






'''simple docstring'''




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor

class   _UpperCAmelCase     (	unittest.TestCase		):




   """simple docstring"""

   def __init__(						self					:      List[Any] , __UpperCAmelCase					:      Dict , __UpperCAmelCase					:      Union[str, Any]=7 , __UpperCAmelCase					:      Any=3 , __UpperCAmelCase					:      Tuple=30 , __UpperCAmelCase					:      Optional[Any]=400 , __UpperCAmelCase					:      int=True , __UpperCAmelCase					:      Any=None , __UpperCAmelCase					:      Tuple=True , __UpperCAmelCase					:      Dict=[0.5, 0.5, 0.5] , __UpperCAmelCase					:      Any=[0.5, 0.5, 0.5] , __UpperCAmelCase					:      str=True , __UpperCAmelCase					:      Any=1 / 255 , __UpperCAmelCase					:      Dict=True , ):





         '''simple docstring'''





         _A     	=      size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
         _A     	=      parent
         _A     	=      batch_size
         _A     	=      num_channels
         _A     	=      min_resolution
         _A     	=      max_resolution
         _A     	=      do_resize
         _A     	=      size
         _A     	=      do_normalize
         _A     	=      image_mean
         _A     	=      image_std
         _A     	=      do_rescale
         _A     	=      rescale_factor
         _A     	=      do_pad

   def 		lowerCAmelCase					(						self					:      Any						):





         '''simple docstring'''





         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }

   def 		lowerCAmelCase					(						self					:      List[str] , __UpperCAmelCase					:      List[Any] , __UpperCAmelCase					:      int=False						):





         '''simple docstring'''





         if not batched:
               _A     	=      image_inputs[0]
               if isinstance(_UpperCAmelCase , Image.Image						):
                     _A     	=      image.size
               else:
                     _A     	=      image.shape[1], image.shape[2]
               if w < h:
                     _A     	=      int(self.size["shortest_edge"] * h / w						)
                     _A     	=      self.size['shortest_edge']
               elif w > h:
                     _A     	=      self.size['shortest_edge']
                     _A     	=      int(self.size["shortest_edge"] * w / h						)
               else:
                     _A     	=      self.size['shortest_edge']
                     _A     	=      self.size['shortest_edge']

         else:
               _A     	=      []
               for image in image_inputs:
                     _A     	=      self.get_expected_values([image]						)
                     expected_values.append((expected_height, expected_width)						)
               _A     	=      max(_UpperCAmelCase , key=lambda __UpperCAmelCase						: item[0]						)[0]
               _A     	=      max(_UpperCAmelCase , key=lambda __UpperCAmelCase						: item[1]						)[1]

         return expected_height, expected_width





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




   """simple docstring"""

   snake_case       							=		DeformableDetrImageProcessor if is_vision_available() else None

   def 		lowerCAmelCase					(						self					:      Optional[int]						):





         '''simple docstring'''





         _A     	=      DeformableDetrImageProcessingTester(self						)

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





         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()

   def 		lowerCAmelCase					(						self					:      Optional[Any]						):





         '''simple docstring'''





         _A     	=      self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(_UpperCAmelCase , "image_mean"						)						)
         self.assertTrue(hasattr(_UpperCAmelCase , "image_std"						)						)
         self.assertTrue(hasattr(_UpperCAmelCase , "do_normalize"						)						)
         self.assertTrue(hasattr(_UpperCAmelCase , "do_resize"						)						)
         self.assertTrue(hasattr(_UpperCAmelCase , "do_rescale"						)						)
         self.assertTrue(hasattr(_UpperCAmelCase , "do_pad"						)						)
         self.assertTrue(hasattr(_UpperCAmelCase , "size"						)						)

   def 		lowerCAmelCase					(						self					:      Tuple						):





         '''simple docstring'''





         _A     	=      self.image_processing_class.from_dict(self.image_processor_dict						)
         self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1333}						)
         self.assertEqual(image_processor.do_pad , _UpperCAmelCase						)

         _A     	=      self.image_processing_class.from_dict(
             self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_UpperCAmelCase						)
         self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84}						)
         self.assertEqual(image_processor.do_pad , _UpperCAmelCase						)

   def 		lowerCAmelCase					(						self					:      Optional[Any]						):





