



"""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 ConditionalDetrImageProcessor

class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					def __init__( self							:						Any,						lowerCamelCase							:						Dict,						lowerCamelCase							:						str=7,						lowerCamelCase							:						Optional[int]=3,						lowerCamelCase							:						int=30,						lowerCamelCase							:						Tuple=400,						lowerCamelCase							:						Any=True,						lowerCamelCase							:						Tuple=None,						lowerCamelCase							:						List[Any]=True,						lowerCamelCase							:						int=[0.5, 0.5, 0.5],						lowerCamelCase							:						Any=[0.5, 0.5, 0.5],						lowerCamelCase							:						int=True,						lowerCamelCase							:						Dict=1 / 255,						lowerCamelCase							:						Union[str, Any]=True,						)->      Any:
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								lowerCamelCase__	:	List[Any]				=size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
								lowerCamelCase__	:	Dict				=parent
								lowerCamelCase__	:	str				=batch_size
								lowerCamelCase__	:	Optional[Any]				=num_channels
								lowerCamelCase__	:	Optional[int]				=min_resolution
								lowerCamelCase__	:	Dict				=max_resolution
								lowerCamelCase__	:	Dict				=do_resize
								lowerCamelCase__	:	List[str]				=size
								lowerCamelCase__	:	str				=do_normalize
								lowerCamelCase__	:	Dict				=image_mean
								lowerCamelCase__	:	List[Any]				=image_std
								lowerCamelCase__	:	List[Any]				=do_rescale
								lowerCamelCase__	:	Optional[Any]				=rescale_factor
								lowerCamelCase__	:	Dict				=do_pad



					def 	snake_case ( self							:						List[str]  )->      List[Any]:
								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 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						List[str],						lowerCamelCase							:						Tuple=False  )->      List[Any]:
								if not batched:
											lowerCamelCase__	:	Any				=image_inputs[0]
											if isinstance(lowerCamelCase,						Image.Image  ):
														lowerCamelCase__	:	List[str]				=image.size
											else:
														lowerCamelCase__	:	List[str]				=image.shape[1], image.shape[2]
											if w < h:
														lowerCamelCase__	:	Union[str, Any]				=int(self.size['''shortest_edge'''] * h / w  )
														lowerCamelCase__	:	List[Any]				=self.size['''shortest_edge''']
											elif w > h:
														lowerCamelCase__	:	Optional[Any]				=self.size['''shortest_edge''']
														lowerCamelCase__	:	Union[str, Any]				=int(self.size['''shortest_edge'''] * w / h  )
											else:
														lowerCamelCase__	:	List[Any]				=self.size['''shortest_edge''']
														lowerCamelCase__	:	List[Any]				=self.size['''shortest_edge''']

								else:
											lowerCamelCase__	:	Optional[Any]				=[]
											for image in image_inputs:
														lowerCamelCase__	:	Any				=self.get_expected_values([image]  )
														expected_values.append((expected_height, expected_width)  )
											lowerCamelCase__	:	List[str]				=max(lowerCamelCase,						key=lambda lowerCamelCase  : item[0]  )[0]
											lowerCamelCase__	:	Optional[int]				=max(lowerCamelCase,						key=lambda lowerCamelCase  : item[1]  )[1]

								return expected_height, expected_width





@require_torch
@require_vision
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							ConditionalDetrImageProcessor if is_vision_available() else None



					def 	snake_case ( self							:						Optional[int]  )->      Tuple:
								lowerCamelCase__	:	Any				=ConditionalDetrImageProcessingTester(self  )



					@property
					def 	snake_case ( self							:						Union[str, Any]  )->      Tuple:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 	snake_case ( self							:						Tuple  )->      Tuple:
								lowerCamelCase__	:	str				=self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(lowerCamelCase,						'''image_mean'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,						'''image_std'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,						'''do_normalize'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,						'''do_resize'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,						'''size'''  )  )



					def 	snake_case ( self							:						Tuple  )->      Union[str, Any]:
								lowerCamelCase__	:	str				=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,						lowerCamelCase  )

								lowerCamelCase__	:	Dict				=self.image_processing_class.from_dict(
								    self.image_processor_dict,						size=42,						max_size=84,						pad_and_return_pixel_mask=lowerCamelCase  )
								self.assertEqual(image_processor.size,						{'''shortest_edge''': 42, '''longest_edge''': 84}  )
								self.assertEqual(image_processor.do_pad,						lowerCamelCase  )



					def 	snake_case ( self							:						Dict  )->      List[str]:
								pass



					def 	snake_case ( self							:						Optional[Any]  )->      str:
								# Initialize image_processing
								lowerCamelCase__	:	Any				=self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								lowerCamelCase__	:	Any				=prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase  )
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,						Image.Image  )

								# Test not batched input
								lowerCamelCase__	:	Any				=image_processing(image_inputs[0],						return_tensors='''pt'''  ).pixel_values

								lowerCamelCase__	:	Tuple				=self.image_processor_tester.get_expected_values(lowerCamelCase  )

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

								# Test batched
								lowerCamelCase__	:	int				=self.image_processor_tester.get_expected_values(lowerCamelCase,						batched=lowerCamelCase  )

								lowerCamelCase__	:	Optional[Any]				=image_processing(lowerCamelCase,						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 	snake_case ( self							:						Any  )->      str:
								# Initialize image_processing
								lowerCamelCase__	:	List[str]				=self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								lowerCamelCase__	:	List[str]				=prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase,						numpify=lowerCamelCase  )
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,						np.ndarray  )

								# Test not batched input
								lowerCamelCase__	:	List[Any]				=image_processing(image_inputs[0],						return_tensors='''pt'''  ).pixel_values

								lowerCamelCase__	:	Dict				=self.image_processor_tester.get_expected_values(lowerCamelCase  )

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

								# Test batched
								lowerCamelCase__	:	Optional[int]				=image_processing(lowerCamelCase,						return_tensors='''pt'''  ).pixel_values

								lowerCamelCase__	:	Any				=self.image_processor_tester.get_expected_values(lowerCamelCase,						batched=lowerCamelCase  )

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



					def 	snake_case ( self							:						Tuple  )->      Tuple:
								# Initialize image_processing
								lowerCamelCase__	:	Optional[int]				=self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								lowerCamelCase__	:	Dict				=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
								lowerCamelCase__	:	List[Any]				=image_processing(image_inputs[0],						return_tensors='''pt'''  ).pixel_values

								lowerCamelCase__	:	int				=self.image_processor_tester.get_expected_values(lowerCamelCase  )

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

								# Test batched
								lowerCamelCase__	:	int				=image_processing(lowerCamelCase,						return_tensors='''pt'''  ).pixel_values

								lowerCamelCase__	:	List[Any]				=self.image_processor_tester.get_expected_values(lowerCamelCase,						batched=lowerCamelCase  )

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



					@slow
					def 	snake_case ( self							:						str  )->      Optional[int]:
								# prepare image and target
								lowerCamelCase__	:	Optional[int]				=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''',						'''r'''  ) as f:
											lowerCamelCase__	:	List[Any]				=json.loads(f.read()  )

								lowerCamelCase__	:	int				={'''image_id''': 3_9769, '''annotations''': target}

								# encode them
								lowerCamelCase__	:	Optional[int]				=ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''  )
								lowerCamelCase__	:	List[str]				=image_processing(images=lowerCamelCase,						annotations=lowerCamelCase,						return_tensors='''pt'''  )

								# verify pixel values
								lowerCamelCase__	:	str				=torch.Size([1, 3, 800, 1066]  )
								self.assertEqual(encoding['''pixel_values'''].shape,						lowerCamelCase  )

								lowerCamelCase__	:	Union[str, Any]				=torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
								self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3],						lowerCamelCase,						atol=1E-4  )  )

								# verify area
								lowerCamelCase__	:	Tuple				=torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''],						lowerCamelCase  )  )
								# verify boxes
								lowerCamelCase__	:	str				=torch.Size([6, 4]  )
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,						lowerCamelCase  )
								lowerCamelCase__	:	List[str]				=torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],						lowerCamelCase,						atol=1E-3  )  )
								# verify image_id
								lowerCamelCase__	:	str				=torch.tensor([3_9769]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],						lowerCamelCase  )  )
								# verify is_crowd
								lowerCamelCase__	:	Dict				=torch.tensor([0, 0, 0, 0, 0, 0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],						lowerCamelCase  )  )
								# verify class_labels
								lowerCamelCase__	:	Dict				=torch.tensor([75, 75, 63, 65, 17, 17]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],						lowerCamelCase  )  )
								# verify orig_size
								lowerCamelCase__	:	List[Any]				=torch.tensor([480, 640]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],						lowerCamelCase  )  )
								# verify size
								lowerCamelCase__	:	str				=torch.tensor([800, 1066]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],						lowerCamelCase  )  )




