



import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


lowercase_	       =			'''\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",
    author = "Lin, Chin-Yew  and
      Och, Franz Josef",
    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",
    month = "aug 23{--}aug 27",
    year = "2004",
    address = "Geneva, Switzerland",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/C04-1072",
    pages = "501--507",
}
'''

lowercase_	       =			'''\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,
the better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
'''

lowercase_	       =			'''
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    \'bleu\': bleu score,
    \'precisions\': geometric mean of n-gram precisions,
    \'brevity_penalty\': brevity penalty,
    \'length_ratio\': ratio of lengths,
    \'translation_length\': translation_length,
    \'reference_length\': reference_length
Examples:

    >>> predictions = [
    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample
    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)
    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric("bleu")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results["bleu"])
    1.0
'''






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



	def 		UpperCamelCase							(						self							:    Any)->							Tuple:
						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"""),
						        })				,	codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]				,	reference_urls=[
						        """https://en.wikipedia.org/wiki/BLEU""",
						        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
						    ]				,	)



	def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    Dict				,	snake_case_							:    List[str]				,	snake_case_							:    Optional[int]=4				,	snake_case_							:    List[str]=False)->							Dict:
						__lowerCAmelCase	   =compute_bleu(
						    reference_corpus=snake_case_				,	translation_corpus=snake_case_				,	max_order=snake_case_				,	smooth=snake_case_)
						((__lowerCAmelCase)   ,	(__lowerCAmelCase)   ,	(__lowerCAmelCase)   ,	(__lowerCAmelCase)   ,	(__lowerCAmelCase)   ,	(__lowerCAmelCase))	   =score
						return {
						    "bleu": bleu,
						    "precisions": precisions,
						    "brevity_penalty": bp,
						    "length_ratio": ratio,
						    "translation_length": translation_length,
						    "reference_length": reference_length,
						}









import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]	)  ->						int:  # picklable for multiprocessing
					return x.sum()






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Any	)  ->						Any:  # picklable for multiprocessing
					return i + 1






@dataclass
class 						__a :
	SCREAMING_SNAKE_CASE       =       42
	SCREAMING_SNAKE_CASE       =       42






class 						__a (     SCREAMING_SNAKE_CASE				):



	def 		UpperCamelCase							(						self							:    List[Any])->							Tuple:
						__lowerCAmelCase	   ={}
						__lowerCAmelCase	   =[]
						__lowerCAmelCase	   =1
						__lowerCAmelCase	   =[1, 2]
						__lowerCAmelCase	   ={"""a""": 1, """b""": 2}
						__lowerCAmelCase	   ={"""a""": [1, 2], """b""": [3, 4]}
						__lowerCAmelCase	   ={"""a""": {"""1""": 1}, """b""": 2}
						__lowerCAmelCase	   ={"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
						__lowerCAmelCase	   ={}
						__lowerCAmelCase	   =[]
						__lowerCAmelCase	   =2
						__lowerCAmelCase	   =[2, 3]
						__lowerCAmelCase	   ={"""a""": 2, """b""": 3}
						__lowerCAmelCase	   ={"""a""": [2, 3], """b""": [4, 5]}
						__lowerCAmelCase	   ={"""a""": {"""1""": 2}, """b""": 3}
						__lowerCAmelCase	   ={"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
						self.assertEqual(map_nested(snake_case_				,	snake_case_)				,	snake_case_)
						self.assertEqual(map_nested(snake_case_				,	snake_case_)				,	snake_case_)
						self.assertEqual(map_nested(snake_case_				,	snake_case_)				,	snake_case_)
						self.assertEqual(map_nested(snake_case_				,	snake_case_)				,	snake_case_)
						self.assertEqual(map_nested(snake_case_				,	snake_case_)				,	snake_case_)
						self.assertEqual(map_nested(snake_case_				,	snake_case_)				,	snake_case_)
						self.assertEqual(map_nested(snake_case_				,	snake_case_)				,	snake_case_)
						self.assertEqual(map_nested(snake_case_				,	snake_case_)				,	snake_case_)

						__lowerCAmelCase	   =2
						self.assertEqual(map_nested(snake_case_				,	snake_case_				,	num_proc=snake_case_)				,	snake_case_)
						self.assertEqual(map_nested(snake_case_				,	snake_case_				,	num_proc=snake_case_)				,	snake_case_)
						self.assertEqual(map_nested(snake_case_				,	snake_case_				,	num_proc=snake_case_)				,	snake_case_)
						self.assertEqual(map_nested(snake_case_				,	snake_case_				,	num_proc=snake_case_)				,	snake_case_)
						self.assertEqual(map_nested(snake_case_				,	snake_case_				,	num_proc=snake_case_)				,	snake_case_)
						self.assertEqual(map_nested(snake_case_				,	snake_case_				,	num_proc=snake_case_)				,	snake_case_)
						self.assertEqual(map_nested(snake_case_				,	snake_case_				,	num_proc=snake_case_)				,	snake_case_)
						self.assertEqual(map_nested(snake_case_				,	snake_case_				,	num_proc=snake_case_)				,	snake_case_)

