



import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


lowerCAmelCase					:				Optional[int]   	="\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = \"Text Chunking using Transformation-Based Learning\",\n    author = \"Ramshaw, Lance  and\n      Marcus, Mitch\",\n    booktitle = \"Third Workshop on Very Large Corpora\",\n    year = \"1995\",\n    url = \"https://www.aclweb.org/anthology/W95-0107\",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n"

lowerCAmelCase					:				List[Any]   	="\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n"

lowerCAmelCase					:				int   	="\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode=\"strict\". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        \"warn\". \"warn\" acts as 0, but the warning is raised.\n\nReturns:\n    'scores': dict. Summary of the scores for overall and per type\n        Overall:\n            'accuracy': accuracy,\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> seqeval = datasets.load_metric(\"seqeval\")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']\n    >>> print(results[\"overall_f1\"])\n    0.5\n    >>> print(results[\"PER\"][\"f1\"])\n    1.0\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     , _KWARGS_DESCRIPTION  )
class 	_a			(      datasets.Metric  ):






			def 							_snake_case   (    self )	->					List[str]:
									return datasets.MetricInfo(
									    description=_DESCRIPTION    ,  citation=_CITATION    ,  homepage="""https://github.com/chakki-works/seqeval"""    ,  inputs_description=_KWARGS_DESCRIPTION    ,  features=datasets.Features(
									        {
									            """predictions""": datasets.Sequence(datasets.Value("""string"""    ,  id="""label""" )    ,  id="""sequence""" ),
									            """references""": datasets.Sequence(datasets.Value("""string"""    ,  id="""label""" )    ,  id="""sequence""" ),
									        } )    ,  codebase_urls=["""https://github.com/chakki-works/seqeval"""]    ,  reference_urls=["""https://github.com/chakki-works/seqeval"""]    ,  )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_ = False    ,  lowercase_ = None    ,  lowercase_ = None    ,  lowercase_ = None    ,  lowercase_ = "warn"    ,  )	->					str:





									if scheme is not None:
															try:
																					lowerCAmelCase	:   List[str]					     =					importlib.import_module("""seqeval.scheme""" )
																					lowerCAmelCase	:   int					     =					getattr(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE )
															except AttributeError:
																					raise ValueError(f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}""" )
									lowerCAmelCase	:   List[Any]					     =					classification_report(
									    y_true=__SCREAMING_SNAKE_CASE    ,  y_pred=__SCREAMING_SNAKE_CASE    ,  suffix=__SCREAMING_SNAKE_CASE    ,  output_dict=__SCREAMING_SNAKE_CASE    ,  scheme=__SCREAMING_SNAKE_CASE    ,  mode=__SCREAMING_SNAKE_CASE    ,  sample_weight=__SCREAMING_SNAKE_CASE    ,  zero_division=__SCREAMING_SNAKE_CASE    ,  )
									report.pop("""macro avg""" )
									report.pop("""weighted avg""" )
									lowerCAmelCase	:   int					     =					report.pop("""micro avg""" )

									lowerCAmelCase	:   List[Any]					     =					{
									    type_name: {
									        '''precision''': score['''precision'''],
									        '''recall''': score['''recall'''],
									        '''f1''': score['''f1-score'''],
									        '''number''': score['''support'''],
									    }
									    for type_name, score in report.items()
									}
									lowerCAmelCase	:   Optional[Any]					     =					overall_score['''precision''']
									lowerCAmelCase	:   Optional[Any]					     =					overall_score['''recall''']
									lowerCAmelCase	:   Optional[int]					     =					overall_score['''f1-score''']
									lowerCAmelCase	:   Optional[int]					     =					accuracy_score(y_true=__SCREAMING_SNAKE_CASE    ,  y_pred=__SCREAMING_SNAKE_CASE )

									return scores



'''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 DeiTImageProcessor






class 					lowerCAmelCase__  (    unittest.TestCase	):






					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							,				__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]							,				__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]							,				):


											"""simple docstring"""





											lowercase_      :	Union[str, Any]						=			size if size is not None else {'''height''': 20, '''width''': 20}
											lowercase_      :	List[Any]						=			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

											lowercase_      :	List[str]						=			parent
											lowercase_      :	Optional[Any]						=			batch_size
											lowercase_      :	int						=			num_channels
											lowercase_      :	List[str]						=			image_size
											lowercase_      :	Optional[Any]						=			min_resolution
											lowercase_      :	Tuple						=			max_resolution
											lowercase_      :	Union[str, Any]						=			do_resize
											lowercase_      :	List[str]						=			size
											lowercase_      :	Tuple						=			do_center_crop
											lowercase_      :	Dict						=			crop_size
											lowercase_      :	int						=			do_normalize
											lowercase_      :	Optional[int]						=			image_mean
											lowercase_      :	List[str]						=			image_std






					def       _snake_case       (  self						):


											"""simple docstring"""





											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}







@require_torch
@require_vision
class 					lowerCAmelCase__  (    lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	DeiTImageProcessor if is_vision_available() else None
					lowerCAmelCase_	  =	True






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			DeiTImageProcessingTester(self						)






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											return self.image_processor_tester.prepare_image_processor_dict()






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	str						=			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_normalize'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''image_mean'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''image_std'''						)						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size							,				{'''height''': 20, '''width''': 20}						)
											self.assertEqual(image_processor.crop_size							,				{'''height''': 18, '''width''': 18}						)

											lowercase_      :	List[str]						=			self.image_processing_class.from_dict(self.image_processor_dict							,				size=42							,				crop_size=84						)
											self.assertEqual(image_processor.size							,				{'''height''': 42, '''width''': 42}						)
											self.assertEqual(image_processor.crop_size							,				{'''height''': 84, '''width''': 84}						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											lowercase_      :	Optional[int]						=			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
											lowercase_      :	List[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
											lowercase_      :	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       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[Any]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											lowercase_      :	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
											lowercase_      :	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
											lowercase_      :	Union[str, 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'''],
											    )							,				)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											lowercase_      :	List[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
											lowercase_      :	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
											lowercase_      :	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'''],
											    )							,				)



