


'''simple docstring'''



import unittest

import numpy as np
from datasets import load_dataset

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

if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor






class 	UpperCamelCase__(       unittest.TestCase	):




    def __init__(		self      :			Dict				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Optional[Any]=7				,   lowerCAmelCase      :			Union[str, Any]=3				,   lowerCAmelCase      :			int=18				,   lowerCAmelCase      :			Optional[int]=30				,   lowerCAmelCase      :			str=400				,   lowerCAmelCase      :			Tuple=True				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			Dict=True				,   lowerCAmelCase      :			Union[str, Any]=None				,   lowerCAmelCase      :			List[Any]=True				,   lowerCAmelCase      :			Any=[0.5, 0.5, 0.5]				,   lowerCAmelCase      :			Optional[int]=[0.5, 0.5, 0.5]				,   lowerCAmelCase      :			Tuple=False				,   )->					List[Any]:

          """simple docstring"""




          UpperCAmelCase					    =     size if size is not None else {'''height''': 20, '''width''': 20}
          UpperCAmelCase					    =     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
          UpperCAmelCase					    =     parent
          UpperCAmelCase					    =     batch_size
          UpperCAmelCase					    =     num_channels
          UpperCAmelCase					    =     image_size
          UpperCAmelCase					    =     min_resolution
          UpperCAmelCase					    =     max_resolution
          UpperCAmelCase					    =     do_resize
          UpperCAmelCase					    =     size
          UpperCAmelCase					    =     do_center_crop
          UpperCAmelCase					    =     crop_size
          UpperCAmelCase					    =     do_normalize
          UpperCAmelCase					    =     image_mean
          UpperCAmelCase					    =     image_std
          UpperCAmelCase					    =     do_reduce_labels





    def 					a__(		self      :			Any				)->					Tuple:

          """simple docstring"""




          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_reduce_labels": self.do_reduce_labels,
          }







def 				lowerCamelCase__				(	):




      '''simple docstring'''






      UpperCAmelCase					    =     load_dataset('''hf-internal-testing/fixtures_ade20k'''				,       split='''test'''					)

      UpperCAmelCase					    =     Image.open(dataset[0]['''file''']					)
      UpperCAmelCase					    =     Image.open(dataset[1]['''file''']					)

      return image, map


def 				lowerCamelCase__				(	):




      '''simple docstring'''






      UpperCAmelCase					    =     load_dataset('''hf-internal-testing/fixtures_ade20k'''				,       split='''test'''					)

      UpperCAmelCase					    =     Image.open(ds[0]['''file''']					)
      UpperCAmelCase					    =     Image.open(ds[1]['''file''']					)
      UpperCAmelCase					    =     Image.open(ds[2]['''file''']					)
      UpperCAmelCase					    =     Image.open(ds[3]['''file''']					)

      return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class 	UpperCamelCase__(       lowerCAmelCase						,    unittest.TestCase	):
    __magic_name__    :				Tuple  		=	BeitImageProcessor if is_vision_available() else None




    def 					a__(		self      :			Optional[int]				)->					Union[str, Any]:

          """simple docstring"""




          UpperCAmelCase					    =     BeitImageProcessingTester(self				)




    @property
    def 					a__(		self      :			Optional[int]				)->					Tuple:

          """simple docstring"""




          return self.image_processor_tester.prepare_image_processor_dict()




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

          """simple docstring"""




          UpperCAmelCase					    =     self.image_processing_class(**self.image_processor_dict				)
          self.assertTrue(hasattr(lowerCAmelCase				,   '''do_resize'''				)				)
          self.assertTrue(hasattr(lowerCAmelCase				,   '''size'''				)				)
          self.assertTrue(hasattr(lowerCAmelCase				,   '''do_center_crop'''				)				)
          self.assertTrue(hasattr(lowerCAmelCase				,   '''center_crop'''				)				)
          self.assertTrue(hasattr(lowerCAmelCase				,   '''do_normalize'''				)				)
          self.assertTrue(hasattr(lowerCAmelCase				,   '''image_mean'''				)				)
          self.assertTrue(hasattr(lowerCAmelCase				,   '''image_std'''				)				)




    def 					a__(		self      :			Optional[int]				)->					Union[str, Any]:

