from sklearn.metrics import precision_score

import datasets


lowerCamelCase__           =				'\nPrecision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:\nPrecision = TP / (TP + FP)\nwhere TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).\n'


lowerCamelCase__           =				'\nArgs:\n    predictions (`list` of `int`): Predicted class labels.\n    references (`list` of `int`): Actual class labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`. If `average` is `None`, it should be the label order. 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    zero_division (`int` or `string`): Sets the value to return when there is a zero division. Defaults to \'warn\'.\n\n        - 0: Returns 0 when there is a zero division.\n        - 1: Returns 1 when there is a zero division.\n        - \'warn\': Raises warnings and then returns 0 when there is a zero division.\n\nReturns:\n    precision (`float` or `array` of `float`): Precision score or list of precision scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate that fewer negative examples were incorrectly labeled as positive, which means that, generally, higher scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> precision_metric = datasets.load_metric("precision")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {\'precision\': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> precision_metric = datasets.load_metric("precision")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results[\'precision\'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> precision_metric = datasets.load_metric("precision")\n        >>> results = precision_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(results)\n        {\'precision\': 0.23529411764705882}\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 = precision_metric.compute(predictions=predictions, references=references, average=\'macro\')\n        >>> print(results)\n        {\'precision\': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'micro\')\n        >>> print(results)\n        {\'precision\': 0.3333333333333333}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'weighted\')\n        >>> print(results)\n        {\'precision\': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print([round(res, 2) for res in results[\'precision\']])\n        [0.67, 0.0, 0.0]\n'


lowerCamelCase__           =				'\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 					UpperCamelCase	(     datasets.Metric      ):




					def 							UpperCamelCase_						( self :					Optional[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.precision_score.html"]      ,)







					def 							UpperCamelCase_						( self :					str      ,_lowerCAmelCase :					List[str]      ,_lowerCAmelCase :					Dict      ,_lowerCAmelCase :					List[Any]=None      ,_lowerCAmelCase :					List[str]=1      ,_lowerCAmelCase :					int="binary"      ,_lowerCAmelCase :					List[Any]=None      ,_lowerCAmelCase :					List[str]="warn"      ,):


							"""simple docstring"""




							__snake_case		       =     precision_score(
							    _lowerCAmelCase      ,_lowerCAmelCase      ,labels=_lowerCAmelCase      ,pos_label=_lowerCAmelCase      ,average=_lowerCAmelCase      ,sample_weight=_lowerCAmelCase      ,zero_division=_lowerCAmelCase      ,)
							return {"precision": float(_lowerCAmelCase						) if score.size == 1 else score}






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
		lowerCamelCase__           =				False

if is_vision_available():
		from PIL import Image

		from transformers import (
		    AutoProcessor,
		    PixaStructImageProcessor,
		    PixaStructProcessor,
		    PreTrainedTokenizerFast,
		    TaTokenizer,
		)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,      reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,      )
@require_vision
@require_torch
class 					UpperCamelCase	(     unittest.TestCase      ):




					def 							UpperCamelCase_						( self :					Optional[int]						):


							"""simple docstring"""




							__snake_case		       =     tempfile.mkdtemp()

							__snake_case		       =     PixaStructImageProcessor()
							__snake_case		       =     TaTokenizer.from_pretrained("t5-small"						)

							__snake_case		       =     PixaStructProcessor(_lowerCAmelCase      ,_lowerCAmelCase						)

							processor.save_pretrained(self.tmpdirname						)




					def 							UpperCamelCase_						( self :					Dict      ,**_lowerCAmelCase :					List[str]						):


							"""simple docstring"""




							return AutoProcessor.from_pretrained(self.tmpdirname      ,**_lowerCAmelCase						).tokenizer




					def 							UpperCamelCase_						( self :					Any      ,**_lowerCAmelCase :					List[Any]						):


							"""simple docstring"""




							return AutoProcessor.from_pretrained(self.tmpdirname      ,**_lowerCAmelCase						).image_processor




					def 							UpperCamelCase_						( self :					Optional[int]						):


							"""simple docstring"""




							shutil.rmtree(self.tmpdirname						)




					def 							UpperCamelCase_						( self :					int						):


							"""simple docstring"""