						__lowerCAmelCase	   ={"""a""": np.eye(2), """b""": np.zeros(3), """c""": np.ones(2)}
						__lowerCAmelCase	   ={"""a""": 2, """b""": 0, """c""": 2}
						__lowerCAmelCase	   ={
						    """a""": np.eye(2).astype(snake_case_),
						    """b""": np.zeros(3).astype(snake_case_),
						    """c""": np.ones(2).astype(snake_case_),
						}
						self.assertEqual(map_nested(snake_case_				,	snake_case_				,	map_numpy=snake_case_)				,	snake_case_)
						self.assertEqual(
						    {k: v.tolist() for k, v in map_nested(snake_case_				,	snake_case_				,	map_numpy=snake_case_).items()}				,	{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}				,	)
						self.assertEqual(map_nested(snake_case_				,	snake_case_				,	map_numpy=snake_case_				,	num_proc=snake_case_)				,	snake_case_)
						self.assertEqual(
						    {k: v.tolist() for k, v in map_nested(snake_case_				,	snake_case_				,	map_numpy=snake_case_				,	num_proc=snake_case_).items()}				,	{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}				,	)
						with self.assertRaises(snake_case_):  # can't pickle a local lambda
											map_nested(lambda snake_case_: x + 1				,	snake_case_				,	num_proc=snake_case_)



	def 		UpperCamelCase							(						self							:    Any)->							int:
						__lowerCAmelCase	   ={"""a""": 1, """b""": 2}
						__lowerCAmelCase	   ={"""a""": 3, """b""": 4}
						__lowerCAmelCase	   ={"""a""": 5, """b""": 6}
						__lowerCAmelCase	   =sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))])
						self.assertEqual(sorted(zip_dict(snake_case_				,	snake_case_				,	snake_case_))				,	snake_case_)



	def 		UpperCamelCase							(						self							:    Optional[Any])->							Optional[int]:






						class 						__a :
							SCREAMING_SNAKE_CASE       =       "bar"


						__lowerCAmelCase	   =Foo()
						self.assertEqual(foo.my_attr				,	"""bar""")
						with temporary_assignment(snake_case_				,	"""my_attr"""				,	"""BAR"""):
											self.assertEqual(foo.my_attr				,	"""BAR""")
						self.assertEqual(foo.my_attr				,	"""bar""")






@pytest.mark.parametrize(
    """iterable_length, num_proc, expected_num_proc"""				,   [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]				,   )
def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[str]				,   __lowerCamelCase	:    Any				,   __lowerCamelCase	:    Dict	)  ->						Union[str, Any]:
					with patch("""datasets.utils.py_utils._single_map_nested"""	) as mock_single_map_nested, patch(
					    """datasets.parallel.parallel.Pool"""	) as mock_multiprocessing_pool:
										__lowerCAmelCase	   ={f"""{i}""": i for i in range(__lowerCamelCase	)}
										__lowerCAmelCase	   =map_nested(lambda __lowerCamelCase	: x + 10				,   __lowerCamelCase				,   num_proc=__lowerCamelCase				,   parallel_min_length=16	)
										if expected_num_proc == 1:
															assert mock_single_map_nested.called
															assert not mock_multiprocessing_pool.called
										else:
															assert not mock_single_map_nested.called
															assert mock_multiprocessing_pool.called
															assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc






class 						__a (     SCREAMING_SNAKE_CASE				):



	@require_tf
	def 		UpperCamelCase							(						self							:    Tuple)->							Optional[int]:
						import tensorflow as tf
						from tensorflow.keras import layers

						__lowerCAmelCase	   =layers.Dense(2)

						def gen_random_output():
											__lowerCAmelCase	   =tf.random.uniform((1, 3))
											return model(snake_case_).numpy()

						with temp_seed(42				,	set_tensorflow=snake_case_):
											__lowerCAmelCase	   =gen_random_output()
						with temp_seed(42				,	set_tensorflow=snake_case_):
											__lowerCAmelCase	   =gen_random_output()
						__lowerCAmelCase	   =gen_random_output()

						np.testing.assert_equal(snake_case_				,	snake_case_)
						self.assertGreater(np.abs(outa - outa).sum()				,	0)



