


"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
    from PIL import Image

    from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor


@require_vision
class 			A__	(						unittest.TestCase):


     """simple docstring"""

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

           lowerCamelCase					:					Optional[Any]        =	[
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """的""",
               """价""",
               """格""",
               """是""",
               """15""",
               """便""",
               """alex""",
               """##andra""",
               """，""",
               """。""",
               """-""",
               """t""",
               """shirt""",
           ]
           lowerCamelCase					:					Optional[int]        =	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					:					str        =	{
               """do_resize""": True,
               """size""": {"""height""": 224, """width""": 224},
               """do_center_crop""": True,
               """crop_size""": {"""height""": 18, """width""": 18},
               """do_normalize""": True,
               """image_mean""": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
               """image_std""": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
               """do_convert_rgb""": True,
           }
           lowerCamelCase					:					Tuple        =	os.path.join(self.tmpdirname     ,		__a	)
           with open(self.image_processor_file     ,		"""w"""     ,		encoding="""utf-8"""	) as fp:
                 json.dump(__a     ,		__a	)

     def 		a__						(	self:						int     ,		**__a:						str	)->    Dict:
           return BertTokenizer.from_pretrained(self.tmpdirname     ,		**__a	)

     def 		a__						(	self:						Any     ,		**__a:						str	)->    Optional[int]:
           return BertTokenizerFast.from_pretrained(self.tmpdirname     ,		**__a	)

     def 		a__						(	self:						Tuple     ,		**__a:						List[Any]	)->    Union[str, Any]:
           return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname     ,		**__a	)

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

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

           lowerCamelCase					:					List[Any]        =	[Image.fromarray(np.moveaxis(__a     ,		0     ,		-1	)	) for x in image_inputs]

           return image_inputs

     def 		a__						(	self:						Dict	)->    Tuple:
           lowerCamelCase					:					List[Any]        =	self.get_tokenizer()
           lowerCamelCase					:					Optional[Any]        =	self.get_rust_tokenizer()
           lowerCamelCase					:					Optional[Any]        =	self.get_image_processor()

           lowerCamelCase					:					Any        =	ChineseCLIPProcessor(tokenizer=__a     ,		image_processor=__a	)
           processor_slow.save_pretrained(self.tmpdirname	)
           lowerCamelCase					:					Tuple        =	ChineseCLIPProcessor.from_pretrained(self.tmpdirname     ,		use_fast=__a	)

           lowerCamelCase					:					Tuple        =	ChineseCLIPProcessor(tokenizer=__a     ,		image_processor=__a	)
           processor_fast.save_pretrained(self.tmpdirname	)
           lowerCamelCase					:					List[str]        =	ChineseCLIPProcessor.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 		a__						(	self:						List[str]	)->    Union[str, Any]:
           lowerCamelCase					:					List[Any]        =	ChineseCLIPProcessor(tokenizer=self.get_tokenizer()     ,		image_processor=self.get_image_processor()	)
           processor.save_pretrained(self.tmpdirname	)

           lowerCamelCase					:					Union[str, Any]        =	self.get_tokenizer(cls_token="""(CLS)"""     ,		sep_token="""(SEP)"""	)
           lowerCamelCase					:					int        =	self.get_image_processor(do_normalize=__a	)

           lowerCamelCase					:					Tuple        =	ChineseCLIPProcessor.from_pretrained(
               self.tmpdirname     ,		cls_token="""(CLS)"""     ,		sep_token="""(SEP)"""     ,		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 		a__						(	self:						List[Any]	)->    List[str]:
           lowerCamelCase					:					Optional[int]        =	self.get_image_processor()
           lowerCamelCase					:					Union[str, Any]        =	self.get_tokenizer()

           lowerCamelCase					:					Any        =	ChineseCLIPProcessor(tokenizer=__a     ,		image_processor=__a	)

           lowerCamelCase					:					Union[str, Any]        =	self.prepare_image_inputs()

           lowerCamelCase					:					Optional[int]        =	image_processor(__a     ,		return_tensors="""np"""	)
           lowerCamelCase					:					List[Any]        =	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 		a__						(	self:						Union[str, Any]	)->    Optional[Any]:
           lowerCamelCase					:					Dict        =	self.get_image_processor()
           lowerCamelCase					:					Any        =	self.get_tokenizer()

           lowerCamelCase					:					Dict        =	ChineseCLIPProcessor(tokenizer=__a     ,		image_processor=__a	)

           lowerCamelCase					:					Tuple        =	"""Alexandra，T-shirt的价格是15便士。"""

           lowerCamelCase					:					Tuple        =	processor(text=__a	)

           lowerCamelCase					:					str        =	tokenizer(__a	)

