




import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
			import jax.numpy as jnp

if is_tf_available():
			import tensorflow as tf

if is_torch_available():
			import torch




class       a__	( unittest.TestCase				):







					def       lowerCAmelCase_    (     self			)  ->   Tuple:



										'''simple docstring'''



										a     =				{
										    "task_specific_params": {
										        "summarization": {"length_penalty": 1.0, "max_length": 128, "min_length": 12, "num_beams": 4},
										        "summarization_cnn": {"length_penalty": 2.0, "max_length": 142, "min_length": 56, "num_beams": 4},
										        "summarization_xsum": {"length_penalty": 1.0, "max_length": 62, "min_length": 11, "num_beams": 6},
										    }
										}
										a     =				{
										    "task_specific_params.summarization.length_penalty": 1.0,
										    "task_specific_params.summarization.max_length": 128,
										    "task_specific_params.summarization.min_length": 12,
										    "task_specific_params.summarization.num_beams": 4,
										    "task_specific_params.summarization_cnn.length_penalty": 2.0,
										    "task_specific_params.summarization_cnn.max_length": 142,
										    "task_specific_params.summarization_cnn.min_length": 56,
										    "task_specific_params.summarization_cnn.num_beams": 4,
										    "task_specific_params.summarization_xsum.length_penalty": 1.0,
										    "task_specific_params.summarization_xsum.max_length": 62,
										    "task_specific_params.summarization_xsum.min_length": 11,
										    "task_specific_params.summarization_xsum.num_beams": 6,
										}

										self.assertEqual(flatten_dict(A			)  ,		A			)







					def       lowerCAmelCase_    (     self			)  ->   Dict:



										'''simple docstring'''



										a     =				np.random.randn(3  ,		4			)
										self.assertTrue(np.allclose(transpose(A			)  ,		x.transpose()			)			)

										a     =				np.random.randn(3  ,		4  ,		5			)
										self.assertTrue(np.allclose(transpose(A  ,		axes=(1, 2, 0)			)  ,		x.transpose((1, 2, 0)			)			)			)







					@require_torch
					def       lowerCAmelCase_    (     self			)  ->   int:



										'''simple docstring'''



										a     =				np.random.randn(3  ,		4			)
										a     =				torch.tensor(A			)
										self.assertTrue(np.allclose(transpose(A			)  ,		transpose(A			).numpy()			)			)

										a     =				np.random.randn(3  ,		4  ,		5			)
										a     =				torch.tensor(A			)
										self.assertTrue(np.allclose(transpose(A  ,		axes=(1, 2, 0)			)  ,		transpose(A  ,		axes=(1, 2, 0)			).numpy()			)			)







					@require_tf
					def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



										'''simple docstring'''



										a     =				np.random.randn(3  ,		4			)
										a     =				tf.constant(A			)
										self.assertTrue(np.allclose(transpose(A			)  ,		transpose(A			).numpy()			)			)

										a     =				np.random.randn(3  ,		4  ,		5			)
										a     =				tf.constant(A			)
										self.assertTrue(np.allclose(transpose(A  ,		axes=(1, 2, 0)			)  ,		transpose(A  ,		axes=(1, 2, 0)			).numpy()			)			)







					@require_flax
					def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



										'''simple docstring'''



										a     =				np.random.randn(3  ,		4			)
										a     =				jnp.array(A			)
										self.assertTrue(np.allclose(transpose(A			)  ,		np.asarray(transpose(A			)			)			)			)

										a     =				np.random.randn(3  ,		4  ,		5			)
										a     =				jnp.array(A			)
										self.assertTrue(np.allclose(transpose(A  ,		axes=(1, 2, 0)			)  ,		np.asarray(transpose(A  ,		axes=(1, 2, 0)			)			)			)			)







					def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



										'''simple docstring'''



										a     =				np.random.randn(3  ,		4			)
										self.assertTrue(np.allclose(reshape(A  ,		(4, 3)			)  ,		np.reshape(A  ,		(4, 3)			)			)			)

										a     =				np.random.randn(3  ,		4  ,		5			)
										self.assertTrue(np.allclose(reshape(A  ,		(12, 5)			)  ,		np.reshape(A  ,		(12, 5)			)			)			)







					@require_torch
					def       lowerCAmelCase_    (     self			)  ->   List[Any]:



										'''simple docstring'''



										a     =				np.random.randn(3  ,		4			)
										a     =				torch.tensor(A			)
										self.assertTrue(np.allclose(reshape(A  ,		(4, 3)			)  ,		reshape(A  ,		(4, 3)			).numpy()			)			)

										a     =				np.random.randn(3  ,		4  ,		5			)
										a     =				torch.tensor(A			)
										self.assertTrue(np.allclose(reshape(A  ,		(12, 5)			)  ,		reshape(A  ,		(12, 5)			).numpy()			)			)







					@require_tf
					def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



										'''simple docstring'''



										a     =				np.random.randn(3  ,		4			)
										a     =				tf.constant(A			)
										self.assertTrue(np.allclose(reshape(A  ,		(4, 3)			)  ,		reshape(A  ,		(4, 3)			).numpy()			)			)

										a     =				np.random.randn(3  ,		4  ,		5			)
										a     =				tf.constant(A			)
										self.assertTrue(np.allclose(reshape(A  ,		(12, 5)			)  ,		reshape(A  ,		(12, 5)			).numpy()			)			)







