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 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Any:
									A							:					Tuple              =						tempfile.mkdtemp()

									A							:					Optional[int]              =						[
									    "[UNK]",
									    "[CLS]",
									    "[SEP]",
									    "[PAD]",
									    "[MASK]",
									    "的",
									    "价",
									    "格",
									    "是",
									    "15",
									    "便",
									    "alex",
									    "##andra",
									    "，",
									    "。",
									    "-",
									    "t",
									    "shirt",
									]
									A							:					Union[str, Any]              =						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							:					Optional[Any]              =						{
									    "do_resize": True,
									    "size": {"height": 2_24, "width": 2_24},
									    "do_center_crop": True,
									    "crop_size": {"height": 18, "width": 18},
									    "do_normalize": True,
									    "image_mean": [0.48145466, 0.4578275, 0.40821073],
									    "image_std": [0.26862954, 0.26130258, 0.27577711],
									    "do_convert_rgb": True,
									}
									A							:					Tuple              =						os.path.join(self.tmpdirname	,	__lowerCamelCase )
									with open(self.image_processor_file	,	"w"	,	encoding="utf-8" ) as fp:
												json.dump(__lowerCamelCase	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	**__lowerCamelCase      :			Dict )      ->				Optional[Any]:
									return BertTokenizer.from_pretrained(self.tmpdirname	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	**__lowerCamelCase      :			Optional[int] )      ->				Tuple:
									return BertTokenizerFast.from_pretrained(self.tmpdirname	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	**__lowerCamelCase      :			List[Any] )      ->				str:
									return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									shutil.rmtree(self.tmpdirname )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				List[str]:
									A							:					Any              =						[np.random.randint(2_55	,	size=(3, 30, 4_00)	,	dtype=np.uinta )]

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

									return image_inputs







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[Any]:
									A							:					List[Any]              =						self.get_tokenizer()
									A							:					int              =						self.get_rust_tokenizer()
									A							:					Dict              =						self.get_image_processor()

									A							:					Tuple              =						ChineseCLIPProcessor(tokenizer=__lowerCamelCase	,	image_processor=__lowerCamelCase )
									processor_slow.save_pretrained(self.tmpdirname )
									A							:					Tuple              =						ChineseCLIPProcessor.from_pretrained(self.tmpdirname	,	use_fast=__lowerCamelCase )

									A							:					Optional[int]              =						ChineseCLIPProcessor(tokenizer=__lowerCamelCase	,	image_processor=__lowerCamelCase )
									processor_fast.save_pretrained(self.tmpdirname )
									A							:					Dict              =						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	,	__lowerCamelCase )
									self.assertIsInstance(processor_fast.tokenizer	,	__lowerCamelCase )

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Union[str, Any]:
									A							:					int              =						ChineseCLIPProcessor(tokenizer=self.get_tokenizer()	,	image_processor=self.get_image_processor() )
									processor.save_pretrained(self.tmpdirname )

									A							:					Union[str, Any]              =						self.get_tokenizer(cls_token="(CLS)"	,	sep_token="(SEP)" )
									A							:					Optional[Any]              =						self.get_image_processor(do_normalize=__lowerCamelCase )

									A							:					Union[str, Any]              =						ChineseCLIPProcessor.from_pretrained(
									    self.tmpdirname	,	cls_token="(CLS)"	,	sep_token="(SEP)"	,	do_normalize=__lowerCamelCase )

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

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Union[str, Any]:
									A							:					List[str]              =						self.get_image_processor()
									A							:					Dict              =						self.get_tokenizer()

									A							:					Tuple              =						ChineseCLIPProcessor(tokenizer=__lowerCamelCase	,	image_processor=__lowerCamelCase )

									A							:					List[Any]              =						self.prepare_image_inputs()

									A							:					Tuple              =						image_processor(__lowerCamelCase	,	return_tensors="np" )
									A							:					Optional[int]              =						processor(images=__lowerCamelCase	,	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 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				int:
									A							:					int              =						self.get_image_processor()
									A							:					Optional[int]              =						self.get_tokenizer()

									A							:					Any              =						ChineseCLIPProcessor(tokenizer=__lowerCamelCase	,	image_processor=__lowerCamelCase )

									A							:					int              =						"Alexandra，T-shirt的价格是15便士。"

									A							:					Tuple              =						processor(text=__lowerCamelCase )

									A							:					Tuple              =						tokenizer(__lowerCamelCase )

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				List[str]:
									A							:					List[str]              =						self.get_image_processor()
									A							:					str              =						self.get_tokenizer()

									A							:					Optional[int]              =						ChineseCLIPProcessor(tokenizer=__lowerCamelCase	,	image_processor=__lowerCamelCase )

									A							:					Optional[int]              =						"Alexandra，T-shirt的价格是15便士。"
									A							:					List[str]              =						self.prepare_image_inputs()

									A							:					List[str]              =						processor(text=__lowerCamelCase	,	images=__lowerCamelCase )

									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(__lowerCamelCase ):
												processor()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				str:
									A							:					Tuple              =						self.get_image_processor()
									A							:					Dict              =						self.get_tokenizer()

									A							:					Any              =						ChineseCLIPProcessor(tokenizer=__lowerCamelCase	,	image_processor=__lowerCamelCase )

									A							:					Union[str, Any]              =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									A							:					Tuple              =						processor.batch_decode(__lowerCamelCase )
									A							:					Any              =						tokenizer.batch_decode(__lowerCamelCase )

									self.assertListEqual(__lowerCamelCase	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[Any]:
									A							:					Any              =						self.get_image_processor()
									A							:					Union[str, Any]              =						self.get_tokenizer()

									A							:					Tuple              =						ChineseCLIPProcessor(tokenizer=__lowerCamelCase	,	image_processor=__lowerCamelCase )

									A							:					Any              =						"Alexandra，T-shirt的价格是15便士。"
									A							:					int              =						self.prepare_image_inputs()

									A							:					Optional[int]              =						processor(text=__lowerCamelCase	,	images=__lowerCamelCase )

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




import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__SCREAMING_SNAKE_CASE           =  """."""


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
							__SCREAMING_SNAKE_CASE           =  []
							__SCREAMING_SNAKE_CASE           =  []
							with open(doctest_file_path) as fp:
														for line in fp:
																					__SCREAMING_SNAKE_CASE           =  line.strip()
																					__SCREAMING_SNAKE_CASE           =  os.path.join(REPO_PATH, line)
																					if not (os.path.isfile(path) or os.path.isdir(path)):
																												non_existent_paths.append(line)
																					all_paths.append(path)
							if len(non_existent_paths) > 0:
														__SCREAMING_SNAKE_CASE           =  """\n""".join(non_existent_paths)
														raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
							if all_paths != sorted(all_paths):
														raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")