






"""simple docstring"""





import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import FlavaImageProcessor, FlavaProcessor
    from transformers.models.flava.image_processing_flava import (
        FLAVA_CODEBOOK_MEAN,
        FLAVA_CODEBOOK_STD,
        FLAVA_IMAGE_MEAN,
        FLAVA_IMAGE_STD,
    )


@require_vision
class   _lowercase			(  unittest.TestCase    ):


      def _UpperCamelCase    (     self					:						Any    ):





            """simple docstring"""







            __snake_case   :     str	      =tempfile.mkdtemp()

            # fmt: off
            __snake_case   :     Any	      =['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
            # fmt: on
            __snake_case   :     Tuple	      =os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES['''vocab_file''']    )

            with open(self.vocab_file				, '''w'''				, encoding='''utf-8'''    ) as fp:
                  fp.write(''''''.join([x + '''\n''' for x in vocab_tokens]    )    )

            __snake_case   :     int	      ={
                '''image_mean''': FLAVA_IMAGE_MEAN,
                '''image_std''': FLAVA_IMAGE_STD,
                '''do_normalize''': True,
                '''do_resize''': True,
                '''size''': 2_2_4,
                '''do_center_crop''': True,
                '''crop_size''': 2_2_4,
                '''input_size_patches''': 1_4,
                '''total_mask_patches''': 7_5,
                '''mask_group_max_patches''': None,
                '''mask_group_min_patches''': 1_6,
                '''mask_group_min_aspect_ratio''': 0.3,
                '''mask_group_max_aspect_ratio''': None,
                '''codebook_do_resize''': True,
                '''codebook_size''': 1_1_2,
                '''codebook_do_center_crop''': True,
                '''codebook_crop_size''': 1_1_2,
                '''codebook_do_map_pixels''': True,
                '''codebook_do_normalize''': True,
                '''codebook_image_mean''': FLAVA_CODEBOOK_MEAN,
                '''codebook_image_std''': FLAVA_CODEBOOK_STD,
            }

            __snake_case   :     Tuple	      =os.path.join(self.tmpdirname				, a    )
            with open(self.image_processor_file				, '''w'''				, encoding='''utf-8'''    ) as fp:
                  json.dump(a				, a    )


      def _UpperCamelCase    (     self					:						Optional[Any]				, **a					:						List[str]    ):





            """simple docstring"""







            return BertTokenizer.from_pretrained(self.tmpdirname				, **a    )


      def _UpperCamelCase    (     self					:						Any				, **a					:						Any    ):





            """simple docstring"""







            return BertTokenizerFast.from_pretrained(self.tmpdirname				, **a    )


      def _UpperCamelCase    (     self					:						Dict				, **a					:						int    ):





            """simple docstring"""







            return FlavaImageProcessor.from_pretrained(self.tmpdirname				, **a    )


      def _UpperCamelCase    (     self					:						str    ):





            """simple docstring"""







            shutil.rmtree(self.tmpdirname    )


      def _UpperCamelCase    (     self					:						Dict    ):





            """simple docstring"""







            __snake_case   :     Union[str, Any]	      =[np.random.randint(2_5_5				, size=(3, 3_0, 4_0_0)				, dtype=np.uinta    )]

            __snake_case   :     Optional[Any]	      =[Image.fromarray(np.moveaxis(a				, 0				, -1    )    ) for x in image_inputs]

            return image_inputs


      def _UpperCamelCase    (     self					:						Any    ):





            """simple docstring"""







            __snake_case   :     List[Any]	      =self.get_tokenizer()
            __snake_case   :     Optional[Any]	      =self.get_rust_tokenizer()
            __snake_case   :     Optional[int]	      =self.get_image_processor()

            __snake_case   :     List[str]	      =FlavaProcessor(tokenizer=a				, image_processor=a    )
            processor_slow.save_pretrained(self.tmpdirname    )
            __snake_case   :     str	      =FlavaProcessor.from_pretrained(self.tmpdirname				, use_fast=a    )

