
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		):




							def 			A__				(    self):
												lowercase						     =						tempfile.mkdtemp()

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

												lowercase						     =						{
												    '''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],
												}
												lowercase						     =						os.path.join(self.tmpdirname       ,A__)
												with open(self.image_processor_file       ,'''w'''       ,encoding='''utf-8''') as fp:
																	json.dump(A__       ,A__)




							def 			A__				(    self       ,**A__):
												return BertTokenizer.from_pretrained(self.tmpdirname       ,**A__)




							def 			A__				(    self       ,**A__):
												return ViTImageProcessor.from_pretrained(self.tmpdirname       ,**A__)




							def 			A__				(    self):
												shutil.rmtree(self.tmpdirname)




							def 			A__				(    self):
												lowercase						     =						[np.random.randint(2_5_5       ,size=(3, 3_0, 4_0_0)       ,dtype=np.uinta)]

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

												return image_inputs




							def 			A__				(    self):
												lowercase						     =						self.get_tokenizer()
												lowercase						     =						self.get_image_processor()

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

												processor.save_pretrained(self.tmpdirname)
												lowercase						     =						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       ,A__)




							def 			A__				(    self):
												lowercase						     =						VisionTextDualEncoderProcessor(
												    tokenizer=self.get_tokenizer()       ,image_processor=self.get_image_processor())
												processor.save_pretrained(self.tmpdirname)

												lowercase						     =						self.get_tokenizer(bos_token='''(BOS)'''       ,eos_token='''(EOS)''')
												lowercase						     =						self.get_image_processor(do_normalize=A__       ,padding_value=1.0)

												lowercase						     =						VisionTextDualEncoderProcessor.from_pretrained(
												    self.tmpdirname       ,bos_token='''(BOS)'''       ,eos_token='''(EOS)'''       ,do_normalize=A__       ,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       ,A__)




							def 			A__				(    self):
												lowercase						     =						self.get_image_processor()
												lowercase						     =						self.get_tokenizer()

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

												lowercase						     =						self.prepare_image_inputs()

												lowercase						     =						image_processor(A__       ,return_tensors='''np''')
												lowercase						     =						processor(images=A__       ,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 			A__				(    self):
												lowercase						     =						self.get_image_processor()
												lowercase						     =						self.get_tokenizer()

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

												lowercase						     =						'''lower newer'''

												lowercase						     =						processor(text=A__)

												lowercase						     =						tokenizer(A__)

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




							def 			A__				(    self):
												lowercase						     =						self.get_image_processor()
												lowercase						     =						self.get_tokenizer()

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

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

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

												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(A__):
																	processor()




							def 			A__				(    self):
												lowercase						     =						self.get_image_processor()
												lowercase						     =						self.get_tokenizer()

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

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

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

												self.assertListEqual(A__       ,A__)





							def 			A__				(    self):
												lowercase						     =						self.get_image_processor()
												lowercase						     =						self.get_tokenizer()

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

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

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

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






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__		:Optional[int]			  =   logging.get_logger(__name__)

lowercase__		:List[Any]			  =   {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	List[Any]	 ='''camembert'''




							def __init__(    self       ,A__=3_0_5_2_2       ,A__=7_6_8       ,A__=1_2       ,A__=1_2       ,A__=3_0_7_2       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=5_1_2       ,A__=2       ,A__=0.02       ,A__=1E-12       ,A__=1       ,A__=0       ,A__=2       ,A__="absolute"       ,A__=True       ,A__=None       ,**A__       ,):
												super().__init__(pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,**A__)

												lowercase						     =						vocab_size
												lowercase						     =						hidden_size
												lowercase						     =						num_hidden_layers
												lowercase						     =						num_attention_heads
												lowercase						     =						hidden_act
												lowercase						     =						intermediate_size
												lowercase						     =						hidden_dropout_prob
												lowercase						     =						attention_probs_dropout_prob
												lowercase						     =						max_position_embeddings
												lowercase						     =						type_vocab_size
												lowercase						     =						initializer_range
												lowercase						     =						layer_norm_eps
												lowercase						     =						position_embedding_type
												lowercase						     =						use_cache
												lowercase						     =						classifier_dropout







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							@property
							def 			A__				(    self):
												if self.task == "multiple-choice":
																	lowercase						     =						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	lowercase						     =						{0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ])




