

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
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						       =	{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     List[str]		     =      "table-transformer"
     __snake_case   :     Any		     =      ["past_key_values"]
     __snake_case   :     Optional[Any]		     =      {
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(							self						:   List[str]						,lowerCamelCase__						:   Optional[int]=True						,lowerCamelCase__						:   List[str]=None						,lowerCamelCase__						:   Optional[int]=3						,lowerCamelCase__						:   Optional[int]=100						,lowerCamelCase__						:   Optional[int]=6						,lowerCamelCase__						:   Optional[int]=2048						,lowerCamelCase__						:   List[str]=8						,lowerCamelCase__						:   List[str]=6						,lowerCamelCase__						:   Dict=2048						,lowerCamelCase__						:   int=8						,lowerCamelCase__						:   Dict=0.0						,lowerCamelCase__						:   Optional[Any]=0.0						,lowerCamelCase__						:   int=True						,lowerCamelCase__						:   Optional[Any]="relu"						,lowerCamelCase__						:   int=256						,lowerCamelCase__						:   List[str]=0.1						,lowerCamelCase__						:   int=0.0						,lowerCamelCase__						:   int=0.0						,lowerCamelCase__						:   List[str]=0.02						,lowerCamelCase__						:   int=1.0						,lowerCamelCase__						:   str=False						,lowerCamelCase__						:   Any="sine"						,lowerCamelCase__						:   Union[str, Any]="resnet50"						,lowerCamelCase__						:   Any=True						,lowerCamelCase__						:   Union[str, Any]=False						,lowerCamelCase__						:   Optional[int]=1						,lowerCamelCase__						:   Tuple=5						,lowerCamelCase__						:   Optional[Any]=2						,lowerCamelCase__						:   int=1						,lowerCamelCase__						:   int=1						,lowerCamelCase__						:   Union[str, Any]=5						,lowerCamelCase__						:   Optional[int]=2						,lowerCamelCase__						:   Optional[int]=0.1						,**lowerCamelCase__						:   List[Any]						,)					->		Any:




           '''simple docstring'''


           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."""	)
                       SCREAMING_SNAKE_CASE		=						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]	)
                 elif isinstance(lowerCamelCase__						,lowerCamelCase__	):
                       SCREAMING_SNAKE_CASE		=						backbone_config.get("""model_type"""	)
                       SCREAMING_SNAKE_CASE		=						CONFIG_MAPPING[backbone_model_type]
                       SCREAMING_SNAKE_CASE		=						config_class.from_dict(lowerCamelCase__	)
                 # set timm attributes to None
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						None, None, None

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






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		int:




           '''simple docstring'''


           return self.encoder_attention_heads






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		int:




           '''simple docstring'''


           return self.d_model

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     int		     =      version.parse("1.11"       )






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		Mapping[str, Mapping[int, str]]:




           '''simple docstring'''


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






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		float:




           '''simple docstring'''


           return 1e-5






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		int:




           '''simple docstring'''


           return 12









import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import DPTImageProcessor

class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def __init__(							self						:   Optional[Any]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Optional[Any]=7						,lowerCamelCase__						:   Optional[Any]=3						,lowerCamelCase__						:   Union[str, Any]=18						,lowerCamelCase__						:   Optional[int]=30						,lowerCamelCase__						:   int=400						,lowerCamelCase__						:   List[str]=True						,lowerCamelCase__						:   Union[str, Any]=None						,lowerCamelCase__						:   Union[str, Any]=True						,lowerCamelCase__						:   Optional[int]=[0.5, 0.5, 0.5]						,lowerCamelCase__						:   Any=[0.5, 0.5, 0.5]						,)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						size if size is not None else {"""height""": 18, """width""": 18}
           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						min_resolution
           SCREAMING_SNAKE_CASE		=						max_resolution
           SCREAMING_SNAKE_CASE		=						do_resize
           SCREAMING_SNAKE_CASE		=						size
           SCREAMING_SNAKE_CASE		=						do_normalize
           SCREAMING_SNAKE_CASE		=						image_mean
           SCREAMING_SNAKE_CASE		=						image_std






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Union[str, Any]:




           '''simple docstring'''


           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
           }

@require_torch
@require_vision
class 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     Tuple		     =      DPTImageProcessor if is_vision_available() else None






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						DPTImageProcessingTester(self	)






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		int:




           '''simple docstring'''


           return self.image_processor_tester.prepare_image_processor_dict()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict	)
           self.assertTrue(hasattr(lowerCamelCase__						,"""image_mean"""	)	)
           self.assertTrue(hasattr(lowerCamelCase__						,"""image_std"""	)	)
           self.assertTrue(hasattr(lowerCamelCase__						,"""do_normalize"""	)	)
           self.assertTrue(hasattr(lowerCamelCase__						,"""do_resize"""	)	)
           self.assertTrue(hasattr(lowerCamelCase__						,"""size"""	)	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class.from_dict(self.image_processor_dict	)
           self.assertEqual(image_processor.size						,{"""height""": 18, """width""": 18}	)

           SCREAMING_SNAKE_CASE		=						self.image_processing_class.from_dict(self.image_processor_dict						,size=42	)
           self.assertEqual(image_processor.size						,{"""height""": 42, """width""": 42}	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict	)
           # create random PIL images
           SCREAMING_SNAKE_CASE		=						prepare_image_inputs(self.image_processor_tester						,equal_resolution=lowerCamelCase__	)
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__						,Image.Image	)

           # Test not batched input
           SCREAMING_SNAKE_CASE		=						image_processing(image_inputs[0]						,return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)

           # Test batched
           SCREAMING_SNAKE_CASE		=						image_processing(lowerCamelCase__						,return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict	)
           # create random numpy tensors
           SCREAMING_SNAKE_CASE		=						prepare_image_inputs(self.image_processor_tester						,equal_resolution=lowerCamelCase__						,numpify=lowerCamelCase__	)
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__						,np.ndarray	)

           # Test not batched input
           SCREAMING_SNAKE_CASE		=						image_processing(image_inputs[0]						,return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)

           # Test batched
           SCREAMING_SNAKE_CASE		=						image_processing(lowerCamelCase__						,return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict	)
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE		=						prepare_image_inputs(self.image_processor_tester						,equal_resolution=lowerCamelCase__						,torchify=lowerCamelCase__	)
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__						,torch.Tensor	)

           # Test not batched input
           SCREAMING_SNAKE_CASE		=						image_processing(image_inputs[0]						,return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)

           # Test batched
           SCREAMING_SNAKE_CASE		=						image_processing(lowerCamelCase__						,return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_images.shape						,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)