          """simple docstring"""




          UpperCAmelCase					    =     self.image_processing_class.from_dict(self.image_processor_dict				)
          self.assertEqual(image_processor.size				,   {'''height''': 20, '''width''': 20}				)
          self.assertEqual(image_processor.crop_size				,   {'''height''': 18, '''width''': 18}				)
          self.assertEqual(image_processor.do_reduce_labels				,   lowerCAmelCase				)

          UpperCAmelCase					    =     self.image_processing_class.from_dict(
              self.image_processor_dict				,   size=42				,   crop_size=84				,   reduce_labels=lowerCAmelCase				)
          self.assertEqual(image_processor.size				,   {'''height''': 42, '''width''': 42}				)
          self.assertEqual(image_processor.crop_size				,   {'''height''': 84, '''width''': 84}				)
          self.assertEqual(image_processor.do_reduce_labels				,   lowerCAmelCase				)




    def 					a__(		self      :			Tuple				)->					Optional[int]:

          """simple docstring"""




          pass




    def 					a__(		self      :			Optional[Any]				)->					List[Any]:

          """simple docstring"""




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

          # Test not batched input
          UpperCAmelCase					    =     image_processing(image_inputs[0]				,   return_tensors='''pt'''				).pixel_values
          self.assertEqual(
              encoded_images.shape				,   (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,   )

          # Test batched
          UpperCAmelCase					    =     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,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,   )




    def 					a__(		self      :			Optional[Any]				)->					int:

          """simple docstring"""




          UpperCAmelCase					    =     self.image_processing_class(**self.image_processor_dict				)
          # create random numpy tensors
          UpperCAmelCase					    =     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
          UpperCAmelCase					    =     image_processing(image_inputs[0]				,   return_tensors='''pt'''				).pixel_values
          self.assertEqual(
              encoded_images.shape				,   (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,   )

          # Test batched
          UpperCAmelCase					    =     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,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,   )




    def 					a__(		self      :			Any				)->					Dict:

          """simple docstring"""




          UpperCAmelCase					    =     self.image_processing_class(**self.image_processor_dict				)
          # create random PyTorch tensors
          UpperCAmelCase					    =     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
          UpperCAmelCase					    =     image_processing(image_inputs[0]				,   return_tensors='''pt'''				).pixel_values
          self.assertEqual(
              encoded_images.shape				,   (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,   )

          # Test batched
          UpperCAmelCase					    =     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,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,   )




    def 					a__(		self      :			int				)->					Optional[Any]:

          """simple docstring"""




          UpperCAmelCase					    =     self.image_processing_class(**self.image_processor_dict				)
          # create random PyTorch tensors
          UpperCAmelCase					    =     prepare_image_inputs(self.image_processor_tester				,   equal_resolution=lowerCAmelCase				,   torchify=lowerCAmelCase				)
          UpperCAmelCase					    =     []
          for image in image_inputs:
                self.assertIsInstance(lowerCAmelCase				,   torch.Tensor				)
                maps.append(torch.zeros(image.shape[-2:]				).long()				)

          # Test not batched input
          UpperCAmelCase					    =     image_processing(image_inputs[0]				,   maps[0]				,   return_tensors='''pt'''				)
          self.assertEqual(
              encoding['''pixel_values'''].shape				,   (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,   )
          self.assertEqual(
              encoding['''labels'''].shape				,   (
                  1,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,   )
          self.assertEqual(encoding['''labels'''].dtype				,   torch.long				)
          self.assertTrue(encoding['''labels'''].min().item() >= 0				)
          self.assertTrue(encoding['''labels'''].max().item() <= 255				)

          # Test batched
          UpperCAmelCase					    =     image_processing(lowerCAmelCase				,   lowerCAmelCase				,   return_tensors='''pt'''				)
          self.assertEqual(
              encoding['''pixel_values'''].shape				,   (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,   )
          self.assertEqual(
              encoding['''labels'''].shape				,   (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,   )
          self.assertEqual(encoding['''labels'''].dtype				,   torch.long				)
          self.assertTrue(encoding['''labels'''].min().item() >= 0				)
          self.assertTrue(encoding['''labels'''].max().item() <= 255				)