					@slow
					def 	snake_case ( self							:						List[Any]  )->      Any:
								# prepare image, target and masks_path
								lowerCamelCase__	:	Tuple				=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''',						'''r'''  ) as f:
											lowerCamelCase__	:	Optional[int]				=json.loads(f.read()  )

								lowerCamelCase__	:	Optional[int]				={'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

								lowerCamelCase__	:	int				=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''  )

								# encode them
								lowerCamelCase__	:	List[Any]				=ConditionalDetrImageProcessor(format='''coco_panoptic'''  )
								lowerCamelCase__	:	str				=image_processing(images=lowerCamelCase,						annotations=lowerCamelCase,						masks_path=lowerCamelCase,						return_tensors='''pt'''  )

								# verify pixel values
								lowerCamelCase__	:	Optional[int]				=torch.Size([1, 3, 800, 1066]  )
								self.assertEqual(encoding['''pixel_values'''].shape,						lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
								self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3],						lowerCamelCase,						atol=1E-4  )  )

								# verify area
								lowerCamelCase__	:	Any				=torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''],						lowerCamelCase  )  )
								# verify boxes
								lowerCamelCase__	:	Tuple				=torch.Size([6, 4]  )
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,						lowerCamelCase  )
								lowerCamelCase__	:	Tuple				=torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],						lowerCamelCase,						atol=1E-3  )  )
								# verify image_id
								lowerCamelCase__	:	Optional[int]				=torch.tensor([3_9769]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],						lowerCamelCase  )  )
								# verify is_crowd
								lowerCamelCase__	:	Optional[int]				=torch.tensor([0, 0, 0, 0, 0, 0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],						lowerCamelCase  )  )
								# verify class_labels
								lowerCamelCase__	:	List[Any]				=torch.tensor([17, 17, 63, 75, 75, 93]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],						lowerCamelCase  )  )
								# verify masks
								lowerCamelCase__	:	Optional[int]				=82_2873
								self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item(),						lowerCamelCase  )
								# verify orig_size
								lowerCamelCase__	:	List[str]				=torch.tensor([480, 640]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],						lowerCamelCase  )  )
								# verify size
								lowerCamelCase__	:	Optional[int]				=torch.tensor([800, 1066]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],						lowerCamelCase  )  )









"""simple docstring"""






from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)

@flax.struct.dataclass
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							42
					_a         =							42





class  __SCREAMING_SNAKE_CASE			(   nn.Module     ):


					'''simple docstring'''





					_a         =							42
					_a         =							(1_6, 3_2, 9_6, 2_5_6)
					_a         =							jnp.floataa



					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Tuple				=nn.Conv(
								    self.block_out_channels[0],						kernel_size=(3, 3),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)

								lowerCamelCase__	:	Dict				=[]
								for i in range(len(self.block_out_channels  ) - 1  ):
											lowerCamelCase__	:	Dict				=self.block_out_channels[i]
											lowerCamelCase__	:	Dict				=self.block_out_channels[i + 1]
											lowerCamelCase__	:	List[str]				=nn.Conv(
											    lowerCamelCase,						kernel_size=(3, 3),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)
											blocks.append(lowerCamelCase  )
											lowerCamelCase__	:	Optional[int]				=nn.Conv(
											    lowerCamelCase,						kernel_size=(3, 3),						strides=(2, 2),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)
											blocks.append(lowerCamelCase  )
								lowerCamelCase__	:	Any				=blocks

								lowerCamelCase__	:	Optional[int]				=nn.Conv(
								    self.conditioning_embedding_channels,						kernel_size=(3, 3),						padding=((1, 1), (1, 1)),						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)




					def __call__( self							:						Any,						lowerCamelCase							:						int  )->      List[str]:
								lowerCamelCase__	:	Tuple				=self.conv_in(lowerCamelCase  )
								lowerCamelCase__	:	Dict				=nn.silu(lowerCamelCase  )

								for block in self.blocks:
											lowerCamelCase__	:	str				=block(lowerCamelCase  )
											lowerCamelCase__	:	List[str]				=nn.silu(lowerCamelCase  )

								lowerCamelCase__	:	Any				=self.conv_out(lowerCamelCase  )

								return embedding





@flax_register_to_config
class  __SCREAMING_SNAKE_CASE			(   nn.Module	,     lowerCAmelCase_	,     lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							3_2
					_a         =							4
					_a         =							(
					    "CrossAttnDownBlock2D",
					    "CrossAttnDownBlock2D",
					    "CrossAttnDownBlock2D",
					    "DownBlock2D",
					)
					_a         =							False
					_a         =							(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
					_a         =							2
					_a         =							8
					_a         =							None
					_a         =							1_2_8_0
					_a         =							0.0
					_a         =							False
					_a         =							jnp.floataa
					_a         =							True
					_a         =							0
					_a         =							"rgb"
					_a         =							(1_6, 3_2, 9_6, 2_5_6)



