


"""simple docstring"""


from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


_snake_case          =							"\\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"

_snake_case          =							"\\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"

_snake_case          =							"\\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   _a      (      datasets.Metric ):
    def      _UpperCamelCase       ( self							:					Any				):
          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      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[List[List[str]]]							,					SCREAMING_SNAKE_CASE__							:					List[List[str]]							,					SCREAMING_SNAKE_CASE__							:					int = 1							,					SCREAMING_SNAKE_CASE__							:					int = 4							,					):
          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__				)
          }







"""simple docstring"""


from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
   import tensorflow as tf

   from transformers import TFBlipTextModel
   from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST



class   _a      :
    def __init__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=12							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=7							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=True							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=True							,					SCREAMING_SNAKE_CASE__							:					str=True							,					SCREAMING_SNAKE_CASE__							:					Any=99							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=32							,					SCREAMING_SNAKE_CASE__							:					str=32							,					SCREAMING_SNAKE_CASE__							:					Tuple=2							,					SCREAMING_SNAKE_CASE__							:					str=4							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=37							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=0.1							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=0.1							,					SCREAMING_SNAKE_CASE__							:					str=5_12							,					SCREAMING_SNAKE_CASE__							:					str=0.02							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=0							,					SCREAMING_SNAKE_CASE__							:					Any=None							,					):
          lowerCamelCase__           =		parent
          lowerCamelCase__           =		batch_size
          lowerCamelCase__           =		seq_length
          lowerCamelCase__           =		is_training
          lowerCamelCase__           =		use_input_mask
          lowerCamelCase__           =		use_labels
          lowerCamelCase__           =		vocab_size
          lowerCamelCase__           =		hidden_size
          lowerCamelCase__           =		projection_dim
          lowerCamelCase__           =		num_hidden_layers
          lowerCamelCase__           =		num_attention_heads
          lowerCamelCase__           =		intermediate_size
          lowerCamelCase__           =		dropout
          lowerCamelCase__           =		attention_dropout
          lowerCamelCase__           =		max_position_embeddings
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		scope
          lowerCamelCase__           =		bos_token_id
    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size				)

          lowerCamelCase__           =		None
          if self.use_input_mask:
                lowerCamelCase__           =		random_attention_mask([self.batch_size, self.seq_length]				)

          if input_mask is not None:
                lowerCamelCase__           =		input_mask.numpy()
                lowerCamelCase__    ,  lowerCamelCase__           =		input_mask.shape
                lowerCamelCase__           =		np.random.randint(1							,					seq_length - 1							,					size=(batch_size,)				)
                for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE__				):
                      lowerCamelCase__           =		1
                      lowerCamelCase__           =		0

          lowerCamelCase__           =		self.get_config()

          return config, input_ids, tf.convert_to_tensor(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Any				):
          return BlipTextConfig(
              vocab_size=self.vocab_size							,					hidden_size=self.hidden_size							,					projection_dim=self.projection_dim							,					num_hidden_layers=self.num_hidden_layers							,					num_attention_heads=self.num_attention_heads							,					intermediate_size=self.intermediate_size							,					dropout=self.dropout							,					attention_dropout=self.attention_dropout							,					max_position_embeddings=self.max_position_embeddings							,					initializer_range=self.initializer_range							,					bos_token_id=self.bos_token_id							,					)
    def      _UpperCamelCase       ( self							:					Any							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[str]				):
          lowerCamelCase__           =		TFBlipTextModel(config=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__							,					attention_mask=SCREAMING_SNAKE_CASE__							,					training=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__							,					training=SCREAMING_SNAKE_CASE__				)
          self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)				)
          self.parent.assertEqual(result.pooler_output.shape							,					(self.batch_size, self.hidden_size)				)


    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		self.prepare_config_and_inputs()
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		config_and_inputs
          lowerCamelCase__           =		{'input_ids': input_ids, 'attention_mask': input_mask}
          return config, inputs_dict





@require_tf
class   _a      (      SCREAMING_SNAKE_CASE_		,		unittest.TestCase ):
    a_    :				str             =    (TFBlipTextModel,) if is_tf_available() else ()
    a_    :				List[str]             =    False
    a_    :				Optional[Any]             =    False
    a_    :				Union[str, Any]             =    False
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		BlipTextModelTester(self				)
          lowerCamelCase__           =		ConfigTester(self							,					config_class=SCREAMING_SNAKE_CASE__							,					hidden_size=37				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          self.config_tester.run_common_tests()
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          pass
    def      _UpperCamelCase       ( self							:					Tuple				):
          pass
    @unittest.skip(reason='Blip does not use inputs_embeds'				)
    def      _UpperCamelCase       ( self							:					List[str]				):
          pass
    @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
    def      _UpperCamelCase       ( self							:					Dict				):
          pass
    @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
    def      _UpperCamelCase       ( self							:					List[Any]				):
          pass
    @slow
    def      _UpperCamelCase       ( self							:					str				):
          for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                lowerCamelCase__           =		TFBlipTextModel.from_pretrained(SCREAMING_SNAKE_CASE__				)
                self.assertIsNotNone(SCREAMING_SNAKE_CASE__				)


    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[Any]=True				):
          super().test_pt_tf_model_equivalence(allow_missing_keys=SCREAMING_SNAKE_CASE__				)



