






"""simple docstring"""




import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort






class        UpperCamelCase			(	snake_case_		,     unittest.TestCase      ):
 UpperCamelCase					:       List[str]			=					'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


 def _lowercase	(     self		:						Tuple							,						UpperCAmelCase__		:						Union[str, Any]=0							)					->       int:
   _a  :       Dict             =	np.random.RandomState(UpperCAmelCase__							)
   _a  :       str             =	{
       """prompt""": """A painting of a squirrel eating a burger""",
       """generator""": generator,
       """num_inference_steps""": 2,
       """guidance_scale""": 7.5,
       """output_type""": """numpy""",
   }
   return inputs


 def _lowercase	(     self		:						Any							)					->       Union[str, Any]:
   _a  :       Any             =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,						provider="""CPUExecutionProvider"""							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Any             =	self.get_dummy_inputs()
   _a  :       Union[str, Any]             =	pipe(**UpperCAmelCase__							).images
   _a  :       Tuple             =	image[0, -3:, -3:, -1]

   assert image.shape == (1, 128, 128, 3)
   _a  :       Optional[int]             =	np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2


 def _lowercase	(     self		:						Union[str, Any]							)					->       int:
   _a  :       Optional[Any]             =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,						provider="""CPUExecutionProvider"""							)
   _a  :       Dict             =	PNDMScheduler.from_config(pipe.scheduler.config							,						skip_prk_steps=UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       List[str]             =	self.get_dummy_inputs()
   _a  :       List[str]             =	pipe(**UpperCAmelCase__							).images
   _a  :       Union[str, Any]             =	image[0, -3:, -3:, -1]

   assert image.shape == (1, 128, 128, 3)
   _a  :       str             =	np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2


 def _lowercase	(     self		:						int							)					->       Any:
   _a  :       Union[str, Any]             =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,						provider="""CPUExecutionProvider"""							)
   _a  :       Tuple             =	LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Dict             =	self.get_dummy_inputs()
   _a  :       Any             =	pipe(**UpperCAmelCase__							).images
   _a  :       Any             =	image[0, -3:, -3:, -1]

   assert image.shape == (1, 128, 128, 3)
   _a  :       Union[str, Any]             =	np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2


 def _lowercase	(     self		:						Optional[int]							)					->       str:
   _a  :       Tuple             =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,						provider="""CPUExecutionProvider"""							)
   _a  :       Union[str, Any]             =	EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Optional[Any]             =	self.get_dummy_inputs()
   _a  :       int             =	pipe(**UpperCAmelCase__							).images
   _a  :       Any             =	image[0, -3:, -3:, -1]

   assert image.shape == (1, 128, 128, 3)
   _a  :       List[Any]             =	np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2


 def _lowercase	(     self		:						Optional[int]							)					->       int:
   _a  :       Tuple             =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,						provider="""CPUExecutionProvider"""							)
   _a  :       Union[str, Any]             =	EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       List[Any]             =	self.get_dummy_inputs()
   _a  :       Union[str, Any]             =	pipe(**UpperCAmelCase__							).images
   _a  :       Optional[Any]             =	image[0, -3:, -3:, -1]

   assert image.shape == (1, 128, 128, 3)
   _a  :       str             =	np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2


 def _lowercase	(     self		:						List[str]							)					->       str:
   _a  :       Dict             =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,						provider="""CPUExecutionProvider"""							)
   _a  :       Optional[Any]             =	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Union[str, Any]             =	self.get_dummy_inputs()
   _a  :       Any             =	pipe(**UpperCAmelCase__							).images
   _a  :       int             =	image[0, -3:, -3:, -1]

   assert image.shape == (1, 128, 128, 3)
   _a  :       Optional[int]             =	np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2


 def _lowercase	(     self		:						List[str]							)					->       List[str]:
   _a  :       str             =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,						provider="""CPUExecutionProvider"""							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       List[Any]             =	self.get_dummy_inputs()
   _a  :       Optional[int]             =	3 * [inputs["""prompt"""]]

   # forward
   _a  :       Dict             =	pipe(**UpperCAmelCase__							)
   _a  :       Tuple             =	output.images[0, -3:, -3:, -1]

   _a  :       str             =	self.get_dummy_inputs()
   _a  :       Union[str, Any]             =	3 * [inputs.pop("""prompt"""							)]

