
"""simple docstring"""






import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    LayoutLMvaConfig,
					    LayoutLMvaForQuestionAnswering,
					    LayoutLMvaForSequenceClassification,
					    LayoutLMvaForTokenClassification,
					    LayoutLMvaModel,
					)
					from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor




class 				lowercase__:
	'''simple docstring'''

	def __init__(						self    :Tuple		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Union[str, Any]=2		, lowerCamelCase_    :Any=3		, lowerCamelCase_    :Union[str, Any]=4		, lowerCamelCase_    :List[str]=2		, lowerCamelCase_    :str=7		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :Dict=True		, lowerCamelCase_    :int=True		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :Optional[Any]=99		, lowerCamelCase_    :Any=36		, lowerCamelCase_    :Any=3		, lowerCamelCase_    :str=4		, lowerCamelCase_    :Tuple=37		, lowerCamelCase_    :Optional[int]="gelu"		, lowerCamelCase_    :str=0.1		, lowerCamelCase_    :Optional[int]=0.1		, lowerCamelCase_    :Tuple=5_12		, lowerCamelCase_    :Optional[Any]=16		, lowerCamelCase_    :List[str]=2		, lowerCamelCase_    :Optional[int]=0.0_2		, lowerCamelCase_    :int=6		, lowerCamelCase_    :str=6		, lowerCamelCase_    :Optional[Any]=3		, lowerCamelCase_    :Union[str, Any]=4		, lowerCamelCase_    :List[Any]=None		, lowerCamelCase_    :Tuple=10_00		, )							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= parent
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= batch_size
			SCREAMING_SNAKE_CASE       :     List[str]				= num_channels
			SCREAMING_SNAKE_CASE       :     str				= image_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= patch_size
			SCREAMING_SNAKE_CASE       :     Tuple				= text_seq_length
			SCREAMING_SNAKE_CASE       :     Optional[int]				= is_training
			SCREAMING_SNAKE_CASE       :     Dict				= use_input_mask
			SCREAMING_SNAKE_CASE       :     Any				= use_token_type_ids
			SCREAMING_SNAKE_CASE       :     List[Any]				= use_labels
			SCREAMING_SNAKE_CASE       :     List[Any]				= vocab_size
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= hidden_size
			SCREAMING_SNAKE_CASE       :     Tuple				= num_hidden_layers
			SCREAMING_SNAKE_CASE       :     Optional[int]				= num_attention_heads
			SCREAMING_SNAKE_CASE       :     Optional[int]				= intermediate_size
			SCREAMING_SNAKE_CASE       :     List[str]				= hidden_act
			SCREAMING_SNAKE_CASE       :     Any				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     int				= type_vocab_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= type_sequence_label_size
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= initializer_range
			SCREAMING_SNAKE_CASE       :     Dict				= coordinate_size
			SCREAMING_SNAKE_CASE       :     List[Any]				= shape_size
			SCREAMING_SNAKE_CASE       :     Dict				= num_labels
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= num_choices
			SCREAMING_SNAKE_CASE       :     List[str]				= scope
			SCREAMING_SNAKE_CASE       :     Optional[int]				= range_bbox

			# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
			SCREAMING_SNAKE_CASE       :     str				= text_seq_length
			SCREAMING_SNAKE_CASE       :     int				= (image_size // patch_size) ** 2 + 1
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.text_seq_length + self.image_seq_length





	def   __lowerCAmelCase (						self    :List[Any]					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= ids_tensor([self.batch_size, self.text_seq_length]		, self.vocab_size					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= ids_tensor([self.batch_size, self.text_seq_length, 4]		, self.range_bbox					)
			# Ensure that bbox is legal
			for i in range(bbox.shape[0]					):
					for j in range(bbox.shape[1]					):
							if bbox[i, j, 3] < bbox[i, j, 1]:
									SCREAMING_SNAKE_CASE       :     str				= bbox[i, j, 3]
									SCREAMING_SNAKE_CASE       :     List[str]				= bbox[i, j, 1]
									SCREAMING_SNAKE_CASE       :     Any				= t
							if bbox[i, j, 2] < bbox[i, j, 0]:
									SCREAMING_SNAKE_CASE       :     Any				= bbox[i, j, 2]
									SCREAMING_SNAKE_CASE       :     Any				= bbox[i, j, 0]
									SCREAMING_SNAKE_CASE       :     Optional[Any]				= t

			SCREAMING_SNAKE_CASE       :     Dict				= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= None
			if self.use_input_mask:
					SCREAMING_SNAKE_CASE       :     Dict				= random_attention_mask([self.batch_size, self.text_seq_length]					)

			SCREAMING_SNAKE_CASE       :     Any				= None
			if self.use_token_type_ids:
					SCREAMING_SNAKE_CASE       :     Tuple				= ids_tensor([self.batch_size, self.text_seq_length]		, self.type_vocab_size					)

