

"""simple docstring"""





import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType




@require_torch
class    _lowerCAmelCase    :







 """simple docstring"""







 __UpperCAmelCase      :   int  						=      ()
 __UpperCAmelCase      :   List[Any]  						=      True


 def    _lowercase					(     self				:     List[str]      ):
    __lowercase								=		self.config_class

    # test default config
    __lowercase								=		config_class()
    self.assertIsNotNone(UpperCAmelCase__      )
    __lowercase								=		["stem"] + [F"""stage{idx}""" for idx in range(1,   len(config.depths      ) + 1      )]
    self.assertEqual(config.stage_names,   UpperCAmelCase__      )
    self.assertTrue(set(config.out_features      ).issubset(set(config.stage_names      )      )      )

    # Test out_features and out_indices are correctly set
    # out_features and out_indices both None
    __lowercase								=		config_class(out_features=UpperCAmelCase__,   out_indices=UpperCAmelCase__      )
    self.assertEqual(config.out_features,   [config.stage_names[-1]]      )
    self.assertEqual(config.out_indices,   [len(config.stage_names      ) - 1]      )

    # out_features and out_indices both set
    __lowercase								=		config_class(out_features=["stem", "stage1"],   out_indices=[0, 1]      )
    self.assertEqual(config.out_features,   ["stem", "stage1"]      )
    self.assertEqual(config.out_indices,   [0, 1]      )

    # Only out_features set
    __lowercase								=		config_class(out_features=["stage1", "stage3"]      )
    self.assertEqual(config.out_features,   ["stage1", "stage3"]      )
    self.assertEqual(config.out_indices,   [1, 3]      )

    # Only out_indices set
    __lowercase								=		config_class(out_indices=[0, 2]      )
    self.assertEqual(config.out_features,   [config.stage_names[0], config.stage_names[2]]      )
    self.assertEqual(config.out_indices,   [0, 2]      )

    # Error raised when out_indices do not correspond to out_features
    with self.assertRaises(UpperCAmelCase__      ):
       __lowercase								=		config_class(out_features=["stage1", "stage2"],   out_indices=[0, 2]      )


 def    _lowercase					(     self				:     Tuple      ):
    __lowercase						,__lowercase								=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowercase								=		model_class(UpperCAmelCase__      )
       __lowercase								=		inspect.signature(model.forward      )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __lowercase								=		[*signature.parameters.keys()]
       __lowercase								=		["pixel_values"]
       self.assertListEqual(arg_names[:1],   UpperCAmelCase__      )


 def    _lowercase					(     self				:     List[str]      ):
    __lowercase						,__lowercase								=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowercase								=		model_class(UpperCAmelCase__      )
       self.assertEqual(len(model.channels      ),   len(config.out_features      )      )
       __lowercase								=		model.num_features
       __lowercase								=		[config.stage_names.index(UpperCAmelCase__      ) for feat in config.out_features]
       __lowercase								=		[num_features[idx] for idx in out_indices]
       self.assertListEqual(model.channels,   UpperCAmelCase__      )

       __lowercase								=		copy.deepcopy(UpperCAmelCase__      )
       __lowercase								=		None
       __lowercase								=		model_class(UpperCAmelCase__      )
       self.assertEqual(len(model.channels      ),   1      )
       self.assertListEqual(model.channels,   [num_features[-1]]      )

       __lowercase								=		copy.deepcopy(UpperCAmelCase__      )
       __lowercase								=		None
       __lowercase								=		model_class(UpperCAmelCase__      )
       self.assertEqual(len(model.channels      ),   1      )
       self.assertListEqual(model.channels,   [num_features[-1]]      )


 def    _lowercase					(     self				:     Tuple      ):
    __lowercase						,__lowercase								=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowercase								=		model_class(UpperCAmelCase__      )
       model.to(UpperCAmelCase__      )
       model.eval()
       __lowercase								=		model(**UpperCAmelCase__      )

       self.assertEqual(len(result.feature_maps      ),   len(config.out_features      )      )
       self.assertEqual(len(model.channels      ),   len(config.out_features      )      )
       self.assertEqual(len(result.feature_maps      ),   len(config.out_indices      )      )
       self.assertEqual(len(model.channels      ),   len(config.out_indices      )      )

       # Check output of last stage is taken if out_features=None, out_indices=None
       __lowercase								=		copy.deepcopy(UpperCAmelCase__      )
       __lowercase								=		None
       __lowercase								=		model_class(UpperCAmelCase__      )
       model.to(UpperCAmelCase__      )
       model.eval()
       __lowercase								=		model(**UpperCAmelCase__      )

       self.assertEqual(len(result.feature_maps      ),   1      )
       self.assertEqual(len(model.channels      ),   1      )

