
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import MaskFormerSwinBackbone
						from transformers.models.maskformer import MaskFormerSwinModel






class 	snake_case__       :







		"""simple docstring"""

		def __init__(      self				:			str  ,	UpperCamelCase__				:			List[Any]  ,	UpperCamelCase__				:			List[str]=13  ,	UpperCamelCase__				:			List[str]=32  ,	UpperCamelCase__				:			Tuple=2  ,	UpperCamelCase__				:			Optional[Any]=3  ,	UpperCamelCase__				:			List[str]=16  ,	UpperCamelCase__				:			int=[1, 2, 1]  ,	UpperCamelCase__				:			Any=[2, 2, 4]  ,	UpperCamelCase__				:			Any=2  ,	UpperCamelCase__				:			List[Any]=2.0  ,	UpperCamelCase__				:			Dict=True  ,	UpperCamelCase__				:			str=0.0  ,	UpperCamelCase__				:			str=0.0  ,	UpperCamelCase__				:			Optional[Any]=0.1  ,	UpperCamelCase__				:			List[str]="gelu"  ,	UpperCamelCase__				:			Optional[Any]=False  ,	UpperCamelCase__				:			Optional[int]=True  ,	UpperCamelCase__				:			Union[str, Any]=0.02  ,	UpperCamelCase__				:			str=1e-5  ,	UpperCamelCase__				:			str=True  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			Any=True  ,	UpperCamelCase__				:			Optional[int]=10  ,	UpperCamelCase__				:			Union[str, Any]=8  ,	UpperCamelCase__				:			Optional[int]=["stage1", "stage2", "stage3"]  ,	UpperCamelCase__				:			Any=[1, 2, 3]  ,	)						->			str:






				"""simple docstring"""







				snake_case  :       Tuple     			=						parent
				snake_case  :       Optional[int]     			=						batch_size
				snake_case  :       Optional[int]     			=						image_size
				snake_case  :       Optional[int]     			=						patch_size
				snake_case  :       List[str]     			=						num_channels
				snake_case  :       Optional[Any]     			=						embed_dim
				snake_case  :       Optional[int]     			=						depths
				snake_case  :       List[str]     			=						num_heads
				snake_case  :       Optional[Any]     			=						window_size
				snake_case  :       List[Any]     			=						mlp_ratio
				snake_case  :       Union[str, Any]     			=						qkv_bias
				snake_case  :       List[str]     			=						hidden_dropout_prob
				snake_case  :       List[Any]     			=						attention_probs_dropout_prob
				snake_case  :       Union[str, Any]     			=						drop_path_rate
				snake_case  :       Dict     			=						hidden_act
				snake_case  :       List[str]     			=						use_absolute_embeddings
				snake_case  :       Any     			=						patch_norm
				snake_case  :       Tuple     			=						layer_norm_eps
				snake_case  :       List[str]     			=						initializer_range
				snake_case  :       Union[str, Any]     			=						is_training
				snake_case  :       List[str]     			=						scope
				snake_case  :       Tuple     			=						use_labels
				snake_case  :       List[str]     			=						type_sequence_label_size
				snake_case  :       Optional[Any]     			=						encoder_stride
				snake_case  :       int     			=						out_features
				snake_case  :       List[Any]     			=						out_indices



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






				"""simple docstring"""







				snake_case  :       Tuple     			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

				snake_case  :       int     			=						None
				if self.use_labels:
						snake_case  :       List[Any]     			=						ids_tensor([self.batch_size]  ,	self.type_sequence_label_size      )

				snake_case  :       Any     			=						self.get_config()

				return config, pixel_values, labels



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






				"""simple docstring"""







				return MaskFormerSwinConfig(
				    image_size=self.image_size  ,	patch_size=self.patch_size  ,	num_channels=self.num_channels  ,	embed_dim=self.embed_dim  ,	depths=self.depths  ,	num_heads=self.num_heads  ,	window_size=self.window_size  ,	mlp_ratio=self.mlp_ratio  ,	qkv_bias=self.qkv_bias  ,	hidden_dropout_prob=self.hidden_dropout_prob  ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,	drop_path_rate=self.drop_path_rate  ,	hidden_act=self.hidden_act  ,	use_absolute_embeddings=self.use_absolute_embeddings  ,	path_norm=self.patch_norm  ,	layer_norm_eps=self.layer_norm_eps  ,	initializer_range=self.initializer_range  ,	encoder_stride=self.encoder_stride  ,	out_features=self.out_features  ,	out_indices=self.out_indices  ,	)



