




import unittest

import numpy as np
import requests

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

   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   lowerCamelCase__					:  Tuple      							=				False

if is_vision_available():
   from PIL import Image

   from transformers import PixaStructImageProcessor





class 			lowerCamelCase_				( unittest.TestCase   ):


 '''simple docstring'''







 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						Tuple							,							_lowerCAmelCase    :						Optional[Any]=7							,							_lowerCAmelCase    :						List[Any]=3							,							_lowerCAmelCase    :						Tuple=18							,							_lowerCAmelCase    :						str=30							,							_lowerCAmelCase    :						str=400							,							_lowerCAmelCase    :						Union[str, Any]=None							,							_lowerCAmelCase    :						int=True							,							_lowerCAmelCase    :						List[Any]=True							,							_lowerCAmelCase    :						Any=None							,							):
       SCREAMING_SNAKE_CASE_      		=     size if size is not None else {'height': 20, 'width': 20}
       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_      		=     size
       SCREAMING_SNAKE_CASE_      		=     do_normalize
       SCREAMING_SNAKE_CASE_      		=     do_convert_rgb
       SCREAMING_SNAKE_CASE_      		=     [512, 1_024, 2_048, 4_096]
       SCREAMING_SNAKE_CASE_      		=     patch_size if patch_size is not None else {'height': 16, 'width': 16}



 def  lowerCAmelCase_    (				self    :						List[str]		):
       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




 def  lowerCAmelCase_    (				self    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
       SCREAMING_SNAKE_CASE_      		=     Image.open(requests.get(_lowerCAmelCase							,							stream=_lowerCAmelCase		).raw		).convert('RGB'		)
       return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,  reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,  )
@require_torch
@require_vision
class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  unittest.TestCase   ):


 '''simple docstring'''







 lowercase_							       =       PixaStructImageProcessor if is_vision_available() else None



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     PixaStructImageProcessingTester(self		)



 @property
 def  lowerCAmelCase_    (				self    :						int		):
       return self.image_processor_tester.prepare_image_processor_dict()



 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(_lowerCAmelCase							,							'do_normalize'		)		)
       self.assertTrue(hasattr(_lowerCAmelCase							,							'do_convert_rgb'		)		)



 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.image_processor_tester.prepare_dummy_image()

       SCREAMING_SNAKE_CASE_      		=     self.image_processing_class(**self.image_processor_dict		)
       SCREAMING_SNAKE_CASE_      		=     2_048

       SCREAMING_SNAKE_CASE_      		=     image_processor(_lowerCAmelCase							,							return_tensors='pt'							,							max_patches=_lowerCAmelCase		)
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,							torch.tensor(0.0606		)							,							atol=1E-3							,							rtol=1E-3		)		)



 def  lowerCAmelCase_    (				self    :						str		):
       # Initialize image_processor
       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_      		=     (
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             SCREAMING_SNAKE_CASE_      		=     image_processor(
                 image_inputs[0]							,							return_tensors='pt'							,							max_patches=_lowerCAmelCase		).flattened_patches
             self.assertEqual(
                 encoded_images.shape							,							(1, max_patch, expected_hidden_dim)							,							)

             # Test batched
             SCREAMING_SNAKE_CASE_      		=     image_processor(
                 _lowerCAmelCase							,							return_tensors='pt'							,							max_patches=_lowerCAmelCase		).flattened_patches
             self.assertEqual(
                 encoded_images.shape							,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,							)



 def  lowerCAmelCase_    (				self    :						int		):
       # Initialize image_processor
       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_      		=     (
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       SCREAMING_SNAKE_CASE_      		=     True

       for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             with self.assertRaises(_lowerCAmelCase		):
                   SCREAMING_SNAKE_CASE_      		=     image_processor(
                       image_inputs[0]							,							return_tensors='pt'							,							max_patches=_lowerCAmelCase		).flattened_patches

             SCREAMING_SNAKE_CASE_      		=     'Hello'

             SCREAMING_SNAKE_CASE_      		=     image_processor(
                 image_inputs[0]							,							return_tensors='pt'							,							max_patches=_lowerCAmelCase							,							header_text=_lowerCAmelCase		).flattened_patches
             self.assertEqual(
                 encoded_images.shape							,							(1, max_patch, expected_hidden_dim)							,							)