       __lowercase								=		copy.deepcopy(UpperCAmelCase__      )
       __lowercase								=		None
       __lowercase								=		model_class(UpperCAmelCase__      )
       model.to(UpperCAmelCase__      )
       model.eval()
       __lowercase								=		model(**UpperCAmelCase__      )

       self.assertEqual(len(result.feature_maps      ),   1      )
       self.assertEqual(len(model.channels      ),   1      )

       # Check backbone can be initialized with fresh weights
       __lowercase								=		copy.deepcopy(UpperCAmelCase__      )
       __lowercase								=		False
       __lowercase								=		model_class(UpperCAmelCase__      )
       model.to(UpperCAmelCase__      )
       model.eval()
       __lowercase								=		model(**UpperCAmelCase__      )


 def    _lowercase					(     self				:     Optional[Any]      ):
    __lowercase						,__lowercase								=		self.model_tester.prepare_config_and_inputs_for_common()

    for backbone_class in self.all_model_classes:
       __lowercase								=		backbone_class(UpperCAmelCase__      )

       self.assertTrue(hasattr(UpperCAmelCase__,   "backbone_type"      )      )
       self.assertTrue(hasattr(UpperCAmelCase__,   "stage_names"      )      )
       self.assertTrue(hasattr(UpperCAmelCase__,   "num_features"      )      )
       self.assertTrue(hasattr(UpperCAmelCase__,   "out_indices"      )      )
       self.assertTrue(hasattr(UpperCAmelCase__,   "out_features"      )      )
       self.assertTrue(hasattr(UpperCAmelCase__,   "out_feature_channels"      )      )
       self.assertTrue(hasattr(UpperCAmelCase__,   "channels"      )      )

       self.assertIsInstance(backbone.backbone_type,   UpperCAmelCase__      )
       # Verify num_features has been initialized in the backbone init
       self.assertIsNotNone(backbone.num_features      )
       self.assertTrue(len(backbone.channels      ) == len(backbone.out_indices      )      )
       self.assertTrue(len(backbone.stage_names      ) == len(backbone.num_features      )      )
       self.assertTrue(len(backbone.channels      ) <= len(backbone.num_features      )      )
       self.assertTrue(len(backbone.out_feature_channels      ) == len(backbone.stage_names      )      )



 def    _lowercase					(     self				:     int      ):
    __lowercase						,__lowercase								=		self.model_tester.prepare_config_and_inputs_for_common()
    __lowercase								=		inputs_dict["pixel_values"].shape[0]

    for backbone_class in self.all_model_classes:
       __lowercase								=		backbone_class(UpperCAmelCase__      )
       backbone.to(UpperCAmelCase__      )
       backbone.eval()

       __lowercase								=		backbone(**UpperCAmelCase__      )

       # Test default outputs and verify feature maps
       self.assertIsInstance(outputs.feature_maps,   UpperCAmelCase__      )
       self.assertTrue(len(outputs.feature_maps      ) == len(backbone.channels      )      )
       for feature_map, n_channels in zip(outputs.feature_maps,   backbone.channels      ):
          self.assertTrue(feature_map.shape[:2],   (batch_size, n_channels)      )
       self.assertIsNone(outputs.hidden_states      )
       self.assertIsNone(outputs.attentions      )

       # Test output_hidden_states=True
       __lowercase								=		backbone(**UpperCAmelCase__,   output_hidden_states=UpperCAmelCase__      )
       self.assertIsNotNone(outputs.hidden_states      )
       self.assertTrue(len(outputs.hidden_states      ),   len(backbone.stage_names      )      )
       for hidden_state, n_channels in zip(outputs.hidden_states,   backbone.channels      ):
          self.assertTrue(hidden_state.shape[:2],   (batch_size, n_channels)      )

       # Test output_attentions=True
       if self.has_attentions:
          __lowercase								=		backbone(**UpperCAmelCase__,   output_attentions=UpperCAmelCase__      )
          self.assertIsNotNone(outputs.attentions      )







"""simple docstring"""





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_a					 =				'\\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'

_a					 =				'\\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'

_a					 =				'\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    _lowerCAmelCase    (      datasets.Metric  ):







 """simple docstring"""







 def    _lowercase					(     self				:     Optional[int]      ):
    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				:     Optional[Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     bool = False,   UpperCAmelCase__				:     Optional[str] = None,   UpperCAmelCase__				:     Optional[str] = None,   UpperCAmelCase__				:     Optional[List[int]] = None,   UpperCAmelCase__				:     Union[str, int] = "warn",   ):
    if scheme is not None:
       try:
          __lowercase								=		importlib.import_module("seqeval.scheme"      )
          __lowercase								=		getattr(UpperCAmelCase__,   UpperCAmelCase__      )
       except AttributeError:
          raise ValueError(F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""      )
    __lowercase								=		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"      )
    __lowercase								=		report.pop("micro avg"      )

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

    return scores




