



'''simple docstring'''


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,
						    BertTokenizerFast,
						    BlipImageProcessor,
						    GPTaTokenizer,
						    InstructBlipProcessor,
						    PreTrainedTokenizerFast,
						)




@require_vision
class      __UpperCamelCase       (unittest.TestCase			):







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							tempfile.mkdtemp()

							lowercase        =							BlipImageProcessor()
							lowercase        =							GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""						)
							lowercase        =							BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""						)

							lowercase        =							InstructBlipProcessor(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)

							processor.save_pretrained(self.tmpdirname						)







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    Optional[int]:
							'''simple docstring'''






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







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    Any:
							'''simple docstring'''






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







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    Dict:
							'''simple docstring'''






							return AutoProcessor.from_pretrained(self.tmpdirname ,				**_lowerCAmelCase						).qformer_tokenizer







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							shutil.rmtree(self.tmpdirname						)







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






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

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

							return image_inputs







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							InstructBlipProcessor(
							    tokenizer=self.get_tokenizer() ,				image_processor=self.get_image_processor() ,				qformer_tokenizer=self.get_qformer_tokenizer() ,				)
							processor.save_pretrained(self.tmpdirname						)

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

							lowercase        =							InstructBlipProcessor.from_pretrained(
							    self.tmpdirname ,				bos_token="""(BOS)""" ,				eos_token="""(EOS)""" ,				do_normalize=_lowerCAmelCase ,				padding_value=1.0						)

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

							self.assertEqual(processor.image_processor.to_json_string() ,				image_processor_add_kwargs.to_json_string()						)
							self.assertIsInstance(processor.image_processor ,				_lowerCAmelCase						)
							self.assertIsInstance(processor.qformer_tokenizer ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase        =							self.get_image_processor()
							lowercase        =							self.get_tokenizer()
							lowercase        =							self.get_qformer_tokenizer()

							lowercase        =							InstructBlipProcessor(
							    tokenizer=_lowerCAmelCase ,				image_processor=_lowerCAmelCase ,				qformer_tokenizer=_lowerCAmelCase						)

							lowercase        =							self.prepare_image_inputs()

							lowercase        =							image_processor(_lowerCAmelCase ,				return_tensors="""np"""						)
							lowercase        =							processor(images=_lowerCAmelCase ,				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						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							self.get_image_processor()
							lowercase        =							self.get_tokenizer()
							lowercase        =							self.get_qformer_tokenizer()

							lowercase        =							InstructBlipProcessor(
							    tokenizer=_lowerCAmelCase ,				image_processor=_lowerCAmelCase ,				qformer_tokenizer=_lowerCAmelCase						)

							lowercase        =							"""lower newer"""

							lowercase        =							processor(text=_lowerCAmelCase						)

							lowercase        =							tokenizer(_lowerCAmelCase ,				return_token_type_ids=_lowerCAmelCase						)
							lowercase        =							qformer_tokenizer(_lowerCAmelCase ,				return_token_type_ids=_lowerCAmelCase						)

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

							for key in encoded_tokens_qformer.keys():
													self.assertListEqual(encoded_tokens_qformer[key] ,				encoded_processor["""qformer_""" + key]						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.get_image_processor()
							lowercase        =							self.get_tokenizer()
							lowercase        =							self.get_qformer_tokenizer()

							lowercase        =							InstructBlipProcessor(
							    tokenizer=_lowerCAmelCase ,				image_processor=_lowerCAmelCase ,				qformer_tokenizer=_lowerCAmelCase						)

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

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

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

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







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							self.get_image_processor()
							lowercase        =							self.get_tokenizer()
							lowercase        =							self.get_qformer_tokenizer()

							lowercase        =							InstructBlipProcessor(
							    tokenizer=_lowerCAmelCase ,				image_processor=_lowerCAmelCase ,				qformer_tokenizer=_lowerCAmelCase						)

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

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

							self.assertListEqual(_lowerCAmelCase ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							self.get_image_processor()
							lowercase        =							self.get_tokenizer()
							lowercase        =							self.get_qformer_tokenizer()

							lowercase        =							InstructBlipProcessor(
							    tokenizer=_lowerCAmelCase ,				image_processor=_lowerCAmelCase ,				qformer_tokenizer=_lowerCAmelCase						)

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

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

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







'''simple docstring'''


from ... import PretrainedConfig


lowercase_      :				int   					=    {
    '''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A			  =					'''nezha'''







	def __init__(    self ,				_lowerCAmelCase=2_1128 ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=512 ,				_lowerCAmelCase=64 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-12 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    int:
							'''simple docstring'''






							super().__init__(pad_token_id=_lowerCAmelCase ,				bos_token_id=_lowerCAmelCase ,				eos_token_id=_lowerCAmelCase ,				**_lowerCAmelCase						)

							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        =							max_relative_position
							lowercase        =							type_vocab_size
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							classifier_dropout
							lowercase        =							use_cache


