





'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase       :int       =  logging.get_logger(__name__)







def       UpperCAmelCase							(     _lowerCamelCase      :  int     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Dict   =					SwinConfig(
						    embed_dim=192			,   depths=(2, 2, 18, 2)			,   num_heads=(6, 12, 24, 48)			,   window_size=12			,   out_features=["stage2", "stage3", "stage4"]			,   )

						SCREAMING_SNAKE_CASE__     :				Tuple   =					DetaConfig(
						    backbone_config=_lowerCamelCase			,   num_queries=900			,   encoder_ffn_dim=2_048			,   decoder_ffn_dim=2_048			,   num_feature_levels=5			,   assign_first_stage=_lowerCamelCase			,   with_box_refine=_lowerCamelCase			,   two_stage=_lowerCamelCase			,   )

						# set labels
						SCREAMING_SNAKE_CASE__     :				Tuple   =					"huggingface/label-files"
						if "o365" in model_name:
												SCREAMING_SNAKE_CASE__     :				Any   =					366
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					"object365-id2label.json"
						else:
												SCREAMING_SNAKE_CASE__     :				Dict   =					91
												SCREAMING_SNAKE_CASE__     :				Tuple   =					"coco-detection-id2label.json"

						SCREAMING_SNAKE_CASE__     :				Dict   =					num_labels
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					json.load(open(cached_download(hf_hub_url(_lowerCamelCase			,   _lowerCamelCase			,   repo_type="dataset"     )     )			,   "r"     )     )
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					{int(_lowerCamelCase     ): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE__     :				Tuple   =					idalabel
						SCREAMING_SNAKE_CASE__     :				Dict   =					{v: k for k, v in idalabel.items()}

						return config







def       UpperCAmelCase							(     _lowerCamelCase      :  Dict     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Dict   =					[]

						# stem
						# fmt: off
						rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")     )
						rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")     )
						rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")     )
						rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")     )
						# stages
						for i in range(len(config.backbone_config.depths     )     ):
												for j in range(config.backbone_config.depths[i]     ):
																		rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")     )
																		rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")     )
																		rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")     )
																		rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")     )
																		rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")     )
																		rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")     )
																		rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")     )
																		rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")     )
																		rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")     )
																		rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")     )
																		rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")     )
																		rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")     )

												if i < 3:
																		rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.reduction.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.reduction.weight""")     )
																		rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.weight""")     )
																		rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.bias""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.bias""")     )

						rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")     )
						rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")     )
						rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")     )
						rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")     )
						rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")     )
						rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")     )

						# transformer encoder
						for i in range(config.encoder_layers     ):
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""")     )
												rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""")     )

						# transformer decoder
						for i in range(config.decoder_layers     ):
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""")     )
												rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""")     )

						# fmt: on

						return rename_keys







def       UpperCAmelCase							(     _lowerCamelCase      :  List[str]			,   _lowerCamelCase      :  Optional[int]			,   _lowerCamelCase      :  Dict     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					dct.pop(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Dict   =					val







def       UpperCAmelCase							(     _lowerCamelCase      :  List[Any]			,   _lowerCamelCase      :  Dict     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Tuple   =					[int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
						for i in range(len(backbone_config.depths     )     ):
												SCREAMING_SNAKE_CASE__     :				str   =					num_features[i]
												for j in range(backbone_config.depths[i]     ):
																		# fmt: off
																		# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""     )
																		SCREAMING_SNAKE_CASE__     :				Optional[int]   =					state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"""     )
																		# next, add query, keys and values (in that order) to the state dict
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					in_proj_weight[:dim, :]
																		SCREAMING_SNAKE_CASE__     :				Dict   =					in_proj_bias[: dim]
																		SCREAMING_SNAKE_CASE__     :				Any   =					in_proj_weight[
																		    dim : dim * 2, :
																		]
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					in_proj_bias[
																		    dim : dim * 2
																		]
																		SCREAMING_SNAKE_CASE__     :				List[str]   =					in_proj_weight[
																		    -dim :, :
																		]
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					in_proj_bias[-dim :]
																		# fmt: on







def       UpperCAmelCase							(     _lowerCamelCase      :  Dict			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					config.d_model
						for i in range(config.decoder_layers     ):
												# read in weights + bias of input projection layer of self-attention
												SCREAMING_SNAKE_CASE__     :				List[str]   =					state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""     )
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""     )
												# next, add query, keys and values (in that order) to the state dict
												SCREAMING_SNAKE_CASE__     :				Dict   =					in_proj_weight[:hidden_size, :]
												SCREAMING_SNAKE_CASE__     :				List[str]   =					in_proj_bias[:hidden_size]
												SCREAMING_SNAKE_CASE__     :				int   =					in_proj_weight[
												    hidden_size : hidden_size * 2, :
												]
												SCREAMING_SNAKE_CASE__     :				Dict   =					in_proj_bias[hidden_size : hidden_size * 2]
												SCREAMING_SNAKE_CASE__     :				Dict   =					in_proj_weight[-hidden_size:, :]
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					in_proj_bias[-hidden_size:]