					@require_flax
					def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



										'''simple docstring'''



										a     =				np.random.randn(3  ,		4			)
										a     =				jnp.array(A			)
										self.assertTrue(np.allclose(reshape(A  ,		(4, 3)			)  ,		np.asarray(reshape(A  ,		(4, 3)			)			)			)			)

										a     =				np.random.randn(3  ,		4  ,		5			)
										a     =				jnp.array(A			)
										self.assertTrue(np.allclose(reshape(A  ,		(12, 5)			)  ,		np.asarray(reshape(A  ,		(12, 5)			)			)			)			)







					def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



										'''simple docstring'''



										a     =				np.random.randn(1  ,		3  ,		4			)
										self.assertTrue(np.allclose(squeeze(A			)  ,		np.squeeze(A			)			)			)

										a     =				np.random.randn(1  ,		4  ,		1  ,		5			)
										self.assertTrue(np.allclose(squeeze(A  ,		axis=2			)  ,		np.squeeze(A  ,		axis=2			)			)			)







					@require_torch
					def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



										'''simple docstring'''



										a     =				np.random.randn(1  ,		3  ,		4			)
										a     =				torch.tensor(A			)
										self.assertTrue(np.allclose(squeeze(A			)  ,		squeeze(A			).numpy()			)			)

										a     =				np.random.randn(1  ,		4  ,		1  ,		5			)
										a     =				torch.tensor(A			)
										self.assertTrue(np.allclose(squeeze(A  ,		axis=2			)  ,		squeeze(A  ,		axis=2			).numpy()			)			)







					@require_tf
					def       lowerCAmelCase_    (     self			)  ->   str:



										'''simple docstring'''



										a     =				np.random.randn(1  ,		3  ,		4			)
										a     =				tf.constant(A			)
										self.assertTrue(np.allclose(squeeze(A			)  ,		squeeze(A			).numpy()			)			)

										a     =				np.random.randn(1  ,		4  ,		1  ,		5			)
										a     =				tf.constant(A			)
										self.assertTrue(np.allclose(squeeze(A  ,		axis=2			)  ,		squeeze(A  ,		axis=2			).numpy()			)			)







					@require_flax
					def       lowerCAmelCase_    (     self			)  ->   Tuple:



										'''simple docstring'''



										a     =				np.random.randn(1  ,		3  ,		4			)
										a     =				jnp.array(A			)
										self.assertTrue(np.allclose(squeeze(A			)  ,		np.asarray(squeeze(A			)			)			)			)

										a     =				np.random.randn(1  ,		4  ,		1  ,		5			)
										a     =				jnp.array(A			)
										self.assertTrue(np.allclose(squeeze(A  ,		axis=2			)  ,		np.asarray(squeeze(A  ,		axis=2			)			)			)			)







					def       lowerCAmelCase_    (     self			)  ->   Dict:



										'''simple docstring'''



										a     =				np.random.randn(3  ,		4			)
										self.assertTrue(np.allclose(expand_dims(A  ,		axis=1			)  ,		np.expand_dims(A  ,		axis=1			)			)			)







					@require_torch
					def       lowerCAmelCase_    (     self			)  ->   Any:



										'''simple docstring'''



										a     =				np.random.randn(3  ,		4			)
										a     =				torch.tensor(A			)
										self.assertTrue(np.allclose(expand_dims(A  ,		axis=1			)  ,		expand_dims(A  ,		axis=1			).numpy()			)			)







					@require_tf
					def       lowerCAmelCase_    (     self			)  ->   str:



										'''simple docstring'''



										a     =				np.random.randn(3  ,		4			)
										a     =				tf.constant(A			)
										self.assertTrue(np.allclose(expand_dims(A  ,		axis=1			)  ,		expand_dims(A  ,		axis=1			).numpy()			)			)







					@require_flax
					def       lowerCAmelCase_    (     self			)  ->   Dict:



										'''simple docstring'''



										a     =				np.random.randn(3  ,		4			)
										a     =				jnp.array(A			)
										self.assertTrue(np.allclose(expand_dims(A  ,		axis=1			)  ,		np.asarray(expand_dims(A  ,		axis=1			)			)			)			)







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


lowercase__				:      Optional[Any]	   = logging.get_logger(__name__)

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




class       a__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     """table-transformer"""
     a		:      Tuple		 =     ["""past_key_values"""]
     a		:      int		 =     {
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }







     def __init__(     self  ,		A=True  ,		A=None  ,		A=3  ,		A=100  ,		A=6  ,		A=2048  ,		A=8  ,		A=6  ,		A=2048  ,		A=8  ,		A=0.0  ,		A=0.0  ,		A=True  ,		A="relu"  ,		A=256  ,		A=0.1  ,		A=0.0  ,		A=0.0  ,		A=0.0_2  ,		A=1.0  ,		A=False  ,		A="sine"  ,		A="resnet50"  ,		A=True  ,		A=False  ,		A=1  ,		A=5  ,		A=2  ,		A=1  ,		A=1  ,		A=5  ,		A=2  ,		A=0.1  ,		**A  ,		)  ->   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."			)
                    a     =				CONFIG_MAPPING["resnet"](out_features=["stage4"]			)
               elif isinstance(A  ,		A			):
                    a     =				backbone_config.get("model_type"			)
                    a     =				CONFIG_MAPPING[backbone_model_type]
                    a     =				config_class.from_dict(A			)
               # set timm attributes to None
               a  ,				a  ,				a     =				None, None, None

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







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.encoder_attention_heads







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.d_model







class       a__	( UpperCamelCase__				):
     a		:      Any		 =     version.parse("""1.11"""				)







     @property
     def       lowerCAmelCase_    (     self			)  ->   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       lowerCAmelCase_    (     self			)  ->   float:



          '''simple docstring'''



          return 1e-5







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return 12

