



"""simple docstring"""







import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
			import ctypes
			import msvcrt  # noqa






			class       UpperCamelCase__			(   ctypes.Structure      ):

						"""simple docstring"""


						A__	:					List[Any]							  =     [("size", ctypes.c_int), ("visible", ctypes.c_byte)]

def       _lowerCamelCase				(							)	-> Optional[int]:






	"""simple docstring"""





	if os.name == "nt":
		A__											=					CursorInfo()
		A__											=					ctypes.windll.kernelaa.GetStdHandle(-11     )
		ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase_,	ctypes.byref(UpperCAmelCase_     )     )
		A__											=					False
		ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase_,	ctypes.byref(UpperCAmelCase_     )     )
	elif os.name == "posix":
		sys.stdout.write("\033[?25l"     )
		sys.stdout.flush()

def       _lowerCamelCase				(							)	-> str:






	"""simple docstring"""





	if os.name == "nt":
		A__											=					CursorInfo()
		A__											=					ctypes.windll.kernelaa.GetStdHandle(-11     )
		ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase_,	ctypes.byref(UpperCAmelCase_     )     )
		A__											=					True
		ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase_,	ctypes.byref(UpperCAmelCase_     )     )
	elif os.name == "posix":
		sys.stdout.write("\033[?25h"     )
		sys.stdout.flush()




@contextmanager
def       _lowerCamelCase				(							)	-> Dict:






	"""simple docstring"""





	try:
		hide_cursor()
		yield
	finally:
		show_cursor()









"""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 IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AlignProcessor, EfficientNetImageProcessor






@require_vision
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Any:
				A__											=					tempfile.mkdtemp()

				A__											=					[
				    "[UNK]",
				    "[CLS]",
				    "[SEP]",
				    "[PAD]",
				    "[MASK]",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				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": 20,
				    "do_center_crop": True,
				    "crop_size": 18,
				    "do_normalize": True,
				    "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
				    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
				}
				A__											=					os.path.join(self.tmpdirname						,	SCREAMING_SNAKE_CASE__		)
				with open(self.image_processor_file						,	"w"						,	encoding="utf-8"		) as fp:
					json.dump(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	List[Any]:
				return BertTokenizer.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Tuple:
				return BertTokenizerFast.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	str:
				return EfficientNetImageProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	List[str]:
				shutil.rmtree(self.tmpdirname		)


			def   snake_case__	(      self		)      ->	Any:
				A__											=					[np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta		)]
				A__											=					[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__						,	0						,	-1		)		) for x in image_inputs]
				return image_inputs


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					self.get_tokenizer()
				A__											=					self.get_rust_tokenizer()
				A__											=					self.get_image_processor()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)
				processor_slow.save_pretrained(self.tmpdirname		)
				A__											=					AlignProcessor.from_pretrained(self.tmpdirname						,	use_fast=SCREAMING_SNAKE_CASE__		)

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)
				processor_fast.save_pretrained(self.tmpdirname		)
				A__											=					AlignProcessor.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						,	SCREAMING_SNAKE_CASE__		)
				self.assertIsInstance(processor_fast.tokenizer						,	SCREAMING_SNAKE_CASE__		)

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


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					AlignProcessor(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=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

				A__											=					AlignProcessor.from_pretrained(
				    self.tmpdirname						,	bos_token="(BOS)"						,	eos_token="(EOS)"						,	do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

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

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


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					self.prepare_image_inputs()

				A__											=					image_processor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)
				A__											=					processor(images=SCREAMING_SNAKE_CASE__						,	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   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"

				A__											=					processor(text=SCREAMING_SNAKE_CASE__		)

				A__											=					tokenizer(SCREAMING_SNAKE_CASE__						,	padding="max_length"						,	max_length=64		)

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


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

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

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

				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(SCREAMING_SNAKE_CASE__		):
					processor()


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

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

				A__											=					processor.batch_decode(SCREAMING_SNAKE_CASE__		)
				A__											=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

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

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

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




