




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType






class  __magic_name__						(  lowerCAmelCase__):
				'''simple docstring'''







				def __init__(							self:   str		,					_lowerCamelCase:   str		):
											SCREAMING_SNAKE_CASE_   	=    data





				def __iter__(							self:   List[Any]		):
											for element in self.data:
																		yield element






def 			a   (_lowerCAmelCase=True     ):
							SCREAMING_SNAKE_CASE_   	=    Accelerator(even_batches=_A     )
							assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
							return accelerator

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase = False     ):
							if iterable:
														SCREAMING_SNAKE_CASE_   	=    DummyIterableDataset(torch.as_tensor(range(_A     )     )     )
							else:
														SCREAMING_SNAKE_CASE_   	=    TensorDataset(torch.as_tensor(range(_A     )     )     )

							SCREAMING_SNAKE_CASE_   	=    DataLoader(_A  ,    batch_size=_A     )
							SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(_A     )

							return dl

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    ):
							SCREAMING_SNAKE_CASE_   	=    create_dataloader(accelerator=_A  ,    dataset_size=_A  ,    batch_size=_A     )

							SCREAMING_SNAKE_CASE_   	=    [len(batch[0]     ) for batch in dl]

							if accelerator.process_index == 0:
														assert batch_sizes == process_0_expected_batch_sizes
							elif accelerator.process_index == 1:
														assert batch_sizes == process_1_expected_batch_sizes

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    create_accelerator()

							# without padding, we would expect a different number of batches
							verify_dataloader_batch_sizes(
							    _A  ,    dataset_size=3  ,    batch_size=1  ,    process_0_expected_batch_sizes=[1, 1]  ,    process_1_expected_batch_sizes=[1, 1]  ,    )

							# without padding, we would expect the same number of batches, but different sizes
							verify_dataloader_batch_sizes(
							    _A  ,    dataset_size=7  ,    batch_size=2  ,    process_0_expected_batch_sizes=[2, 2]  ,    process_1_expected_batch_sizes=[2, 2]  ,    )

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    create_accelerator(even_batches=_A     )

							verify_dataloader_batch_sizes(
							    _A  ,    dataset_size=3  ,    batch_size=1  ,    process_0_expected_batch_sizes=[1, 1]  ,    process_1_expected_batch_sizes=[1]  ,    )

							verify_dataloader_batch_sizes(
							    _A  ,    dataset_size=7  ,    batch_size=2  ,    process_0_expected_batch_sizes=[2, 2]  ,    process_1_expected_batch_sizes=[2, 1]  ,    )

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    create_accelerator(even_batches=_A     )

							SCREAMING_SNAKE_CASE_   	=    torch.nn.Linear(1  ,    1     )
							SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(_A     )

							SCREAMING_SNAKE_CASE_   	=    create_dataloader(_A  ,    dataset_size=3  ,    batch_size=1     )

							SCREAMING_SNAKE_CASE_   	=    []
							with accelerator.join_uneven_inputs([ddp_model]     ):
														for batch_idx, batch in enumerate(_A     ):
																					SCREAMING_SNAKE_CASE_   	=    ddp_model(batch[0].float()     )
																					SCREAMING_SNAKE_CASE_   	=    output.sum()
																					loss.backward()
																					batch_idxs.append(_A     )

							accelerator.wait_for_everyone()

							if accelerator.process_index == 0:
														assert batch_idxs == [0, 1]
							elif accelerator.process_index == 1:
														assert batch_idxs == [0]

def 			a   (_lowerCAmelCase     ):
							with warnings.catch_warnings(record=_A     ) as w:
														with accelerator.join_uneven_inputs([Mock()]     ):
																					pass

														assert issubclass(w[-1].category  ,    _A     )
														assert "only supported for multi-GPU" in str(w[-1].message     )

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    True
							SCREAMING_SNAKE_CASE_   	=    False
							SCREAMING_SNAKE_CASE_   	=    create_accelerator(even_batches=_A     )
							SCREAMING_SNAKE_CASE_   	=    torch.nn.Linear(1  ,    1     )
							SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(_A     )
							SCREAMING_SNAKE_CASE_   	=    create_dataloader(_A  ,    dataset_size=3  ,    batch_size=1     )
							SCREAMING_SNAKE_CASE_   	=    create_dataloader(_A  ,    dataset_size=3  ,    batch_size=1     )

							with accelerator.join_uneven_inputs([ddp_model]  ,    even_batches=_A     ):
														SCREAMING_SNAKE_CASE_   	=    train_dl.batch_sampler.even_batches
														SCREAMING_SNAKE_CASE_   	=    valid_dl.batch_sampler.even_batches

							assert train_dl_overridden_value == overridden_even_batches
							assert valid_dl_overridden_value == overridden_even_batches
							assert train_dl.batch_sampler.even_batches == default_even_batches
							assert valid_dl.batch_sampler.even_batches == default_even_batches