	@require_torch
	def 		UpperCamelCase							(						self							:    Dict)->							Dict:
						import torch

						def gen_random_output():
											__lowerCAmelCase	   =torch.nn.Linear(3				,	2)
											__lowerCAmelCase	   =torch.rand(1				,	3)
											return model(snake_case_).detach().numpy()

						with temp_seed(42				,	set_pytorch=snake_case_):
											__lowerCAmelCase	   =gen_random_output()
						with temp_seed(42				,	set_pytorch=snake_case_):
											__lowerCAmelCase	   =gen_random_output()
						__lowerCAmelCase	   =gen_random_output()

						np.testing.assert_equal(snake_case_				,	snake_case_)
						self.assertGreater(np.abs(outa - outa).sum()				,	0)



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							List[Any]:
						def gen_random_output():
											return np.random.rand(1				,	3)

						with temp_seed(42):
											__lowerCAmelCase	   =gen_random_output()
						with temp_seed(42):
											__lowerCAmelCase	   =gen_random_output()
						__lowerCAmelCase	   =gen_random_output()

						np.testing.assert_equal(snake_case_				,	snake_case_)
						self.assertGreater(np.abs(outa - outa).sum()				,	0)






@pytest.mark.parametrize("""input_data"""				,   [{}]	)
def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[str]	)  ->						List[str]:
					__lowerCAmelCase	   =NestedDataStructure(__lowerCamelCase	).data
					assert output_data == input_data






@pytest.mark.parametrize(
    """data, expected_output"""				,   [
        ({}, []),
        ([], []),
        ("""foo""", ["""foo"""]),
        (["""foo""", """bar"""], ["""foo""", """bar"""]),
        ([["""foo""", """bar"""]], ["""foo""", """bar"""]),
        ([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]),
        ([[["""foo"""], """bar"""]], ["""foo""", """bar"""]),
        ({"""a""": 1, """b""": 2}, [1, 2]),
        ({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]),
        ({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]),
    ]				,   )
def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]				,   __lowerCamelCase	:    int	)  ->						List[str]:
					__lowerCAmelCase	   =NestedDataStructure(__lowerCamelCase	).flatten()
					assert output == expected_output






def 					__lowerCAmelCase		(  )  ->						int:
					__lowerCAmelCase	   =A(x=1				,   y="""foobar"""	)
					__lowerCAmelCase	   ={"""x""": 1, """y""": """foobar"""}
					assert asdict(__lowerCamelCase	) == expected_output

					__lowerCAmelCase	   ={"""a""": {"""b""": A(x=10				,   y="""foo"""	)}, """c""": [A(x=20				,   y="""bar"""	)]}
					__lowerCAmelCase	   ={"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
					assert asdict(__lowerCamelCase	) == expected_output

					with pytest.raises(__lowerCamelCase	):
										asdict([1, A(x=10				,   y="""foo"""	)]	)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str	)  ->						Dict:
					return text.split()






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]	)  ->						Any:
					yield (time.time(), content)
					time.sleep(2	)
					yield (time.time(), content)






def 					__lowerCAmelCase		(  )  ->						Any:
					with Pool(2	) as pool:
										__lowerCAmelCase	   =list(iflatmap_unordered(__lowerCamelCase				,   _split_text				,   kwargs_iterable=[{"""text""": """hello there"""}] * 10	)	)
										assert out.count("""hello"""	) == 10
										assert out.count("""there"""	) == 10
										assert len(__lowerCamelCase	) == 20

					# check multiprocess from pathos (uses dill for pickling)
					with multiprocess.Pool(2	) as pool:
										__lowerCAmelCase	   =list(iflatmap_unordered(__lowerCamelCase				,   _split_text				,   kwargs_iterable=[{"""text""": """hello there"""}] * 10	)	)
										assert out.count("""hello"""	) == 10
										assert out.count("""there"""	) == 10
										assert len(__lowerCamelCase	) == 20

					# check that we get items as fast as possible
					with Pool(2	) as pool:
										__lowerCAmelCase	   =[]
										for yield_time, content in iflatmap_unordered(
										    __lowerCamelCase				,   _aseconds_generator_of_aitems_with_timing				,   kwargs_iterable=[{"""content""": """a"""}, {"""content""": """b"""}]	):
															assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
															out.append(__lowerCamelCase	)
										assert out.count("""a"""	) == 2
										assert out.count("""b"""	) == 2
										assert len(__lowerCamelCase	) == 4




