




import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


UpperCamelCase								=					logging.get_logger(__name__)

UpperCamelCase								=					{
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				"detr"
    snake_case__ =				["past_key_values"]
    snake_case__ =				{
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }


    def __init__(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=True       ,     SCREAMING_SNAKE_CASE__					:					List[str]=None       ,     SCREAMING_SNAKE_CASE__					:					int=3       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=100       ,     SCREAMING_SNAKE_CASE__					:					Dict=6       ,     SCREAMING_SNAKE_CASE__					:					Dict=2_048       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=8       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=6       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=2_048       ,     SCREAMING_SNAKE_CASE__					:					Tuple=8       ,     SCREAMING_SNAKE_CASE__					:					str=0.0       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=0.0       ,     SCREAMING_SNAKE_CASE__					:					Dict=True       ,     SCREAMING_SNAKE_CASE__					:					str="relu"       ,     SCREAMING_SNAKE_CASE__					:					int=256       ,     SCREAMING_SNAKE_CASE__					:					int=0.1       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=0.0       ,     SCREAMING_SNAKE_CASE__					:					Dict=0.0       ,     SCREAMING_SNAKE_CASE__					:					str=0.02       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=1.0       ,     SCREAMING_SNAKE_CASE__					:					Tuple=False       ,     SCREAMING_SNAKE_CASE__					:					List[str]="sine"       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]="resnet50"       ,     SCREAMING_SNAKE_CASE__					:					Tuple=True       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=False       ,     SCREAMING_SNAKE_CASE__					:					int=1       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=5       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=2       ,     SCREAMING_SNAKE_CASE__					:					Tuple=1       ,     SCREAMING_SNAKE_CASE__					:					str=1       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=5       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=2       ,     SCREAMING_SNAKE_CASE__					:					Tuple=0.1       ,     **SCREAMING_SNAKE_CASE__					:					List[str]       ,     )				->	str:
         if backbone_config is not None and use_timm_backbone:
              raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."       )

         if not use_timm_backbone:
              if backbone_config is None:
                   logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."       )
                   lowerCAmelCase__          =			CONFIG_MAPPING["resnet"](out_features=["stage4"]       )
              elif isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ):
                   lowerCAmelCase__          =			backbone_config.get("model_type"       )
                   lowerCAmelCase__          =			CONFIG_MAPPING[backbone_model_type]
                   lowerCAmelCase__          =			config_class.from_dict(SCREAMING_SNAKE_CASE__       )
              # set timm attributes to None
              lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			None, None, None

         lowerCAmelCase__          =			use_timm_backbone
         lowerCAmelCase__          =			backbone_config
         lowerCAmelCase__          =			num_channels
         lowerCAmelCase__          =			num_queries
         lowerCAmelCase__          =			d_model
         lowerCAmelCase__          =			encoder_ffn_dim
         lowerCAmelCase__          =			encoder_layers
         lowerCAmelCase__          =			encoder_attention_heads
         lowerCAmelCase__          =			decoder_ffn_dim
         lowerCAmelCase__          =			decoder_layers
         lowerCAmelCase__          =			decoder_attention_heads
         lowerCAmelCase__          =			dropout
         lowerCAmelCase__          =			attention_dropout
         lowerCAmelCase__          =			activation_dropout
         lowerCAmelCase__          =			activation_function
         lowerCAmelCase__          =			init_std
         lowerCAmelCase__          =			init_xavier_std
         lowerCAmelCase__          =			encoder_layerdrop
         lowerCAmelCase__          =			decoder_layerdrop
         lowerCAmelCase__          =			encoder_layers
         lowerCAmelCase__          =			auxiliary_loss
         lowerCAmelCase__          =			position_embedding_type
         lowerCAmelCase__          =			backbone
         lowerCAmelCase__          =			use_pretrained_backbone
         lowerCAmelCase__          =			dilation
         # Hungarian matcher
         lowerCAmelCase__          =			class_cost
         lowerCAmelCase__          =			bbox_cost
         lowerCAmelCase__          =			giou_cost
         # Loss coefficients
         lowerCAmelCase__          =			mask_loss_coefficient
         lowerCAmelCase__          =			dice_loss_coefficient
         lowerCAmelCase__          =			bbox_loss_coefficient
         lowerCAmelCase__          =			giou_loss_coefficient
         lowerCAmelCase__          =			eos_coefficient
         super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    @property
    def        a				(	self					:					Optional[int]       )				->	int:
         return self.encoder_attention_heads


    @property
    def        a				(	self					:					Tuple       )				->	int:
         return self.d_model


    @classmethod
    def        a				(	cls					:					str       ,     SCREAMING_SNAKE_CASE__					:					PretrainedConfig       ,     **SCREAMING_SNAKE_CASE__					:					Any       )				->	Optional[int]:
         return cls(backbone_config=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Dict       )				->	Dict[str, any]:
         lowerCAmelCase__          =			copy.deepcopy(self.__dict__       )
         if output["backbone_config"] is not None:
              lowerCAmelCase__          =			self.backbone_config.to_dict()
         lowerCAmelCase__          =			self.__class__.model_type
         return output






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				version.parse("1.11"   )


    @property
    def        a				(	self					:					Dict       )				->	Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                 ("pixel_mask", {0: "batch"}),
             ]       )


    @property
    def        a				(	self					:					str       )				->	float:
         return 1e-5


    @property
    def        a				(	self					:					str       )				->	int:
         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 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					Dict       )				->	Optional[int]:
         lowerCAmelCase__          =			tempfile.mkdtemp()

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

         lowerCAmelCase__          =			BlipaProcessor(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         processor.save_pretrained(self.tmpdirname       )


    def        a				(	self					:					int       ,     **SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE__       ).tokenizer


    def        a				(	self					:					Tuple       ,     **SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE__       ).image_processor


    def        a				(	self					:					str       )				->	int:
         shutil.rmtree(self.tmpdirname       )


    def        a				(	self					:					List[Any]       )				->	Any:
         lowerCAmelCase__          =			[np.random.randint(255       ,     size=(3, 30, 400)       ,     dtype=np.uinta       )]

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

         return image_inputs


    def        a				(	self					:					str       )				->	Dict:
         lowerCAmelCase__          =			BlipaProcessor(tokenizer=self.get_tokenizer()       ,     image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

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

         lowerCAmelCase__          =			BlipaProcessor.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       ,     SCREAMING_SNAKE_CASE__       )

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


    def        a				(	self					:					int       )				->	str:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			image_processor(SCREAMING_SNAKE_CASE__       ,     return_tensors="np"       )
         lowerCAmelCase__          =			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        a				(	self					:					Tuple       )				->	int:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			tokenizer(SCREAMING_SNAKE_CASE__       ,     return_token_type_ids=SCREAMING_SNAKE_CASE__       )

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


    def        a				(	self					:					Dict       )				->	str:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       ,     images=SCREAMING_SNAKE_CASE__       )

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

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


    def        a				(	self					:					str       )				->	List[str]:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			processor.batch_decode(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__       )

         self.assertListEqual(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[str]       )				->	Any:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       ,     images=SCREAMING_SNAKE_CASE__       )

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