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    True
							SCREAMING_SNAKE_CASE_   	=    False
							SCREAMING_SNAKE_CASE_   	=    create_accelerator(even_batches=_A     )
							SCREAMING_SNAKE_CASE_   	=    torch.nn.Linear(1  ,    1     )
							SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(_A     )
							create_dataloader(_A  ,    dataset_size=3  ,    batch_size=1  ,    iterable=_A     )
							SCREAMING_SNAKE_CASE_   	=    create_dataloader(_A  ,    dataset_size=3  ,    batch_size=1     )

							with warnings.catch_warnings():
														warnings.filterwarnings('''ignore'''     )
														try:
																					with accelerator.join_uneven_inputs([ddp_model]  ,    even_batches=_A     ):
																												SCREAMING_SNAKE_CASE_   	=    batch_dl.batch_sampler.even_batches
														except AttributeError:
																					# ensure attribute error is not raised when processing iterable dl
																					raise AssertionError

							assert batch_dl_overridden_value == overridden_even_batches
							assert batch_dl.batch_sampler.even_batches == default_even_batches

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    create_accelerator()
							SCREAMING_SNAKE_CASE_   	=    torch.nn.Linear(1  ,    1     )
							SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(_A     )
							create_dataloader(_A  ,    dataset_size=3  ,    batch_size=1  ,    iterable=_A     )

							with warnings.catch_warnings(record=_A     ) as w:
														with accelerator.join_uneven_inputs([ddp_model]  ,    even_batches=_A     ):
																					pass

														assert issubclass(w[-1].category  ,    _A     )
														assert "only supported for map-style datasets" in str(w[-1].message     )

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    create_accelerator()

							accelerator.print('''Test that even_batches variable ensures uniform batches across processes'''     )
							test_default_ensures_even_batch_sizes()

							accelerator.print('''Run tests with even_batches disabled'''     )
							test_can_disable_even_batches()

							accelerator.print('''Test joining uneven inputs'''     )
							test_can_join_uneven_inputs()

							accelerator.print('''Test overriding even_batches when joining uneven inputs'''     )
							test_join_can_override_even_batches()

							accelerator.print('''Test overriding even_batches for mixed dataloader types'''     )
							test_join_can_override_for_mixed_type_dataloaders()

							accelerator.print('''Test overriding even_batches raises a warning for iterable dataloaders'''     )
							test_join_raises_warning_for_iterable_when_overriding_even_batches()

							accelerator.print('''Test join with non DDP distributed raises warning'''     )
							SCREAMING_SNAKE_CASE_   	=    accelerator.state.distributed_type
							SCREAMING_SNAKE_CASE_   	=    DistributedType.FSDP
							test_join_raises_warning_for_non_ddp_distributed(_A     )
							SCREAMING_SNAKE_CASE_   	=    original_state


if __name__ == "__main__":
			main()








import re
import string

import numpy as np

import datasets


__SCREAMING_SNAKE_CASE 						="""
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""

__SCREAMING_SNAKE_CASE 						="""
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    25.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    50.0


    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    75.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results[\"exact_match\"], 1))
    100.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]
    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    33.3

"""

__SCREAMING_SNAKE_CASE 						="""
"""






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







				def     _A							(							self:   Optional[int]		):
											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     _A							(							self:   Tuple		,					_lowerCamelCase:   int		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   str=False		,					_lowerCamelCase:   List[str]=False		,					_lowerCamelCase:   List[str]=False		,					):
											if regexes_to_ignore is not None:
																		for s in regexes_to_ignore:
																									SCREAMING_SNAKE_CASE_   	=    np.array([re.sub(_lowerCamelCase		,					''''''		,					_lowerCamelCase		) for x in predictions]		)
																									SCREAMING_SNAKE_CASE_   	=    np.array([re.sub(_lowerCamelCase		,					''''''		,					_lowerCamelCase		) for x in references]		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)

											if ignore_case:
																		SCREAMING_SNAKE_CASE_   	=    np.char.lower(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.lower(_lowerCamelCase		)

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

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

											SCREAMING_SNAKE_CASE_   	=    predictions == references

											return {"exact_match": np.mean(_lowerCamelCase		) * 1_00}


