
'''simple docstring'''



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     UpperCAmelCase_	(						datasets.Metric					):






						'''simple docstring'''






						def    _lowercase      (   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""" ),
												        } )       ,  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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase=4       ,  _lowercase=False ):

												"""simple docstring"""



												_lowerCAmelCase    		=					compute_bleu(
												    reference_corpus=_lowercase       ,  translation_corpus=_lowercase       ,  max_order=_lowercase       ,  smooth=_lowercase )
												((_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 os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      ['image_processor', 'tokenizer']
 __UpperCamelCase       =      'BlipImageProcessor'
 __UpperCamelCase       =      'AutoTokenizer'

 def __init__(  self,		A_,		A_,		A_		)	->       str:
    super().__init__(A_,		A_		)

    # add QFormer tokenizer
    UpperCAmelCase__						=qformer_tokenizer

 def __call__(  self,		A_ = None,		A_ = None,		A_ = True,		A_ = False,		A_ = None,		A_ = None,		A_ = 0,		A_ = None,		A_ = None,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = True,		A_ = None,		**A_,		)	->       BatchFeature:
    if images is None and text is None:
       raise ValueError("You have to specify at least images or text."		)

    UpperCAmelCase__						=BatchFeature()

    if text is not None:
       UpperCAmelCase__						=self.tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       encoding.update(A_		)
       UpperCAmelCase__						=self.qformer_tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       UpperCAmelCase__						=qformer_text_encoding.pop("input_ids"		)
       UpperCAmelCase__						=qformer_text_encoding.pop("attention_mask"		)

    if images is not None:
       UpperCAmelCase__						=self.image_processor(A_,		return_tensors=A_		)
       encoding.update(A_		)

    return encoding

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       List[Any]:
    return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)

 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.tokenizer.model_input_names
    UpperCAmelCase__						=self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)

 def  __UpperCAmelCase						(  self,		A_,		**A_		)	->       str:
    if os.path.isfile(A_		):
       raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""		)
    os.makedirs(A_,		exist_ok=A_		)
    UpperCAmelCase__						=os.path.join(A_,		"qformer_tokenizer"		)
    self.qformer_tokenizer.save_pretrained(A_		)
    return super().save_pretrained(A_,		**A_		)






 @classmethod
 def  __UpperCAmelCase						(  cls,		A_,		**A_		)	->       int:
    UpperCAmelCase__						=AutoTokenizer.from_pretrained(A_,		subfolder="qformer_tokenizer"		)
    UpperCAmelCase__						=cls._get_arguments_from_pretrained(A_,		**A_		)
    args.append(A_		)
    return cls(*A_		)