			SCREAMING_SNAKE_CASE       :     Tuple				= None
			SCREAMING_SNAKE_CASE       :     Optional[int]				= None
			if self.use_labels:
					SCREAMING_SNAKE_CASE       :     List[str]				= ids_tensor([self.batch_size]		, self.type_sequence_label_size					)
					SCREAMING_SNAKE_CASE       :     Dict				= ids_tensor([self.batch_size, self.text_seq_length]		, self.num_labels					)

			SCREAMING_SNAKE_CASE       :     List[str]				= LayoutLMvaConfig(
			    vocab_size=self.vocab_size		, hidden_size=self.hidden_size		, num_hidden_layers=self.num_hidden_layers		, num_attention_heads=self.num_attention_heads		, intermediate_size=self.intermediate_size		, hidden_act=self.hidden_act		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, max_position_embeddings=self.max_position_embeddings		, type_vocab_size=self.type_vocab_size		, initializer_range=self.initializer_range		, coordinate_size=self.coordinate_size		, shape_size=self.shape_size		, input_size=self.image_size		, patch_size=self.patch_size		, )

			return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :int		, lowerCamelCase_    :str		, lowerCamelCase_    :int		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Dict		, lowerCamelCase_    :Union[str, Any]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= LayoutLMvaModel(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()

			# text + image
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(lowerCamelCase_		, pixel_values=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= model(
			    lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, attention_mask=lowerCamelCase_		, token_type_ids=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= model(lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, token_type_ids=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_					)

			self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)					)

			# text only
			SCREAMING_SNAKE_CASE       :     List[str]				= model(lowerCamelCase_					)
			self.parent.assertEqual(
			    result.last_hidden_state.shape		, (self.batch_size, self.text_seq_length, self.hidden_size)					)

			# image only
			SCREAMING_SNAKE_CASE       :     List[str]				= model(pixel_values=lowerCamelCase_					)
			self.parent.assertEqual(
			    result.last_hidden_state.shape		, (self.batch_size, self.image_seq_length, self.hidden_size)					)





	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Any		, lowerCamelCase_    :int		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :List[str]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.num_labels
			SCREAMING_SNAKE_CASE       :     Dict				= LayoutLMvaForSequenceClassification(lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     int				= model(
			    lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, attention_mask=lowerCamelCase_		, token_type_ids=lowerCamelCase_		, labels=lowerCamelCase_		, )
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_labels)					)





	def   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Any		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :int		, lowerCamelCase_    :str		, lowerCamelCase_    :Any					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.num_labels
			SCREAMING_SNAKE_CASE       :     int				= LayoutLMvaForTokenClassification(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(
			    lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, attention_mask=lowerCamelCase_		, token_type_ids=lowerCamelCase_		, labels=lowerCamelCase_		, )
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.text_seq_length, self.num_labels)					)





	def   __lowerCAmelCase (						self    :Optional[Any]		, lowerCamelCase_    :Dict		, lowerCamelCase_    :Dict		, lowerCamelCase_    :Dict		, lowerCamelCase_    :int		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :int		, lowerCamelCase_    :str		, lowerCamelCase_    :Optional[int]					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= LayoutLMvaForQuestionAnswering(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     List[Any]				= model(
			    lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, attention_mask=lowerCamelCase_		, token_type_ids=lowerCamelCase_		, start_positions=lowerCamelCase_		, end_positions=lowerCamelCase_		, )
			self.parent.assertEqual(result.start_logits.shape		, (self.batch_size, self.seq_length)					)
			self.parent.assertEqual(result.end_logits.shape		, (self.batch_size, self.seq_length)					)







	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.prepare_config_and_inputs()
			(
			    (
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					
			)       :     Tuple				= config_and_inputs
			SCREAMING_SNAKE_CASE       :     Dict				= {
			    '''input_ids''': input_ids,
			    '''bbox''': bbox,
			    '''pixel_values''': pixel_values,
			    '''token_type_ids''': token_type_ids,
			    '''attention_mask''': input_mask,
			}
			return config, inputs_dict





@require_torch
class 				lowercase__(				_UpperCAmelCase		,						_UpperCAmelCase		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  False
	UpperCamelCase				=  False
	UpperCamelCase				=  False

	UpperCamelCase				=  (
	    (
	        LayoutLMvaModel,
	        LayoutLMvaForSequenceClassification,
	        LayoutLMvaForTokenClassification,
	        LayoutLMvaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	UpperCamelCase				=  (
	    {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
	    if is_torch_available()
	    else {}
	)





	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :str		, lowerCamelCase_    :str		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :List[str]					)							->			Union[str, Any]:







			'''simple docstring'''




			return True





	def   __lowerCAmelCase (						self    :List[str]					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= LayoutLMvaModelTester(self					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= ConfigTester(self		, config_class=lowerCamelCase_		, hidden_size=37					)





