




from collections import OrderedDict
from typing import Mapping

from packaging import version

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


UpperCAmelCase__				     = logging.get_logger(__name__)

UpperCAmelCase__				     = {
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       Any       	=		'yolos'






						def __init__( self :     Dict				,							lowerCamelCase__ :     str=768				,							lowerCamelCase__ :     List[str]=12				,							lowerCamelCase__ :     Union[str, Any]=12				,							lowerCamelCase__ :     str=3_072				,							lowerCamelCase__ :     Any="gelu"				,							lowerCamelCase__ :     List[str]=0.0				,							lowerCamelCase__ :     List[Any]=0.0				,							lowerCamelCase__ :     Dict=0.0_2				,							lowerCamelCase__ :     Optional[int]=1e-1_2				,							lowerCamelCase__ :     Any=[512, 864]				,							lowerCamelCase__ :     Optional[Any]=16				,							lowerCamelCase__ :     Union[str, Any]=3				,							lowerCamelCase__ :     Any=True				,							lowerCamelCase__ :     str=100				,							lowerCamelCase__ :     Tuple=True				,							lowerCamelCase__ :     Optional[int]=False				,							lowerCamelCase__ :     Dict=1				,							lowerCamelCase__ :     List[Any]=5				,							lowerCamelCase__ :     int=2				,							lowerCamelCase__ :     int=5				,							lowerCamelCase__ :     Dict=2				,							lowerCamelCase__ :     Optional[Any]=0.1				,							**lowerCamelCase__ :     str				,							)    ->						Optional[Any]:
													"""simple docstring"""


													super().__init__(**lowerCamelCase__				)

													__lowercase      =    hidden_size
													__lowercase      =    num_hidden_layers
													__lowercase      =    num_attention_heads
													__lowercase      =    intermediate_size
													__lowercase      =    hidden_act
													__lowercase      =    hidden_dropout_prob
													__lowercase      =    attention_probs_dropout_prob
													__lowercase      =    initializer_range
													__lowercase      =    layer_norm_eps
													__lowercase      =    image_size
													__lowercase      =    patch_size
													__lowercase      =    num_channels
													__lowercase      =    qkv_bias
													__lowercase      =    num_detection_tokens
													__lowercase      =    use_mid_position_embeddings
													__lowercase      =    auxiliary_loss
													# Hungarian matcher
													__lowercase      =    class_cost
													__lowercase      =    bbox_cost
													__lowercase      =    giou_cost
													# Loss coefficients
													__lowercase      =    bbox_loss_coefficient
													__lowercase      =    giou_loss_coefficient
													__lowercase      =    eos_coefficient







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       Dict       	=		version.parse('1.11'  )






						@property
						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						Mapping[str, Mapping[int, str]]:
													"""simple docstring"""


													return OrderedDict(
													    [
													        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
													    ]				)






						@property
						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						float:
													"""simple docstring"""


													return 1e-4






						@property
						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						int:
													"""simple docstring"""


													return 12












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 a (     unittest.TestCase  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    tempfile.mkdtemp()

													__lowercase      =    BlipImageProcessor()
													__lowercase      =    GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''				)

													__lowercase      =    BlipaProcessor(lowerCamelCase__				,							lowerCamelCase__				)

													processor.save_pretrained(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							**lowerCamelCase__ :     Any				)    ->						str:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Dict				,							**lowerCamelCase__ :     List[str]				)    ->						Dict:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						str:
													"""simple docstring"""


													shutil.rmtree(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     Any				)    ->						Any:
													"""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     UpperCAmelCase_      ( self :     List[Any]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    BlipaProcessor(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=lowerCamelCase__				,							padding_value=1.0				)

													__lowercase      =    BlipaProcessor.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__				)






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=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     UpperCAmelCase_      ( self :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    '''lower newer'''

													__lowercase      =    processor(text=lowerCamelCase__				)

													__lowercase      =    tokenizer(lowerCamelCase__				,							return_token_type_ids=lowerCamelCase__				)

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






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

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

													__lowercase      =    processor(text=lowerCamelCase__				,							images=lowerCamelCase__				)

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

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






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=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     UpperCAmelCase_      ( self :     List[str]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

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

													__lowercase      =    processor(text=lowerCamelCase__				,							images=lowerCamelCase__				)

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






