






import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import FlavaImageProcessor, FlavaProcessor
  from transformers.models.flava.image_processing_flava import (
      FLAVA_CODEBOOK_MEAN,
      FLAVA_CODEBOOK_STD,
      FLAVA_IMAGE_MEAN,
      FLAVA_IMAGE_STD,
  )





@require_vision
class  snake_case__      (							unittest.TestCase   ):
 def     __magic_name__       (       self				)							->   Tuple:
       __magic_name__    :     List[str]     		=      tempfile.mkdtemp()

       # fmt: off
       __magic_name__    :     Dict     		=      ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
       # fmt: on
       __magic_name__    :     str     		=      os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES["""vocab_file"""]				)

       with open(self.vocab_file    ,     """w"""    ,     encoding="""utf-8"""				) as fp:
             fp.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

       __magic_name__    :     int     		=      {
           """image_mean""": FLAVA_IMAGE_MEAN,
           """image_std""": FLAVA_IMAGE_STD,
           """do_normalize""": True,
           """do_resize""": True,
           """size""": 2_24,
           """do_center_crop""": True,
           """crop_size""": 2_24,
           """input_size_patches""": 14,
           """total_mask_patches""": 75,
           """mask_group_max_patches""": None,
           """mask_group_min_patches""": 16,
           """mask_group_min_aspect_ratio""": 0.3,
           """mask_group_max_aspect_ratio""": None,
           """codebook_do_resize""": True,
           """codebook_size""": 1_12,
           """codebook_do_center_crop""": True,
           """codebook_crop_size""": 1_12,
           """codebook_do_map_pixels""": True,
           """codebook_do_normalize""": True,
           """codebook_image_mean""": FLAVA_CODEBOOK_MEAN,
           """codebook_image_std""": FLAVA_CODEBOOK_STD,
       }

       __magic_name__    :     Any     		=      os.path.join(self.tmpdirname    ,     lowerCAmelCase__				)
       with open(self.image_processor_file    ,     """w"""    ,     encoding="""utf-8"""				) as fp:
             json.dump(lowerCAmelCase__    ,     lowerCAmelCase__				)
 def     __magic_name__       (       self    ,     **lowerCAmelCase__				)							->   Union[str, Any]:
       return BertTokenizer.from_pretrained(self.tmpdirname    ,     **lowerCAmelCase__				)
 def     __magic_name__       (       self    ,     **lowerCAmelCase__				)							->   int:
       return BertTokenizerFast.from_pretrained(self.tmpdirname    ,     **lowerCAmelCase__				)
 def     __magic_name__       (       self    ,     **lowerCAmelCase__				)							->   str:
       return FlavaImageProcessor.from_pretrained(self.tmpdirname    ,     **lowerCAmelCase__				)
 def     __magic_name__       (       self				)							->   Tuple:
       shutil.rmtree(self.tmpdirname				)
 def     __magic_name__       (       self				)							->   Any:
       __magic_name__    :     Any     		=      [np.random.randint(2_55    ,     size=(3, 30, 4_00)    ,     dtype=np.uinta				)]

       __magic_name__    :     List[Any]     		=      [Image.fromarray(np.moveaxis(lowerCAmelCase__    ,     0    ,     -1				)				) for x in image_inputs]

       return image_inputs
 def     __magic_name__       (       self				)							->   Tuple:
       __magic_name__    :     List[str]     		=      self.get_tokenizer()
       __magic_name__    :     List[str]     		=      self.get_rust_tokenizer()
       __magic_name__    :     Union[str, Any]     		=      self.get_image_processor()

       __magic_name__    :     Optional[Any]     		=      FlavaProcessor(tokenizer=lowerCAmelCase__    ,     image_processor=lowerCAmelCase__				)
       processor_slow.save_pretrained(self.tmpdirname				)
       __magic_name__    :     Dict     		=      FlavaProcessor.from_pretrained(self.tmpdirname    ,     use_fast=lowerCAmelCase__				)

       __magic_name__    :     List[Any]     		=      FlavaProcessor(tokenizer=lowerCAmelCase__    ,     image_processor=lowerCAmelCase__				)
       processor_fast.save_pretrained(self.tmpdirname				)
       __magic_name__    :     int     		=      FlavaProcessor.from_pretrained(self.tmpdirname				)

       self.assertEqual(processor_slow.tokenizer.get_vocab()    ,     tokenizer_slow.get_vocab()				)
       self.assertEqual(processor_fast.tokenizer.get_vocab()    ,     tokenizer_fast.get_vocab()				)
       self.assertEqual(tokenizer_slow.get_vocab()    ,     tokenizer_fast.get_vocab()				)
       self.assertIsInstance(processor_slow.tokenizer    ,     lowerCAmelCase__				)
       self.assertIsInstance(processor_fast.tokenizer    ,     lowerCAmelCase__				)

