
"""simple docstring"""




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

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


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



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__=-1					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     label_idx



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	List[str]:



											"""simple docstring"""




											if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: List[str]     =     mode.value
											SCREAMING_SNAKE_CASE__		: str     =     os.path.join(SCREAMING_SNAKE_CASE__     , F'''{mode}.txt'''					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     1
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     []
											with open(SCREAMING_SNAKE_CASE__     , encoding="""utf-8"""					) as f:
																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     []
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     []
																		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
																																							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     []
																																							SCREAMING_SNAKE_CASE__		: List[Any]     =     []
																									else:
																																SCREAMING_SNAKE_CASE__		: List[Any]     =     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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     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]:
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	int:



											"""simple docstring"""




											if path:
																		with open(SCREAMING_SNAKE_CASE__     , """r"""					) as f:
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     f.read().splitlines()
																		if "O" not in labels:
																									SCREAMING_SNAKE_CASE__		: 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 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def __init__(self					)      ->	List[str]:



											"""simple docstring"""




											super().__init__(label_idx=-2					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Tuple:



											"""simple docstring"""




											if path:
																		with open(SCREAMING_SNAKE_CASE__     , """r"""					) as f:
																									SCREAMING_SNAKE_CASE__		: Optional[int]     =     f.read().splitlines()
																		if "O" not in labels:
																									SCREAMING_SNAKE_CASE__		: Optional[int]     =     ["""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 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	str:



											"""simple docstring"""




											if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: List[str]     =     mode.value
											SCREAMING_SNAKE_CASE__		: int     =     os.path.join(SCREAMING_SNAKE_CASE__     , F'''{mode}.txt'''					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     1
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     []

											with open(SCREAMING_SNAKE_CASE__     , encoding="""utf-8"""					) as f:
																		for sentence in parse_incr(SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: Tuple     =     []
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     []
																									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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     0
											for sentence in parse_incr(SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: Tuple     =     preds_list[example_id]
																		SCREAMING_SNAKE_CASE__		: List[str]     =     """"""
																		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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Union[str, Any]:



											"""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",
																		]






"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import MobileViTImageProcessor



class 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=7     , SCREAMING_SNAKE_CASE__=3     , SCREAMING_SNAKE_CASE__=18     , SCREAMING_SNAKE_CASE__=30     , SCREAMING_SNAKE_CASE__=4_00     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=True     , )      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     size if size is not None else {"""shortest_edge""": 20}
											SCREAMING_SNAKE_CASE__		: List[Any]     =     crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											SCREAMING_SNAKE_CASE__		: Any     =     parent
											SCREAMING_SNAKE_CASE__		: List[str]     =     batch_size
											SCREAMING_SNAKE_CASE__		: Dict     =     num_channels
											SCREAMING_SNAKE_CASE__		: List[Any]     =     image_size
											SCREAMING_SNAKE_CASE__		: Dict     =     min_resolution
											SCREAMING_SNAKE_CASE__		: Dict     =     max_resolution
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     do_resize
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     size
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     do_center_crop
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     crop_size
											SCREAMING_SNAKE_CASE__		: List[str]     =     do_flip_channel_order



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "do_flip_channel_order": self.do_flip_channel_order,
											}






@require_torch
@require_vision
class 			lowerCAmelCase_		(a__				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		str       =     MobileViTImageProcessor if is_vision_available() else None



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     MobileViTImageProcessingTester(self					)



				@property
				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.image_processing_class(**self.image_processor_dict					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """do_resize"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """size"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """do_center_crop"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """center_crop"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """do_flip_channel_order"""					)					)



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     self.image_processing_class.from_dict(self.image_processor_dict					)
											self.assertEqual(image_processor.size     , {"""shortest_edge""": 20}					)
											self.assertEqual(image_processor.crop_size     , {"""height""": 18, """width""": 18}					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.image_processing_class.from_dict(self.image_processor_dict     , size=42     , crop_size=84					)
											self.assertEqual(image_processor.size     , {"""shortest_edge""": 42}					)
											self.assertEqual(image_processor.crop_size     , {"""height""": 84, """width""": 84}					)



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



											"""simple docstring"""




											pass



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     self.image_processing_class(**self.image_processor_dict					)
											# create random PIL images
											SCREAMING_SNAKE_CASE__		: List[str]     =     prepare_image_inputs(self.image_processor_tester     , equal_resolution=SCREAMING_SNAKE_CASE__					)
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__     , Image.Image					)

											# Test not batched input
											SCREAMING_SNAKE_CASE__		: Dict     =     image_processing(image_inputs[0]     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )

											# Test batched
											SCREAMING_SNAKE_CASE__		: List[str]     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.image_processing_class(**self.image_processor_dict					)
											# create random numpy tensors
											SCREAMING_SNAKE_CASE__		: Tuple     =     prepare_image_inputs(self.image_processor_tester     , equal_resolution=SCREAMING_SNAKE_CASE__     , numpify=SCREAMING_SNAKE_CASE__					)
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__     , np.ndarray					)

											# Test not batched input
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     image_processing(image_inputs[0]     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )

											# Test batched
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     self.image_processing_class(**self.image_processor_dict					)
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE__		: Any     =     prepare_image_inputs(self.image_processor_tester     , equal_resolution=SCREAMING_SNAKE_CASE__     , torchify=SCREAMING_SNAKE_CASE__					)
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__     , torch.Tensor					)

											# Test not batched input
											SCREAMING_SNAKE_CASE__		: List[str]     =     image_processing(image_inputs[0]     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )

											# Test batched
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )