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






				"""simple docstring"""







				snake_case  :       List[Any]     			=						MaskFormerSwinModel(config=UpperCamelCase__      )
				model.to(UpperCamelCase__      )
				model.eval()
				snake_case  :       Union[str, Any]     			=						model(UpperCamelCase__      )

				snake_case  :       Tuple     			=						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
				snake_case  :       Dict     			=						int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

				self.parent.assertEqual(result.last_hidden_state.shape  ,	(self.batch_size, expected_seq_len, expected_dim)      )



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






				"""simple docstring"""







				snake_case  :       List[Any]     			=						MaskFormerSwinBackbone(config=UpperCamelCase__      )
				model.to(UpperCamelCase__      )
				model.eval()
				snake_case  :       int     			=						model(UpperCamelCase__      )

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps      )  ,	len(config.out_features      )      )
				self.parent.assertListEqual(list(result.feature_maps[0].shape      )  ,	[13, 16, 16, 16]      )

				# verify channels
				self.parent.assertEqual(len(model.channels      )  ,	len(config.out_features      )      )
				self.parent.assertListEqual(model.channels  ,	[16, 32, 64]      )

				# verify ValueError
				with self.parent.assertRaises(UpperCamelCase__      ):
						snake_case  :       str     			=						["stem"]
						snake_case  :       Any     			=						MaskFormerSwinBackbone(config=UpperCamelCase__      )



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






				"""simple docstring"""







				snake_case  :       int     			=						self.prepare_config_and_inputs()
				snake_case  :       Union[str, Any]     			=						config_and_inputs
				snake_case  :       Optional[int]     			=						{"pixel_values": pixel_values}
				return config, inputs_dict







@require_torch
class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						(
		    (
		        MaskFormerSwinModel,
		        MaskFormerSwinBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		lowerCamelCase     							=						{"""feature-extraction""": MaskFormerSwinModel} if is_torch_available() else {}
		lowerCamelCase     							=						False
		lowerCamelCase     							=						False
		lowerCamelCase     							=						False
		lowerCamelCase     							=						False
		lowerCamelCase     							=						False



		def       lowerCAmelCase				(      self				:			Dict      )						->			str:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						MaskFormerSwinModelTester(self      )
				snake_case  :       str     			=						ConfigTester(self  ,	config_class=UpperCamelCase__  ,	embed_dim=37      )



		@require_torch_multi_gpu
		@unittest.skip(
		    reason=(
		        '''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
		        ''' `nn.DataParallel`'''
		    )      )
		def       lowerCAmelCase				(      self				:			Any      )						->			List[str]:






				"""simple docstring"""







				pass



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






				"""simple docstring"""







				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()



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






				"""simple docstring"""







				return



		def       lowerCAmelCase				(      self				:			str      )						->			Any:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCamelCase__      )



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






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_backbone(*UpperCamelCase__      )



		@unittest.skip('''Swin does not use inputs_embeds'''      )
		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			Dict:






				"""simple docstring"""







				pass



		@unittest.skip('''Swin does not support feedforward chunking'''      )
		def       lowerCAmelCase				(      self				:			str      )						->			Any:






				"""simple docstring"""







				pass



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






				"""simple docstring"""







				snake_case  :       Tuple     			=						self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						snake_case  :       List[Any]     			=						model_class(UpperCamelCase__      )
						self.assertIsInstance(model.get_input_embeddings()  ,	(nn.Module)      )
						snake_case  :       Optional[Any]     			=						model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(UpperCamelCase__  ,	nn.Linear      )      )



		def       lowerCAmelCase				(      self				:			int      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						snake_case  :       Any     			=						model_class(UpperCamelCase__      )
						snake_case  :       Tuple     			=						inspect.signature(model.forward      )
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						snake_case  :       str     			=						[*signature.parameters.keys()]

						snake_case  :       Optional[Any]     			=						["pixel_values"]
						self.assertListEqual(arg_names[:1]  ,	UpperCamelCase__      )



		@unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'''      )
		def       lowerCAmelCase				(      self				:			Tuple      )						->			int:






				"""simple docstring"""







				pass



		@unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone'''      )
		def       lowerCAmelCase				(      self				:			Tuple      )						->			List[str]:






				"""simple docstring"""







				pass



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			Dict  ,	UpperCamelCase__				:			Dict  ,	UpperCamelCase__				:			Optional[Any]      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						model_class(UpperCamelCase__      )
				model.to(UpperCamelCase__      )
				model.eval()

				with torch.no_grad():
						snake_case  :       List[Any]     			=						model(**self._prepare_for_class(UpperCamelCase__  ,	UpperCamelCase__      )      )

				snake_case  :       str     			=						outputs.hidden_states

				snake_case  :       List[Any]     			=						getattr(
				    self.model_tester  ,	'''expected_num_hidden_layers'''  ,	len(self.model_tester.depths      ) + 1      )
				self.assertEqual(len(UpperCamelCase__      )  ,	UpperCamelCase__      )

				# Swin has a different seq_length
				snake_case  :       Union[str, Any]     			=						(
				    config.patch_size
				    if isinstance(config.patch_size  ,	collections.abc.Iterable      )
				    else (config.patch_size, config.patch_size)
				)

				snake_case  :       List[str]     			=						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

				self.assertListEqual(
				    list(hidden_states[0].shape[-2:]      )  ,	[num_patches, self.model_tester.embed_dim]  ,	)



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






				"""simple docstring"""







				snake_case  :       Dict     			=						self.model_tester.prepare_config_and_inputs_for_common()

				snake_case  :       Optional[int]     			=						(
				    self.model_tester.image_size
				    if isinstance(self.model_tester.image_size  ,	collections.abc.Iterable      )
				    else (self.model_tester.image_size, self.model_tester.image_size)
				)

				for model_class in self.all_model_classes:
						snake_case  :       Dict     			=						True
						self.check_hidden_states_output(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						snake_case  :       int     			=						True

						self.check_hidden_states_output(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )



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






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						self.model_tester.prepare_config_and_inputs_for_common()
				snake_case  :       Dict     			=						3

				snake_case  :       Optional[Any]     			=						(
				    self.model_tester.image_size
				    if isinstance(self.model_tester.image_size  ,	collections.abc.Iterable      )
				    else (self.model_tester.image_size, self.model_tester.image_size)
				)
				snake_case  :       Optional[int]     			=						(
				    config.patch_size
				    if isinstance(config.patch_size  ,	collections.abc.Iterable      )
				    else (config.patch_size, config.patch_size)
				)

				snake_case  :       int     			=						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
				snake_case  :       Optional[Any]     			=						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

				for model_class in self.all_model_classes:
						snake_case  :       Union[str, Any]     			=						True
						self.check_hidden_states_output(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	(padded_height, padded_width)      )

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						snake_case  :       int     			=						True
						self.check_hidden_states_output(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	(padded_height, padded_width)      )



		@unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints'''      )
		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			Optional[Any]:






				"""simple docstring"""







				pass



		@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''      )
		def       lowerCAmelCase				(      self				:			Dict      )						->			Any:






				"""simple docstring"""







				pass



		@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''      )
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			int:






				"""simple docstring"""







				pass



		def       lowerCAmelCase				(      self				:			Any      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.model_tester.prepare_config_and_inputs_for_common()

				def set_nan_tensor_to_zero(UpperCamelCase__				:			Dict      ):
						snake_case  :       Dict     			=						0
						return t

				def check_equivalence(UpperCamelCase__				:			Dict  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			List[Any]={}      ):
						with torch.no_grad():
								snake_case  :       Optional[Any]     			=						model(**UpperCamelCase__  ,	return_dict=UpperCamelCase__  ,	**UpperCamelCase__      )
								snake_case  :       List[str]     			=						model(**UpperCamelCase__  ,	return_dict=UpperCamelCase__  ,	**UpperCamelCase__      ).to_tuple()

								def recursive_check(UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			str      ):
										if isinstance(UpperCamelCase__  ,	(List, Tuple)      ):
												for tuple_iterable_value, dict_iterable_value in zip(UpperCamelCase__  ,	UpperCamelCase__      ):
														recursive_check(UpperCamelCase__  ,	UpperCamelCase__      )
										elif isinstance(UpperCamelCase__  ,	UpperCamelCase__      ):
												for tuple_iterable_value, dict_iterable_value in zip(
												    tuple_object.values()  ,	dict_object.values()      ):
														recursive_check(UpperCamelCase__  ,	UpperCamelCase__      )
										elif tuple_object is None:
												return
										else:
												self.assertTrue(
												    torch.allclose(
												        set_nan_tensor_to_zero(UpperCamelCase__      )  ,	set_nan_tensor_to_zero(UpperCamelCase__      )  ,	atol=1e-5      )  ,	msg=(
												        '''Tuple and dict output are not equal. Difference:'''
												        f' {torch.max(torch.abs(tuple_object - dict_object      )      )}. Tuple has `nan`:'
												        f' {torch.isnan(UpperCamelCase__      ).any()} and `inf`: {torch.isinf(UpperCamelCase__      )}. Dict has'
												        f' `nan`: {torch.isnan(UpperCamelCase__      ).any()} and `inf`: {torch.isinf(UpperCamelCase__      )}.'
												    )  ,	)

								recursive_check(UpperCamelCase__  ,	UpperCamelCase__      )

				for model_class in self.all_model_classes:
						snake_case  :       List[Any]     			=						model_class(UpperCamelCase__      )
						model.to(UpperCamelCase__      )
						model.eval()

						snake_case  :       str     			=						self._prepare_for_class(UpperCamelCase__  ,	UpperCamelCase__      )
						snake_case  :       Dict     			=						self._prepare_for_class(UpperCamelCase__  ,	UpperCamelCase__      )
						check_equivalence(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

						snake_case  :       Optional[int]     			=						self._prepare_for_class(UpperCamelCase__  ,	UpperCamelCase__  ,	return_labels=UpperCamelCase__      )
						snake_case  :       Any     			=						self._prepare_for_class(UpperCamelCase__  ,	UpperCamelCase__  ,	return_labels=UpperCamelCase__      )
						check_equivalence(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

						snake_case  :       Optional[Any]     			=						self._prepare_for_class(UpperCamelCase__  ,	UpperCamelCase__      )
						snake_case  :       Union[str, Any]     			=						self._prepare_for_class(UpperCamelCase__  ,	UpperCamelCase__      )
						check_equivalence(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	{'''output_hidden_states''': True}      )

						snake_case  :       Any     			=						self._prepare_for_class(UpperCamelCase__  ,	UpperCamelCase__  ,	return_labels=UpperCamelCase__      )
						snake_case  :       int     			=						self._prepare_for_class(UpperCamelCase__  ,	UpperCamelCase__  ,	return_labels=UpperCamelCase__      )
						check_equivalence(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	{'''output_hidden_states''': True}      )







@require_torch
class 	snake_case__       (      unittest.TestCase    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						(MaskFormerSwinBackbone,) if is_torch_available() else ()
		lowerCamelCase     							=						MaskFormerSwinConfig



		def       lowerCAmelCase				(      self				:			int      )						->			Dict:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						MaskFormerSwinModelTester(self      )