       self.assertEqual(processor_slow.image_processor.to_json_string()    ,     image_processor.to_json_string()				)
       self.assertEqual(processor_fast.image_processor.to_json_string()    ,     image_processor.to_json_string()				)
       self.assertIsInstance(processor_slow.image_processor    ,     lowerCAmelCase__				)
       self.assertIsInstance(processor_fast.image_processor    ,     lowerCAmelCase__				)
 def     __magic_name__       (       self				)							->   Optional[int]:
       __magic_name__    :     Dict     		=      FlavaProcessor(tokenizer=self.get_tokenizer()    ,     image_processor=self.get_image_processor()				)
       processor.save_pretrained(self.tmpdirname				)

       __magic_name__    :     Optional[Any]     		=      self.get_tokenizer(bos_token="""(BOS)"""    ,     eos_token="""(EOS)"""				)
       __magic_name__    :     Dict     		=      self.get_image_processor(do_normalize=lowerCAmelCase__    ,     padding_value=1.0				)

       __magic_name__    :     Optional[Any]     		=      FlavaProcessor.from_pretrained(
           self.tmpdirname    ,     bos_token="""(BOS)"""    ,     eos_token="""(EOS)"""    ,     do_normalize=lowerCAmelCase__    ,     padding_value=1.0				)

       self.assertEqual(processor.tokenizer.get_vocab()    ,     tokenizer_add_kwargs.get_vocab()				)
       self.assertIsInstance(processor.tokenizer    ,     lowerCAmelCase__				)

       self.assertEqual(processor.image_processor.to_json_string()    ,     image_processor_add_kwargs.to_json_string()				)
       self.assertIsInstance(processor.image_processor    ,     lowerCAmelCase__				)
 def     __magic_name__       (       self				)							->   Tuple:
       __magic_name__    :     Union[str, Any]     		=      self.get_image_processor()
       __magic_name__    :     Dict     		=      self.get_tokenizer()

       __magic_name__    :     Union[str, Any]     		=      FlavaProcessor(tokenizer=lowerCAmelCase__    ,     image_processor=lowerCAmelCase__				)

       __magic_name__    :     List[Any]     		=      self.prepare_image_inputs()

       __magic_name__    :     Union[str, Any]     		=      image_processor(lowerCAmelCase__    ,     return_tensors="""np"""				)
       __magic_name__    :     Tuple     		=      processor(images=lowerCAmelCase__    ,     return_tensors="""np"""				)

       for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()    ,     input_processor[key].sum()    ,     delta=1e-2				)

       # With rest of the args
       random.seed(12_34				)
       __magic_name__    :     Dict     		=      image_processor(
           lowerCAmelCase__    ,     return_image_mask=lowerCAmelCase__    ,     return_codebook_pixels=lowerCAmelCase__    ,     return_tensors="""np"""				)
       random.seed(12_34				)
       __magic_name__    :     Optional[int]     		=      processor(
           images=lowerCAmelCase__    ,     return_image_mask=lowerCAmelCase__    ,     return_codebook_pixels=lowerCAmelCase__    ,     return_tensors="""np"""				)

       for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()    ,     input_processor[key].sum()    ,     delta=1e-2				)
 def     __magic_name__       (       self				)							->   Any:
       __magic_name__    :     str     		=      self.get_image_processor()
       __magic_name__    :     List[Any]     		=      self.get_tokenizer()

       __magic_name__    :     Optional[int]     		=      FlavaProcessor(tokenizer=lowerCAmelCase__    ,     image_processor=lowerCAmelCase__				)

       __magic_name__    :     Tuple     		=      """lower newer"""

       __magic_name__    :     str     		=      processor(text=lowerCAmelCase__				)

       __magic_name__    :     int     		=      tokenizer(lowerCAmelCase__				)

       for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]    ,     encoded_processor[key]				)
 def     __magic_name__       (       self				)							->   List[str]:
       __magic_name__    :     Optional[Any]     		=      self.get_image_processor()
       __magic_name__    :     List[Any]     		=      self.get_tokenizer()

       __magic_name__    :     Optional[int]     		=      FlavaProcessor(tokenizer=lowerCAmelCase__    ,     image_processor=lowerCAmelCase__				)

       __magic_name__    :     str     		=      """lower newer"""
       __magic_name__    :     Optional[Any]     		=      self.prepare_image_inputs()

       __magic_name__    :     Optional[int]     		=      processor(text=lowerCAmelCase__    ,     images=lowerCAmelCase__				)

       self.assertListEqual(list(inputs.keys()				)    ,     ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]				)