            __snake_case   :     Dict	      =FlavaProcessor(tokenizer=a				, image_processor=a    )
            processor_fast.save_pretrained(self.tmpdirname    )
            __snake_case   :     Union[str, Any]	      =FlavaProcessor.from_pretrained(self.tmpdirname    )

            self.assertEqual(processor_slow.tokenizer.get_vocab()				, tokenizer_slow.get_vocab()    )
            self.assertEqual(processor_fast.tokenizer.get_vocab()				, tokenizer_fast.get_vocab()    )
            self.assertEqual(tokenizer_slow.get_vocab()				, tokenizer_fast.get_vocab()    )
            self.assertIsInstance(processor_slow.tokenizer				, a    )
            self.assertIsInstance(processor_fast.tokenizer				, a    )

            self.assertEqual(processor_slow.image_processor.to_json_string()				, image_processor.to_json_string()    )
            self.assertEqual(processor_fast.image_processor.to_json_string()				, image_processor.to_json_string()    )
            self.assertIsInstance(processor_slow.image_processor				, a    )
            self.assertIsInstance(processor_fast.image_processor				, a    )


      def _UpperCamelCase    (     self					:						Tuple    ):





            """simple docstring"""







            __snake_case   :     Tuple	      =FlavaProcessor(tokenizer=self.get_tokenizer()				, image_processor=self.get_image_processor()    )
            processor.save_pretrained(self.tmpdirname    )

            __snake_case   :     Dict	      =self.get_tokenizer(bos_token='''(BOS)'''				, eos_token='''(EOS)'''    )
            __snake_case   :     Tuple	      =self.get_image_processor(do_normalize=a				, padding_value=1.0    )

            __snake_case   :     Dict	      =FlavaProcessor.from_pretrained(
                self.tmpdirname				, bos_token='''(BOS)'''				, eos_token='''(EOS)'''				, do_normalize=a				, padding_value=1.0    )

            self.assertEqual(processor.tokenizer.get_vocab()				, tokenizer_add_kwargs.get_vocab()    )
            self.assertIsInstance(processor.tokenizer				, a    )

            self.assertEqual(processor.image_processor.to_json_string()				, image_processor_add_kwargs.to_json_string()    )
            self.assertIsInstance(processor.image_processor				, a    )


      def _UpperCamelCase    (     self					:						Tuple    ):





            """simple docstring"""







            __snake_case   :     str	      =self.get_image_processor()
            __snake_case   :     Tuple	      =self.get_tokenizer()

            __snake_case   :     Tuple	      =FlavaProcessor(tokenizer=a				, image_processor=a    )

            __snake_case   :     Union[str, Any]	      =self.prepare_image_inputs()

            __snake_case   :     List[Any]	      =image_processor(a				, return_tensors='''np'''    )
            __snake_case   :     Optional[Any]	      =processor(images=a				, return_tensors='''np'''    )

            for key in input_feat_extract.keys():
                  self.assertAlmostEqual(input_feat_extract[key].sum()				, input_processor[key].sum()				, delta=1e-2    )

            # With rest of the args
            random.seed(1_2_3_4    )
            __snake_case   :     str	      =image_processor(
                a				, return_image_mask=a				, return_codebook_pixels=a				, return_tensors='''np'''    )
            random.seed(1_2_3_4    )
            __snake_case   :     Union[str, Any]	      =processor(
                images=a				, return_image_mask=a				, return_codebook_pixels=a				, return_tensors='''np'''    )

            for key in input_feat_extract.keys():
                  self.assertAlmostEqual(input_feat_extract[key].sum()				, input_processor[key].sum()				, delta=1e-2    )


      def _UpperCamelCase    (     self					:						int    ):





            """simple docstring"""







            __snake_case   :     str	      =self.get_image_processor()
            __snake_case   :     Any	      =self.get_tokenizer()