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






				"""simple docstring"""







				snake_case  :       List[str]     			=						self.model_tester.prepare_config_and_inputs_for_common()
				snake_case  :       int     			=						inputs_dict["pixel_values"].shape[0]

				for backbone_class in self.all_model_classes:
						snake_case  :       Optional[Any]     			=						backbone_class(UpperCamelCase__      )
						backbone.to(UpperCamelCase__      )
						backbone.eval()

						snake_case  :       List[Any]     			=						backbone(**UpperCamelCase__      )

						# Test default outputs and verify feature maps
						self.assertIsInstance(outputs.feature_maps  ,	UpperCamelCase__      )
						self.assertTrue(len(outputs.feature_maps      ) == len(backbone.channels      )      )
						for feature_map, n_channels in zip(outputs.feature_maps  ,	backbone.channels      ):
								self.assertTrue(feature_map.shape[:2]  ,	(batch_size, n_channels)      )
						self.assertIsNone(outputs.hidden_states      )
						self.assertIsNone(outputs.attentions      )

						# Test output_hidden_states=True
						snake_case  :       Dict     			=						backbone(**UpperCamelCase__  ,	output_hidden_states=UpperCamelCase__      )
						self.assertIsNotNone(outputs.hidden_states      )
						self.assertTrue(len(outputs.hidden_states      )  ,	len(backbone.stage_names      )      )
						# We skip the stem layer
						for hidden_states, n_channels in zip(outputs.hidden_states[1:]  ,	backbone.channels      ):
								for hidden_state in hidden_states:
										# Hidden states are in the format (batch_size, (height * width), n_channels)
										snake_case  :       Any     			=						hidden_state.shape
										self.assertTrue((h_batch_size, h_n_channels)  ,	(batch_size, n_channels)      )

            # Test output_attentions=True
						if self.has_attentions:
								snake_case  :       Optional[Any]     			=						backbone(**UpperCamelCase__  ,	output_attentions=UpperCamelCase__      )
								self.assertIsNotNone(outputs.attentions      )






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

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


SCREAMING_SNAKE_CASE__     					=			logging.getLogger(__name__)



class 						_UpperCAmelCase      (     lowercase							):






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






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     Union[Split, str]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						mode.value
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(UpperCAmelCase   ,  F"{mode}.txt")
				SCREAMING_SNAKE_CASE_				:Tuple				     =						1
				SCREAMING_SNAKE_CASE_				:str				     =						[]
				with open(UpperCAmelCase   ,  encoding="utf-8") as f:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
						SCREAMING_SNAKE_CASE_				:int				     =						[]
						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
												SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
												SCREAMING_SNAKE_CASE_				:Any				     =						[]
								else:
										SCREAMING_SNAKE_CASE_				:int				     =						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 _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     List):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						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]:
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						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 _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								SCREAMING_SNAKE_CASE_				:Any				     =						f.read().splitlines()
						if "O" not in labels:
								SCREAMING_SNAKE_CASE_				: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      (     lowercase							):






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







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						f.read().splitlines()
						if "O" not in labels:
								SCREAMING_SNAKE_CASE_				:Dict				     =						["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      (     lowercase							):






		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     Union[Split, str]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						mode.value
				SCREAMING_SNAKE_CASE_				:List[str]				     =						os.path.join(UpperCAmelCase   ,  F"{mode}.txt")
				SCREAMING_SNAKE_CASE_				:Dict				     =						1
				SCREAMING_SNAKE_CASE_				:List[str]				     =						[]

				with open(UpperCAmelCase   ,  encoding="utf-8") as f:
						for sentence in parse_incr(UpperCAmelCase):
								SCREAMING_SNAKE_CASE_				:List[str]				     =						[]
								SCREAMING_SNAKE_CASE_				:int				     =						[]
								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 _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     List):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						0
				for sentence in parse_incr(UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:str				     =						preds_list[example_id]
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						""
						for token in sentence:
								out += F"{token['form']} ({token['upos']}|{s_p.pop(0)}) "
						out += "\n"
						writer.write(UpperCAmelCase)
						example_id += 1







		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     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",
						]






