

'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize







class lowerCamelCase   :

				def __init__(       self,						lowercase_,						lowercase_=sys.maxsize )    ->							List[str]:
					snake_case							=					'bilinear'
					snake_case							=					max_size
					snake_case							=					short_edge_length






				def __call__(       self,						lowercase_ )    ->							Tuple:
					snake_case							=					[]
					for img in imgs:
						snake_case			,			snake_case							=					img.shape[:2]
						# later: provide list and randomly choose index for resize
						snake_case							=					np.random.randint(self.short_edge_length[0],						self.short_edge_length[1] + 1 )
						if size == 0:
							return img
						snake_case							=					size * 1.0 / min(lowercase_,						lowercase_ )
						if h < w:
							snake_case			,			snake_case							=					size, scale * w
						else:
							snake_case			,			snake_case							=					scale * h, size
						if max(lowercase_,						lowercase_ ) > self.max_size:
							snake_case							=					self.max_size * 1.0 / max(lowercase_,						lowercase_ )
							snake_case							=					newh * scale
							snake_case							=					neww * scale
						snake_case							=					int(neww + 0.5 )
						snake_case							=					int(newh + 0.5 )

						if img.dtype == np.uinta:
							snake_case							=					Image.fromarray(lowercase_ )
							snake_case							=					pil_image.resize((neww, newh),						PILImageResampling.BILINEAR )
							snake_case							=					np.asarray(lowercase_ )
						else:
							snake_case							=					img.permute(2,						0,						1 ).unsqueeze(0 )  # 3, 0, 1)  # hw(c) -> nchw
							snake_case							=					nn.functional.interpolate(
							    lowercase_,						(newh, neww),						mode=self.interp_method,						align_corners=lowercase_ ).squeeze(0 )
						img_augs.append(lowercase_ )

					return img_augs







class lowerCamelCase   :

				def __init__(       self,						lowercase_ )    ->							Tuple:
					snake_case							=					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],						cfg.INPUT.MAX_SIZE_TEST )
					snake_case							=					cfg.INPUT.FORMAT
					snake_case							=					cfg.SIZE_DIVISIBILITY
					snake_case							=					cfg.PAD_VALUE
					snake_case							=					cfg.INPUT.MAX_SIZE_TEST
					snake_case							=					cfg.MODEL.DEVICE
					snake_case							=					torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ),						1,						1 )
					snake_case							=					torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ),						1,						1 )
					snake_case							=					lambda lowercase_ : (x - self.pixel_mean) / self.pixel_std

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							int:
					snake_case							=					tuple(max(lowercase_ ) for s in zip(*[img.shape for img in images] ) )
					snake_case							=					[im.shape[-2:] for im in images]
					snake_case							=					[
					    nn.functional.pad(
					        lowercase_,						[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],						value=self.pad_value,						)
					    for size, im in zip(lowercase_,						lowercase_ )
					]

					return torch.stack(lowercase_ ), torch.tensor(lowercase_ )






				def __call__(       self,						lowercase_,						lowercase_=False )    ->							Optional[Any]:
					with torch.no_grad():
						if not isinstance(lowercase_,						lowercase_ ):
							snake_case							=					[images]
						if single_image:
							assert len(lowercase_ ) == 1
						for i in range(len(lowercase_ ) ):
							if isinstance(images[i],						torch.Tensor ):
								images.insert(lowercase_,						images.pop(lowercase_ ).to(self.device ).float() )
							elif not isinstance(images[i],						torch.Tensor ):
								images.insert(
								    lowercase_,						torch.as_tensor(img_tensorize(images.pop(lowercase_ ),						input_format=self.input_format ) )
								    .to(self.device )
								    .float(),						)
            # resize smallest edge
						snake_case							=					torch.tensor([im.shape[:2] for im in images] )
						snake_case							=					self.aug(lowercase_ )
						# transpose images and convert to torch tensors
						# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
						# now normalize before pad to avoid useless arithmetic
						snake_case							=					[self.normalizer(lowercase_ ) for x in images]
						# now pad them to do the following operations
						snake_case			,			snake_case							=					self.pad(lowercase_ )
						# Normalize

						if self.size_divisibility > 0:
							raise NotImplementedError()
						# pad
						snake_case							=					torch.true_divide(lowercase_,						lowercase_ )
						if single_image:
							return images[0], sizes[0], scales_yx[0]
						else:
							return images, sizes, scales_yx

def      __magic_name__							(			A       , A				)      ->		str:
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes


def      __magic_name__							(			A       , A				)      ->		Any:
	assert torch.isfinite(A				).all(), "Box tensor contains infinite or NaN!"
	snake_case			,			snake_case							=					box_size
	tensor[:, 0].clamp_(min=0       , max=A				)
	tensor[:, 1].clamp_(min=0       , max=A				)
	tensor[:, 2].clamp_(min=0       , max=A				)
	tensor[:, 3].clamp_(min=0       , max=A				)









"""simple docstring"""






import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


SCREAMING_SNAKE_CASE__				:  Optional[Any]						= "\\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"

SCREAMING_SNAKE_CASE__				:  str						= "\\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"

SCREAMING_SNAKE_CASE__				:  Any						= "\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   ):





  """simple docstring"""

  def 							lowercase_     (						self						)				->					Tuple:
     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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = False  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "warn"  ,					)				->					List[Any]:
     if scheme is not None:
        try:
           a   :	str						=    importlib.import_module('seqeval.scheme'						)
           a   :	List[str]						=    getattr(__UpperCAmelCase  ,					__UpperCAmelCase						)
        except AttributeError:
           raise ValueError(f'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'						)
     a   :	str						=    classification_report(
         y_true=__UpperCAmelCase  ,					y_pred=__UpperCAmelCase  ,					suffix=__UpperCAmelCase  ,					output_dict=__UpperCAmelCase  ,					scheme=__UpperCAmelCase  ,					mode=__UpperCAmelCase  ,					sample_weight=__UpperCAmelCase  ,					zero_division=__UpperCAmelCase  ,					)
     report.pop('macro avg'						)
     report.pop('weighted avg'						)
     a   :	Optional[Any]						=    report.pop('micro avg'						)

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

     return scores