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

     def 		a__						(	self:						Optional[int]	)->    Optional[Any]:
           lowerCamelCase					:					List[Any]        =	self.get_image_processor()
           lowerCamelCase					:					List[Any]        =	self.get_tokenizer()

           lowerCamelCase					:					Optional[int]        =	ChineseCLIPProcessor(tokenizer=__a     ,		image_processor=__a	)

           lowerCamelCase					:					Tuple        =	"""Alexandra，T-shirt的价格是15便士。"""
           lowerCamelCase					:					List[Any]        =	self.prepare_image_inputs()

           lowerCamelCase					:					int        =	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 pytest.raises(__a	):
                 processor()

     def 		a__						(	self:						Tuple	)->    str:
           lowerCamelCase					:					Dict        =	self.get_image_processor()
           lowerCamelCase					:					int        =	self.get_tokenizer()

           lowerCamelCase					:					str        =	ChineseCLIPProcessor(tokenizer=__a     ,		image_processor=__a	)

           lowerCamelCase					:					int        =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowerCamelCase					:					List[Any]        =	processor.batch_decode(__a	)
           lowerCamelCase					:					Union[str, Any]        =	tokenizer.batch_decode(__a	)

           self.assertListEqual(__a     ,		__a	)







     def 		a__						(	self:						Union[str, Any]	)->    Union[str, Any]:
           lowerCamelCase					:					int        =	self.get_image_processor()
           lowerCamelCase					:					List[Any]        =	self.get_tokenizer()

           lowerCamelCase					:					List[str]        =	ChineseCLIPProcessor(tokenizer=__a     ,		image_processor=__a	)

           lowerCamelCase					:					List[str]        =	"""Alexandra，T-shirt的价格是15便士。"""
           lowerCamelCase					:					Union[str, Any]        =	self.prepare_image_inputs()

           lowerCamelCase					:					Dict        =	processor(text=__a     ,		images=__a	)

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








"""simple docstring"""


import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase      :str	    =		get_tests_dir('fixtures/spiece.model')


@require_sentencepiece
@require_tokenizers
class 			A__	(						__lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					List[str]													=AlbertTokenizer
     snake_case__	:					Optional[Any]													=AlbertTokenizerFast
     snake_case__	:					Optional[int]													=True
     snake_case__	:					Any													=True
     snake_case__	:					Optional[int]													=True

     def 		a__						(	self:						Dict	)->    Optional[Any]:
           super().setUp()

           # We have a SentencePiece fixture for testing
           lowerCamelCase					:					int        =	AlbertTokenizer(__a	)
           tokenizer.save_pretrained(self.tmpdirname	)

     def 		a__						(	self:						Tuple     ,		__a:						Tuple	)->    Union[str, Any]:
           lowerCamelCase					:					List[str]        =	"""this is a test"""
           lowerCamelCase					:					int        =	"""this is a test"""
           return input_text, output_text

     def 		a__						(	self:						Any	)->    List[Any]:
           lowerCamelCase					:					int        =	"""<pad>"""
           lowerCamelCase					:					Optional[int]        =	0

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a	)     ,		__a	)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a	)     ,		__a	)

     def 		a__						(	self:						Tuple	)->    str:
           lowerCamelCase					:					Optional[int]        =	list(self.get_tokenizer().get_vocab().keys()	)

           self.assertEqual(vocab_keys[0]     ,		"""<pad>"""	)
           self.assertEqual(vocab_keys[1]     ,		"""<unk>"""	)
           self.assertEqual(vocab_keys[-1]     ,		"""▁eloquent"""	)
           self.assertEqual(len(__a	)     ,		30_000	)

     def 		a__						(	self:						List[str]	)->    Any:
           self.assertEqual(self.get_tokenizer().vocab_size     ,		30_000	)

     def 		a__						(	self:						Optional[Any]	)->    Union[str, Any]:
           if not self.test_rust_tokenizer:
                 return

           lowerCamelCase					:					str        =	self.get_tokenizer()
           lowerCamelCase					:					Tuple        =	self.get_rust_tokenizer()

           lowerCamelCase					:					Union[str, Any]        =	"""I was born in 92000, and this is falsé."""

