
from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


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

lowercase__							:							Any									=			"\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

lowercase__							:							int									=			"\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"






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

							"""simple docstring"""






							def     A__		(  self			)->   MetricInfo:





													'''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     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = 4     ,	)->   Dict[str, float]:





													'''simple docstring'''






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






import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowercase__							:							Optional[Any]									=			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowercase__							:							Dict									=			"    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"






class        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			tempfile.mkdtemp()
													os.makedirs(os.path.join(self.diffusers_dir     ,	'''schedulers/'''			)			)
													__UpperCamelCase       =			self.diffusers_dir
													shutil.copy(
													    os.path.join(SCREAMING_SNAKE_CASE_     ,	'''src/diffusers/schedulers/scheduling_ddpm.py'''			)     ,	os.path.join(self.diffusers_dir     ,	'''schedulers/scheduling_ddpm.py'''			)     ,	)



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			'''src/diffusers'''
													shutil.rmtree(self.diffusers_dir			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			comment + F"\nclass {class_name}(nn.Module):\n" + class_code
													if overwrite_result is not None:
																			__UpperCamelCase       =			comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
													__UpperCamelCase       =			black.Mode(target_versions={black.TargetVersion.PYaa}     ,	line_length=119			)
													__UpperCamelCase       =			black.format_str(SCREAMING_SNAKE_CASE_     ,	mode=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			os.path.join(self.diffusers_dir     ,	'''new_code.py'''			)
													with open(SCREAMING_SNAKE_CASE_     ,	'''w'''     ,	newline='''\n'''			) as f:
																			f.write(SCREAMING_SNAKE_CASE_			)
													if overwrite_result is None:
																			self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE_			)			) == 0			)
													else:
																			check_copies.is_copy_consistent(f.name     ,	overwrite=SCREAMING_SNAKE_CASE_			)
																			with open(SCREAMING_SNAKE_CASE_     ,	'''r'''			) as f:
																									self.assertTrue(f.read()     ,	SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''			)
													self.assertEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													self.check_copy_consistency(
													    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''     ,	'''DDPMSchedulerOutput'''     ,	REFERENCE_CODE + '''\n'''     ,	)

													# With no empty line at the end
													self.check_copy_consistency(
													    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''     ,	'''DDPMSchedulerOutput'''     ,	SCREAMING_SNAKE_CASE_     ,	)

													# Copy consistency with rename
													self.check_copy_consistency(
													    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''     ,	'''TestSchedulerOutput'''     ,	re.sub('''DDPM'''     ,	'''Test'''     ,	SCREAMING_SNAKE_CASE_			)     ,	)

													# Copy consistency with a really long name
													__UpperCamelCase       =			'''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
													self.check_copy_consistency(
													    F"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"     ,	F"{long_class_name}SchedulerOutput"     ,	re.sub('''Bert'''     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)     ,	)

													# Copy consistency with overwrite
													self.check_copy_consistency(
													    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''     ,	'''TestSchedulerOutput'''     ,	SCREAMING_SNAKE_CASE_     ,	overwrite_result=re.sub('''DDPM'''     ,	'''Test'''     ,	SCREAMING_SNAKE_CASE_			)     ,	)