       # add extra args
       __magic_name__    :     Any     		=      processor(text=lowerCAmelCase__    ,     images=lowerCAmelCase__    ,     return_codebook_pixels=lowerCAmelCase__    ,     return_image_mask=lowerCAmelCase__				)

       self.assertListEqual(
           list(inputs.keys()				)    ,     [
               """input_ids""",
               """token_type_ids""",
               """attention_mask""",
               """pixel_values""",
               """codebook_pixel_values""",
               """bool_masked_pos""",
           ]    ,     )

       # test if it raises when no input is passed
       with pytest.raises(lowerCAmelCase__				):
             processor()
 def     __magic_name__       (       self				)							->   str:
       __magic_name__    :     Dict     		=      self.get_image_processor()
       __magic_name__    :     Dict     		=      self.get_tokenizer()

       __magic_name__    :     Any     		=      FlavaProcessor(tokenizer=lowerCAmelCase__    ,     image_processor=lowerCAmelCase__				)

       __magic_name__    :     List[str]     		=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       __magic_name__    :     Optional[Any]     		=      processor.batch_decode(lowerCAmelCase__				)
       __magic_name__    :     Dict     		=      tokenizer.batch_decode(lowerCAmelCase__				)

       self.assertListEqual(lowerCAmelCase__    ,     lowerCAmelCase__				)







 def     __magic_name__       (       self				)							->   Any:
       __magic_name__    :     str     		=      self.get_image_processor()
       __magic_name__    :     str     		=      self.get_tokenizer()

       __magic_name__    :     Union[str, Any]     		=      FlavaProcessor(tokenizer=lowerCAmelCase__    ,     image_processor=lowerCAmelCase__				)

       __magic_name__    :     List[Any]     		=      """lower newer"""
       __magic_name__    :     List[Any]     		=      self.prepare_image_inputs()

       __magic_name__    :     Tuple     		=      processor(text=lowerCAmelCase__    ,     images=lowerCAmelCase__				)

       self.assertListEqual(list(inputs.keys()				)    ,     processor.model_input_names				)








import sacrebleu as scb
from packaging import version

import datasets


__magic_name__:  Optional[int]  	=			"\\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__magic_name__:  int  	=			"\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"

__magic_name__:  List[Any]  	=			"\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,						_KWARGS_DESCRIPTION   )
class  snake_case__      (							datasets.Metric   ):
	def     __magic_name__       (       self				)							->   Optional[int]:
							if version.parse(scb.__version__				) < version.parse("""1.4.12"""				):
													raise ImportWarning(
													    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
													    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""				)
							return datasets.MetricInfo(
							    description=_DESCRIPTION    ,     citation=_CITATION    ,     homepage="""https://github.com/mjpost/sacreBLEU"""    ,     inputs_description=_KWARGS_DESCRIPTION    ,     features=datasets.Features(
							        {
							            """predictions""": datasets.Value("""string"""    ,     id="""sequence"""				),
							            """references""": datasets.Sequence(datasets.Value("""string"""    ,     id="""sequence"""				)    ,     id="""references"""				),
							        }				)    ,     codebase_urls=["""https://github.com/mjpost/sacreBLEU"""]    ,     reference_urls=[
							        """https://github.com/mjpost/sacreBLEU""",
							        """https://en.wikipedia.org/wiki/BLEU""",
							        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
							    ]    ,     )







	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__="exp"    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__=False    ,     )							->   Optional[Any]:
							__magic_name__    :     Optional[int]     		=      len(references[0]				)
							if any(len(lowerCAmelCase__				) != references_per_prediction for refs in references				):
													raise ValueError("""Sacrebleu requires the same number of references for each prediction"""				)
							__magic_name__    :     Dict     		=      [[refs[i] for refs in references] for i in range(lowerCAmelCase__				)]
							__magic_name__    :     List[str]     		=      scb.corpus_bleu(
							    lowerCAmelCase__    ,     lowerCAmelCase__    ,     smooth_method=lowerCAmelCase__    ,     smooth_value=lowerCAmelCase__    ,     force=lowerCAmelCase__    ,     lowercase=lowerCAmelCase__    ,     use_effective_order=lowerCAmelCase__    ,     **({"""tokenize""": tokenize} if tokenize else {})    ,     )
							__magic_name__    :     List[str]     		=      {
							    """score""": output.score,
							    """counts""": output.counts,
							    """totals""": output.totals,
							    """precisions""": output.precisions,
							    """bp""": output.bp,
							    """sys_len""": output.sys_len,
							    """ref_len""": output.ref_len,
							}
							return output_dict
