





'''simple docstring'''






import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput




class  _A (					__lowercase		):

		def __init__(   self					:   str  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Union[str, Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     dataset
					__snake_case					:      str							      =     process
					__snake_case					:      Union[str, Any]							      =     params

		def __len__(   self					:   int			)					->    Any:


					"""simple docstring"""

					return len(self.dataset			)

		def __getitem__(   self					:   Any  ,      __magic_name__					:   List[str]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     self.dataset[i]
					__snake_case					:      Dict							      =     self.process(__magic_name__  ,      **self.params			)
					return processed





class  _A (					__lowercase		):

		def __init__(   self					:   List[str]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   int  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   Optional[int]=None			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     loader
					__snake_case					:      List[Any]							      =     infer
					__snake_case					:      Tuple							      =     params
					if loader_batch_size == 1:
								# Let's spare some time by deactivating altogether
								__snake_case					:      Any							      =     None
					__snake_case					:      Tuple							      =     loader_batch_size

					# Internal bookkeeping
					__snake_case					:      Union[str, Any]							      =     None
					__snake_case					:      Optional[Any]							      =     None

		def __len__(   self					:   Tuple			)					->    Optional[int]:


					"""simple docstring"""

					return len(self.loader			)

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


					"""simple docstring"""

					__snake_case					:      Dict							      =     iter(self.loader			)
					return self

		def 	lowercase__     (   self					:   Optional[int]			)					->    Dict:


					"""simple docstring"""

					if isinstance(self._loader_batch_data  ,      torch.Tensor			):
								# Batch data is simple tensor, just fetch the slice
								__snake_case					:      List[Any]							      =     self._loader_batch_data[self._loader_batch_index]
					else:
								# Batch data is assumed to be BaseModelOutput (or dict)
								__snake_case					:      Optional[Any]							      =     {}
								for k, element in self._loader_batch_data.items():
											if isinstance(__magic_name__  ,      __magic_name__			):
														# Convert ModelOutput to tuple first
														__snake_case					:      int							      =     element.to_tuple()
														if isinstance(element[0]  ,      torch.Tensor			):
																	__snake_case					:      List[str]							      =     tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
														elif isinstance(element[0]  ,      np.ndarray			):
																	__snake_case					:      Optional[Any]							      =     tuple(np.expand_dims(el[self._loader_batch_index]  ,      0			) for el in element			)
														continue
											if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(__magic_name__  ,      __magic_name__			):
														# Those are stored as lists of tensors so need specific unbatching.
														if isinstance(element[0]  ,      torch.Tensor			):
																	__snake_case					:      Any							      =     tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
														elif isinstance(element[0]  ,      np.ndarray			):
																	__snake_case					:      Optional[Any]							      =     tuple(np.expand_dims(el[self._loader_batch_index]  ,      0			) for el in element			)
														continue
											if element is None:
														# This can happen for optional data that get passed around
														__snake_case					:      Tuple							      =     None
											elif isinstance(element[self._loader_batch_index]  ,      torch.Tensor			):
														# Take correct batch data, but make it looked like batch_size=1
														# For compatibility with other methods within transformers

														__snake_case					:      List[str]							      =     element[self._loader_batch_index].unsqueeze(0			)
											elif isinstance(element[self._loader_batch_index]  ,      np.ndarray			):
														# Take correct batch data, but make it looked like batch_size=1
														# For compatibility with other methods within transformers
														__snake_case					:      Union[str, Any]							      =     np.expand_dims(element[self._loader_batch_index]  ,      0			)
											else:
														# This is typically a list, so no need to `unsqueeze`.
														__snake_case					:      int							      =     element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
								__snake_case					:      Union[str, Any]							      =     self._loader_batch_data.__class__(__magic_name__			)
					self._loader_batch_index += 1
					return result

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


					"""simple docstring"""

					if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
								# We are currently unrolling a batch so we just need to return
								# the current item within a batch
								return self.loader_batch_item()

					# We're out of items within a batch
					__snake_case					:      List[Any]							      =     next(self.iterator			)
					__snake_case					:      Union[str, Any]							      =     self.infer(__magic_name__  ,      **self.params			)
					# We now have a batch of "inferred things".
					if self.loader_batch_size is not None:
								# Try to infer the size of the batch
								if isinstance(__magic_name__  ,      torch.Tensor			):
											__snake_case					:      List[Any]							      =     processed
								else:
											__snake_case					:      Optional[Any]							      =     list(processed.keys()			)[0]
											__snake_case					:      Dict							      =     processed[key]
								if isinstance(__magic_name__  ,      __magic_name__			):
											__snake_case					:      List[str]							      =     len(__magic_name__			)
								else:
											__snake_case					:      int							      =     first_tensor.shape[0]
								if 0 < observed_batch_size < self.loader_batch_size:
											# could be last batch so we can't unroll as many
											# elements.
											__snake_case					:      Optional[int]							      =     observed_batch_size
								# Setting internal index to unwrap the batch
								__snake_case					:      Union[str, Any]							      =     processed
								__snake_case					:      Any							      =     0
								return self.loader_batch_item()
					else:
								# We're not unrolling batches
								return processed





class  _A (					__lowercase		):

		def __init__(   self					:   Optional[int]  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   int=None			)					->    Tuple:


					"""simple docstring"""

					super().__init__(__magic_name__  ,      __magic_name__  ,      __magic_name__			)

		def __iter__(   self					:   Tuple			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     iter(self.loader			)
					__snake_case					:      List[str]							      =     None
					return self

		def 	lowercase__     (   self					:   Tuple			)					->    Tuple:


					"""simple docstring"""

					if self.subiterator is None:
								__snake_case					:      Any							      =     self.infer(next(self.iterator			)  ,      **self.params			)
					try:
								# Try to return next item
								__snake_case					:      List[Any]							      =     next(self.subiterator			)
					except StopIteration:
								# When a preprocess iterator ends, we can start lookig at the next item
								# ChunkIterator will keep feeding until ALL elements of iterator
								# all have created their subiterator and have been iterating against.
								#
								# Another way to look at it, is we're basically flattening lists of lists
								# into a single list, but with generators
								__snake_case					:      List[Any]							      =     self.infer(next(self.iterator			)  ,      **self.params			)
								__snake_case					:      List[str]							      =     next(self.subiterator			)
					return processed





class  _A (					__lowercase		):

		def __iter__(   self					:   Optional[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     iter(self.loader			)
					return self

		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Dict							      =     False
					__snake_case					:      Union[str, Any]							      =     []
					if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
								while self._loader_batch_index < self.loader_batch_size:
											__snake_case					:      Union[str, Any]							      =     self.loader_batch_item()
											__snake_case					:      List[str]							      =     item.pop("""is_last"""			)
											accumulator.append(__magic_name__			)
											if is_last:
														return accumulator

					while not is_last:
								__snake_case					:      Union[str, Any]							      =     self.infer(next(self.iterator			)  ,      **self.params			)
								if self.loader_batch_size is not None:
											if isinstance(__magic_name__  ,      torch.Tensor			):
														__snake_case					:      str							      =     processed
											else:
														__snake_case					:      Optional[Any]							      =     list(processed.keys()			)[0]
														__snake_case					:      List[str]							      =     processed[key]
											if isinstance(__magic_name__  ,      __magic_name__			):
														__snake_case					:      Tuple							      =     len(__magic_name__			)
											else:
														__snake_case					:      Dict							      =     first_tensor.shape[0]
											if 0 < observed_batch_size < self.loader_batch_size:
														# could be last batch so we can't unroll as many
														# elements.
														__snake_case					:      List[str]							      =     observed_batch_size
											__snake_case					:      List[Any]							      =     processed
											__snake_case					:      str							      =     0
											while self._loader_batch_index < self.loader_batch_size:
														__snake_case					:      List[Any]							      =     self.loader_batch_item()
														__snake_case					:      List[Any]							      =     item.pop("""is_last"""			)
														accumulator.append(__magic_name__			)
														if is_last:
																	return accumulator
								else:
											__snake_case					:      Tuple							      =     processed
											__snake_case					:      List[Any]							      =     item.pop("""is_last"""			)
											accumulator.append(__magic_name__			)
					return accumulator





class  _A (					__lowercase		):

		def __init__(   self					:   str  ,      __magic_name__					:   Dataset  ,      __magic_name__					:   str			)					->    str:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     dataset
					__snake_case					:      Optional[int]							      =     key

		def __len__(   self					:   str			)					->    Tuple:


					"""simple docstring"""

					return len(self.dataset			)

		def __getitem__(   self					:   Optional[Any]  ,      __magic_name__					:   Dict			)					->    Optional[Any]:


					"""simple docstring"""

					return self.dataset[i][self.key]





class  _A (					__lowercase		):

		def __init__(   self					:   Optional[Any]  ,      __magic_name__					:   Dataset  ,      __magic_name__					:   str  ,      __magic_name__					:   str			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     dataset
					__snake_case					:      Any							      =     keya
					__snake_case					:      int							      =     keya

		def __len__(   self					:   Union[str, Any]			)					->    Tuple:


					"""simple docstring"""

					return len(self.dataset			)

		def __getitem__(   self					:   Union[str, Any]  ,      __magic_name__					:   Tuple			)					->    Dict:


					"""simple docstring"""

					return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}





"""simple docstring"""

from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


_UpperCamelCase		     =    """\
@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}
}
"""

_UpperCamelCase		     =    """\
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.
"""

_UpperCamelCase		     =    """\
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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case = 1			,					snake_case = 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   )
        }