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Dict		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :str=False					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= copy.deepcopy(lowerCamelCase_					)
			if model_class in get_values(lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Tuple				= {
					    k: v.unsqueeze(1					).expand(-1		, self.model_tester.num_choices		, -1					).contiguous()
					    if isinstance(lowerCamelCase_		, torch.Tensor					) and v.ndim > 1
					    else v
					    for k, v in inputs_dict.items()
					}
			if return_labels:
					if model_class in get_values(lowerCamelCase_					):
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.ones(self.model_tester.batch_size		, dtype=torch.long		, device=lowerCamelCase_					)
					elif model_class in get_values(lowerCamelCase_					):
							SCREAMING_SNAKE_CASE       :     List[Any]				= torch.zeros(
							    self.model_tester.batch_size		, dtype=torch.long		, device=lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     Any				= torch.zeros(
							    self.model_tester.batch_size		, dtype=torch.long		, device=lowerCamelCase_					)
					elif model_class in [
					    *get_values(lowerCamelCase_					),
					]:
							SCREAMING_SNAKE_CASE       :     str				= torch.zeros(
							    self.model_tester.batch_size		, dtype=torch.long		, device=lowerCamelCase_					)
					elif model_class in [
					    *get_values(lowerCamelCase_					),
					]:
							SCREAMING_SNAKE_CASE       :     Dict				= torch.zeros(
							    (self.model_tester.batch_size, self.model_tester.text_seq_length)		, dtype=torch.long		, device=lowerCamelCase_		, )

			return inputs_dict





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[int]:







			'''simple docstring'''




			self.config_tester.run_common_tests()





	def   __lowerCAmelCase (						self    :str					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.model_tester.prepare_config_and_inputs()
			for type in ["absolute", "relative_key", "relative_key_query"]:
					SCREAMING_SNAKE_CASE       :     str				= type
					self.model_tester.create_and_check_model(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :str					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :List[str]					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_					)







	@slow
	def   __lowerCAmelCase (						self    :int					)							->			Union[str, Any]:







			'''simple docstring'''




			for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE       :     Optional[int]				= LayoutLMvaModel.from_pretrained(lowerCamelCase_					)
					self.assertIsNotNone(lowerCamelCase_					)





def __A						(   )->							Optional[int]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
		return image




@require_torch
class 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	@cached_property
	def   __lowerCAmelCase (						self    :str					)							->			int:







			'''simple docstring'''




			return LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase_					) if is_vision_available() else None







	@slow
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''					).to(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Any				= self.default_image_processor
			SCREAMING_SNAKE_CASE       :     List[Any]				= prepare_img()
			SCREAMING_SNAKE_CASE       :     Tuple				= image_processor(images=lowerCamelCase_		, return_tensors='''pt'''					).pixel_values.to(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.tensor([[1, 2]]					)
			SCREAMING_SNAKE_CASE       :     Dict				= torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]					).unsqueeze(0					)

			# forward pass
			SCREAMING_SNAKE_CASE       :     Tuple				= model(
			    input_ids=input_ids.to(lowerCamelCase_					)		, bbox=bbox.to(lowerCamelCase_					)		, pixel_values=pixel_values.to(lowerCamelCase_					)		, )

			# verify the logits
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.Size((1, 1_99, 7_68)					)
			self.assertEqual(outputs.last_hidden_state.shape		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= torch.tensor(
			    [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]					).to(lowerCamelCase_					)

			self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]		, lowerCamelCase_		, atol=1E-4					)					)







"""simple docstring"""






from sklearn.metrics import fa_score

import datasets


lowerCamelCase__ :       List[Any]						     =			"\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"


lowerCamelCase__ :       str						     =			"\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {'f1': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results['f1'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(round(results['f1'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n        >>> print(round(results['f1'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'f1': array([0.8, 0. , 0. ])}\n"


lowerCamelCase__ :       int						     =			"\n@article{scikit-learn,\n    title={Scikit-learn: Machine Learning in {P}ython},\n    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n    journal={Journal of Machine Learning Research},\n    volume={12},\n    pages={2825--2830},\n    year={2011}\n}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,						_KWARGS_DESCRIPTION   )
class 				lowercase__(				datasets.Metric   ):
	'''simple docstring'''

	def   __lowerCAmelCase (						self    :str					)							->			Any:







			'''simple docstring'''




			return datasets.MetricInfo(
			    description=_DESCRIPTION		, citation=_CITATION		, inputs_description=_KWARGS_DESCRIPTION		, features=datasets.Features(
			        {
			            '''predictions''': datasets.Sequence(datasets.Value('''int32'''					)					),
			            '''references''': datasets.Sequence(datasets.Value('''int32'''					)					),
			        }
			        if self.config_name == '''multilabel'''
			        else {
			            '''predictions''': datasets.Value('''int32'''					),
			            '''references''': datasets.Value('''int32'''					),
			        }					)		, reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html''']		, )







	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Dict		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :int=None		, lowerCamelCase_    :str=1		, lowerCamelCase_    :Union[str, Any]="binary"		, lowerCamelCase_    :Dict=None					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= fa_score(
			    lowerCamelCase_		, lowerCamelCase_		, labels=lowerCamelCase_		, pos_label=lowerCamelCase_		, average=lowerCamelCase_		, sample_weight=lowerCamelCase_					)
			return {"f1": float(lowerCamelCase_					) if score.size == 1 else score}