							__snake_case		       =     [np.random.randint(255      ,size=(3, 30, 400)      ,dtype=np.uinta						)]

							__snake_case		       =     [Image.fromarray(np.moveaxis(_lowerCAmelCase      ,0      ,-1						)						) for x in image_inputs]

							return image_inputs




					def 							UpperCamelCase_						( self :					Any						):


							"""simple docstring"""




							__snake_case		       =     PixaStructProcessor(tokenizer=self.get_tokenizer()      ,image_processor=self.get_image_processor()						)
							processor.save_pretrained(self.tmpdirname						)

							__snake_case		       =     self.get_tokenizer(bos_token="(BOS)"      ,eos_token="(EOS)"						)
							__snake_case		       =     self.get_image_processor(do_normalize=_lowerCAmelCase      ,padding_value=1.0						)

							__snake_case		       =     PixaStructProcessor.from_pretrained(
							    self.tmpdirname      ,bos_token="(BOS)"      ,eos_token="(EOS)"      ,do_normalize=_lowerCAmelCase      ,padding_value=1.0						)

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

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




					def 							UpperCamelCase_						( self :					Optional[int]						):


							"""simple docstring"""




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

							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)

							__snake_case		       =     self.prepare_image_inputs()

							__snake_case		       =     image_processor(_lowerCAmelCase      ,return_tensors="np"						)
							__snake_case		       =     processor(images=_lowerCAmelCase      ,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 :					List[str]						):


							"""simple docstring"""




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

							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)

							__snake_case		       =     "lower newer"

							__snake_case		       =     processor(text=_lowerCAmelCase						)

							__snake_case		       =     tokenizer(_lowerCAmelCase      ,return_token_type_ids=_lowerCAmelCase      ,add_special_tokens=_lowerCAmelCase						)

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




					def 							UpperCamelCase_						( self :					Any						):


							"""simple docstring"""




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

							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)

							__snake_case		       =     "lower newer"
							__snake_case		       =     self.prepare_image_inputs()

							__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase						)

							self.assertListEqual(
							    list(inputs.keys()						)      ,["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]						)

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




					def 							UpperCamelCase_						( self :					str						):


							"""simple docstring"""




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

							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)

							__snake_case		       =     "lower newer"
							__snake_case		       =     self.prepare_image_inputs()

							__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase						)

							__snake_case		       =     [512, 1_024, 2_048, 4_096]
							__snake_case		       =     [770, 770, 770, 770]
							# with text
							for i, max_patch in enumerate(_lowerCAmelCase						):
									__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase      ,max_patches=_lowerCAmelCase						)
									self.assertEqual(inputs["flattened_patches"][0].shape[0]      ,_lowerCAmelCase						)
									self.assertEqual(inputs["flattened_patches"][0].shape[1]      ,expected_hidden_size[i]						)

							# without text input
							for i, max_patch in enumerate(_lowerCAmelCase						):
									__snake_case		       =     processor(images=_lowerCAmelCase      ,max_patches=_lowerCAmelCase						)
									self.assertEqual(inputs["flattened_patches"][0].shape[0]      ,_lowerCAmelCase						)
									self.assertEqual(inputs["flattened_patches"][0].shape[1]      ,expected_hidden_size[i]						)




					def 							UpperCamelCase_						( self :					Union[str, Any]						):


							"""simple docstring"""




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

							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)

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

							__snake_case		       =     processor.batch_decode(_lowerCAmelCase						)
							__snake_case		       =     tokenizer.batch_decode(_lowerCAmelCase						)

							self.assertListEqual(_lowerCAmelCase      ,_lowerCAmelCase						)







					def 							UpperCamelCase_						( self :					Tuple						):


							"""simple docstring"""




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

							__snake_case		       =     PixaStructProcessor(tokenizer=_lowerCAmelCase      ,image_processor=_lowerCAmelCase						)

							__snake_case		       =     "lower newer"
							__snake_case		       =     self.prepare_image_inputs()

							__snake_case		       =     processor(text=_lowerCAmelCase      ,images=_lowerCAmelCase						)

							# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
							self.assertListEqual(
							    list(inputs.keys()						)      ,["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]						)

							__snake_case		       =     processor(text=_lowerCAmelCase						)

							# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
							self.assertListEqual(list(inputs.keys()						)      ,["input_ids", "attention_mask"]						)