         '''simple docstring'''





         pass

   def 		lowerCAmelCase					(						self					:      str						):





         '''simple docstring'''





         _A     	=      self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         _A     	=      prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase						)
         for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase , Image.Image						)

         # Test not batched input
         _A     	=      image_processing(image_inputs[0] , return_tensors="pt"						).pixel_values

         _A     	=      self.image_processor_tester.get_expected_values(_UpperCAmelCase						)

         self.assertEqual(
             encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

         # Test batched
         _A     	=      self.image_processor_tester.get_expected_values(_UpperCAmelCase , batched=_UpperCAmelCase						)

         _A     	=      image_processing(_UpperCAmelCase , return_tensors="pt"						).pixel_values
         self.assertEqual(
             encoded_images.shape , (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) , )

   def 		lowerCAmelCase					(						self					:      Dict						):





         '''simple docstring'''





         _A     	=      self.image_processing_class(**self.image_processor_dict						)
         # create random numpy tensors
         _A     	=      prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase , numpify=_UpperCAmelCase						)
         for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase , np.ndarray						)

         # Test not batched input
         _A     	=      image_processing(image_inputs[0] , return_tensors="pt"						).pixel_values

         _A     	=      self.image_processor_tester.get_expected_values(_UpperCAmelCase						)

         self.assertEqual(
             encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

         # Test batched
         _A     	=      image_processing(_UpperCAmelCase , return_tensors="pt"						).pixel_values

         _A     	=      self.image_processor_tester.get_expected_values(_UpperCAmelCase , batched=_UpperCAmelCase						)

         self.assertEqual(
             encoded_images.shape , (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) , )

   def 		lowerCAmelCase					(						self					:      str						):





         '''simple docstring'''





         _A     	=      self.image_processing_class(**self.image_processor_dict						)
         # create random PyTorch tensors
         _A     	=      prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase , torchify=_UpperCAmelCase						)
         for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase , torch.Tensor						)

         # Test not batched input
         _A     	=      image_processing(image_inputs[0] , return_tensors="pt"						).pixel_values

         _A     	=      self.image_processor_tester.get_expected_values(_UpperCAmelCase						)

         self.assertEqual(
             encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

         # Test batched
         _A     	=      image_processing(_UpperCAmelCase , return_tensors="pt"						).pixel_values

         _A     	=      self.image_processor_tester.get_expected_values(_UpperCAmelCase , batched=_UpperCAmelCase						)

         self.assertEqual(
             encoded_images.shape , (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) , )

   @slow
   def 		lowerCAmelCase					(						self					:      Dict						):





         '''simple docstring'''





         _A     	=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
         with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r"						) as f:
               _A     	=      json.loads(f.read()						)

         _A     	=      {'image_id': 39769, 'annotations': target}

         # encode them
         _A     	=      DeformableDetrImageProcessor()
         _A     	=      image_processing(images=_UpperCAmelCase , annotations=_UpperCAmelCase , return_tensors="pt"						)

         # verify pixel values
         _A     	=      torch.Size([1, 3, 800, 1066]						)
         self.assertEqual(encoding["pixel_values"].shape , _UpperCAmelCase						)

         _A     	=      torch.tensor([0.2796, 0.3138, 0.3481]						)
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , _UpperCAmelCase , atol=1E-4						)						)

         # verify area
         _A     	=      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , _UpperCAmelCase						)						)
         # verify boxes
         _A     	=      torch.Size([6, 4]						)
         self.assertEqual(encoding["labels"][0]["boxes"].shape , _UpperCAmelCase						)
         _A     	=      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , _UpperCAmelCase , atol=1E-3						)						)
         # verify image_id
         _A     	=      torch.tensor([39769]						)
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , _UpperCAmelCase						)						)
         # verify is_crowd
         _A     	=      torch.tensor([0, 0, 0, 0, 0, 0]						)
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , _UpperCAmelCase						)						)
         # verify class_labels
         _A     	=      torch.tensor([75, 75, 63, 65, 17, 17]						)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , _UpperCAmelCase						)						)
         # verify orig_size
         _A     	=      torch.tensor([480, 640]						)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , _UpperCAmelCase						)						)
         # verify size
         _A     	=      torch.tensor([800, 1066]						)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , _UpperCAmelCase						)						)