					def 	snake_case ( self							:						str,						lowerCamelCase							:						jax.random.KeyArray  )->      FrozenDict:
								# init input tensors
								lowerCamelCase__	:	int				=(1, self.in_channels, self.sample_size, self.sample_size)
								lowerCamelCase__	:	int				=jnp.zeros(lowerCamelCase,						dtype=jnp.floataa  )
								lowerCamelCase__	:	Union[str, Any]				=jnp.ones((1,),						dtype=jnp.intaa  )
								lowerCamelCase__	:	str				=jnp.zeros((1, 1, self.cross_attention_dim),						dtype=jnp.floataa  )
								lowerCamelCase__	:	Any				=(1, 3, self.sample_size * 8, self.sample_size * 8)
								lowerCamelCase__	:	Optional[Any]				=jnp.zeros(lowerCamelCase,						dtype=jnp.floataa  )

								lowerCamelCase__			,     lowerCamelCase__	:	List[Any]				=jax.random.split(lowerCamelCase  )
								lowerCamelCase__	:	Dict				={'''params''': params_rng, '''dropout''': dropout_rng}

								return self.init(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )["params"]



					def 	snake_case ( self							:						Any  )->      Tuple:
								lowerCamelCase__	:	Optional[int]				=self.block_out_channels
								lowerCamelCase__	:	Tuple				=block_out_channels[0] * 4

								# If `num_attention_heads` is not defined (which is the case for most models)
								# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
								# The reason for this behavior is to correct for incorrectly named variables that were introduced
								# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
								# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
								# which is why we correct for the naming here.
								lowerCamelCase__	:	List[Any]				=self.num_attention_heads or self.attention_head_dim

								# input
								lowerCamelCase__	:	int				=nn.Conv(
								    block_out_channels[0],						kernel_size=(3, 3),						strides=(1, 1),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)

								# time
								lowerCamelCase__	:	str				=FlaxTimesteps(
								    block_out_channels[0],						flip_sin_to_cos=self.flip_sin_to_cos,						freq_shift=self.config.freq_shift  )
								lowerCamelCase__	:	Dict				=FlaxTimestepEmbedding(lowerCamelCase,						dtype=self.dtype  )

								lowerCamelCase__	:	List[Any]				=FlaxControlNetConditioningEmbedding(
								    conditioning_embedding_channels=block_out_channels[0],						block_out_channels=self.conditioning_embedding_out_channels,						)

								lowerCamelCase__	:	Dict				=self.only_cross_attention
								if isinstance(lowerCamelCase,						lowerCamelCase  ):
											lowerCamelCase__	:	int				=(only_cross_attention,) * len(self.down_block_types  )

								if isinstance(lowerCamelCase,						lowerCamelCase  ):
											lowerCamelCase__	:	List[str]				=(num_attention_heads,) * len(self.down_block_types  )

								# down
								lowerCamelCase__	:	Union[str, Any]				=[]
								lowerCamelCase__	:	Dict				=[]

								lowerCamelCase__	:	List[Any]				=block_out_channels[0]

								lowerCamelCase__	:	List[Any]				=nn.Conv(
								    lowerCamelCase,						kernel_size=(1, 1),						padding='''VALID''',						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)
								controlnet_down_blocks.append(lowerCamelCase  )

								for i, down_block_type in enumerate(self.down_block_types  ):
											lowerCamelCase__	:	List[Any]				=output_channel
											lowerCamelCase__	:	str				=block_out_channels[i]
											lowerCamelCase__	:	Dict				=i == len(lowerCamelCase  ) - 1

											if down_block_type == "CrossAttnDownBlock2D":
														lowerCamelCase__	:	str				=FlaxCrossAttnDownBlockaD(
														    in_channels=lowerCamelCase,						out_channels=lowerCamelCase,						dropout=self.dropout,						num_layers=self.layers_per_block,						num_attention_heads=num_attention_heads[i],						add_downsample=not is_final_block,						use_linear_projection=self.use_linear_projection,						only_cross_attention=only_cross_attention[i],						dtype=self.dtype,						)
											else:
														lowerCamelCase__	:	List[Any]				=FlaxDownBlockaD(
														    in_channels=lowerCamelCase,						out_channels=lowerCamelCase,						dropout=self.dropout,						num_layers=self.layers_per_block,						add_downsample=not is_final_block,						dtype=self.dtype,						)

