
import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
					from PIL import Image

					from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor





@require_tokenizers
@require_vision
class 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= tempfile.mkdtemp()

					# fmt: off
					__A   			= ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
					# fmt: on
					__A   			= os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES["vocab_file"] )
					with open(self.vocab_file							,"w"							,encoding="utf-8" ) as vocab_writer:
									vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

					__A   			= {
					    "do_resize": True,
					    "size": {"height": 18, "width": 18},
					    "do_normalize": True,
					    "image_mean": [0.5, 0.5, 0.5],
					    "image_std": [0.5, 0.5, 0.5],
					}
					__A   			= os.path.join(self.tmpdirname							,A )
					with open(self.image_processor_file							,"w"							,encoding="utf-8" ) as fp:
									json.dump(A							,A )






	def 				UpperCamelCase_  (       self					: int							,**A					: List[Any] ):
					return BertTokenizer.from_pretrained(self.tmpdirname							,**A )






	def 				UpperCamelCase_  (       self					: int							,**A					: Optional[Any] ):
					return ViTImageProcessor.from_pretrained(self.tmpdirname							,**A )






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					shutil.rmtree(self.tmpdirname )






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= [np.random.randint(2_55							,size=(3, 30, 4_00)							,dtype=np.uinta )]

					__A   			= [Image.fromarray(np.moveaxis(A							,0							,-1 ) ) for x in image_inputs]

					return image_inputs






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.get_tokenizer()
					__A   			= self.get_image_processor()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					processor.save_pretrained(self.tmpdirname )
					__A   			= VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

					self.assertEqual(processor.tokenizer.get_vocab()							,tokenizer.get_vocab() )
					self.assertIsInstance(processor.tokenizer							,(BertTokenizer, BertTokenizerFast) )

					self.assertEqual(processor.image_processor.to_json_string()							,image_processor.to_json_string() )
					self.assertIsInstance(processor.image_processor							,A )






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= VisionTextDualEncoderProcessor(
					    tokenizer=self.get_tokenizer()							,image_processor=self.get_image_processor() )
					processor.save_pretrained(self.tmpdirname )

					__A   			= self.get_tokenizer(bos_token="(BOS)"							,eos_token="(EOS)" )
					__A   			= self.get_image_processor(do_normalize=A							,padding_value=1.0 )

					__A   			= VisionTextDualEncoderProcessor.from_pretrained(
					    self.tmpdirname							,bos_token="(BOS)"							,eos_token="(EOS)"							,do_normalize=A							,padding_value=1.0 )

					self.assertEqual(processor.tokenizer.get_vocab()							,tokenizer_add_kwargs.get_vocab() )
					self.assertIsInstance(processor.tokenizer							,(BertTokenizer, BertTokenizerFast) )

					self.assertEqual(processor.image_processor.to_json_string()							,image_processor_add_kwargs.to_json_string() )
					self.assertIsInstance(processor.image_processor							,A )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					__A   			= self.prepare_image_inputs()

					__A   			= image_processor(A							,return_tensors="np" )
					__A   			= processor(images=A							,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 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					__A   			= "lower newer"

					__A   			= processor(text=A )

					__A   			= tokenizer(A )

					for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]							,encoded_processor[key] )






	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					__A   			= "lower newer"
					__A   			= self.prepare_image_inputs()

					__A   			= processor(text=A							,images=A )

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

					# test if it raises when no input is passed
					with self.assertRaises(A ):
									processor()






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					__A   			= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__A   			= processor.batch_decode(A )
					__A   			= tokenizer.batch_decode(A )

					self.assertListEqual(A							,A )







	def 				UpperCamelCase_  (       self					: int ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					__A   			= "lower newer"
					__A   			= self.prepare_image_inputs()

					__A   			= processor(text=A							,images=A )

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


import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


snake_case :		List[Any]							      =  "\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n"

snake_case :		Any							      =  "\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

snake_case :		str							      =  "\nCalculates average rouge scores for a list of hypotheses and 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    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,						_KWARGS_DESCRIPTION				)
class       _snake_case					(     datasets.Metric				):







			def 			SCREAMING_SNAKE_CASE    (				self		):
				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/google-research/google-research/tree/master/rouge"]						,   reference_urls=[
				        "https://en.wikipedia.org/wiki/ROUGE_(metric)",
				        "https://github.com/google-research/google-research/tree/master/rouge",
				    ]						,   )







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a=None						,   _a=True						,   _a=False		):
				if rouge_types is None:
					__magic_name__		:		str	   =     ["rouge1", "rouge2", "rougeL", "rougeLsum"]

				__magic_name__		:		List[str]	   =     rouge_scorer.RougeScorer(rouge_types=_a						,   use_stemmer=_a		)
				if use_aggregator:
					__magic_name__		:		Dict	   =     scoring.BootstrapAggregator()
				else:
					__magic_name__		:		str	   =     []

				for ref, pred in zip(_a						,   _a		):
					__magic_name__		:		Union[str, Any]	   =     scorer.score(_a						,   _a		)
					if use_aggregator:
						aggregator.add_scores(_a		)
					else:
						scores.append(_a		)

				if use_aggregator:
					__magic_name__		:		Any	   =     aggregator.aggregate()
				else:
					__magic_name__		:		List[Any]	   =     {}
					for key in scores[0]:
						__magic_name__		:		str	   =     [score[key] for score in scores]

				return result