   @slow
   def 		lowerCAmelCase					(						self					:      Union[str, Any]						):





         '''simple docstring'''





         _A     	=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
         with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r"						) as f:
               _A     	=      json.loads(f.read()						)

         _A     	=      {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

         _A     	=      pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"						)

         # encode them
         _A     	=      DeformableDetrImageProcessor(format="coco_panoptic"						)
         _A     	=      image_processing(images=_UpperCAmelCase , annotations=_UpperCAmelCase , masks_path=_UpperCAmelCase , return_tensors="pt"						)

         # verify pixel values
         _A     	=      torch.Size([1, 3, 800, 1066]						)
         self.assertEqual(encoding["pixel_values"].shape , _UpperCAmelCase						)

         _A     	=      torch.tensor([0.2796, 0.3138, 0.3481]						)
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , _UpperCAmelCase , atol=1E-4						)						)

         # verify area
         _A     	=      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]						)
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , _UpperCAmelCase						)						)
         # verify boxes
         _A     	=      torch.Size([6, 4]						)
         self.assertEqual(encoding["labels"][0]["boxes"].shape , _UpperCAmelCase						)
         _A     	=      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , _UpperCAmelCase , atol=1E-3						)						)
         # verify image_id
         _A     	=      torch.tensor([39769]						)
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , _UpperCAmelCase						)						)
         # verify is_crowd
         _A     	=      torch.tensor([0, 0, 0, 0, 0, 0]						)
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , _UpperCAmelCase						)						)
         # verify class_labels
         _A     	=      torch.tensor([17, 17, 63, 75, 75, 93]						)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , _UpperCAmelCase						)						)
         # verify masks
         _A     	=      822873
         self.assertEqual(encoding["labels"][0]["masks"].sum().item() , _UpperCAmelCase						)
         # verify orig_size
         _A     	=      torch.tensor([480, 640]						)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , _UpperCAmelCase						)						)
         # verify size
         _A     	=      torch.tensor([800, 1066]						)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , _UpperCAmelCase						)						)












'''simple docstring'''




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block


@dataclass
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			42




class 					SCREAMING_SNAKE_CASE       (a__   ,						a__				):


     @register_to_config
     def __init__(		self		,      _UpperCAmelCase = 6_5536		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 2		,      _UpperCAmelCase = 2		,      _UpperCAmelCase = 0		,      _UpperCAmelCase = "fourier"		,      _UpperCAmelCase = True		,      _UpperCAmelCase = False		,      _UpperCAmelCase = 0.0		,      _UpperCAmelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,      _UpperCAmelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,      _UpperCAmelCase = "UNetMidBlock1D"		,      _UpperCAmelCase = None		,      _UpperCAmelCase = (32, 32, 64)		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 8		,      _UpperCAmelCase = 1		,      _UpperCAmelCase = False		,      ):

          '''simple docstring'''






          super().__init__()
          __A		:					Dict						=			sample_size

          # time
          if time_embedding_type == "fourier":
               __A		:					int						=			GaussianFourierProjection(
                   embedding_size=8		,      set_W_to_weight=_UpperCAmelCase		,      log=_UpperCAmelCase		,      flip_sin_to_cos=_UpperCAmelCase)
               __A		:					Any						=			2 * block_out_channels[0]
          elif time_embedding_type == "positional":
               __A		:					List[str]						=			Timesteps(
                   block_out_channels[0]		,      flip_sin_to_cos=_UpperCAmelCase		,      downscale_freq_shift=_UpperCAmelCase)
               __A		:					List[str]						=			block_out_channels[0]

          if use_timestep_embedding:
               __A		:					Optional[Any]						=			block_out_channels[0] * 4
               __A		:					Optional[int]						=			TimestepEmbedding(
                   in_channels=_UpperCAmelCase		,      time_embed_dim=_UpperCAmelCase		,      act_fn=_UpperCAmelCase		,      out_dim=block_out_channels[0]		,      )

          __A		:					Dict						=			nn.ModuleList([])
          __A		:					Dict						=			None
          __A		:					Tuple						=			nn.ModuleList([])
          __A		:					Tuple						=			None

