



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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast



@require_vision
class 						UpperCAmelCase_					( unittest.TestCase):







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					tempfile.mkdtemp()

														_lowerCAmelCase  :							Optional[int]				     =					BlipImageProcessor()
														_lowerCAmelCase  :							Union[str, Any]				     =					GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")

														_lowerCAmelCase  :							str				     =					BlipaProcessor(A_,    A_)

														processor.save_pretrained(self.tmpdirname)







							def      snake_case__   (     self,    **__a):

														'''simple docstring'''

														return AutoProcessor.from_pretrained(self.tmpdirname,    **A_).tokenizer







							def      snake_case__   (     self,    **__a):

														'''simple docstring'''

														return AutoProcessor.from_pretrained(self.tmpdirname,    **A_).image_processor







							def      snake_case__   (     self):

														'''simple docstring'''

														shutil.rmtree(self.tmpdirname)







							def      snake_case__   (     self):

														'''simple docstring'''

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

														_lowerCAmelCase  :							str				     =					[Image.fromarray(np.moveaxis(A_,    0,    -1)) for x in image_inputs]

														return image_inputs







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					BlipaProcessor(tokenizer=self.get_tokenizer(),    image_processor=self.get_image_processor())
														processor.save_pretrained(self.tmpdirname)

														_lowerCAmelCase  :							Optional[Any]				     =					self.get_tokenizer(bos_token="(BOS)",    eos_token="(EOS)")
														_lowerCAmelCase  :							str				     =					self.get_image_processor(do_normalize=A_,    padding_value=1.0)

														_lowerCAmelCase  :							List[str]				     =					BlipaProcessor.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,    A_)

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					self.get_image_processor()
														_lowerCAmelCase  :							Dict				     =					self.get_tokenizer()

														_lowerCAmelCase  :							Optional[Any]				     =					BlipaProcessor(tokenizer=A_,    image_processor=A_)

														_lowerCAmelCase  :							Any				     =					self.prepare_image_inputs()

														_lowerCAmelCase  :							List[Any]				     =					image_processor(A_,    return_tensors="np")
														_lowerCAmelCase  :							Any				     =					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      snake_case__   (     self):

														'''simple docstring'''

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

														_lowerCAmelCase  :							int				     =					BlipaProcessor(tokenizer=A_,    image_processor=A_)

														_lowerCAmelCase  :							Any				     =					"lower newer"

														_lowerCAmelCase  :							int				     =					processor(text=A_)

														_lowerCAmelCase  :							Optional[int]				     =					tokenizer(A_,    return_token_type_ids=A_)

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					self.get_image_processor()
														_lowerCAmelCase  :							List[str]				     =					self.get_tokenizer()

														_lowerCAmelCase  :							int				     =					BlipaProcessor(tokenizer=A_,    image_processor=A_)

														_lowerCAmelCase  :							Union[str, Any]				     =					"lower newer"
														_lowerCAmelCase  :							Optional[Any]				     =					self.prepare_image_inputs()

														_lowerCAmelCase  :							Union[str, Any]				     =					processor(text=A_,    images=A_)

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

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					self.get_image_processor()
														_lowerCAmelCase  :							Any				     =					self.get_tokenizer()

														_lowerCAmelCase  :							Any				     =					BlipaProcessor(tokenizer=A_,    image_processor=A_)

														_lowerCAmelCase  :							Union[str, Any]				     =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														_lowerCAmelCase  :							Optional[int]				     =					processor.batch_decode(A_)
														_lowerCAmelCase  :							Optional[Any]				     =					tokenizer.batch_decode(A_)

														self.assertListEqual(A_,    A_)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					self.get_image_processor()
														_lowerCAmelCase  :							Dict				     =					self.get_tokenizer()

														_lowerCAmelCase  :							List[Any]				     =					BlipaProcessor(tokenizer=A_,    image_processor=A_)

														_lowerCAmelCase  :							Tuple				     =					"lower newer"
														_lowerCAmelCase  :							Union[str, Any]				     =					self.prepare_image_inputs()

														_lowerCAmelCase  :							str				     =					processor(text=A_,    images=A_)

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'roc_bert'







							def __init__(     self,    __a=3_0522,    __a=768,    __a=12,    __a=12,    __a=3072,    __a="gelu",    __a=0.1,    __a=0.1,    __a=512,    __a=2,    __a=0.02,    __a=1E-12,    __a=True,    __a=0,    __a="absolute",    __a=None,    __a=True,    __a=True,    __a=768,    __a=910,    __a=512,    __a=2_4858,    __a=True,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					vocab_size
														_lowerCAmelCase  :							Optional[Any]				     =					max_position_embeddings
														_lowerCAmelCase  :							Union[str, Any]				     =					hidden_size
														_lowerCAmelCase  :							str				     =					num_hidden_layers
														_lowerCAmelCase  :							List[Any]				     =					num_attention_heads
														_lowerCAmelCase  :							int				     =					intermediate_size
														_lowerCAmelCase  :							Optional[int]				     =					hidden_act
														_lowerCAmelCase  :							Optional[Any]				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Optional[Any]				     =					attention_probs_dropout_prob
														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							Optional[Any]				     =					type_vocab_size
														_lowerCAmelCase  :							int				     =					layer_norm_eps
														_lowerCAmelCase  :							Union[str, Any]				     =					use_cache
														_lowerCAmelCase  :							Optional[int]				     =					enable_pronunciation
														_lowerCAmelCase  :							Dict				     =					enable_shape
														_lowerCAmelCase  :							Optional[Any]				     =					pronunciation_embed_dim
														_lowerCAmelCase  :							Any				     =					pronunciation_vocab_size
														_lowerCAmelCase  :							List[str]				     =					shape_embed_dim
														_lowerCAmelCase  :							int				     =					shape_vocab_size
														_lowerCAmelCase  :							Optional[int]				     =					concat_input
														_lowerCAmelCase  :							Dict				     =					position_embedding_type
														_lowerCAmelCase  :							Tuple				     =					classifier_dropout
														super().__init__(pad_token_id=__a,    **__a)
