
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       _lowerCAmelCase   (      unittest.TestCase			):




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      tempfile.mkdtemp()

							# fmt: off
							lowerCAmelCase_         =      ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
							# fmt: on
							lowerCAmelCase_         =      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]	)	)

							lowerCAmelCase_         =      {
							    "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],
							}
							lowerCAmelCase_         =      os.path.join(self.tmpdirname							,							_UpperCamelCase	)
							with open(self.image_processor_file							,							"w"							,							encoding="utf-8"	) as fp:
										json.dump(_UpperCamelCase							,							_UpperCamelCase	)




				def 	__a		(   self							,							**_UpperCamelCase	)							-> Optional[int]:
							return BertTokenizer.from_pretrained(self.tmpdirname							,							**_UpperCamelCase	)




				def 	__a		(   self							,							**_UpperCamelCase	)							-> Dict:
							return ViTImageProcessor.from_pretrained(self.tmpdirname							,							**_UpperCamelCase	)




				def 	__a		(   self	)							-> int:
							shutil.rmtree(self.tmpdirname	)




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      [np.random.randint(255							,							size=(3, 30, 400)							,							dtype=np.uinta	)]

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

							return image_inputs




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      self.get_tokenizer()
							lowerCAmelCase_         =      self.get_image_processor()

							lowerCAmelCase_         =      VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase							,							image_processor=_UpperCamelCase	)

							processor.save_pretrained(self.tmpdirname	)
							lowerCAmelCase_         =      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							,							_UpperCamelCase	)




				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      VisionTextDualEncoderProcessor(
							    tokenizer=self.get_tokenizer()							,							image_processor=self.get_image_processor()	)
							processor.save_pretrained(self.tmpdirname	)

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

							lowerCAmelCase_         =      VisionTextDualEncoderProcessor.from_pretrained(
							    self.tmpdirname							,							bos_token="(BOS)"							,							eos_token="(EOS)"							,							do_normalize=_UpperCamelCase							,							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							,							_UpperCamelCase	)




				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      self.get_image_processor()
							lowerCAmelCase_         =      self.get_tokenizer()

							lowerCAmelCase_         =      VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase							,							image_processor=_UpperCamelCase	)

							lowerCAmelCase_         =      self.prepare_image_inputs()

							lowerCAmelCase_         =      image_processor(_UpperCamelCase							,							return_tensors="np"	)
							lowerCAmelCase_         =      processor(images=_UpperCamelCase							,							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 	__a		(   self	)							-> Tuple:
							lowerCAmelCase_         =      self.get_image_processor()
							lowerCAmelCase_         =      self.get_tokenizer()

							lowerCAmelCase_         =      VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase							,							image_processor=_UpperCamelCase	)

							lowerCAmelCase_         =      "lower newer"

							lowerCAmelCase_         =      processor(text=_UpperCamelCase	)

							lowerCAmelCase_         =      tokenizer(_UpperCamelCase	)

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




				def 	__a		(   self	)							-> Optional[Any]:
							lowerCAmelCase_         =      self.get_image_processor()
							lowerCAmelCase_         =      self.get_tokenizer()

							lowerCAmelCase_         =      VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase							,							image_processor=_UpperCamelCase	)

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

							lowerCAmelCase_         =      processor(text=_UpperCamelCase							,							images=_UpperCamelCase	)

							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(_UpperCamelCase	):
										processor()




				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      self.get_image_processor()
							lowerCAmelCase_         =      self.get_tokenizer()

							lowerCAmelCase_         =      VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase							,							image_processor=_UpperCamelCase	)

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

							lowerCAmelCase_         =      processor.batch_decode(_UpperCamelCase	)
							lowerCAmelCase_         =      tokenizer.batch_decode(_UpperCamelCase	)

							self.assertListEqual(_UpperCamelCase							,							_UpperCamelCase	)




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.get_image_processor()
							lowerCAmelCase_         =      self.get_tokenizer()

							lowerCAmelCase_         =      VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase							,							image_processor=_UpperCamelCase	)

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

							lowerCAmelCase_         =      processor(text=_UpperCamelCase							,							images=_UpperCamelCase	)

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





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version






def   lowerCamelCase__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Optional[str] = None							):




			"""simple docstring"""


			if version.parse(hfh.__version__							).release < version.parse("0.11.0"							).release:
						# old versions of hfh don't url-encode the file path
						lowerCAmelCase_         =      quote(__lowerCAmelCase							)
			return hfh.hf_hub_url(__lowerCAmelCase       ,				__lowerCAmelCase       ,				repo_type="dataset"       ,				revision=__lowerCAmelCase							)



