






"""simple docstring"""







import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__a       :	Any             = []
for i in range(6):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
		)
		rename_keys.append(
		    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
		)
		rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
		rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
		rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
		rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
		rename_keys.append(
		    (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
		)
		rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
		rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
		rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
		)
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
		)
		rename_keys.append(
		    (
		        F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
		        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
		    )
		)
		rename_keys.append(
		    (
		        F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
		        F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
		    )
		)
		rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
		rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
		rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
		rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
		)
		rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
		)
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
		)
		rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
		rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

		# q, k, v projections in self/cross-attention in decoder for conditional DETR
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
		)
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
		)
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
		)
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
		)
		rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
		)
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
		)
		rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
		)

		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
		)
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
		)
		rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
		rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
		rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
		)
		rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
		rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
		)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  state_dict.pop(lowerCamelCase_)
					a__          =  val





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  OrderedDict()
					for key, value in state_dict.items():
										if "backbone.0.body" in key:
															a__          =  key.replace('''backbone.0.body''' ,     '''backbone.conv_encoder.model''')
															a__          =  value
										else:
															a__          =  value

					return new_state_dict





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False):
					a__          =  ''''''
					if is_panoptic:
										a__          =  '''conditional_detr.'''

					# first: transformer encoder
					for i in range(6):
										# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
										a__          =  state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight')
										a__          =  state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias')
										# next, add query, keys and values (in that order) to the state dict
										a__          =  in_proj_weight[:256, :]
										a__          =  in_proj_bias[:256]
										a__          =  in_proj_weight[256:512, :]
										a__          =  in_proj_bias[256:512]
										a__          =  in_proj_weight[-256:, :]
										a__          =  in_proj_bias[-256:]





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					a__          =  Image.open(requests.get(lowerCamelCase_ ,     stream=lowerCamelCase_).raw)

					return im





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  ConditionalDetrConfig()
					# set backbone and dilation attributes
					if "resnet101" in model_name:
										a__          =  '''resnet101'''
					if "dc5" in model_name:
										a__          =  True
					a__          =  '''panoptic''' in model_name
					if is_panoptic:
										a__          =  250
					else:
										a__          =  91
										a__          =  '''huggingface/label-files'''
										a__          =  '''coco-detection-id2label.json'''
										a__          =  json.load(open(hf_hub_download(lowerCamelCase_ ,     lowerCamelCase_ ,     repo_type='''dataset''') ,     '''r'''))
										a__          =  {int(lowerCamelCase_): v for k, v in idalabel.items()}
										a__          =  idalabel
										a__          =  {v: k for k, v in idalabel.items()}

					# load image processor
					a__          =  '''coco_panoptic''' if is_panoptic else '''coco_detection'''
					a__          =  ConditionalDetrImageProcessor(format=lowerCamelCase_)

					# prepare image
					a__          =  prepare_img()
					a__          =  image_processor(images=lowerCamelCase_ ,     return_tensors='''pt''')
					a__          =  encoding['''pixel_values''']

					logger.info(f'Converting model {model_name}...')

					# load original model from torch hub
					a__          =  torch.hub.load('''DeppMeng/ConditionalDETR''' ,     lowerCamelCase_ ,     pretrained=lowerCamelCase_).eval()
					a__          =  conditional_detr.state_dict()
					# rename keys
					for src, dest in rename_keys:
										if is_panoptic:
															a__          =  '''conditional_detr.''' + src
										rename_key(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					a__          =  rename_backbone_keys(lowerCamelCase_)
					# query, key and value matrices need special treatment
					read_in_q_k_v(lowerCamelCase_ ,     is_panoptic=lowerCamelCase_)
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					a__          =  '''conditional_detr.model.''' if is_panoptic else '''model.'''
					for key in state_dict.copy().keys():
										if is_panoptic:
															if (
															    key.startswith('''conditional_detr''')
															    and not key.startswith('''class_labels_classifier''')
															    and not key.startswith('''bbox_predictor''')
															):
																				a__          =  state_dict.pop(lowerCamelCase_)
																				a__          =  val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				a__          =  state_dict.pop(lowerCamelCase_)
																				a__          =  val
															elif key.startswith('''bbox_attention''') or key.startswith('''mask_head'''):
																				continue
															else:
																				a__          =  state_dict.pop(lowerCamelCase_)
																				a__          =  val
										else:
															if not key.startswith('''class_labels_classifier''') and not key.startswith('''bbox_predictor'''):
																				a__          =  state_dict.pop(lowerCamelCase_)
																				a__          =  val
    # finally, create HuggingFace model and load state dict
					a__          =  ConditionalDetrForSegmentation(lowerCamelCase_) if is_panoptic else ConditionalDetrForObjectDetection(lowerCamelCase_)
					model.load_state_dict(lowerCamelCase_)
					model.eval()
					model.push_to_hub(repo_id=lowerCamelCase_ ,     organization='''DepuMeng''' ,     commit_message='''Add model''')
					# verify our conversion
					a__          =  conditional_detr(lowerCamelCase_)
					a__          =  model(lowerCamelCase_)
					assert torch.allclose(outputs.logits ,     original_outputs['''pred_logits'''] ,     atol=1E-4)
					assert torch.allclose(outputs.pred_boxes ,     original_outputs['''pred_boxes'''] ,     atol=1E-4)
					if is_panoptic:
										assert torch.allclose(outputs.pred_masks ,     original_outputs['''pred_masks'''] ,     atol=1E-4)

