




import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
			import torch

if is_soundfile_availble():
			import soundfile as sf

if is_vision_available():
			from PIL import Image

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase="")      ->					str:
					a     =				tempfile.mkdtemp()
					return os.path.join(__UpperCamelCase		, str(uuid.uuida()) + suffix)




@require_soundfile
@require_torch
class       a__	( unittest.TestCase				):







					def       lowerCAmelCase_    (     self			)  ->   Any:



										'''simple docstring'''



										a     =				torch.rand(12  ,		dtype=torch.floataa			) - 0.5
										a     =				AgentAudio(A			)
										a     =				str(agent_type.to_string()			)

										# Ensure that the tensor and the agent_type's tensor are the same
										self.assertTrue(torch.allclose(A  ,		agent_type.to_raw()  ,		atol=1e-4			)			)

										del agent_type

										# Ensure the path remains even after the object deletion
										self.assertTrue(os.path.exists(A			)			)

										# Ensure that the file contains the same value as the original tensor
										a  ,				a     =				sf.read(A			)
										self.assertTrue(torch.allclose(A  ,		torch.tensor(A			)  ,		atol=1e-4			)			)







					def       lowerCAmelCase_    (     self			)  ->   str:



										'''simple docstring'''



										a     =				torch.rand(12  ,		dtype=torch.floataa			) - 0.5
										a     =				get_new_path(suffix=".wav"			)
										sf.write(A  ,		A  ,		16000			)

										a     =				AgentAudio(A			)

										self.assertTrue(torch.allclose(A  ,		agent_type.to_raw()  ,		atol=1e-4			)			)
										self.assertEqual(agent_type.to_string()  ,		A			)







@require_vision
@require_torch
class       a__	( unittest.TestCase				):







					def       lowerCAmelCase_    (     self			)  ->   Tuple:



										'''simple docstring'''



										a     =				torch.randint(0  ,		256  ,		(64, 64, 3)			)
										a     =				AgentImage(A			)
										a     =				str(agent_type.to_string()			)

										# Ensure that the tensor and the agent_type's tensor are the same
										self.assertTrue(torch.allclose(A  ,		agent_type._tensor  ,		atol=1e-4			)			)

										self.assertIsInstance(agent_type.to_raw()  ,		Image.Image			)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(A			)			)







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



										'''simple docstring'''



										a     =				Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"
										a     =				Image.open(A			)
										a     =				AgentImage(A			)

										self.assertTrue(path.samefile(agent_type.to_string()			)			)
										self.assertTrue(image == agent_type.to_raw()			)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(A			)			)







					def       lowerCAmelCase_    (     self			)  ->   Dict:



										'''simple docstring'''



										a     =				Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"
										a     =				Image.open(A			)
										a     =				AgentImage(A			)

										self.assertFalse(path.samefile(agent_type.to_string()			)			)
										self.assertTrue(image == agent_type.to_raw()			)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(A			)			)







class       a__	( unittest.TestCase				):







					def       lowerCAmelCase_    (     self			)  ->   Tuple:



										'''simple docstring'''



										a     =				"Hey!"
										a     =				AgentText(A			)

										self.assertEqual(A  ,		agent_type.to_string()			)
										self.assertEqual(A  ,		agent_type.to_raw()			)
										self.assertEqual(A  ,		A			)







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

