


import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	from transformers.models.roberta.modeling_flax_roberta import (
	    FlaxRobertaForCausalLM,
	    FlaxRobertaForMaskedLM,
	    FlaxRobertaForMultipleChoice,
	    FlaxRobertaForQuestionAnswering,
	    FlaxRobertaForSequenceClassification,
	    FlaxRobertaForTokenClassification,
	    FlaxRobertaModel,
	)




class       lowercase_					(unittest.TestCase   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_=13       ,  lowercase_=7       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=99       ,  lowercase_=32       ,  lowercase_=5       ,  lowercase_=4       ,  lowercase_=37       ,  lowercase_="gelu"       ,  lowercase_=0.1       ,  lowercase_=0.1       ,  lowercase_=512       ,  lowercase_=16       ,  lowercase_=2       ,  lowercase_=0.02       ,  lowercase_=4       ,  )  -> Dict:
											a__      =parent
											a__      =batch_size
											a__      =seq_length
											a__      =is_training
											a__      =use_attention_mask
											a__      =use_token_type_ids
											a__      =use_labels
											a__      =vocab_size
											a__      =hidden_size
											a__      =num_hidden_layers
											a__      =num_attention_heads
											a__      =intermediate_size
											a__      =hidden_act
											a__      =hidden_dropout_prob
											a__      =attention_probs_dropout_prob
											a__      =max_position_embeddings
											a__      =type_vocab_size
											a__      =type_sequence_label_size
											a__      =initializer_range
											a__      =num_choices





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size)

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

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

											a__      =RobertaConfig(
											    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=lowercase_       ,  initializer_range=self.initializer_range       ,  )

											return config, input_ids, token_type_ids, attention_mask





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =self.prepare_config_and_inputs()
											a__    ,     a__    ,     a__    ,     a__      =config_and_inputs
											a__      ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =self.prepare_config_and_inputs()
											a__    ,     a__    ,     a__    ,     a__      =config_and_inputs

											a__      =True
											a__      =floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
											a__      =ids_tensor([self.batch_size, self.seq_length]       ,  vocab_size=2)

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)







@require_flax
class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =True

				snake_case	    =(
				    (
				        FlaxRobertaModel,
				        FlaxRobertaForCausalLM,
				        FlaxRobertaForMaskedLM,
				        FlaxRobertaForSequenceClassification,
				        FlaxRobertaForTokenClassification,
				        FlaxRobertaForMultipleChoice,
				        FlaxRobertaForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =FlaxRobertaModelTester(self)





				@slow
				def 						__UpperCamelCase    (				self)  -> List[Any]:
											for model_class_name in self.all_model_classes:
																		a__      =model_class_name.from_pretrained('roberta-base'       ,  from_pt=lowercase_)
																		a__      =model(np.ones((1, 1)))
																		self.assertIsNotNone(lowercase_)





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class       lowercase_					(unittest.TestCase   ):





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =tempfile.mkdtemp()

											a__      =BlipImageProcessor()
											a__      =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel')

											a__      =BlipProcessor(lowercase_       ,  lowercase_)

											processor.save_pretrained(self.tmpdirname)





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> str:
											return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowercase_).tokenizer





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> List[str]:
											return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowercase_).image_processor





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											shutil.rmtree(self.tmpdirname)





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =[np.random.randint(255       ,  size=(3, 30, 400)       ,  dtype=np.uinta)]

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

											return image_inputs





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =BlipProcessor(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=lowercase_       ,  padding_value=1.0)

											a__      =BlipProcessor.from_pretrained(
											    self.tmpdirname       ,  bos_token='(BOS)'       ,  eos_token='(EOS)'       ,  do_normalize=lowercase_       ,  padding_value=1.0)

											self.assertEqual(processor.tokenizer.get_vocab()       ,  tokenizer_add_kwargs.get_vocab())
											self.assertIsInstance(processor.tokenizer       ,  lowercase_)

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





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      =self.prepare_image_inputs()

											a__      =image_processor(lowercase_       ,  return_tensors='np')
											a__      =processor(images=lowercase_       ,  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 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      ='lower newer'

											a__      =processor(text=lowercase_)

											a__      =tokenizer(lowercase_       ,  return_token_type_ids=lowercase_)

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





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

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

											a__      =processor(text=lowercase_       ,  images=lowercase_)

											self.assertListEqual(list(inputs.keys())       ,  ['pixel_values', 'input_ids', 'attention_mask'])

											# test if it raises when no input is passed
											with pytest.raises(lowercase_):
																		processor()





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

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

											a__      =processor.batch_decode(lowercase_)
											a__      =tokenizer.batch_decode(lowercase_)

											self.assertListEqual(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

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

											a__      =processor(text=lowercase_       ,  images=lowercase_)

											# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
											self.assertListEqual(list(inputs.keys())       ,  ['pixel_values', 'input_ids', 'attention_mask'])