					# Save model and image processor
					logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...')
					Path(lowerCamelCase_).mkdir(exist_ok=lowerCamelCase_)
					model.save_pretrained(lowerCamelCase_)
					image_processor.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
		__a       :	List[str]             = argparse.ArgumentParser()

		parser.add_argument(
		    '--model_name',
		    default='conditional_detr_resnet50',
		    type=str,
		    help='Name of the CONDITIONAL_DETR model you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
		)
		__a       :	Union[str, Any]             = parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










"""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        _SCREAMING_SNAKE_CASE						(  nn.Module							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =42
							_SCREAMING_SNAKE_CASE    =42
							_SCREAMING_SNAKE_CASE    =0.0
							_SCREAMING_SNAKE_CASE    =1
							_SCREAMING_SNAKE_CASE    =1
							_SCREAMING_SNAKE_CASE    =True
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =jnp.floataa




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  []
												a__          =  []

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

																	a__          =  FlaxResnetBlockaD(
																	    in_channels=__A				,		out_channels=self.out_channels				,		dropout_prob=self.dropout				,		dtype=self.dtype				,		)
																	resnets.append(__A    )

																	a__          =  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(__A    )

												a__          =  resnets
												a__          =  attentions

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





							def __call__(   self:     Optional[int]				,		__A:     Union[str, Any]				,		__A:     str				,		__A:     Optional[Any]				,		__A:     Any=True    ):

												'''simple docstring'''

												a__          =  ()

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

												if self.add_downsample:
																	a__          =  self.downsamplers_a(__A    )
																	output_states += (hidden_states,)

												return hidden_states, output_states







class        _SCREAMING_SNAKE_CASE						(  nn.Module							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =42
							_SCREAMING_SNAKE_CASE    =42
							_SCREAMING_SNAKE_CASE    =0.0
							_SCREAMING_SNAKE_CASE    =1
							_SCREAMING_SNAKE_CASE    =True
							_SCREAMING_SNAKE_CASE    =jnp.floataa




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  []

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

																	a__          =  FlaxResnetBlockaD(
																	    in_channels=__A				,		out_channels=self.out_channels				,		dropout_prob=self.dropout				,		dtype=self.dtype				,		)
																	resnets.append(__A    )
												a__          =  resnets

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





							def __call__(   self:     Dict				,		__A:     int				,		__A:     Dict				,		__A:     Optional[Any]=True    ):

												'''simple docstring'''

												a__          =  ()

												for resnet in self.resnets:
																	a__          =  resnet(__A				,		__A				,		deterministic=__A    )
																	output_states += (hidden_states,)

												if self.add_downsample:
																	a__          =  self.downsamplers_a(__A    )
																	output_states += (hidden_states,)

												return hidden_states, output_states







class        _SCREAMING_SNAKE_CASE						(  nn.Module							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =42
							_SCREAMING_SNAKE_CASE    =42
							_SCREAMING_SNAKE_CASE    =42
							_SCREAMING_SNAKE_CASE    =0.0
							_SCREAMING_SNAKE_CASE    =1
							_SCREAMING_SNAKE_CASE    =1
							_SCREAMING_SNAKE_CASE    =True
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =jnp.floataa




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  []
												a__          =  []

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

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

																	a__          =  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(__A    )

												a__          =  resnets
												a__          =  attentions

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





							def __call__(   self:     Any				,		__A:     Optional[int]				,		__A:     List[Any]				,		__A:     List[str]				,		__A:     Optional[Any]				,		__A:     Any=True    ):

												'''simple docstring'''

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

																	a__          =  resnet(__A				,		__A				,		deterministic=__A    )
																	a__          =  attn(__A				,		__A				,		deterministic=__A    )

												if self.add_upsample:
																	a__          =  self.upsamplers_a(__A    )

												return hidden_states







class        _SCREAMING_SNAKE_CASE						(  nn.Module							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =42
							_SCREAMING_SNAKE_CASE    =42
							_SCREAMING_SNAKE_CASE    =42
							_SCREAMING_SNAKE_CASE    =0.0
							_SCREAMING_SNAKE_CASE    =1
							_SCREAMING_SNAKE_CASE    =True
							_SCREAMING_SNAKE_CASE    =jnp.floataa




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  []

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

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

												a__          =  resnets

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





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

												'''simple docstring'''

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

																	a__          =  resnet(__A				,		__A				,		deterministic=__A    )

												if self.add_upsample:
																	a__          =  self.upsamplers_a(__A    )

												return hidden_states







class        _SCREAMING_SNAKE_CASE						(  nn.Module							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =42
							_SCREAMING_SNAKE_CASE    =0.0
							_SCREAMING_SNAKE_CASE    =1
							_SCREAMING_SNAKE_CASE    =1
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =jnp.floataa




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

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

												a__          =  []

												for _ in range(self.num_layers    ):
																	a__          =  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(__A    )

																	a__          =  FlaxResnetBlockaD(
																	    in_channels=self.in_channels				,		out_channels=self.in_channels				,		dropout_prob=self.dropout				,		dtype=self.dtype				,		)
																	resnets.append(__A    )

												a__          =  resnets
												a__          =  attentions





							def __call__(   self:     Any				,		__A:     Optional[int]				,		__A:     int				,		__A:     Tuple				,		__A:     str=True    ):

												'''simple docstring'''

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

												return hidden_states