   _a  :       Any             =	pipe.tokenizer(
       UpperCAmelCase__							,						padding="""max_length"""							,						max_length=pipe.tokenizer.model_max_length							,						truncation=UpperCAmelCase__							,						return_tensors="""np"""							,						)
   _a  :       Tuple             =	text_inputs["""input_ids"""]

   _a  :       Dict             =	pipe.text_encoder(input_ids=text_inputs.astype(np.intaa							)							)[0]

   _a  :       int             =	prompt_embeds

   # forward
   _a  :       List[str]             =	pipe(**UpperCAmelCase__							)
   _a  :       Tuple             =	output.images[0, -3:, -3:, -1]

   assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()							).max() < 1E-4







 def _lowercase	(     self		:						Any							)					->       int:
   _a  :       Optional[Any]             =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,						provider="""CPUExecutionProvider"""							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Tuple             =	self.get_dummy_inputs()
   _a  :       List[str]             =	3 * ["""this is a negative prompt"""]
   _a  :       Optional[Any]             =	negative_prompt
   _a  :       Optional[int]             =	3 * [inputs["""prompt"""]]

   # forward
   _a  :       Any             =	pipe(**UpperCAmelCase__							)
   _a  :       List[Any]             =	output.images[0, -3:, -3:, -1]

   _a  :       int             =	self.get_dummy_inputs()
   _a  :       int             =	3 * [inputs.pop("""prompt"""							)]

   _a  :       Tuple             =	[]
   for p in [prompt, negative_prompt]:
     _a  :       Optional[int]             =	pipe.tokenizer(
         UpperCAmelCase__							,						padding="""max_length"""							,						max_length=pipe.tokenizer.model_max_length							,						truncation=UpperCAmelCase__							,						return_tensors="""np"""							,						)
     _a  :       Optional[int]             =	text_inputs["""input_ids"""]

     embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa							)							)[0]							)

   _a      ,  _a  :       str             =	embeds

   # forward
   _a  :       List[str]             =	pipe(**UpperCAmelCase__							)
   _a  :       Optional[int]             =	output.images[0, -3:, -3:, -1]

   assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()							).max() < 1E-4







@nightly
@require_onnxruntime
@require_torch_gpu
class        UpperCamelCase			(	unittest.TestCase      ):


 @property
 def _lowercase	(     self		:						int							)					->       Optional[int]:
   return (
       "CUDAExecutionProvider",
       {
           "gpu_mem_limit": "15000000000",  # 15GB
           "arena_extend_strategy": "kSameAsRequested",
       },
   )


 @property
 def _lowercase	(     self		:						Optional[Any]							)					->       List[Any]:
   _a  :       Optional[Any]             =	ort.SessionOptions()
   _a  :       Dict             =	False
   return options


 def _lowercase	(     self		:						str							)					->       Any:
   # using the PNDM scheduler by default
   _a  :       int             =	OnnxStableDiffusionPipeline.from_pretrained(
       """CompVis/stable-diffusion-v1-4"""							,						revision="""onnx"""							,						safety_checker=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							,						provider=self.gpu_provider							,						sess_options=self.gpu_options							,						)
   sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Optional[Any]             =	"""A painting of a squirrel eating a burger"""
   np.random.seed(0							)
   _a  :       Dict             =	sd_pipe([prompt]							,						guidance_scale=6.0							,						num_inference_steps=10							,						output_type="""np"""							)
   _a  :       Union[str, Any]             =	output.images

   _a  :       Tuple             =	image[0, -3:, -3:, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Optional[Any]             =	np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3


 def _lowercase	(     self		:						Union[str, Any]							)					->       List[str]:
   _a  :       Any             =	DDIMScheduler.from_pretrained(
       """runwayml/stable-diffusion-v1-5"""							,						subfolder="""scheduler"""							,						revision="""onnx"""							)
   _a  :       str             =	OnnxStableDiffusionPipeline.from_pretrained(
       """runwayml/stable-diffusion-v1-5"""							,						revision="""onnx"""							,						scheduler=UpperCAmelCase__							,						safety_checker=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							,						provider=self.gpu_provider							,						sess_options=self.gpu_options							,						)
   sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Tuple             =	"""open neural network exchange"""
   _a  :       List[str]             =	np.random.RandomState(0							)
   _a  :       Optional[int]             =	sd_pipe([prompt]							,						guidance_scale=7.5							,						num_inference_steps=10							,						generator=UpperCAmelCase__							,						output_type="""np"""							)
   _a  :       List[Any]             =	output.images
   _a  :       Optional[int]             =	image[0, -3:, -3:, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Dict             =	np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3