            __snake_case   :     List[str]	      =FlavaProcessor(tokenizer=a				, image_processor=a    )

            __snake_case   :     Dict	      ='''lower newer'''

            __snake_case   :     Union[str, Any]	      =processor(text=a    )

            __snake_case   :     List[Any]	      =tokenizer(a    )

            for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]				, encoded_processor[key]    )


      def _UpperCamelCase    (     self					:						List[str]    ):





            """simple docstring"""







            __snake_case   :     Optional[int]	      =self.get_image_processor()
            __snake_case   :     Dict	      =self.get_tokenizer()

            __snake_case   :     Optional[int]	      =FlavaProcessor(tokenizer=a				, image_processor=a    )

            __snake_case   :     Dict	      ='''lower newer'''
            __snake_case   :     List[str]	      =self.prepare_image_inputs()

            __snake_case   :     Union[str, Any]	      =processor(text=a				, images=a    )

            self.assertListEqual(list(inputs.keys()    )				, ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']    )

            # add extra args
            __snake_case   :     str	      =processor(text=a				, images=a				, return_codebook_pixels=a				, return_image_mask=a    )

            self.assertListEqual(
                list(inputs.keys()    )				, [
                    '''input_ids''',
                    '''token_type_ids''',
                    '''attention_mask''',
                    '''pixel_values''',
                    '''codebook_pixel_values''',
                    '''bool_masked_pos''',
                ]				, )

            # test if it raises when no input is passed
            with pytest.raises(a    ):
                  processor()


      def _UpperCamelCase    (     self					:						Optional[Any]    ):





            """simple docstring"""







            __snake_case   :     int	      =self.get_image_processor()
            __snake_case   :     int	      =self.get_tokenizer()

            __snake_case   :     Any	      =FlavaProcessor(tokenizer=a				, image_processor=a    )

            __snake_case   :     Optional[int]	      =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            __snake_case   :     Optional[int]	      =processor.batch_decode(a    )
            __snake_case   :     Tuple	      =tokenizer.batch_decode(a    )

            self.assertListEqual(a				, a    )







      def _UpperCamelCase    (     self					:						Optional[int]    ):





            """simple docstring"""







            __snake_case   :     Any	      =self.get_image_processor()
            __snake_case   :     str	      =self.get_tokenizer()

            __snake_case   :     Any	      =FlavaProcessor(tokenizer=a				, image_processor=a    )

            __snake_case   :     Union[str, Any]	      ='''lower newer'''
            __snake_case   :     Tuple	      =self.prepare_image_inputs()

            __snake_case   :     Optional[int]	      =processor(text=a				, images=a    )

            self.assertListEqual(list(inputs.keys()    )				, processor.model_input_names    )










"""simple docstring"""





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


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

UpperCamelCase_						:					Any      				=			"""\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

UpperCamelCase_						:					int      				=			"""
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode=\"strict\". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        \"warn\". \"warn\" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = datasets.load_metric(\"seqeval\")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results[\"overall_f1\"])
    0.5
    >>> print(results[\"PER\"][\"f1\"])
    1.0
"""


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


      def _UpperCamelCase    (     self					:						int    ):





            """simple docstring"""







            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 _UpperCamelCase    (     self					:						List[Any]				, a					:						List[str]				, a					:						Optional[Any]				, a					:						bool = False				, a					:						Optional[str] = None				, a					:						Optional[str] = None				, a					:						Optional[List[int]] = None				, a					:						Union[str, int] = "warn"				, ):





            """simple docstring"""







            if scheme is not None:
                  try:
                        __snake_case   :     List[str]	      =importlib.import_module('''seqeval.scheme'''    )
                        __snake_case   :     int	      =getattr(a				, a    )
                  except AttributeError:
                        raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''    )
            __snake_case   :     Optional[Any]	      =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'''    )
            __snake_case   :     Union[str, Any]	      =report.pop('''micro avg'''    )

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

            return scores


