    ->int:



														"""simple docstring"""



														snake_case_							=						ModelForTest()
														snake_case_							=						torch.randn(2						,				3				)
														snake_case_							=						test_model(UpperCAmelCase__				)

														snake_case_							=						PostForwardHook()
														add_hook_to_module(UpperCAmelCase__						,				UpperCAmelCase__				)
														snake_case_							=						test_model(UpperCAmelCase__				)
														self.assertTrue(torch.allclose(UpperCAmelCase__						,				output + 1						,				atol=1E-5				)				)

														# Attaching a hook to a model when it already has one replaces, does not chain
														snake_case_							=						PostForwardHook()
														add_hook_to_module(UpperCAmelCase__						,				UpperCAmelCase__				)
														snake_case_							=						test_model(UpperCAmelCase__				)
														self.assertTrue(torch.allclose(UpperCAmelCase__						,				output + 1						,				atol=1E-5				)				)

														# You need to use the sequential hook to chain two or more hooks
														snake_case_							=						SequentialHook(PostForwardHook()						,				PostForwardHook()				)
														add_hook_to_module(UpperCAmelCase__						,				UpperCAmelCase__				)

														snake_case_							=						test_model(UpperCAmelCase__				)
														assert torch.allclose(UpperCAmelCase__						,				output + 2						,				atol=1E-5				)



							def 						lowerCAmelCase				( self							:					Dict				)    ->Optional[Any]:



														"""simple docstring"""



														snake_case_							=						ModelForTest()
														snake_case_							=						torch.randn(2						,				3				)
														snake_case_							=						test_model(UpperCAmelCase__				)

														snake_case_							=						PostForwardHook()
														add_hook_to_module(UpperCAmelCase__						,				UpperCAmelCase__				)
														snake_case_							=						test_model(UpperCAmelCase__				)
														self.assertTrue(torch.allclose(UpperCAmelCase__						,				output + 1				)				)
														self.assertTrue(outputa.requires_grad				)

														snake_case_							=						True
														snake_case_							=						test_model(UpperCAmelCase__				)
														self.assertFalse(outputa.requires_grad				)



							@require_multi_gpu
							def 						lowerCAmelCase				( self							:					Optional[Any]				)    ->str:



														"""simple docstring"""



														snake_case_							=						ModelForTest()
														# Everything is on CPU
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)

														# This will move each submodule on different devices
														add_hook_to_module(model.lineara						,				AlignDevicesHook(execution_device=0				)				)
														add_hook_to_module(model.batchnorm						,				AlignDevicesHook(execution_device=0				)				)
														add_hook_to_module(model.lineara						,				AlignDevicesHook(execution_device=1				)				)

														self.assertEqual(model.lineara.weight.device						,				torch.device(0				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device(0				)				)
														self.assertEqual(model.batchnorm.running_mean.device						,				torch.device(0				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device(1				)				)

														# We can still make a forward pass. The input does not need to be on any particular device
														snake_case_							=						torch.randn(2						,				3				)
														snake_case_							=						model(UpperCAmelCase__				)
														self.assertEqual(output.device						,				torch.device(1				)				)

														# We can add a general hook to put back output on same device as input.
														add_hook_to_module(UpperCAmelCase__						,				AlignDevicesHook(io_same_device=UpperCAmelCase__				)				)
														snake_case_							=						torch.randn(2						,				3				).to(0				)
														snake_case_							=						model(UpperCAmelCase__				)
														self.assertEqual(output.device						,				torch.device(0				)				)



							def 						lowerCAmelCase				( self							:					str				)    ->Union[str, Any]:



														"""simple docstring"""



														snake_case_							=						ModelForTest()

														# Everything is on CPU
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)

														# This will move each submodule on different devices
														snake_case_							=						{"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True}

														add_hook_to_module(model.lineara						,				AlignDevicesHook(**UpperCAmelCase__				)				)
														add_hook_to_module(model.batchnorm						,				AlignDevicesHook(**UpperCAmelCase__				)				)
														add_hook_to_module(model.lineara						,				AlignDevicesHook(**UpperCAmelCase__				)				)