          # Test not batched input (PIL images)
          UpperCAmelCase    ,    UpperCAmelCase					    =     prepare_semantic_single_inputs()

          UpperCAmelCase					    =     image_processing(lowerCAmelCase				,   lowerCAmelCase				,   return_tensors='''pt'''				)
          self.assertEqual(
              encoding['''pixel_values'''].shape				,   (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,   )
          self.assertEqual(
              encoding['''labels'''].shape				,   (
                  1,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,   )
          self.assertEqual(encoding['''labels'''].dtype				,   torch.long				)
          self.assertTrue(encoding['''labels'''].min().item() >= 0				)
          self.assertTrue(encoding['''labels'''].max().item() <= 255				)

          # Test batched input (PIL images)
          UpperCAmelCase    ,    UpperCAmelCase					    =     prepare_semantic_batch_inputs()

          UpperCAmelCase					    =     image_processing(lowerCAmelCase				,   lowerCAmelCase				,   return_tensors='''pt'''				)
          self.assertEqual(
              encoding['''pixel_values'''].shape				,   (
                  2,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,   )
          self.assertEqual(
              encoding['''labels'''].shape				,   (
                  2,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,   )
          self.assertEqual(encoding['''labels'''].dtype				,   torch.long				)
          self.assertTrue(encoding['''labels'''].min().item() >= 0				)
          self.assertTrue(encoding['''labels'''].max().item() <= 255				)





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

          """simple docstring"""




          UpperCAmelCase					    =     self.image_processing_class(**self.image_processor_dict				)

          # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
          UpperCAmelCase    ,    UpperCAmelCase					    =     prepare_semantic_single_inputs()
          UpperCAmelCase					    =     image_processing(lowerCAmelCase				,   lowerCAmelCase				,   return_tensors='''pt'''				)
          self.assertTrue(encoding['''labels'''].min().item() >= 0				)
          self.assertTrue(encoding['''labels'''].max().item() <= 150				)

          UpperCAmelCase					    =     True
          UpperCAmelCase					    =     image_processing(lowerCAmelCase				,   lowerCAmelCase				,   return_tensors='''pt'''				)
          self.assertTrue(encoding['''labels'''].min().item() >= 0				)
          self.assertTrue(encoding['''labels'''].max().item() <= 255				)









'''simple docstring'''



import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			str				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=lowerCAmelCase				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

																UpperCAmelCase					    =     FlaxTransformeraDModel(
																    in_channels=self.out_channels				,   n_heads=self.num_attention_heads				,   d_head=self.out_channels // self.num_attention_heads				,   depth=1				,   use_linear_projection=self.use_linear_projection				,   only_cross_attention=self.only_cross_attention				,   use_memory_efficient_attention=self.use_memory_efficient_attention				,   dtype=self.dtype				,   )
																attentions.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets
										UpperCAmelCase					    =     attentions

										if self.add_downsample:
																UpperCAmelCase					    =     FlaxDownsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			List[Any]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			List[str]=True				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     ()

										for resnet, attn in zip(self.resnets				,   self.attentions				):
																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																UpperCAmelCase					    =     attn(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																output_states += (hidden_states,)

										if self.add_downsample:
																UpperCAmelCase					    =     self.downsamplers_a(lowerCAmelCase				)
																output_states += (hidden_states,)

										return hidden_states, output_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			List[str]				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=lowerCAmelCase				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)
										UpperCAmelCase					    =     resnets

										if self.add_downsample:
																UpperCAmelCase					    =     FlaxDownsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			List[str]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			List[str]=True				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     ()

										for resnet in self.resnets:
																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																output_states += (hidden_states,)

										if self.add_downsample:
																UpperCAmelCase					    =     self.downsamplers_a(lowerCAmelCase				)
																output_states += (hidden_states,)

										return hidden_states, output_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			List[str]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if (i == self.num_layers - 1) else self.out_channels
																UpperCAmelCase					    =     self.prev_output_channel if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=resnet_in_channels + res_skip_channels				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

