

'''simple docstring'''


from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


lowerCAmelCase__						:       List[Any]										=						"""\
@misc{wu2016googles,
      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

lowerCAmelCase__						:       Optional[Any]										=						"""\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""

lowerCAmelCase__						:       Tuple										=						"""\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    'google_bleu': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.44

    Example 2:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.61

    Example 3:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results[\"google_bleu\"], 2))
        0.53

    Example 4:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results[\"google_bleu\"], 2))
        0.4
"""




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






      """simple docstring"""




      def       __magic_name__							(							self					:      List[Any]  ):



        '''simple docstring'''



        return datasets.MetricInfo(
            description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
                {
                    '''predictions''': datasets.Sequence(datasets.Value('''string'''			,			id='''token'''  )			,			id='''sequence'''  ),
                    '''references''': datasets.Sequence(
                        datasets.Sequence(datasets.Value('''string'''			,			id='''token'''  )			,			id='''sequence'''  )			,			id='''references'''  ),
                }  )			,			)







      def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      List[List[List[str]]]			,			snake_case_					:      List[List[str]]			,			snake_case_					:      int = 1			,			snake_case_					:      int = 4			,			):



        '''simple docstring'''



        return {
            "google_bleu": gleu_score.corpus_gleu(
                list_of_references=snake_case_			,			hypotheses=snake_case_			,			min_len=snake_case_			,			max_len=snake_case_  )
        }






'''simple docstring'''


# Copyright 2022 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 argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase__						:       List[Any]										=						"""Run commands across TPU VMs for initial setup before running `accelerate launch`."""




def 	_a       (				__lowerCAmelCase		:      Any=None     ):

		"""simple docstring"""

		if subparsers is not None:
				snake_case__						:       Tuple       						=  subparsers.add_parser('''tpu-config'''   ,							description=_description     )
		else:
				snake_case__						:       str       						=  argparse.ArgumentParser('''Accelerate tpu-config command'''   ,							description=_description     )
		# Core arguments
		snake_case__						:       Any       						=  parser.add_argument_group(
		    '''Config Arguments'''   ,							'''Arguments that can be configured through `accelerate config`.'''     )
		config_args.add_argument(
		    '''--config_file'''   ,							type=__lowerCAmelCase   ,							default=__lowerCAmelCase   ,							help='''Path to the config file to use for accelerate.'''   ,							)
		config_args.add_argument(
		    '''--tpu_name'''   ,							default=__lowerCAmelCase   ,							help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''   ,							)
		config_args.add_argument(
		    '''--tpu_zone'''   ,							default=__lowerCAmelCase   ,							help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''   ,							)
		snake_case__						:       Optional[Any]       						=  parser.add_argument_group('''TPU Arguments'''   ,							'''Arguments for options ran inside the TPU.'''     )
		pod_args.add_argument(
		    '''--use_alpha'''   ,							action='''store_true'''   ,							help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''   ,							)
		pod_args.add_argument(
		    '''--command_file'''   ,							default=__lowerCAmelCase   ,							help='''The path to the file containing the commands to run on the pod on startup.'''   ,							)
		pod_args.add_argument(
		    '''--command'''   ,							action='''append'''   ,							nargs='''+'''   ,							help='''A command to run on the pod. Can be passed multiple times.'''   ,							)
		pod_args.add_argument(
		    '''--install_accelerate'''   ,							action='''store_true'''   ,							help='''Whether to install accelerate on the pod. Defaults to False.'''   ,							)
		pod_args.add_argument(
		    '''--accelerate_version'''   ,							default='''latest'''   ,							help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''   ,							)
		pod_args.add_argument(
		    '''--debug'''   ,							action='''store_true'''   ,							help='''If set, will print the command that would be run instead of running it.'''     )

		if subparsers is not None:
				parser.set_defaults(func=__lowerCAmelCase     )
		return parser




def 	_a       (				__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		snake_case__						:       Optional[int]       						=  None

		# Get the default from the config file if it exists.
		if args.config_file is not None or os.path.isfile(__lowerCAmelCase     ):
				snake_case__						:       Tuple       						=  load_config_from_file(args.config_file     )
				if not args.command_file and defaults.command_file is not None and not args.command:
						snake_case__						:       Optional[int]       						=  defaults.command_file
				if not args.command and defaults.commands is not None:
						snake_case__						:       Union[str, Any]       						=  defaults.commands
				if not args.tpu_name:
						snake_case__						:       Union[str, Any]       						=  defaults.tpu_name
				if not args.tpu_zone:
						snake_case__						:       Optional[Any]       						=  defaults.tpu_zone
		if args.accelerate_version == "dev":
				snake_case__						:       Tuple       						=  '''git+https://github.com/huggingface/accelerate.git'''
		elif args.accelerate_version == "latest":
				snake_case__						:       Union[str, Any]       						=  '''accelerate -U'''
		elif isinstance(parse(args.accelerate_version     )   ,							__lowerCAmelCase     ):
				snake_case__						:       Any       						=  F"""accelerate=={args.accelerate_version}"""

		if not args.command_file and not args.command:
				raise ValueError('''You must specify either a command file or a command to run on the pod.'''     )

		if args.command_file:
				with open(args.command_file   ,							'''r'''     ) as f:
						snake_case__						:       str       						=  [f.read().splitlines()]

    # To turn list of lists into list of strings
		if isinstance(args.command[0]   ,							__lowerCAmelCase     ):
				snake_case__						:       List[Any]       						=  [line for cmd in args.command for line in cmd]
		# Default to the shared folder and install accelerate
		snake_case__						:       Optional[int]       						=  ['''cd /usr/share''']
		if args.install_accelerate:
				new_cmd += [F"""pip install {args.accelerate_version}"""]
		new_cmd += args.command
		snake_case__						:       Tuple       						=  '''; '''.join(__lowerCAmelCase     )

		# Then send it to gcloud
		# Eventually try to use google-api-core to do this instead of subprocess
		snake_case__						:       str       						=  ['''gcloud''']
		if args.use_alpha:
				cmd += ["alpha"]
		cmd += [
		    "compute",
		    "tpus",
		    "tpu-vm",
		    "ssh",
		    args.tpu_name,
		    "--zone",
		    args.tpu_zone,
		    "--command",
		    args.command,
		    "--worker",
		    "all",
		]
		if args.debug:
				print(F"""Running {' '.join(__lowerCAmelCase     )}"""     )
				return
		subprocess.run(__lowerCAmelCase     )
		print('''Successfully setup pod.'''     )





def 	_a       (				):

		"""simple docstring"""

		snake_case__						:       Dict       						=  tpu_command_parser()
		snake_case__						:       Dict       						=  parser.parse_args()

		tpu_command_launcher(__lowerCAmelCase     )



