"""simple docstring"""




import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


__lowerCamelCase									=	'\\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									=	'\\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									=	'\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      UpperCamelCase__(      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		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = False		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = "warn"		,)       ->					Union[str, Any]:






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

													A__						  =				{
													    type_name: {
													        '''precision''': score['''precision'''],
													        '''recall''': score['''recall'''],
													        '''f1''': score['''f1-score'''],
													        '''number''': score['''support'''],
													    }
													    for type_name, score in report.items()
													}
													A__						  =				overall_score['''precision''']
													A__						  =				overall_score['''recall''']
													A__						  =				overall_score['''f1-score''']
													A__						  =				accuracy_score(y_true=a__		,y_pred=a__  )

													return scores









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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import TvltImageProcessor






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=10			,  __lowerCamelCase=10			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)		->Tuple:


 '''simple docstring'''
 snake_case :			Dict							=   []
 for i in range(image_processor_tester.num_frames							):
  video.append(np.random.randint(255			,  size=(image_processor_tester.num_channels, width, height)			,  dtype=np.uinta							)							)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  snake_case :			Optional[int]							=   [Image.fromarray(np.moveaxis(__lowerCamelCase			,  0			,  -1							)							) for frame in video]

 if torchify:
  snake_case :			int							=   [torch.from_numpy(__lowerCamelCase							) for frame in video]

 return video






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=False			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)		->Optional[Any]:


 '''simple docstring'''
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 snake_case :			Any							=   []
 for i in range(image_processor_tester.batch_size							):
  if equal_resolution:
   snake_case :			Optional[int]							=   image_processor_tester.max_resolution
  else:
   snake_case	,       snake_case :			str							=   np.random.choice(
       np.arange(image_processor_tester.min_resolution			,  image_processor_tester.max_resolution							)			,  2							)
   snake_case :			Any							=   prepare_video(
       image_processor_tester=__lowerCamelCase			,  width=__lowerCamelCase			,  height=__lowerCamelCase			,  numpify=__lowerCamelCase			,  torchify=__lowerCamelCase			,  )
  video_inputs.append(__lowerCamelCase							)

 return video_inputs

class   lowerCamelCase						(	unittest.TestCase  ):

       def __init__(      self						,    a__						,    a__=7						,    a__=3						,    a__=4						,    a__=18						,    a__=30						,    a__=4_00						,    a__=True						,    a__=None						,    a__=True						,    a__=[0.5, 0.5, 0.5]						,    a__=[0.5, 0.5, 0.5]						,    a__=True						,    a__=None						,    ):






        '''simple docstring'''






        snake_case :			Tuple							=   size if size is not None else {'''shortest_edge''': 18}
        snake_case :			List[str]							=   crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

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



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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







@require_torch
@require_vision
class   lowerCamelCase						(	SCREAMING_SNAKE_CASE,      unittest.TestCase  ):
       _UpperCamelCase     		=   TvltImageProcessor if is_vision_available() else None

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   TvltImageProcessorTester(self       )

       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   self.image_processing_class(**self.image_processor_dict       )
        self.assertTrue(hasattr(a__						,    '''image_mean'''       )       )
        self.assertTrue(hasattr(a__						,    '''image_std'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_normalize'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_resize'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_center_crop'''       )       )
        self.assertTrue(hasattr(a__						,    '''size'''       )       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.image_processing_class(**self.image_processor_dict       )
        # create random PIL videos
        snake_case :			List[str]							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    Image.Image       )

        # Test not batched input
        snake_case :			int							=   image_processor(video_inputs[0]						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )

        # Test batched
        snake_case :			Optional[Any]							=   image_processor(a__						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.image_processing_class(**self.image_processor_dict       )
        # create random numpy tensors
        snake_case :			Union[str, Any]							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__						,    numpify=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    np.ndarray       )

        # Test not batched input
        snake_case :			List[str]							=   image_processor(video_inputs[0]						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )

        # Test batched
        snake_case :			Tuple							=   image_processor(a__						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			str							=   self.image_processing_class(**self.image_processor_dict       )
        # create random PyTorch tensors
        snake_case :			str							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__						,    torchify=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    torch.Tensor       )

        # Test not batched input
        snake_case :			Tuple							=   image_processor(video_inputs[0]						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )

        # Test batched
        snake_case :			Optional[int]							=   image_processor(a__						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,    (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )						,    )






