


'''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    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= tempfile.mkdtemp()

							# fmt: off
							UpperCamelCase__   :List[str]												= ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
							# fmt: on
							UpperCamelCase__   :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]							)							)

							UpperCamelCase__   :int												= {
							    '''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],
							}
							UpperCamelCase__   :Union[str, Any]												= os.path.join(self.tmpdirname    ,   UpperCamelCase_							)
							with open(self.image_processor_file    ,   '''w'''    ,   encoding='''utf-8'''							) as fp:
												json.dump(UpperCamelCase_    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   **UpperCamelCase_							):

							'''simple docstring'''


							return BertTokenizer.from_pretrained(self.tmpdirname    ,   **UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   **UpperCamelCase_							):

							'''simple docstring'''


							return ViTImageProcessor.from_pretrained(self.tmpdirname    ,   **UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							shutil.rmtree(self.tmpdirname							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= [np.random.randint(255    ,   size=(3, 30, 400)    ,   dtype=np.uinta							)]

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

							return image_inputs




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= self.get_tokenizer()
							UpperCamelCase__   :Dict												= self.get_image_processor()

							UpperCamelCase__   :str												= VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_    ,   image_processor=UpperCamelCase_							)

							processor.save_pretrained(self.tmpdirname							)
							UpperCamelCase__   :Union[str, 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    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= VisionTextDualEncoderProcessor(
							    tokenizer=self.get_tokenizer()    ,   image_processor=self.get_image_processor()							)
							processor.save_pretrained(self.tmpdirname							)

							UpperCamelCase__   :Tuple												= self.get_tokenizer(bos_token='''(BOS)'''    ,   eos_token='''(EOS)'''							)
							UpperCamelCase__   :Union[str, Any]												= self.get_image_processor(do_normalize=UpperCamelCase_    ,   padding_value=1.0							)

							UpperCamelCase__   :Any												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= self.get_image_processor()
							UpperCamelCase__   :List[Any]												= self.get_tokenizer()

							UpperCamelCase__   :Dict												= VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_    ,   image_processor=UpperCamelCase_							)

							UpperCamelCase__   :Optional[Any]												= self.prepare_image_inputs()

							UpperCamelCase__   :List[str]												= image_processor(UpperCamelCase_    ,   return_tensors='''np'''							)
							UpperCamelCase__   :Optional[int]												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= self.get_image_processor()
							UpperCamelCase__   :List[str]												= self.get_tokenizer()

							UpperCamelCase__   :List[Any]												= VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_    ,   image_processor=UpperCamelCase_							)

							UpperCamelCase__   :Any												= '''lower newer'''

							UpperCamelCase__   :Optional[Any]												= processor(text=UpperCamelCase_							)

							UpperCamelCase__   :Tuple												= tokenizer(UpperCamelCase_							)

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= self.get_image_processor()
							UpperCamelCase__   :str												= self.get_tokenizer()

							UpperCamelCase__   :Tuple												= VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_    ,   image_processor=UpperCamelCase_							)

							UpperCamelCase__   :List[Any]												= '''lower newer'''
							UpperCamelCase__   :Optional[int]												= self.prepare_image_inputs()

							UpperCamelCase__   :Optional[int]												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= self.get_image_processor()
							UpperCamelCase__   :List[str]												= self.get_tokenizer()

							UpperCamelCase__   :Tuple												= VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_    ,   image_processor=UpperCamelCase_							)

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

							UpperCamelCase__   :List[str]												= processor.batch_decode(UpperCamelCase_							)
							UpperCamelCase__   :Any												= tokenizer.batch_decode(UpperCamelCase_							)

							self.assertListEqual(UpperCamelCase_    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= self.get_image_processor()
							UpperCamelCase__   :List[Any]												= self.get_tokenizer()

							UpperCamelCase__   :int												= VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_    ,   image_processor=UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= '''lower newer'''
							UpperCamelCase__   :Tuple												= self.prepare_image_inputs()

							UpperCamelCase__   :Dict												= processor(text=UpperCamelCase_    ,   images=UpperCamelCase_							)

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



'''simple docstring'''
from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 			a      (			__a   )  ->	int:



					'''simple docstring'''






					for param in module.parameters():
										UpperCamelCase__   :Dict												= False


def 			a      (			)  ->	Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= '''cuda''' if torch.cuda.is_available() else '''cpu'''
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										UpperCamelCase__   :Optional[int]												= '''mps'''
					if device == "mps":
										print(
										    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
										    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
										    ''' with generations.'''   )
					return device


def 			a      (			__a   )  ->	Any:



					'''simple docstring'''






					UpperCamelCase__   :Dict												= plt.imshow(__a   )
					fig.axes.get_xaxis().set_visible(__a   )
					fig.axes.get_yaxis().set_visible(__a   )
					plt.show()







def 			a      (			)  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :int												= datetime.now()
					UpperCamelCase__   :str												= current_time.strftime('''%H:%M:%S'''   )
					return timestamp