														# Parameters have been offloaded, so on the meta device
														self.assertEqual(model.lineara.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""meta"""				)				)
														# Buffers are not included in the offload by default, so are on the execution device
														snake_case_							=						torch.device(hook_kwargs["""execution_device"""]				)
														self.assertEqual(model.batchnorm.running_mean.device						,				UpperCAmelCase__				)

														snake_case_							=						torch.randn(2						,				3				)
														snake_case_							=						model(UpperCAmelCase__				)
														self.assertEqual(output.device						,				UpperCAmelCase__				)

														# Removing hooks loads back the weights in the model.
														remove_hook_from_module(model.lineara				)
														remove_hook_from_module(model.batchnorm				)
														remove_hook_from_module(model.lineara				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)

														# Now test with buffers included in the offload
														snake_case_							=						{
														    """execution_device""": 0 if torch.cuda.is_available() else """cpu""",
														    """offload""": True,
														    """offload_buffers""": True,
														}

														add_hook_to_module(model.lineara						,				AlignDevicesHook(**UpperCAmelCase__				)				)
														add_hook_to_module(model.batchnorm						,				AlignDevicesHook(**UpperCAmelCase__				)				)
														add_hook_to_module(model.lineara						,				AlignDevicesHook(**UpperCAmelCase__				)				)

														# Parameters have been offloaded, so on the meta device, buffers included
														self.assertEqual(model.lineara.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.batchnorm.running_mean.device						,				torch.device("""meta"""				)				)

														snake_case_							=						torch.randn(2						,				3				)
														snake_case_							=						model(UpperCAmelCase__				)
														self.assertEqual(output.device						,				UpperCAmelCase__				)

														# Removing hooks loads back the weights in the model.
														remove_hook_from_module(model.lineara				)
														remove_hook_from_module(model.batchnorm				)
														remove_hook_from_module(model.lineara				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)



							def 						lowerCAmelCase				( self							:					Tuple				)    ->List[str]:



														"""simple docstring"""



														snake_case_							=						ModelForTest()

														# Everything is on CPU
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)

														# This will move each submodule on different devices
														snake_case_							=						0 if torch.cuda.is_available() else """cpu"""
														attach_align_device_hook(UpperCAmelCase__						,				execution_device=UpperCAmelCase__						,				offload=UpperCAmelCase__				)

														# Parameters have been offloaded, so on the meta device
														self.assertEqual(model.lineara.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""meta"""				)				)
														# Buffers are not included in the offload by default, so are on the execution device
														snake_case_							=						torch.device(UpperCAmelCase__				)
														self.assertEqual(model.batchnorm.running_mean.device						,				UpperCAmelCase__				)

														snake_case_							=						torch.randn(2						,				3				)
														snake_case_							=						model(UpperCAmelCase__				)
														self.assertEqual(output.device						,				UpperCAmelCase__				)

														# Removing hooks loads back the weights in the model.
														remove_hook_from_submodules(UpperCAmelCase__				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)

														# Now test with buffers included in the offload
														attach_align_device_hook(UpperCAmelCase__						,				execution_device=UpperCAmelCase__						,				offload=UpperCAmelCase__						,				offload_buffers=UpperCAmelCase__				)

														# Parameters have been offloaded, so on the meta device, buffers included
														self.assertEqual(model.lineara.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.batchnorm.running_mean.device						,				torch.device("""meta"""				)				)

														snake_case_							=						torch.randn(2						,				3				)
														snake_case_							=						model(UpperCAmelCase__				)
														self.assertEqual(output.device						,				UpperCAmelCase__				)

														# Removing hooks loads back the weights in the model.
														remove_hook_from_submodules(UpperCAmelCase__				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)



							def 						lowerCAmelCase				( self							:					Dict				)    ->str:



														"""simple docstring"""



														snake_case_							=						ModelForTest()

														# Everything is on CPU
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)

														# This will move each submodule on different devices
														snake_case_							=						0 if torch.cuda.is_available() else """cpu"""
														attach_align_device_hook(
														    UpperCAmelCase__						,				execution_device=UpperCAmelCase__						,				offload=UpperCAmelCase__						,				weights_map=model.state_dict()				)

