"""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     UpperCamelCase__(			unittest.TestCase ):





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							tempfile.mkdtemp()

													# fmt: off
													A__	     =							['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
													# fmt: on
													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''': {'''height''': 18, '''width''': 18},
													    '''do_normalize''': True,
													    '''image_mean''': [0.5, 0.5, 0.5],
													    '''image_std''': [0.5, 0.5, 0.5],
													}
													A__	     =							os.path.join(self.tmpdirname		,__UpperCAmelCase     )
													with open(self.image_processor_file		,'w'		,encoding='utf-8'     ) as fp:
																				json.dump(__UpperCAmelCase		,__UpperCAmelCase     )





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





						def 				snake_case__	(       self		,**__UpperCAmelCase     )					->				Tuple:
													return ViTImageProcessor.from_pretrained(self.tmpdirname		,**__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Tuple:
													shutil.rmtree(self.tmpdirname     )





						def 				snake_case__	(       self     )					->				int:
													A__	     =							[np.random.randint(2_55		,size=(3, 30, 4_00)		,dtype=np.uinta     )]

													A__	     =							[Image.fromarray(np.moveaxis(__UpperCAmelCase		,0		,-1     )     ) for x in image_inputs]

													return image_inputs





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

													A__	     =							VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase		,image_processor=__UpperCAmelCase     )

													processor.save_pretrained(self.tmpdirname     )
													A__	     =							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 				snake_case__	(       self     )					->				List[str]:
													A__	     =							VisionTextDualEncoderProcessor(
													    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=__UpperCAmelCase		,padding_value=1.0     )

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

													A__	     =							VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase		,image_processor=__UpperCAmelCase     )

													A__	     =							self.prepare_image_inputs()

													A__	     =							image_processor(__UpperCAmelCase		,return_tensors='np'     )
													A__	     =							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 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							self.get_image_processor()
													A__	     =							self.get_tokenizer()

													A__	     =							VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase		,image_processor=__UpperCAmelCase     )

													A__	     =							'''lower newer'''

													A__	     =							processor(text=__UpperCAmelCase     )

													A__	     =							tokenizer(__UpperCAmelCase     )

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





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

													A__	     =							VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase		,image_processor=__UpperCAmelCase     )

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

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

													A__	     =							VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase		,image_processor=__UpperCAmelCase     )

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

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

													self.assertListEqual(__UpperCAmelCase		,__UpperCAmelCase     )





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

													A__	     =							VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase		,image_processor=__UpperCAmelCase     )

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

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

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






"""simple docstring"""




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		'M-CLIP'





						def __init__(       self		,__UpperCAmelCase=10_24		,__UpperCAmelCase=7_68		,**__UpperCAmelCase     )					->				List[str]:
													A__	     =							transformerDimSize
													A__	     =							imageDimSize
													super().__init__(**__UpperCAmelCase     )






class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		MCLIPConfig





						def __init__(       self		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													super().__init__(__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )
													A__	     =							XLMRobertaModel(__UpperCAmelCase     )
													A__	     =							torch.nn.Linear(
													    in_features=config.transformerDimensions		,out_features=config.numDims     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Tuple:
													A__	     =							self.transformer(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase     )[0]
													A__	     =							(embs * attention_mask.unsqueeze(2     )).sum(dim=1     ) / attention_mask.sum(dim=1     )[:, None]
													return self.LinearTransformation(__UpperCAmelCase     ), embs





