






import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		@require_torch
		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
							# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
							# while running an external program

							# python one-liner segments

							# this must be loaded before socket.socket is monkey-patched
							UpperCAmelCase_    :      str            =						'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

							UpperCAmelCase_    :      Union[str, Any]            =						'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

							UpperCAmelCase_    :      Optional[int]            =						'\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

							# Force fetching the files so that we can use the cache
							UpperCAmelCase_    :      List[Any]            =						'hf-internal-testing/tiny-random-bert'
							BertConfig.from_pretrained(_UpperCamelCase   )
							BertModel.from_pretrained(_UpperCamelCase   )
							BertTokenizer.from_pretrained(_UpperCamelCase   )
							pipeline(task='fill-mask'				,		model=_UpperCamelCase   )

							# baseline - just load from_pretrained with normal network
							UpperCAmelCase_    :      Dict            =						[sys.executable, '-c', '\n'.join([load, run, mock]   )]

							# should succeed
							UpperCAmelCase_    :      str            =						self.get_env()
							# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
							UpperCAmelCase_    :      Tuple            =						'1'
							UpperCAmelCase_    :      Any            =						subprocess.run(_UpperCamelCase				,		env=_UpperCamelCase				,		check=_UpperCamelCase				,		capture_output=_UpperCamelCase   )
							self.assertEqual(result.returncode				,		0				,		result.stderr   )
							self.assertIn('success'				,		result.stdout.decode()   )



		@require_torch
		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							# python one-liner segments
							# this must be loaded before socket.socket is monkey-patched
							UpperCAmelCase_    :      Optional[int]            =						'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

							UpperCAmelCase_    :      List[Any]            =						'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

							UpperCAmelCase_    :      List[str]            =						'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

							# Force fetching the files so that we can use the cache
							UpperCAmelCase_    :      Optional[int]            =						'hf-internal-testing/tiny-random-bert'
							BertConfig.from_pretrained(_UpperCamelCase   )
							BertModel.from_pretrained(_UpperCamelCase   )
							BertTokenizer.from_pretrained(_UpperCamelCase   )
							pipeline(task='fill-mask'				,		model=_UpperCamelCase   )

							# baseline - just load from_pretrained with normal network
							UpperCAmelCase_    :      str            =						[sys.executable, '-c', '\n'.join([load, run, mock]   )]

							# should succeed
							UpperCAmelCase_    :      Tuple            =						self.get_env()
							UpperCAmelCase_    :      Union[str, Any]            =						subprocess.run(_UpperCamelCase				,		env=_UpperCamelCase				,		check=_UpperCamelCase				,		capture_output=_UpperCamelCase   )
							self.assertEqual(result.returncode				,		0				,		result.stderr   )
							self.assertIn('success'				,		result.stdout.decode()   )



		@require_torch
		def 					__UpperCAmelCase			(							self   )	->							str:
							# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
							# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
							# while running an external program

							# python one-liner segments

							# this must be loaded before socket.socket is monkey-patched
							UpperCAmelCase_    :      str            =						'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

							UpperCAmelCase_    :      List[Any]            =						'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

							UpperCAmelCase_    :      str            =						'\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

							# baseline - just load from_pretrained with normal network
							UpperCAmelCase_    :      List[Any]            =						[sys.executable, '-c', '\n'.join([load, run]   )]

							# should succeed
							UpperCAmelCase_    :      str            =						self.get_env()
							UpperCAmelCase_    :      str            =						subprocess.run(_UpperCamelCase				,		env=_UpperCamelCase				,		check=_UpperCamelCase				,		capture_output=_UpperCamelCase   )
							self.assertEqual(result.returncode				,		0				,		result.stderr   )
							self.assertIn('success'				,		result.stdout.decode()   )

							# next emulate no network
							UpperCAmelCase_    :      str            =						[sys.executable, '-c', '\n'.join([load, mock, run]   )]

							# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
							# env["TRANSFORMERS_OFFLINE"] = "0"
							# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
							# self.assertEqual(result.returncode, 1, result.stderr)

							# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
							UpperCAmelCase_    :      List[Any]            =						'1'
							UpperCAmelCase_    :      int            =						subprocess.run(_UpperCamelCase				,		env=_UpperCamelCase				,		check=_UpperCamelCase				,		capture_output=_UpperCamelCase   )
							self.assertEqual(result.returncode				,		0				,		result.stderr   )
							self.assertIn('success'				,		result.stdout.decode()   )



		@require_torch
		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							UpperCAmelCase_    :      Any            =						'\nfrom transformers import pipeline\n        '
							UpperCAmelCase_    :      Optional[int]            =						'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

							UpperCAmelCase_    :      Tuple            =						'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
							UpperCAmelCase_    :      str            =						self.get_env()
							UpperCAmelCase_    :      Optional[Any]            =						'1'
							UpperCAmelCase_    :      str            =						[sys.executable, '-c', '\n'.join([load, mock, run]   )]
							UpperCAmelCase_    :      List[Any]            =						subprocess.run(_UpperCamelCase				,		env=_UpperCamelCase				,		check=_UpperCamelCase				,		capture_output=_UpperCamelCase   )
							self.assertEqual(result.returncode				,		1				,		result.stderr   )
							self.assertIn(
							    'You cannot infer task automatically within `pipeline` when using offline mode'				,		result.stderr.decode().replace('\n'				,		''   )				,		)



		@require_torch
		def 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      Dict            =						'\nfrom transformers import AutoModel\n        '
							UpperCAmelCase_    :      str            =						'\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

							# baseline - just load from_pretrained with normal network
							UpperCAmelCase_    :      Union[str, Any]            =						[sys.executable, '-c', '\n'.join([load, run]   )]

							# should succeed
							UpperCAmelCase_    :      Any            =						self.get_env()
							UpperCAmelCase_    :      List[Any]            =						subprocess.run(_UpperCamelCase				,		env=_UpperCamelCase				,		check=_UpperCamelCase				,		capture_output=_UpperCamelCase   )
							self.assertEqual(result.returncode				,		0				,		result.stderr   )
							self.assertIn('success'				,		result.stdout.decode()   )

							# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
							UpperCAmelCase_    :      Tuple            =						'1'
							UpperCAmelCase_    :      Optional[Any]            =						subprocess.run(_UpperCamelCase				,		env=_UpperCamelCase				,		check=_UpperCamelCase				,		capture_output=_UpperCamelCase   )
							self.assertEqual(result.returncode				,		0				,		result.stderr   )
							self.assertIn('success'				,		result.stdout.decode()   )







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

import datasets


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

UpperCamelCase  							=						'''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''

UpperCamelCase  							=						'''
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
    \'num_edits\' (int): The cumulative number of edits
    \'ref_length\' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?",
        ...                     "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}

    Example 2:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}

    Example 3:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}

    Example 4:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}

    Example 5:
        >>> predictions = ["does this sentence match??",
        ...                    "what about this sentence?",
        ...                    "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''



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






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Tuple:
       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       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     str	,						lowercase_				:     Optional[int]	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						)					->						Any:
       lowercase__ :   Optional[int]    					=				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"  )
       lowercase__ :   Union[str, Any]    					=				[[refs[i] for refs in references] for i in range(lowercase_  )]

       lowercase__ :   str    					=				TER(
           normalized=lowercase_	,						no_punct=lowercase_	,						asian_support=lowercase_	,						case_sensitive=lowercase_	,						)
       lowercase__ :   List[str]    					=				sb_ter.corpus_score(lowercase_	,						lowercase_  )

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



