



import copy
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/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'conditional_detr'
							lowerCamelCase__   			=		['past_key_values']
							lowerCamelCase__   			=		{
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}







							def __init__(   self,			__a=True,			__a=None,			__a=3,			__a=300,			__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=0.02,			__a=1.0,			__a=False,			__a="sine",			__a="resnet50",			__a=True,			__a=False,			__a=2,			__a=5,			__a=2,			__a=1,			__a=1,			__a=2,			__a=5,			__a=2,			__a=0.25,			**__a,			):

														'''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.")
																												_lowerCAmelCase     :							str    =    CONFIG_MAPPING["resnet"](out_features=["stage4"])
																					elif isinstance(__a,			__a):
																												_lowerCAmelCase     :							Union[str, Any]    =    backbone_config.get("model_type")
																												_lowerCAmelCase     :							Optional[Any]    =    CONFIG_MAPPING[backbone_model_type]
																												_lowerCAmelCase     :							Dict    =    config_class.from_dict(__a)

														_lowerCAmelCase     :							Dict    =    use_timm_backbone
														_lowerCAmelCase     :							Optional[int]    =    backbone_config
														_lowerCAmelCase     :							Union[str, Any]    =    num_channels
														_lowerCAmelCase     :							int    =    num_queries
														_lowerCAmelCase     :							Tuple    =    d_model
														_lowerCAmelCase     :							Dict    =    encoder_ffn_dim
														_lowerCAmelCase     :							Any    =    encoder_layers
														_lowerCAmelCase     :							int    =    encoder_attention_heads
														_lowerCAmelCase     :							str    =    decoder_ffn_dim
														_lowerCAmelCase     :							Tuple    =    decoder_layers
														_lowerCAmelCase     :							Optional[Any]    =    decoder_attention_heads
														_lowerCAmelCase     :							Tuple    =    dropout
														_lowerCAmelCase     :							Any    =    attention_dropout
														_lowerCAmelCase     :							List[str]    =    activation_dropout
														_lowerCAmelCase     :							Dict    =    activation_function
														_lowerCAmelCase     :							Union[str, Any]    =    init_std
														_lowerCAmelCase     :							str    =    init_xavier_std
														_lowerCAmelCase     :							Optional[Any]    =    encoder_layerdrop
														_lowerCAmelCase     :							List[str]    =    decoder_layerdrop
														_lowerCAmelCase     :							Dict    =    encoder_layers
														_lowerCAmelCase     :							List[str]    =    auxiliary_loss
														_lowerCAmelCase     :							List[Any]    =    position_embedding_type
														_lowerCAmelCase     :							Union[str, Any]    =    backbone
														_lowerCAmelCase     :							Optional[Any]    =    use_pretrained_backbone
														_lowerCAmelCase     :							int    =    dilation
														# Hungarian matcher
														_lowerCAmelCase     :							Dict    =    class_cost
														_lowerCAmelCase     :							str    =    bbox_cost
														_lowerCAmelCase     :							List[Any]    =    giou_cost
														# Loss coefficients
														_lowerCAmelCase     :							Optional[int]    =    mask_loss_coefficient
														_lowerCAmelCase     :							Optional[int]    =    dice_loss_coefficient
														_lowerCAmelCase     :							Tuple    =    cls_loss_coefficient
														_lowerCAmelCase     :							Dict    =    bbox_loss_coefficient
														_lowerCAmelCase     :							List[Any]    =    giou_loss_coefficient
														_lowerCAmelCase     :							Union[str, Any]    =    focal_alpha
														super().__init__(is_encoder_decoder=__a,			**__a)







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return self.encoder_attention_heads







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return self.d_model







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    copy.deepcopy(self.__dict__)
														if self.backbone_config is not None:
																					_lowerCAmelCase     :							List[str]    =    self.backbone_config.to_dict()
														_lowerCAmelCase     :							int    =    self.__class__.model_type
														return output



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

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







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-5







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 12





import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


_snake_case		=  get_tests_dir("fixtures")



class   UpperCAmelCase_    (       unittest.TestCase):







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    mock.Mock()
														_lowerCAmelCase     :							int    =    500
														_lowerCAmelCase     :							Tuple    =    {}
														_lowerCAmelCase     :							str    =    HTTPError
														_lowerCAmelCase     :							Union[str, Any]    =    {}

														# Download this model to make sure it's in the cache.
														_lowerCAmelCase     :							Tuple    =    ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
														# Under the mock environment we get a 500 error when trying to reach the model.
														with mock.patch("requests.Session.request",			return_value=__a) as mock_head:
																					_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
																					# This check we did call the fake head request
																					mock_head.assert_called()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    ViTImageProcessor.from_pretrained(
														    "https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json")







							def    snake_case__      (   self):

														'''simple docstring'''

														with self.assertRaises(__a):
																					# config is in subfolder, the following should not work without specifying the subfolder
																					_lowerCAmelCase     :							int    =    AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")

														_lowerCAmelCase     :							Optional[Any]    =    AutoImageProcessor.from_pretrained(
														    "hf-internal-testing/stable-diffusion-all-variants",			subfolder="feature_extractor")

														self.assertIsNotNone(__a)



@is_staging_test
class   UpperCAmelCase_    (       unittest.TestCase):







							@classmethod
							def    snake_case__      (   cls):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    TOKEN
														HfFolder.save_token(__a)







							@classmethod
							def    snake_case__      (   cls):

														'''simple docstring'''

														try:
																					delete_repo(token=cls._token,			repo_id="test-image-processor")
														except HTTPError:
																					pass

														try:
																					delete_repo(token=cls._token,			repo_id="valid_org/test-image-processor-org")
														except HTTPError:
																					pass

														try:
																					delete_repo(token=cls._token,			repo_id="test-dynamic-image-processor")
														except HTTPError:
																					pass







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained(__a)
														image_processor.push_to_hub("test-image-processor",			use_auth_token=self._token)

														_lowerCAmelCase     :							str    =    ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))

														# Reset repo
														delete_repo(token=self._token,			repo_id="test-image-processor")

														# Push to hub via save_pretrained
														with tempfile.TemporaryDirectory() as tmp_dir:
																					image_processor.save_pretrained(
																					    __a,			repo_id="test-image-processor",			push_to_hub=__a,			use_auth_token=self._token)

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    ViTImageProcessor.from_pretrained(__a)
														image_processor.push_to_hub("valid_org/test-image-processor",			use_auth_token=self._token)

														_lowerCAmelCase     :							Tuple    =    ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))

														# Reset repo
														delete_repo(token=self._token,			repo_id="valid_org/test-image-processor")

														# Push to hub via save_pretrained
														with tempfile.TemporaryDirectory() as tmp_dir:
																					image_processor.save_pretrained(
																					    __a,			repo_id="valid_org/test-image-processor-org",			push_to_hub=__a,			use_auth_token=self._token)

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))







							def    snake_case__      (   self):

														'''simple docstring'''

														CustomImageProcessor.register_for_auto_class()
														_lowerCAmelCase     :							List[str]    =    CustomImageProcessor.from_pretrained(__a)

														image_processor.push_to_hub("test-dynamic-image-processor",			use_auth_token=self._token)

														# This has added the proper auto_map field to the config
														self.assertDictEqual(
														    image_processor.auto_map,			{"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"},			)

														_lowerCAmelCase     :							Tuple    =    AutoImageProcessor.from_pretrained(
														    f"{USER}/test-dynamic-image-processor",			trust_remote_code=__a)
														# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
														self.assertEqual(new_image_processor.__class__.__name__,			"CustomImageProcessor")