          # down
          __A		:					Any						=			in_channels
          for i, down_block_type in enumerate(_UpperCAmelCase):
               __A		:					Tuple						=			output_channel
               __A		:					Optional[Any]						=			block_out_channels[i]

               if i == 0:
                    input_channel += extra_in_channels

               __A		:					List[str]						=			i == len(_UpperCAmelCase) - 1

               __A		:					int						=			get_down_block(
                   _UpperCAmelCase		,      num_layers=_UpperCAmelCase		,      in_channels=_UpperCAmelCase		,      out_channels=_UpperCAmelCase		,      temb_channels=block_out_channels[0]		,      add_downsample=not is_final_block or downsample_each_block		,      )
               self.down_blocks.append(_UpperCAmelCase)

          # mid
          __A		:					str						=			get_mid_block(
              _UpperCAmelCase		,      in_channels=block_out_channels[-1]		,      mid_channels=block_out_channels[-1]		,      out_channels=block_out_channels[-1]		,      embed_dim=block_out_channels[0]		,      num_layers=_UpperCAmelCase		,      add_downsample=_UpperCAmelCase		,      )

          # up
          __A		:					Optional[int]						=			list(reversed(_UpperCAmelCase))
          __A		:					Optional[int]						=			reversed_block_out_channels[0]
          if out_block_type is None:
               __A		:					str						=			out_channels
          else:
               __A		:					List[Any]						=			block_out_channels[0]

          for i, up_block_type in enumerate(_UpperCAmelCase):
               __A		:					Optional[Any]						=			output_channel
               __A		:					Optional[Any]						=			(
                   reversed_block_out_channels[i + 1] if i < len(_UpperCAmelCase) - 1 else final_upsample_channels
               )

               __A		:					Dict						=			i == len(_UpperCAmelCase) - 1

               __A		:					str						=			get_up_block(
                   _UpperCAmelCase		,      num_layers=_UpperCAmelCase		,      in_channels=_UpperCAmelCase		,      out_channels=_UpperCAmelCase		,      temb_channels=block_out_channels[0]		,      add_upsample=not is_final_block		,      )
               self.up_blocks.append(_UpperCAmelCase)
               __A		:					Optional[int]						=			output_channel

          # out
          __A		:					str						=			norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,      32)
          __A		:					Optional[Any]						=			get_out_block(
              out_block_type=_UpperCAmelCase		,      num_groups_out=_UpperCAmelCase		,      embed_dim=block_out_channels[0]		,      out_channels=_UpperCAmelCase		,      act_fn=_UpperCAmelCase		,      fc_dim=block_out_channels[-1] // 4		,      )


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = True		,      ):

          '''simple docstring'''






          __A		:					Any						=			timestep
          if not torch.is_tensor(_UpperCAmelCase):
               __A		:					Any						=			torch.tensor([timesteps]		,      dtype=torch.long		,      device=sample.device)
          elif torch.is_tensor(_UpperCAmelCase) and len(timesteps.shape) == 0:
               __A		:					Any						=			timesteps[None].to(sample.device)

          __A		:					List[Any]						=			self.time_proj(_UpperCAmelCase)
          if self.config.use_timestep_embedding:
               __A		:					Dict						=			self.time_mlp(_UpperCAmelCase)
          else:
               __A		:					Dict						=			timestep_embed[..., None]
               __A		:					Tuple						=			timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
               __A		:					List[Any]						=			timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

          # 2. down
          __A		:					int						=			()
          for downsample_block in self.down_blocks:
               __A					,__A		:					int						=			downsample_block(hidden_states=_UpperCAmelCase		,      temb=_UpperCAmelCase)
               down_block_res_samples += res_samples

          # 3. mid
          if self.mid_block:
               __A		:					Optional[int]						=			self.mid_block(_UpperCAmelCase		,      _UpperCAmelCase)

          # 4. up
          for i, upsample_block in enumerate(self.up_blocks):
               __A		:					Any						=			down_block_res_samples[-1:]
               __A		:					Optional[int]						=			down_block_res_samples[:-1]
               __A		:					Any						=			upsample_block(_UpperCAmelCase		,      res_hidden_states_tuple=_UpperCAmelCase		,      temb=_UpperCAmelCase)

          # 5. post-process
          if self.out_block:
               __A		:					Dict						=			self.out_block(_UpperCAmelCase		,      _UpperCAmelCase)

          if not return_dict:
               return (sample,)

          return UNetaDOutput(sample=_UpperCAmelCase)