 def _lowercase	(     self		:						Optional[Any]							)					->       List[Any]:
   _a  :       Union[str, Any]             =	LMSDiscreteScheduler.from_pretrained(
       """runwayml/stable-diffusion-v1-5"""							,						subfolder="""scheduler"""							,						revision="""onnx"""							)
   _a  :       Optional[Any]             =	OnnxStableDiffusionPipeline.from_pretrained(
       """runwayml/stable-diffusion-v1-5"""							,						revision="""onnx"""							,						scheduler=UpperCAmelCase__							,						safety_checker=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							,						provider=self.gpu_provider							,						sess_options=self.gpu_options							,						)
   sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Any             =	"""open neural network exchange"""
   _a  :       List[str]             =	np.random.RandomState(0							)
   _a  :       Optional[Any]             =	sd_pipe([prompt]							,						guidance_scale=7.5							,						num_inference_steps=10							,						generator=UpperCAmelCase__							,						output_type="""np"""							)
   _a  :       List[str]             =	output.images
   _a  :       Optional[int]             =	image[0, -3:, -3:, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       List[str]             =	np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3


 def _lowercase	(     self		:						Optional[int]							)					->       str:
   _a  :       Optional[int]             =	0

   def test_callback_fn(UpperCAmelCase__		:						int							,						UpperCAmelCase__		:						int							,						UpperCAmelCase__		:						np.ndarray							) -> None:
     _a  :       Dict             =	True
     nonlocal number_of_steps
     number_of_steps += 1
     if step == 0:
       assert latents.shape == (1, 4, 64, 64)
       _a  :       str             =	latents[0, -3:, -3:, -1]
       _a  :       List[Any]             =	np.array(
           [-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7]							)

       assert np.abs(latents_slice.flatten() - expected_slice							).max() < 1E-3
     elif step == 5:
       assert latents.shape == (1, 4, 64, 64)
       _a  :       Optional[Any]             =	latents[0, -3:, -3:, -1]
       _a  :       List[Any]             =	np.array(
           [-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5]							)

       assert np.abs(latents_slice.flatten() - expected_slice							).max() < 1E-3

   _a  :       List[str]             =	False

   _a  :       Tuple             =	OnnxStableDiffusionPipeline.from_pretrained(
       """runwayml/stable-diffusion-v1-5"""							,						revision="""onnx"""							,						safety_checker=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							,						provider=self.gpu_provider							,						sess_options=self.gpu_options							,						)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Any             =	"""Andromeda galaxy in a bottle"""

   _a  :       Dict             =	np.random.RandomState(0							)
   pipe(
       prompt=UpperCAmelCase__							,						num_inference_steps=5							,						guidance_scale=7.5							,						generator=UpperCAmelCase__							,						callback=UpperCAmelCase__							,						callback_steps=1							,						)
   assert test_callback_fn.has_been_called
   assert number_of_steps == 6







 def _lowercase	(     self		:						List[str]							)					->       Union[str, Any]:
   _a  :       List[str]             =	OnnxStableDiffusionPipeline.from_pretrained(
       """runwayml/stable-diffusion-v1-5"""							,						revision="""onnx"""							,						safety_checker=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							,						provider=self.gpu_provider							,						sess_options=self.gpu_options							,						)
   assert isinstance(UpperCAmelCase__							,						UpperCAmelCase__							)
   assert pipe.safety_checker is None

   _a  :       List[Any]             =	pipe("""example prompt"""							,						num_inference_steps=2							).images[0]
   assert image is not None

   # check that there's no error when saving a pipeline with one of the models being None
   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(UpperCAmelCase__							)
     _a  :       Optional[int]             =	OnnxStableDiffusionPipeline.from_pretrained(UpperCAmelCase__							)

   # sanity check that the pipeline still works
   assert pipe.safety_checker is None
   _a  :       List[Any]             =	pipe("""example prompt"""							,						num_inference_steps=2							).images[0]
   assert image is not None









"""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",
     ]