             # Test batched
             SCREAMING_SNAKE_CASE_      		=     image_processor(
                 _lowerCAmelCase							,							return_tensors='pt'							,							max_patches=_lowerCAmelCase							,							header_text=_lowerCAmelCase		).flattened_patches
             self.assertEqual(
                 encoded_images.shape							,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,							)



 def  lowerCAmelCase_    (				self    :						str		):
       # Initialize image_processor
       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		)

       SCREAMING_SNAKE_CASE_      		=     (
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             SCREAMING_SNAKE_CASE_      		=     image_processor(
                 image_inputs[0]							,							return_tensors='pt'							,							max_patches=_lowerCAmelCase		).flattened_patches
             self.assertEqual(
                 encoded_images.shape							,							(1, max_patch, expected_hidden_dim)							,							)

             # Test batched
             SCREAMING_SNAKE_CASE_      		=     image_processor(
                 _lowerCAmelCase							,							return_tensors='pt'							,							max_patches=_lowerCAmelCase		).flattened_patches
             self.assertEqual(
                 encoded_images.shape							,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,							)




 def  lowerCAmelCase_    (				self    :						str		):
       # Initialize image_processor
       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_      		=     (
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             SCREAMING_SNAKE_CASE_      		=     image_processor(
                 image_inputs[0]							,							return_tensors='pt'							,							max_patches=_lowerCAmelCase		).flattened_patches
             self.assertEqual(
                 encoded_images.shape							,							(1, max_patch, expected_hidden_dim)							,							)

             # Test batched
             SCREAMING_SNAKE_CASE_      		=     image_processor(
                 _lowerCAmelCase							,							return_tensors='pt'							,							max_patches=_lowerCAmelCase		).flattened_patches
             self.assertEqual(
                 encoded_images.shape							,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,							)





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,  reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,  )
@require_torch
@require_vision
class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  unittest.TestCase   ):


 '''simple docstring'''







 lowercase_							       =       PixaStructImageProcessor if is_vision_available() else None



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     PixaStructImageProcessingTester(self							,							num_channels=4		)
       SCREAMING_SNAKE_CASE_      		=     3



 @property
 def  lowerCAmelCase_    (				self    :						Any		):
       return self.image_processor_tester.prepare_image_processor_dict()



 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(_lowerCAmelCase							,							'do_normalize'		)		)
       self.assertTrue(hasattr(_lowerCAmelCase							,							'do_convert_rgb'		)		)




 def  lowerCAmelCase_    (				self    :						List[str]		):
       # Initialize image_processor
       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_      		=     (
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             SCREAMING_SNAKE_CASE_      		=     image_processor(
                 image_inputs[0]							,							return_tensors='pt'							,							max_patches=_lowerCAmelCase		).flattened_patches
             self.assertEqual(
                 encoded_images.shape							,							(1, max_patch, expected_hidden_dim)							,							)

             # Test batched
             SCREAMING_SNAKE_CASE_      		=     image_processor(
                 _lowerCAmelCase							,							return_tensors='pt'							,							max_patches=_lowerCAmelCase		).flattened_patches
             self.assertEqual(
                 encoded_images.shape							,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,							)



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


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     Tuple       =     {
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''conditional_detr'''
     __SCREAMING_SNAKE_CASE     					=    ['''past_key_values''']
     __SCREAMING_SNAKE_CASE     					=    {
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }


     def __init__(  self							,					A_=True							,					A_=None							,					A_=3							,					A_=3_00							,					A_=6							,					A_=20_48							,					A_=8							,					A_=6							,					A_=20_48							,					A_=8							,					A_=0.0							,					A_=0.0							,					A_=True							,					A_="relu"							,					A_=2_56							,					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_=2							,					A_=5							,					A_=2							,					A_=1							,					A_=1							,					A_=2							,					A_=5							,					A_=2							,					A_=0.2_5							,					**A_							,					):
       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		:							int							=     CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]    )
         elif isinstance(A_							,					A_    ):
           _UpperCAmelCase		:							Tuple							=     backbone_config.get("""model_type"""    )
           _UpperCAmelCase		:							Union[str, Any]							=     CONFIG_MAPPING[backbone_model_type]
           _UpperCAmelCase		:							Optional[Any]							=     config_class.from_dict(A_    )

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


     @property
     def        __snake_case(  self    ):
       return self.encoder_attention_heads


     @property
     def        __snake_case(  self    ):
       return self.d_model




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


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    version.parse('''1.11'''     )


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


     @property
     def        __snake_case(  self    ):
       return 1e-5




     @property
     def        __snake_case(  self    ):
       return 12





