





'''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  UpperCAmelCase     :




      def __init__(			self			: int			,			__snake_case			: Union[str, Any]			,			__snake_case			: Tuple=12			,			__snake_case			: Optional[int]=7			,			__snake_case			: List[Any]=True			,			__snake_case			: int=True			,			__snake_case			: Optional[int]=True			,			__snake_case			: Union[str, Any]=99			,			__snake_case			: int=32			,			__snake_case			: int=32			,			__snake_case			: List[Any]=2			,			__snake_case			: int=4			,			__snake_case			: Union[str, Any]=37			,			__snake_case			: Dict=0.1			,			__snake_case			: int=0.1			,			__snake_case			: int=5_12			,			__snake_case			: Tuple=0.02			,			__snake_case			: Optional[Any]=0			,			__snake_case			: Union[str, Any]=None			,			)				->      Optional[int]:
            _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 				lowercase__    (			self			: Optional[int]  )				->      Union[str, Any]:
            _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							     =							input_mask.shape
                  _lowerCAmelCase							     =							np.random.randint(1			,			seq_length - 1			,			size=(batch_size,)  )
                  for batch_idx, start_index in enumerate(_snake_case  ):
                        _lowerCAmelCase							     =							1
                        _lowerCAmelCase							     =							0

            _lowerCAmelCase							     =							self.get_config()

            return config, input_ids, tf.convert_to_tensor(_snake_case  )




      def 				lowercase__    (			self			: Tuple  )				->      str:
            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 				lowercase__    (			self			: List[Any]			,			__snake_case			: Dict			,			__snake_case			: List[Any]			,			__snake_case			: List[Any]  )				->      Dict:
            _lowerCAmelCase							     =							TFBlipTextModel(config=_snake_case  )
            _lowerCAmelCase							     =							model(_snake_case			,			attention_mask=_snake_case			,			training=_snake_case  )
            _lowerCAmelCase							     =							model(_snake_case			,			training=_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 				lowercase__    (			self			: Optional[Any]  )				->      Optional[int]:
            _lowerCAmelCase							     =							self.prepare_config_and_inputs()
            _lowerCAmelCase							     =							config_and_inputs
            _lowerCAmelCase							     =							{'''input_ids''': input_ids, '''attention_mask''': input_mask}
            return config, inputs_dict




@require_tf
class  UpperCAmelCase     (			A_							,		unittest.TestCase   ):
      _lowercase:			int						     =   (TFBlipTextModel,) if is_tf_available() else ()
      _lowercase:			Optional[Any]						     =   False
      _lowercase:			List[Any]						     =   False
      _lowercase:			Union[str, Any]						     =   False




      def 				lowercase__    (			self			: Any  )				->      Any:
            _lowerCAmelCase							     =							BlipTextModelTester(self  )
            _lowerCAmelCase							     =							ConfigTester(self			,			config_class=_snake_case			,			hidden_size=37  )




      def 				lowercase__    (			self			: str  )				->      Optional[int]:
            self.config_tester.run_common_tests()




      def 				lowercase__    (			self			: Optional[int]  )				->      int:
            _lowerCAmelCase							     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_snake_case  )




      def 				lowercase__    (			self			: List[Any]  )				->      Tuple:
            pass




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




      @unittest.skip(reason="""Blip does not use inputs_embeds"""  )
      def 				lowercase__    (			self			: int  )				->      str:
            pass




      @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""  )
      def 				lowercase__    (			self			: Optional[int]  )				->      Any:
            pass




      @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""  )
      def 				lowercase__    (			self			: Optional[Any]  )				->      Dict:
            pass




      @slow
      def 				lowercase__    (			self			: int  )				->      List[Any]:



            for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _lowerCAmelCase							     =							TFBlipTextModel.from_pretrained(_snake_case  )
                  self.assertIsNotNone(_snake_case  )




      def 				lowercase__    (			self			: Dict			,			__snake_case			: List[str]=True  )				->      Optional[int]:
            super().test_pt_tf_model_equivalence(allow_missing_keys=_snake_case  )











"""simple docstring"""







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCAmelCase_   						=   version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
							from nltk import word_tokenize


lowerCAmelCase_   						=   '\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n'

lowerCAmelCase_   						=   '\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'

lowerCAmelCase_   						=   '\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results["meteor"], 4))\n    0.6944\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,_KWARGS_DESCRIPTION    )
class        __A       (	datasets.Metric    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		str:






				"""simple docstring"""



				return datasets.MetricInfo(
				    description=_DESCRIPTION			,citation=_CITATION			,inputs_description=_KWARGS_DESCRIPTION			,features=datasets.Features(
				        {
				            '''predictions''': datasets.Value('''string'''			,id='''sequence'''						),
				            '''references''': datasets.Value('''string'''			,id='''sequence'''						),
				        }						)			,codebase_urls=['''https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py''']			,reference_urls=[
				        '''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
				        '''https://en.wikipedia.org/wiki/METEOR''',
				    ]			,)







		def 					UpperCAmelCase    (	self						:  str			,_snake_case						:  Dict						)					->		Dict:






				"""simple docstring"""



				import nltk

				nltk.download('''wordnet'''						)
				if NLTK_VERSION >= version.Version('''3.6.5'''						):
						nltk.download('''punkt'''						)
				if NLTK_VERSION >= version.Version('''3.6.6'''						):
						nltk.download('''omw-1.4'''						)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Dict			,_snake_case						:  List[str]			,_snake_case						:  Tuple=0.9			,_snake_case						:  Optional[int]=3			,_snake_case						:  Union[str, Any]=0.5						)					->		List[str]:






				"""simple docstring"""



				if NLTK_VERSION >= version.Version('''3.6.5'''						):
						lowercase__   :    int				=			[
						    meteor_score.single_meteor_score(
						        word_tokenize(_snake_case						)			,word_tokenize(_snake_case						)			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]
				else:
						lowercase__   :    Tuple				=			[
						    meteor_score.single_meteor_score(_snake_case			,_snake_case			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]

				return {"meteor": np.mean(_snake_case						)}
