






"""simple docstring"""





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       __A		(   unittest.TestCase			):

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							str:
											__magic_name__:	Optional[Any]							=       tempfile.mkdtemp()

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

											__magic_name__:	Dict							=       {
											    "do_resize": True,
											    "size": {"height": 1_8, "width": 1_8},
											    "do_normalize": True,
											    "image_mean": [0.5, 0.5, 0.5],
											    "image_std": [0.5, 0.5, 0.5],
											}
											__magic_name__:	int							=       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   lowerCamelCase__   (			self  :      List[str]     ,		**__snake_case  :      List[str]					)			->							Dict:
											return BertTokenizer.from_pretrained(self.tmpdirname     ,		**_SCREAMING_SNAKE_CASE					)

					def   lowerCamelCase__   (			self  :      Dict     ,		**__snake_case  :      List[Any]					)			->							Optional[Any]:
											return ViTImageProcessor.from_pretrained(self.tmpdirname     ,		**_SCREAMING_SNAKE_CASE					)

					def   lowerCamelCase__   (			self  :      Any					)			->							Dict:
											shutil.rmtree(self.tmpdirname					)

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Dict:
											__magic_name__:	List[str]							=       [np.random.randint(2_5_5     ,		size=(3, 3_0, 4_0_0)     ,		dtype=np.uinta					)]

											__magic_name__:	str							=       [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE     ,		0     ,		-1					)					) for x in image_inputs]

											return image_inputs

					def   lowerCamelCase__   (			self  :      str					)			->							Dict:
											__magic_name__:	Tuple							=       self.get_tokenizer()
											__magic_name__:	Any							=       self.get_image_processor()

											__magic_name__:	str							=       VisionTextDualEncoderProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,		image_processor=_SCREAMING_SNAKE_CASE					)

											processor.save_pretrained(self.tmpdirname					)
											__magic_name__:	List[Any]							=       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     ,		_SCREAMING_SNAKE_CASE					)

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							str:
											__magic_name__:	Optional[int]							=       VisionTextDualEncoderProcessor(
											    tokenizer=self.get_tokenizer()     ,		image_processor=self.get_image_processor()					)
											processor.save_pretrained(self.tmpdirname					)

											__magic_name__:	int							=       self.get_tokenizer(bos_token="""(BOS)"""     ,		eos_token="""(EOS)"""					)
											__magic_name__:	List[Any]							=       self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE     ,		padding_value=1.0					)

											__magic_name__:	int							=       VisionTextDualEncoderProcessor.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     ,		(BertTokenizer, BertTokenizerFast)					)

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

					def   lowerCamelCase__   (			self  :      List[str]					)			->							int:
											__magic_name__:	Tuple							=       self.get_image_processor()
											__magic_name__:	str							=       self.get_tokenizer()

											__magic_name__:	Optional[int]							=       VisionTextDualEncoderProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,		image_processor=_SCREAMING_SNAKE_CASE					)

											__magic_name__:	List[str]							=       self.prepare_image_inputs()

											__magic_name__:	str							=       image_processor(_SCREAMING_SNAKE_CASE     ,		return_tensors="""np"""					)
											__magic_name__:	Any							=       processor(images=_SCREAMING_SNAKE_CASE     ,		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   lowerCamelCase__   (			self  :      List[Any]					)			->							Optional[Any]:
											__magic_name__:	Dict							=       self.get_image_processor()
											__magic_name__:	List[str]							=       self.get_tokenizer()

											__magic_name__:	Optional[int]							=       VisionTextDualEncoderProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,		image_processor=_SCREAMING_SNAKE_CASE					)

											__magic_name__:	Optional[int]							=       "lower newer"

											__magic_name__:	int							=       processor(text=_SCREAMING_SNAKE_CASE					)

											__magic_name__:	str							=       tokenizer(_SCREAMING_SNAKE_CASE					)

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

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							int:
											__magic_name__:	Optional[int]							=       self.get_image_processor()
											__magic_name__:	List[str]							=       self.get_tokenizer()

											__magic_name__:	str							=       VisionTextDualEncoderProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,		image_processor=_SCREAMING_SNAKE_CASE					)

											__magic_name__:	Optional[Any]							=       "lower newer"
											__magic_name__:	Optional[Any]							=       self.prepare_image_inputs()

											__magic_name__:	Optional[Any]							=       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 self.assertRaises(_SCREAMING_SNAKE_CASE					):
																	processor()

					def   lowerCamelCase__   (			self  :      Tuple					)			->							Dict:
											__magic_name__:	Dict							=       self.get_image_processor()
											__magic_name__:	List[Any]							=       self.get_tokenizer()

											__magic_name__:	List[str]							=       VisionTextDualEncoderProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,		image_processor=_SCREAMING_SNAKE_CASE					)

											__magic_name__:	List[Any]							=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											__magic_name__:	List[Any]							=       processor.batch_decode(_SCREAMING_SNAKE_CASE					)
											__magic_name__:	List[str]							=       tokenizer.batch_decode(_SCREAMING_SNAKE_CASE					)

											self.assertListEqual(_SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE					)






					def   lowerCamelCase__   (			self  :      int					)			->							Optional[Any]:
											__magic_name__:	Optional[Any]							=       self.get_image_processor()
											__magic_name__:	str							=       self.get_tokenizer()

											__magic_name__:	int							=       VisionTextDualEncoderProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,		image_processor=_SCREAMING_SNAKE_CASE					)

											__magic_name__:	List[Any]							=       "lower newer"
											__magic_name__:	List[Any]							=       self.prepare_image_inputs()

											__magic_name__:	List[Any]							=       processor(text=_SCREAMING_SNAKE_CASE     ,		images=_SCREAMING_SNAKE_CASE					)

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








"""simple docstring"""






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case				:  Tuple =       {
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Optional[Any] =       [
	    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AutoformerForPrediction',
	    'AutoformerModel',
	    'AutoformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_autoformer import (
	    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AutoformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_autoformer import (
		    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AutoformerForPrediction,
		    AutoformerModel,
		    AutoformerPreTrainedModel,
		)

else:
	import sys

	__snake_case				:  Optional[int] =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)