														# Parameters have been offloaded, so on the meta device
														self.assertEqual(model.lineara.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""meta"""				)				)
														# Buffers are not included in the offload by default, so are on the execution device
														snake_case_							=						torch.device(UpperCAmelCase__				)
														self.assertEqual(model.batchnorm.running_mean.device						,				UpperCAmelCase__				)

														snake_case_							=						torch.randn(2						,				3				)
														snake_case_							=						model(UpperCAmelCase__				)
														self.assertEqual(output.device						,				UpperCAmelCase__				)

														# Removing hooks loads back the weights in the model.
														remove_hook_from_submodules(UpperCAmelCase__				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)

														# Now test with buffers included in the offload
														attach_align_device_hook(
														    UpperCAmelCase__						,				execution_device=UpperCAmelCase__						,				offload=UpperCAmelCase__						,				weights_map=model.state_dict()						,				offload_buffers=UpperCAmelCase__						,				)

														# Parameters have been offloaded, so on the meta device, buffers included
														self.assertEqual(model.lineara.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""meta"""				)				)
														self.assertEqual(model.batchnorm.running_mean.device						,				torch.device("""meta"""				)				)

														snake_case_							=						torch.randn(2						,				3				)
														snake_case_							=						model(UpperCAmelCase__				)
														self.assertEqual(output.device						,				UpperCAmelCase__				)

														# Removing hooks loads back the weights in the model.
														remove_hook_from_submodules(UpperCAmelCase__				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.batchnorm.weight.device						,				torch.device("""cpu"""				)				)
														self.assertEqual(model.lineara.weight.device						,				torch.device("""cpu"""				)				)











"""simple docstring"""




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

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


_snake_case          =   logging.getLogger(__name__)






class        UpperCamelCase			(	snake_case_      ):


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


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Union[Split, str]							)					->       List[InputExample]:
   if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       Any             =	mode.value
   _a  :       Optional[int]             =	os.path.join(UpperCAmelCase__							,						f"""{mode}.txt"""							)
   _a  :       int             =	1
   _a  :       int             =	[]
   with open(UpperCAmelCase__							,						encoding="""utf-8"""							) as f:
     _a  :       str             =	[]
     _a  :       str             =	[]
     for line in f:
       if line.startswith("""-DOCSTART-"""							) or line == "" or line == "\n":
         if words:
           examples.append(InputExample(guid=f"""{mode}-{guid_index}"""							,						words=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
           guid_index += 1
           _a  :       List[str]             =	[]
           _a  :       str             =	[]
       else:
         _a  :       List[Any]             =	line.split(""" """							)
         words.append(splits[0]							)
         if len(UpperCAmelCase__							) > 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=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
   return examples


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						List							)					->       Union[str, Any]:
   _a  :       List[str]             =	0
   for line in test_input_reader:
     if line.startswith("""-DOCSTART-"""							) or line == "" or line == "\n":
       writer.write(UpperCAmelCase__							)
       if not preds_list[example_id]:
         example_id += 1
     elif preds_list[example_id]:
       _a  :       int             =	line.split()[0] + """ """ + preds_list[example_id].pop(0							) + """\n"""
       writer.write(UpperCAmelCase__							)
     else:
       logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""							,						line.split()[0]							)







 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       _a  :       List[Any]             =	f.read().splitlines()
     if "O" not in labels:
       _a  :       Union[str, Any]             =	["""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			(	snake_case_      ):


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







 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       _a  :       Optional[int]             =	f.read().splitlines()
     if "O" not in labels:
       _a  :       Optional[Any]             =	["""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			(	snake_case_      ):


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						Union[Split, str]							)					->       List[InputExample]:
   if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       List[Any]             =	mode.value
   _a  :       Union[str, Any]             =	os.path.join(UpperCAmelCase__							,						f"""{mode}.txt"""							)
   _a  :       List[str]             =	1
   _a  :       Optional[Any]             =	[]

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


 def _lowercase	(     self		:						Tuple							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						List							)					->       Dict:
   _a  :       Optional[Any]             =	0
   for sentence in parse_incr(UpperCAmelCase__							):
     _a  :       List[str]             =	preds_list[example_id]
     _a  :       str             =	""""""
     for token in sentence:
       out += f"""{token['form']} ({token['upos']}|{s_p.pop(0							)}) """
     out += "\n"
     writer.write(UpperCAmelCase__							)
     example_id += 1







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

