
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_UpperCAmelCase :			int              =	version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
					from nltk import word_tokenize


_UpperCAmelCase :			Union[str, Any]              =	"\\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"

_UpperCAmelCase :			Tuple              =	"\\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"

_UpperCAmelCase :			List[Any]              =	"\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     lowercase (		datasets.Metric       ):



		def __UpperCamelCase			(			self	)  -> 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    ,				A_	)  -> 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    ,				A_    ,				A_    ,				A_=0.9    ,				A_=3    ,				A_=0.5	)  -> Dict:
				"""simple docstring"""
				if NLTK_VERSION >= version.Version('3.6.5'	):
						UpperCamelCase          =					[
						    meteor_score.single_meteor_score(
						        word_tokenize(A_	)    ,				word_tokenize(A_	)    ,				alpha=A_    ,				beta=A_    ,				gamma=A_	)
						    for ref, pred in zip(A_    ,				A_	)
						]
				else:
						UpperCamelCase          =					[
						    meteor_score.single_meteor_score(A_    ,				A_    ,				alpha=A_    ,				beta=A_    ,				gamma=A_	)
						    for ref, pred in zip(A_    ,				A_	)
						]

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


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 OwlViTImageProcessor, OwlViTProcessor


@require_vision
class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					tempfile.mkdtemp()

				# fmt: off
				UpperCamelCase          =					['', 'l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
				# fmt: on
				UpperCamelCase          =					dict(zip(A_    ,				range(len(A_	)	)	)	)
				UpperCamelCase          =					['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
				UpperCamelCase          =					{'unk_token': '<unk>'}

				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['vocab_file']	)
				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['merges_file']	)
				with open(self.vocab_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write(json.dumps(A_	) + '\n'	)
				with open(self.merges_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write('\n'.join(A_	)	)

				UpperCamelCase          =					{
				    'do_resize': True,
				    'size': 20,
				    'do_center_crop': True,
				    'crop_size': 18,
				    'do_normalize': True,
				    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
				    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
				}
				UpperCamelCase          =					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    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				return CLIPTokenizer.from_pretrained(self.tmpdirname    ,				pad_token='!'    ,				**A_	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Tuple:
				"""simple docstring"""
				return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,				pad_token='!'    ,				**A_	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				return OwlViTImageProcessor.from_pretrained(self.tmpdirname    ,				**A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				shutil.rmtree(self.tmpdirname	)



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[np.random.randint(255    ,				size=(3, 30, 400)    ,				dtype=np.uinta	)]

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

				return image_inputs



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_tokenizer()
				UpperCamelCase          =					self.get_rust_tokenizer()
				UpperCamelCase          =					self.get_image_processor()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)
				processor_slow.save_pretrained(self.tmpdirname	)
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(self.tmpdirname    ,				use_fast=A_	)

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)
				processor_fast.save_pretrained(self.tmpdirname	)
				UpperCamelCase          =					OwlViTProcessor.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    ,				A_	)
				self.assertIsInstance(processor_fast.tokenizer    ,				A_	)

				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    ,				A_	)
				self.assertIsInstance(processor_fast.image_processor    ,				A_	)



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					OwlViTProcessor(tokenizer=self.get_tokenizer()    ,				image_processor=self.get_image_processor()	)
				processor.save_pretrained(self.tmpdirname	)

				UpperCamelCase          =					self.get_tokenizer(bos_token='(BOS)'    ,				eos_token='(EOS)'	)
				UpperCamelCase          =					self.get_image_processor(do_normalize=A_	)

				UpperCamelCase          =					OwlViTProcessor.from_pretrained(
				    self.tmpdirname    ,				bos_token='(BOS)'    ,				eos_token='(EOS)'    ,				do_normalize=A_	)

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

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



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					self.prepare_image_inputs()

				UpperCamelCase          =					image_processor(A_    ,				return_tensors='np'	)
				UpperCamelCase          =					processor(images=A_    ,				return_tensors='np'	)

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



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					'lower newer'

				UpperCamelCase          =					processor(text=A_    ,				return_tensors='np'	)

				UpperCamelCase          =					tokenizer(A_    ,				return_tensors='np'	)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key][0].tolist()    ,				encoded_processor[key][0].tolist()	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					'lower newer'
				UpperCamelCase          =					self.prepare_image_inputs()

				UpperCamelCase          =					processor(text=A_    ,				images=A_	)

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

				# test if it raises when no input is passed
				with pytest.raises(A_	):
						processor()



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					['cat', 'nasa badge']
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(2, seq_length)	)

				# test if it raises when no input is passed
				with pytest.raises(A_	):
						processor()



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					[['cat', 'nasa badge'], ['person']]
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				UpperCamelCase          =					len(A_	)
				UpperCamelCase          =					max([len(A_	) for texts in input_texts]	)

				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(batch_size * num_max_text_queries, seq_length)	)

				# test if it raises when no input is passed
				with pytest.raises(A_	):
						processor()



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					['cat', 'nasa badge']
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				UpperCamelCase          =					inputs['input_ids']
				UpperCamelCase          =					[
				    [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
				    [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
				]

				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(2, seq_length)	)
				self.assertListEqual(list(input_ids[0]	)    ,				predicted_ids[0]	)
				self.assertListEqual(list(input_ids[1]	)    ,				predicted_ids[1]	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					self.prepare_image_inputs()
				UpperCamelCase          =					self.prepare_image_inputs()

				UpperCamelCase          =					processor(images=A_    ,				query_images=A_	)

				self.assertListEqual(list(inputs.keys()	)    ,				['query_pixel_values', 'pixel_values']	)

				# test if it raises when no input is passed
				with pytest.raises(A_	):
						processor()



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

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

				UpperCamelCase          =					processor.batch_decode(A_	)
				UpperCamelCase          =					tokenizer.batch_decode(A_	)

				self.assertListEqual(A_    ,				A_	)