											down_blocks.append(lowerCamelCase  )

											for _ in range(self.layers_per_block  ):
														lowerCamelCase__	:	Any				=nn.Conv(
														    lowerCamelCase,						kernel_size=(1, 1),						padding='''VALID''',						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)
														controlnet_down_blocks.append(lowerCamelCase  )

											if not is_final_block:
														lowerCamelCase__	:	Any				=nn.Conv(
														    lowerCamelCase,						kernel_size=(1, 1),						padding='''VALID''',						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)
														controlnet_down_blocks.append(lowerCamelCase  )

								lowerCamelCase__	:	int				=down_blocks
								lowerCamelCase__	:	List[str]				=controlnet_down_blocks

								# mid
								lowerCamelCase__	:	Tuple				=block_out_channels[-1]
								lowerCamelCase__	:	List[Any]				=FlaxUNetMidBlockaDCrossAttn(
								    in_channels=lowerCamelCase,						dropout=self.dropout,						num_attention_heads=num_attention_heads[-1],						use_linear_projection=self.use_linear_projection,						dtype=self.dtype,						)

								lowerCamelCase__	:	List[str]				=nn.Conv(
								    lowerCamelCase,						kernel_size=(1, 1),						padding='''VALID''',						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)




					def __call__( self							:						int,						lowerCamelCase							:						List[Any],						lowerCamelCase							:						Tuple,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						str,						lowerCamelCase							:						float = 1.0,						lowerCamelCase							:						bool = True,						lowerCamelCase							:						bool = False,						)->      Union[FlaxControlNetOutput, Tuple]:
								lowerCamelCase__	:	int				=self.controlnet_conditioning_channel_order
								if channel_order == "bgr":
											lowerCamelCase__	:	int				=jnp.flip(lowerCamelCase,						axis=1  )

								# 1. time
								if not isinstance(lowerCamelCase,						jnp.ndarray  ):
											lowerCamelCase__	:	Any				=jnp.array([timesteps],						dtype=jnp.intaa  )
								elif isinstance(lowerCamelCase,						jnp.ndarray  ) and len(timesteps.shape  ) == 0:
											lowerCamelCase__	:	List[str]				=timesteps.astype(dtype=jnp.floataa  )
											lowerCamelCase__	:	int				=jnp.expand_dims(lowerCamelCase,						0  )

								lowerCamelCase__	:	Optional[Any]				=self.time_proj(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=self.time_embedding(lowerCamelCase  )

								# 2. pre-process
								lowerCamelCase__	:	Optional[int]				=jnp.transpose(lowerCamelCase,						(0, 2, 3, 1)  )
								lowerCamelCase__	:	Dict				=self.conv_in(lowerCamelCase  )

								lowerCamelCase__	:	List[str]				=jnp.transpose(lowerCamelCase,						(0, 2, 3, 1)  )
								lowerCamelCase__	:	int				=self.controlnet_cond_embedding(lowerCamelCase  )
								sample += controlnet_cond

								# 3. down
								lowerCamelCase__	:	Union[str, Any]				=(sample,)
								for down_block in self.down_blocks:
											if isinstance(lowerCamelCase,						lowerCamelCase  ):
														lowerCamelCase__			,     lowerCamelCase__	:	Dict				=down_block(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						deterministic=not train  )
											else:
														lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=down_block(lowerCamelCase,						lowerCamelCase,						deterministic=not train  )
											down_block_res_samples += res_samples

								# 4. mid
								lowerCamelCase__	:	Optional[int]				=self.mid_block(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						deterministic=not train  )

								# 5. contronet blocks
								lowerCamelCase__	:	Optional[Any]				=()
								for down_block_res_sample, controlnet_block in zip(lowerCamelCase,						self.controlnet_down_blocks  ):
											lowerCamelCase__	:	Union[str, Any]				=controlnet_block(lowerCamelCase  )
											controlnet_down_block_res_samples += (down_block_res_sample,)

								lowerCamelCase__	:	List[str]				=controlnet_down_block_res_samples

								lowerCamelCase__	:	List[str]				=self.controlnet_mid_block(lowerCamelCase  )

								# 6. scaling
								lowerCamelCase__	:	Union[str, Any]				=[sample * conditioning_scale for sample in down_block_res_samples]
								mid_block_res_sample *= conditioning_scale

								if not return_dict:
											return (down_block_res_samples, mid_block_res_sample)

								return FlaxControlNetOutput(
								    down_block_res_samples=lowerCamelCase,						mid_block_res_sample=lowerCamelCase  )