           lowerCamelCase					:					List[str]        =	tokenizer.tokenize(__a	)
           lowerCamelCase					:					Tuple        =	rust_tokenizer.tokenize(__a	)
           self.assertListEqual(__a     ,		__a	)

           lowerCamelCase					:					Dict        =	tokenizer.encode(__a     ,		add_special_tokens=__a	)
           lowerCamelCase					:					List[str]        =	rust_tokenizer.encode(__a     ,		add_special_tokens=__a	)
           self.assertListEqual(__a     ,		__a	)

           lowerCamelCase					:					Any        =	self.get_rust_tokenizer()
           lowerCamelCase					:					List[str]        =	tokenizer.encode(__a	)
           lowerCamelCase					:					str        =	rust_tokenizer.encode(__a	)
           self.assertListEqual(__a     ,		__a	)

     def 		a__						(	self:						Tuple	)->    List[Any]:
           lowerCamelCase					:					List[str]        =	AlbertTokenizer(__a     ,		keep_accents=__a	)

           lowerCamelCase					:					int        =	tokenizer.tokenize("""This is a test"""	)
           self.assertListEqual(__a     ,		["""▁this""", """▁is""", """▁a""", """▁test"""]	)

           self.assertListEqual(tokenizer.convert_tokens_to_ids(__a	)     ,		[48, 25, 21, 1_289]	)

           lowerCamelCase					:					List[str]        =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
           self.assertListEqual(
               __a     ,		["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]	)
           lowerCamelCase					:					List[str]        =	tokenizer.convert_tokens_to_ids(__a	)
           self.assertListEqual(__a     ,		[31, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]	)

           lowerCamelCase					:					Tuple        =	tokenizer.convert_ids_to_tokens(__a	)
           self.assertListEqual(
               __a     ,		["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]     ,		)

     def 		a__						(	self:						Tuple	)->    str:
           lowerCamelCase					:					str        =	AlbertTokenizer(__a	)

           lowerCamelCase					:					Union[str, Any]        =	tokenizer.encode("""sequence builders"""	)
           lowerCamelCase					:					List[Any]        =	tokenizer.encode("""multi-sequence build"""	)

           lowerCamelCase					:					Any        =	tokenizer.build_inputs_with_special_tokens(__a	)
           lowerCamelCase					:					str        =	tokenizer.build_inputs_with_special_tokens(__a     ,		__a	)

           assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
           assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
               tokenizer.sep_token_id
           ]







     @slow
     def 		a__						(	self:						Any	)->    Dict:
           # fmt: off
           lowerCamelCase					:					Optional[Any]        =	{"""attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """input_ids""": [[2, 21_970, 13, 5, 6_092, 167, 28, 7_103, 2_153, 673, 8, 7_028, 12_051, 18, 17, 7_103, 2_153, 673, 8, 3_515, 18_684, 8, 4_461, 6, 1_927, 297, 8, 12_060, 2_607, 18, 13, 5, 4_461, 15, 10_538, 38, 8, 135, 15, 822, 58, 15, 993, 10_363, 15, 1_460, 8_005, 4_461, 15, 993, 255, 2_328, 9, 9, 9, 6, 26, 1_112, 816, 3_260, 13, 5, 103, 2_377, 6, 17, 1_112, 816, 2_782, 13, 5, 103, 10_641, 6, 29, 84, 2_512, 2_430, 782, 18_684, 2_761, 19, 808, 2_430, 2_556, 17, 855, 1_480, 9_477, 4_091, 128, 11_712, 15, 7_103, 2_153, 673, 17, 24_883, 9_990, 9, 3], [2, 11_502, 25, 1_006, 20, 782, 8, 11_809, 855, 1_732, 19_393, 18_667, 37, 367, 21_018, 69, 1_854, 34, 11_860, 19_124, 27, 156, 225, 17, 193, 4_141, 19, 65, 9_124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2_231, 886, 2_385, 17_659, 84, 14, 16_792, 1_952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=__a     ,		model_name="""albert-base-v2"""     ,		revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"""     ,		)