																UpperCAmelCase					    =     FlaxTransformeraDModel(
																    in_channels=self.out_channels				,   n_heads=self.num_attention_heads				,   d_head=self.out_channels // self.num_attention_heads				,   depth=1				,   use_linear_projection=self.use_linear_projection				,   only_cross_attention=self.only_cross_attention				,   use_memory_efficient_attention=self.use_memory_efficient_attention				,   dtype=self.dtype				,   )
																attentions.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets
										UpperCAmelCase					    =     attentions

										if self.add_upsample:
																UpperCAmelCase					    =     FlaxUpsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			Union[str, Any]=True				)->					Optional[int]:

										"""simple docstring"""




										for resnet, attn in zip(self.resnets				,   self.attentions				):
																# pop res hidden states
																UpperCAmelCase					    =     res_hidden_states_tuple[-1]
																UpperCAmelCase					    =     res_hidden_states_tuple[:-1]
																UpperCAmelCase					    =     jnp.concatenate((hidden_states, res_hidden_states)				,   axis=-1				)

																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																UpperCAmelCase					    =     attn(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)

										if self.add_upsample:
																UpperCAmelCase					    =     self.upsamplers_a(lowerCAmelCase				)

										return hidden_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	True
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			Optional[int]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     []

										for i in range(self.num_layers				):
																UpperCAmelCase					    =     self.in_channels if (i == self.num_layers - 1) else self.out_channels
																UpperCAmelCase					    =     self.prev_output_channel if i == 0 else self.out_channels

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=resnet_in_channels + res_skip_channels				,   out_channels=self.out_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets

										if self.add_upsample:
																UpperCAmelCase					    =     FlaxUpsampleaD(self.out_channels				,   dtype=self.dtype				)





				def __call__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Union[str, Any]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Dict=True				)->					Tuple:

										"""simple docstring"""




										for resnet in self.resnets:
																# pop res hidden states
																UpperCAmelCase					    =     res_hidden_states_tuple[-1]
																UpperCAmelCase					    =     res_hidden_states_tuple[:-1]
																UpperCAmelCase					    =     jnp.concatenate((hidden_states, res_hidden_states)				,   axis=-1				)

																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)

										if self.add_upsample:
																UpperCAmelCase					    =     self.upsamplers_a(lowerCAmelCase				)

										return hidden_states







class 	UpperCamelCase__(       nn.Module	):
				__magic_name__    :				int
				__magic_name__    :				float  		=	0.0
				__magic_name__    :				int  		=	1
				__magic_name__    :				int  		=	1
				__magic_name__    :				bool  		=	False
				__magic_name__    :				bool  		=	False
				__magic_name__    :				jnp.dtype  		=	jnp.floataa




				def 					a__(		self      :			int				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     [
										    FlaxResnetBlockaD(
										        in_channels=self.in_channels				,   out_channels=self.in_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
										]

										UpperCAmelCase					    =     []

										for _ in range(self.num_layers				):
																UpperCAmelCase					    =     FlaxTransformeraDModel(
																    in_channels=self.in_channels				,   n_heads=self.num_attention_heads				,   d_head=self.in_channels // self.num_attention_heads				,   depth=1				,   use_linear_projection=self.use_linear_projection				,   use_memory_efficient_attention=self.use_memory_efficient_attention				,   dtype=self.dtype				,   )
																attentions.append(lowerCAmelCase				)

																UpperCAmelCase					    =     FlaxResnetBlockaD(
																    in_channels=self.in_channels				,   out_channels=self.in_channels				,   dropout_prob=self.dropout				,   dtype=self.dtype				,   )
																resnets.append(lowerCAmelCase				)

										UpperCAmelCase					    =     resnets
										UpperCAmelCase					    =     attentions





				def __call__(		self      :			Dict				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Any=True				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.resnets[0](lowerCAmelCase				,   lowerCAmelCase				)
										for attn, resnet in zip(self.attentions				,   self.resnets[1:]				):
																UpperCAmelCase					    =     attn(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)
																UpperCAmelCase					    =     resnet(lowerCAmelCase				,   lowerCAmelCase				,   deterministic=lowerCAmelCase				)

										return hidden_states





