




"""simple docstring"""



import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


SCREAMING_SNAKE_CASE				=       "\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

SCREAMING_SNAKE_CASE				=       "\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

SCREAMING_SNAKE_CASE				=       "\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"




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




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




     '''simple docstring'''

     if version.parse(scb.__version__      ) < version.parse("1.4.12"      ):
      raise ImportWarning(
          "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
          "You can install it with `pip install \"sacrebleu>=1.4.12\"`."      )
     return datasets.MetricInfo(
         description=_DESCRIPTION	, citation=_CITATION	, homepage="http://www.cs.umd.edu/~snover/tercom/"	, inputs_description=_KWARGS_DESCRIPTION	, features=datasets.Features(
             {
                 "predictions": datasets.Value("string"	, id="sequence"      ),
                 "references": datasets.Sequence(datasets.Value("string"	, id="sequence"      )	, id="references"      ),
             }      )	, codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"]	, reference_urls=[
             "https://github.com/jhclark/tercom",
         ]	, )




    def 							__magic_name__							(    self						:       Any	, snake_case_						:       Any	, snake_case_						:       Tuple	, snake_case_						:       bool = False	, snake_case_						:       bool = False	, snake_case_						:       bool = False	, snake_case_						:       bool = False	, )      ->							Tuple:




     '''simple docstring'''

     A__       =				len(references[0]      )
     if any(len(lowercase_      ) != references_per_prediction for refs in references      ):
      raise ValueError("Sacrebleu requires the same number of references for each prediction"      )
     A__       =				[[refs[i] for refs in references] for i in range(lowercase_      )]

     A__       =				TER(
         normalized=lowercase_	, no_punct=lowercase_	, asian_support=lowercase_	, case_sensitive=lowercase_	, )
     A__       =				sb_ter.corpus_score(lowercase_	, lowercase_      )

     return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}






'''simple docstring'''




# 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__						(    _UpperCAmelCase):







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  str    ):
										lowercase_					:			int										=       data







			def __iter__(	self				:  int    ):
										for element in self.data:
																	yield element




def 							lowerCamelCase	(      UpperCAmelCase__       :					Any=True )			->      Any:
							lowercase_					:			Optional[int]										=       Accelerator(even_batches=UpperCAmelCase__ )
							assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
							return accelerator




def 							lowerCamelCase	(      UpperCAmelCase__       :					Accelerator      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					bool = False )			->      Optional[Any]:
							if iterable:
														lowercase_					:			Dict										=       DummyIterableDataset(torch.as_tensor(range(UpperCAmelCase__ ) ) )
							else:
														lowercase_					:			Union[str, Any]										=       TensorDataset(torch.as_tensor(range(UpperCAmelCase__ ) ) )

							lowercase_					:			Any										=       DataLoader(UpperCAmelCase__      ,      batch_size=UpperCAmelCase__ )
							lowercase_					:			Any										=       accelerator.prepare(UpperCAmelCase__ )

							return dl




def 							lowerCamelCase	(      UpperCAmelCase__       :					Accelerator      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					List[int]      ,      UpperCAmelCase__       :					List[int]      ,      )			->      int:
							lowercase_					:			List[str]										=       create_dataloader(accelerator=UpperCAmelCase__      ,      dataset_size=UpperCAmelCase__      ,      batch_size=UpperCAmelCase__ )

							lowercase_					:			Tuple										=       [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 							lowerCamelCase	(      )			->      int:
							lowercase_					:			Tuple										=       create_accelerator()

							# without padding, we would expect a different number of batches
							verify_dataloader_batch_sizes(
							    UpperCAmelCase__      ,      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(
							    UpperCAmelCase__      ,      dataset_size=7      ,      batch_size=2      ,      process_0_expected_batch_sizes=[2, 2]      ,      process_1_expected_batch_sizes=[2, 2]      ,      )




def 							lowerCamelCase	(      )			->      Optional[int]:
							lowercase_					:			Optional[int]										=       create_accelerator(even_batches=UpperCAmelCase__ )

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

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




def 							lowerCamelCase	(      )			->      List[str]:
							lowercase_					:			str										=       create_accelerator(even_batches=UpperCAmelCase__ )

							lowercase_					:			Dict										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			Optional[Any]										=       accelerator.prepare(UpperCAmelCase__ )

							lowercase_					:			List[Any]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )

							lowercase_					:			Optional[Any]										=       []
							with accelerator.join_uneven_inputs([ddp_model] ):
														for batch_idx, batch in enumerate(UpperCAmelCase__ ):
																					lowercase_					:			Any										=       ddp_model(batch[0].float() )
																					lowercase_					:			List[str]										=       output.sum()
																					loss.backward()
																					batch_idxs.append(UpperCAmelCase__ )

							accelerator.wait_for_everyone()

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




def 							lowerCamelCase	(      UpperCAmelCase__       :					List[str] )			->      List[str]:
							with warnings.catch_warnings(record=UpperCAmelCase__ ) as w:
														with accelerator.join_uneven_inputs([Mock()] ):
																					pass

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




def 							lowerCamelCase	(      )			->      Any:
							lowercase_					:			str										=       True
							lowercase_					:			Tuple										=       False
							lowercase_					:			str										=       create_accelerator(even_batches=UpperCAmelCase__ )
							lowercase_					:			Union[str, Any]										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			Any										=       accelerator.prepare(UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )
							lowercase_					:			List[Any]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )

							with accelerator.join_uneven_inputs([ddp_model]      ,      even_batches=UpperCAmelCase__ ):
														lowercase_					:			Union[str, Any]										=       train_dl.batch_sampler.even_batches
														lowercase_					:			List[str]										=       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 							lowerCamelCase	(      )			->      Dict:
							lowercase_					:			str										=       True
							lowercase_					:			Optional[Any]										=       False
							lowercase_					:			Union[str, Any]										=       create_accelerator(even_batches=UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			Optional[int]										=       accelerator.prepare(UpperCAmelCase__ )
							create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1      ,      iterable=UpperCAmelCase__ )
							lowercase_					:			List[str]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )

							with warnings.catch_warnings():
														warnings.filterwarnings("""ignore""" )
														try:
																					with accelerator.join_uneven_inputs([ddp_model]      ,      even_batches=UpperCAmelCase__ ):
																												lowercase_					:			Optional[Any]										=       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 							lowerCamelCase	(      )			->      List[Any]:
							lowercase_					:			Optional[Any]										=       create_accelerator()
							lowercase_					:			Optional[int]										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			List[Any]										=       accelerator.prepare(UpperCAmelCase__ )
							create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1      ,      iterable=UpperCAmelCase__ )

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

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




def 							lowerCamelCase	(      )			->      List[str]:
							lowercase_					:			List[Any]										=       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""" )
							lowercase_					:			List[Any]										=       accelerator.state.distributed_type
							lowercase_					:			Union[str, Any]										=       DistributedType.FSDP
							test_join_raises_warning_for_non_ddp_distributed(UpperCAmelCase__ )
							lowercase_					:			str										=       original_state


if __name__ == "__main__":
						main()