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Dict   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					Image.open(requests.get(_lowerCamelCase			,   stream=_lowerCamelCase     ).raw     )

						return im







@torch.no_grad()
def       UpperCAmelCase							(     _lowerCamelCase      :  List[Any]			,   _lowerCamelCase      :  Union[str, Any]			,   _lowerCamelCase      :  Any     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Tuple   =					get_deta_config(_lowerCamelCase     )

						# load original state dict
						if model_name == "deta-swin-large":
												SCREAMING_SNAKE_CASE__     :				Dict   =					hf_hub_download(repo_id="nielsr/deta-checkpoints"			,   filename="adet_swin_ft.pth"     )
						elif model_name == "deta-swin-large-o365":
												SCREAMING_SNAKE_CASE__     :				List[Any]   =					hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"			,   filename="deta_swin_pt_o365.pth"     )
						else:
												raise ValueError(f"""Model name {model_name} not supported"""     )

						SCREAMING_SNAKE_CASE__     :				List[Any]   =					torch.load(_lowerCamelCase			,   map_location="cpu"     )["model"]

						# original state dict
						for name, param in state_dict.items():
												print(_lowerCamelCase			,   param.shape     )

						# rename keys
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					create_rename_keys(_lowerCamelCase     )
						for src, dest in rename_keys:
												rename_key(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )
						read_in_swin_q_k_v(_lowerCamelCase			,   config.backbone_config     )
						read_in_decoder_q_k_v(_lowerCamelCase			,   _lowerCamelCase     )

						# fix some prefixes
						for key in state_dict.copy().keys():
												if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																		SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					state_dict.pop(_lowerCamelCase     )
																		SCREAMING_SNAKE_CASE__     :				Dict   =					val
												if "input_proj" in key:
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					state_dict.pop(_lowerCamelCase     )
																		SCREAMING_SNAKE_CASE__     :				Any   =					val
												if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																		SCREAMING_SNAKE_CASE__     :				Dict   =					state_dict.pop(_lowerCamelCase     )
																		SCREAMING_SNAKE_CASE__     :				int   =					val

    # finally, create HuggingFace model and load state dict
						SCREAMING_SNAKE_CASE__     :				Any   =					DetaForObjectDetection(_lowerCamelCase     )
						model.load_state_dict(_lowerCamelCase     )
						model.eval()

						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					"cuda" if torch.cuda.is_available() else "cpu"
						model.to(_lowerCamelCase     )

						# load image processor
						SCREAMING_SNAKE_CASE__     :				Any   =					DetaImageProcessor(format="coco_detection"     )

						# verify our conversion on image
						SCREAMING_SNAKE_CASE__     :				List[str]   =					prepare_img()
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					processor(images=_lowerCamelCase			,   return_tensors="pt"     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					encoding["pixel_values"]
						SCREAMING_SNAKE_CASE__     :				List[str]   =					model(pixel_values.to(_lowerCamelCase     )     )

						# verify logits
						print("Logits:"			,   outputs.logits[0, :3, :3]     )
						print("Boxes:"			,   outputs.pred_boxes[0, :3, :3]     )
						if model_name == "deta-swin-large":
												SCREAMING_SNAKE_CASE__     :				int   =					torch.tensor(
												    [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]     )
												SCREAMING_SNAKE_CASE__     :				str   =					torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]     )
						elif model_name == "deta-swin-large-o365":
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					torch.tensor(
												    [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]     )
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]     )
						assert torch.allclose(outputs.logits[0, :3, :3]			,   expected_logits.to(_lowerCamelCase     )			,   atol=1E-4     )
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,   expected_boxes.to(_lowerCamelCase     )			,   atol=1E-4     )
						print("Everything ok!"     )

						if pytorch_dump_folder_path:
												# Save model and processor
												logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""     )
												Path(_lowerCamelCase     ).mkdir(exist_ok=_lowerCamelCase     )
												model.save_pretrained(_lowerCamelCase     )
												processor.save_pretrained(_lowerCamelCase     )

						# Push to hub
						if push_to_hub:
												print("Pushing model and processor to hub..."     )
												model.push_to_hub(f"""jozhang97/{model_name}"""     )
												processor.push_to_hub(f"""jozhang97/{model_name}"""     )


if __name__ == "__main__":
				__lowercase       :int       =  argparse.ArgumentParser()

				parser.add_argument(
				    "--model_name",
				    type=str,
				    default="deta-swin-large",
				    choices=["deta-swin-large", "deta-swin-large-o365"],
				    help="Name of the 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.",
				)
				parser.add_argument(
				    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
				)
				__lowercase       :Optional[Any]       =  parser.parse_args()
				convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax
				import jax.numpy as jnp
				from flax.jax_utils import replicate
				from flax.training.common_utils import shard



@slow
@require_flax
class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def      A_  (      self				:     Dict    )	->List[Any]:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()

							def      A_  (      self				:     Dict    )	->Tuple:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-canny"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Dict   =					"bird"
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe.prepare_image_inputs([canny_image] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				int   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Tuple   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					jnp.array(
													    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2






							def      A_  (      self				:     List[Any]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				int   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-openpose"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Any   =					"Chef in the kitchen"
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"    )
													SCREAMING_SNAKE_CASE__     :				str   =					pipe.prepare_image_inputs([pose_image] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Any   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				str   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.array(
													    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2