
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


_snake_case      = logging.get_logger(__name__)

_snake_case      = {
    '''microsoft/table-transformer-detection''': (
        '''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
    ),
}
class       _lowerCAmelCase  (		__A							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[str]       						="table-transformer"
 SCREAMING_SNAKE_CASE_				:					List[Any]       						=["past_key_values"]
 SCREAMING_SNAKE_CASE_				:					Any       						={
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
 }

 def __init__(						self			:			Any					, SCREAMING_SNAKE_CASE__			:			Dict=True					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=None					, SCREAMING_SNAKE_CASE__			:			Dict=3					, SCREAMING_SNAKE_CASE__			:			List[str]=1_00					, SCREAMING_SNAKE_CASE__			:			Optional[int]=6					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=20_48					, SCREAMING_SNAKE_CASE__			:			int=8					, SCREAMING_SNAKE_CASE__			:			str=6					, SCREAMING_SNAKE_CASE__			:			Any=20_48					, SCREAMING_SNAKE_CASE__			:			Any=8					, SCREAMING_SNAKE_CASE__			:			List[str]=0.0					, SCREAMING_SNAKE_CASE__			:			int=0.0					, SCREAMING_SNAKE_CASE__			:			List[str]=True					, SCREAMING_SNAKE_CASE__			:			Optional[int]="relu"					, SCREAMING_SNAKE_CASE__			:			Tuple=2_56					, SCREAMING_SNAKE_CASE__			:			int=0.1					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=0.0					, SCREAMING_SNAKE_CASE__			:			Dict=0.0					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=0.02					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=1.0					, SCREAMING_SNAKE_CASE__			:			Optional[int]=False					, SCREAMING_SNAKE_CASE__			:			str="sine"					, SCREAMING_SNAKE_CASE__			:			Tuple="resnet50"					, SCREAMING_SNAKE_CASE__			:			Optional[int]=True					, SCREAMING_SNAKE_CASE__			:			Optional[int]=False					, SCREAMING_SNAKE_CASE__			:			Any=1					, SCREAMING_SNAKE_CASE__			:			List[str]=5					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=2					, SCREAMING_SNAKE_CASE__			:			Dict=1					, SCREAMING_SNAKE_CASE__			:			Tuple=1					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=5					, SCREAMING_SNAKE_CASE__			:			List[str]=2					, SCREAMING_SNAKE_CASE__			:			List[str]=0.1					, **SCREAMING_SNAKE_CASE__			:			List[str]					, ):






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

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

 @property
 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      return self.encoder_attention_heads






 @property
 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      return self.d_model




class       _lowerCAmelCase  (		__A							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[str]       						=version.parse("1.11"							)

 @property
 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



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

 @property
 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      return 1e-5






 @property
 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      return 12






import unittest

import numpy as np

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

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 ViTImageProcessor


class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A=13  ,       __A=3  ,       __A=224  ,       __A=30  ,       __A=400  ,       __A=True  ,       __A=None  ,       __A=True  ,       __A=[0.5, 0.5, 0.5]  ,       __A=[0.5, 0.5, 0.5]  ,       ):
        __a					=  size if size is not None else {"""height""": 18, """width""": 18}
        __a					=  parent
        __a					=  batch_size
        __a					=  num_channels
        __a					=  image_size
        __a					=  min_resolution
        __a					=  max_resolution
        __a					=  do_resize
        __a					=  size
        __a					=  do_normalize
        __a					=  image_mean
        __a					=  image_std


     def 	snake_case_  (	self    ):
        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							(       __A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      ViTImageProcessor if is_vision_available() else None


     def 	snake_case_  (	self    ):
        __a					=  EfficientFormerImageProcessorTester(self    )


     @property
     def 	snake_case_  (	self    ):
        return self.image_proc_tester.prepare_image_processor_dict()


     def 	snake_case_  (	self    ):
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(__A  ,       """image_mean"""    )    )
        self.assertTrue(hasattr(__A  ,       """image_std"""    )    )
        self.assertTrue(hasattr(__A  ,       """do_normalize"""    )    )
        self.assertTrue(hasattr(__A  ,       """do_resize"""    )    )
        self.assertTrue(hasattr(__A  ,       """size"""    )    )


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       Image.Image    )

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

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A  ,       numpify=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       np.ndarray    )

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

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A  ,       torchify=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       torch.Tensor    )

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

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )



