




'''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      lowerCAmelCase (			unittest.TestCase):


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	tempfile.mkdtemp()

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

            __snake_case            =	{
                '''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],
            }
            __snake_case            =	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     ,	**__SCREAMING_SNAKE_CASE	)   ->	int:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            shutil.rmtree(self.tmpdirname	)


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




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

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

            return image_inputs


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_image_processor()

            __snake_case            =	VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

            processor.save_pretrained(self.tmpdirname	)
            __snake_case            =	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	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	VisionTextDualEncoderProcessor(
                tokenizer=self.get_tokenizer()     ,	image_processor=self.get_image_processor()	)
            processor.save_pretrained(self.tmpdirname	)

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

            __snake_case            =	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	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.prepare_image_inputs()

            __snake_case            =	image_processor(__SCREAMING_SNAKE_CASE     ,	return_tensors='''np'''	)
            __snake_case            =	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[str]:



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	'''lower newer'''

            __snake_case            =	processor(text=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	tokenizer(__SCREAMING_SNAKE_CASE	)

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


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



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

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

            __snake_case            =	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	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

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

            __snake_case            =	processor.batch_decode(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	)

            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)




       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

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

            __snake_case            =	processor(text=__SCREAMING_SNAKE_CASE     ,	images=__SCREAMING_SNAKE_CASE	)

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












'''simple docstring'''



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.roformer.modeling_flax_roformer import (
         FlaxRoFormerForMaskedLM,
         FlaxRoFormerForMultipleChoice,
         FlaxRoFormerForQuestionAnswering,
         FlaxRoFormerForSequenceClassification,
         FlaxRoFormerForTokenClassification,
         FlaxRoFormerModel,
     )





class      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=13     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=99     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=5     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=37     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=512     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=4     ,	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	seq_length
            __snake_case            =	is_training
            __snake_case            =	use_attention_mask
            __snake_case            =	use_token_type_ids
            __snake_case            =	use_labels
            __snake_case            =	vocab_size
            __snake_case            =	hidden_size
            __snake_case            =	num_hidden_layers
            __snake_case            =	num_attention_heads
            __snake_case            =	intermediate_size
            __snake_case            =	hidden_act
            __snake_case            =	hidden_dropout_prob
            __snake_case            =	attention_probs_dropout_prob
            __snake_case            =	max_position_embeddings
            __snake_case            =	type_vocab_size
            __snake_case            =	type_sequence_label_size
            __snake_case            =	initializer_range
            __snake_case            =	num_choices


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



            '''simple docstring'''




            __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size	)

            __snake_case            =	None
            if self.use_attention_mask:
                 __snake_case            =	random_attention_mask([self.batch_size, self.seq_length]	)

            __snake_case            =	None
            if self.use_token_type_ids:
                 __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.type_vocab_size	)

            __snake_case            =	RoFormerConfig(
                vocab_size=self.vocab_size     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	max_position_embeddings=self.max_position_embeddings     ,	type_vocab_size=self.type_vocab_size     ,	is_decoder=__SCREAMING_SNAKE_CASE     ,	initializer_range=self.initializer_range     ,	)

            return config, input_ids, token_type_ids, attention_mask




       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.prepare_config_and_inputs()
            __snake_case       ,     __snake_case       ,     __snake_case       ,     __snake_case            =	config_and_inputs
            __snake_case            =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
            return config, inputs_dict





@require_flax
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Tuple										=      True

       __lowercase	:   Optional[int]										=      (
           (
               FlaxRoFormerModel,
               FlaxRoFormerForMaskedLM,
               FlaxRoFormerForSequenceClassification,
               FlaxRoFormerForTokenClassification,
               FlaxRoFormerForMultipleChoice,
               FlaxRoFormerForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerModelTester(self	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 __snake_case            =	model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''     ,	from_pt=__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model(np.ones((1, 1)	)	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)





@require_flax
class      lowerCAmelCase (			unittest.TestCase):


       @slow
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''	)
            __snake_case            =	jnp.array([[0, 1, 2, 3, 4, 5]]	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)[0]

            __snake_case            =	5_0000

            __snake_case            =	(1, 6, vocab_size)
            self.assertEqual(output.shape     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	jnp.array(
                [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]	)

            self.assertTrue(jnp.allclose(output[:, :3, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-4	)	)






