





import re
import string

import numpy as np

import datasets


UpperCAmelCase_           =	"\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

UpperCAmelCase_           =	"\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

UpperCAmelCase_           =	"\n"




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





     def 		UpperCamelCase(     self					):
        return datasets.MetricInfo(
            description=_DESCRIPTION				,							citation=_CITATION				,							inputs_description=_KWARGS_DESCRIPTION				,							features=datasets.Features(
                {
                    '''predictions''': datasets.Value('''string'''				,							id='''sequence'''					),
                    '''references''': datasets.Value('''string'''				,							id='''sequence'''					),
                }					)				,							reference_urls=[]				,							)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase=None				,							_UpperCamelCase=False				,							_UpperCamelCase=False				,							_UpperCamelCase=False				,							):
        if regexes_to_ignore is not None:
           for s in regexes_to_ignore:
              _UpperCAmelCase           =   np.array([re.sub(_UpperCamelCase				,							''''''				,							_UpperCamelCase					) for x in predictions]					)
              _UpperCAmelCase           =   np.array([re.sub(_UpperCamelCase				,							''''''				,							_UpperCamelCase					) for x in references]					)
        else:
           _UpperCAmelCase           =   np.asarray(_UpperCamelCase					)
           _UpperCAmelCase           =   np.asarray(_UpperCamelCase					)

        if ignore_case:
           _UpperCAmelCase           =   np.char.lower(_UpperCamelCase					)
           _UpperCAmelCase           =   np.char.lower(_UpperCamelCase					)

        if ignore_punctuation:
           _UpperCAmelCase           =   string.punctuation.maketrans(''''''				,							''''''				,							string.punctuation					)
           _UpperCAmelCase           =   np.char.translate(_UpperCamelCase				,							table=_UpperCamelCase					)
           _UpperCAmelCase           =   np.char.translate(_UpperCamelCase				,							table=_UpperCamelCase					)

        if ignore_numbers:
           _UpperCAmelCase           =   string.digits.maketrans(''''''				,							''''''				,							string.digits					)
           _UpperCAmelCase           =   np.char.translate(_UpperCamelCase				,							table=_UpperCamelCase					)
           _UpperCAmelCase           =   np.char.translate(_UpperCamelCase				,							table=_UpperCamelCase					)

        _UpperCAmelCase           =   predictions == references

        return {"exact_match": np.mean(_UpperCamelCase					) * 100}






import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
    import torch

if is_soundfile_availble():
    import soundfile as sf

if is_vision_available():
    from PIL import Image






def 				_UpperCAmelCase			(    A=""     ):




   '''simple docstring'''

   UpperCAmelCase__						=tempfile.mkdtemp()
   return os.path.join(A				,							str(uuid.uuida()     ) + suffix     )

@require_soundfile
@require_torch
class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       str:
    UpperCAmelCase__						=torch.rand(12,		dtype=torch.floataa		) - 0.5
    UpperCAmelCase__						=AgentAudio(A_		)
    UpperCAmelCase__						=str(agent_type.to_string()		)

    # Ensure that the tensor and the agent_type's tensor are the same
    self.assertTrue(torch.allclose(A_,		agent_type.to_raw(),		atol=1E-4		)		)

    del agent_type

    # Ensure the path remains even after the object deletion
    self.assertTrue(os.path.exists(A_		)		)

    # Ensure that the file contains the same value as the original tensor
    UpperCAmelCase__			,							UpperCAmelCase__						=sf.read(A_		)
    self.assertTrue(torch.allclose(A_,		torch.tensor(A_		),		atol=1E-4		)		)






 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=torch.rand(12,		dtype=torch.floataa		) - 0.5
    UpperCAmelCase__						=get_new_path(suffix=".wav"		)
    sf.write(A_,		A_,		1_6000		)

    UpperCAmelCase__						=AgentAudio(A_		)

    self.assertTrue(torch.allclose(A_,		agent_type.to_raw(),		atol=1E-4		)		)
    self.assertEqual(agent_type.to_string(),		A_		)

@require_vision
@require_torch
class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=torch.randint(0,		256,		(64, 64, 3)		)
    UpperCAmelCase__						=AgentImage(A_		)
    UpperCAmelCase__						=str(agent_type.to_string()		)

    # Ensure that the tensor and the agent_type's tensor are the same
    self.assertTrue(torch.allclose(A_,		agent_type._tensor,		atol=1E-4		)		)

    self.assertIsInstance(agent_type.to_raw(),		Image.Image		)

    # Ensure the path remains even after the object deletion
    del agent_type
    self.assertTrue(os.path.exists(A_		)		)

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=Path(get_tests_dir("fixtures/tests_samples/COCO"		)		) / "000000039769.png"
    UpperCAmelCase__						=Image.open(A_		)
    UpperCAmelCase__						=AgentImage(A_		)

    self.assertTrue(path.samefile(agent_type.to_string()		)		)
    self.assertTrue(image == agent_type.to_raw()		)

    # Ensure the path remains even after the object deletion
    del agent_type
    self.assertTrue(os.path.exists(A_		)		)






 def  __UpperCAmelCase						(  self		)	->       Tuple:
    UpperCAmelCase__						=Path(get_tests_dir("fixtures/tests_samples/COCO"		)		) / "000000039769.png"
    UpperCAmelCase__						=Image.open(A_		)
    UpperCAmelCase__						=AgentImage(A_		)

    self.assertFalse(path.samefile(agent_type.to_string()		)		)
    self.assertTrue(image == agent_type.to_raw()		)

    # Ensure the path remains even after the object deletion
    del agent_type
    self.assertTrue(os.path.exists(A_		)		)

class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    UpperCAmelCase__						="Hey!"
    UpperCAmelCase__						=AgentText(A_		)

    self.assertEqual(A_,		agent_type.to_string()		)
    self.assertEqual(A_,		agent_type.to_raw()		)
    self.assertEqual(A_,		A_		)



