
'''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()
__lowerCAmelCase												=	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCAmelCase												=	[]
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				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  val






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					_snake_case       =  key.replace("""backbone.0.body"""							, """backbone.conv_encoder.model"""				)
																					_snake_case       =  value
														else:
																					_snake_case       =  value

							return new_state_dict






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=False				):
							_snake_case       =  """"""
							if is_panoptic:
														_snake_case       =  """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)
														_snake_case       =  state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
														_snake_case       =  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
														_snake_case       =  in_proj_weight[:256, :]
														_snake_case       =  in_proj_bias[:256]
														_snake_case       =  in_proj_weight[256:512, :]
														_snake_case       =  in_proj_bias[256:512]
														_snake_case       =  in_proj_weight[-256:, :]
														_snake_case       =  in_proj_bias[-256:]






def  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
							_snake_case       =  Image.open(requests.get(_SCREAMING_SNAKE_CASE							, stream=_SCREAMING_SNAKE_CASE				).raw				)

							return im






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

							# load image processor
							_snake_case       =  """coco_panoptic""" if is_panoptic else """coco_detection"""
							_snake_case       =  ConditionalDetrImageProcessor(format=_SCREAMING_SNAKE_CASE				)

							# prepare image
							_snake_case       =  prepare_img()
							_snake_case       =  image_processor(images=_SCREAMING_SNAKE_CASE							, return_tensors="""pt"""				)
							_snake_case       =  encoding["""pixel_values"""]

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

							# load original model from torch hub
							_snake_case       =  torch.hub.load("""DeppMeng/ConditionalDETR"""							, _SCREAMING_SNAKE_CASE							, pretrained=_SCREAMING_SNAKE_CASE				).eval()
							_snake_case       =  conditional_detr.state_dict()
							# rename keys
							for src, dest in rename_keys:
														if is_panoptic:
																					_snake_case       =  """conditional_detr.""" + src
														rename_key(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
							_snake_case       =  rename_backbone_keys(_SCREAMING_SNAKE_CASE				)
							# query, key and value matrices need special treatment
							read_in_q_k_v(_SCREAMING_SNAKE_CASE							, is_panoptic=_SCREAMING_SNAKE_CASE				)
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							_snake_case       =  """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"""				)
																					):
																												_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
																												_snake_case       =  val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
																												_snake_case       =  val
																					elif key.startswith("""bbox_attention"""				) or key.startswith("""mask_head"""				):
																												continue
																					else:
																												_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
																												_snake_case       =  val
														else:
																					if not key.startswith("""class_labels_classifier"""				) and not key.startswith("""bbox_predictor"""				):
																												_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)
																												_snake_case       =  val
    # finally, create HuggingFace model and load state dict
							_snake_case       =  ConditionalDetrForSegmentation(_SCREAMING_SNAKE_CASE				) if is_panoptic else ConditionalDetrForObjectDetection(_SCREAMING_SNAKE_CASE				)
							model.load_state_dict(_SCREAMING_SNAKE_CASE				)
							model.eval()
							model.push_to_hub(repo_id=_SCREAMING_SNAKE_CASE							, organization="""DepuMeng"""							, commit_message="""Add model"""				)
							# verify our conversion
							_snake_case       =  conditional_detr(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  model(_SCREAMING_SNAKE_CASE				)
							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(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
							model.save_pretrained(_SCREAMING_SNAKE_CASE				)
							image_processor.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				__lowerCAmelCase												=	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.'
				)
				__lowerCAmelCase												=	parser.parse_args()
				convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

'''simple docstring'''
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       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       Optional[int]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()

							def 	lowercase							(self  )					->       Dict:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-canny"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """bird"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

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

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

														_snake_case       =  pipe(
														    prompt_ids=UpperCAmelCase  ,  image=UpperCAmelCase  ,  params=UpperCAmelCase  ,  prng_seed=UpperCAmelCase  ,  num_inference_steps=50  ,  jit=UpperCAmelCase  ,  ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														_snake_case       =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
														_snake_case       =  images[0, 253:256, 253:256, -1]

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  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 	lowercase							(self  )					->       Optional[int]:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-openpose"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """Chef in the kitchen"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

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

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

														_snake_case       =  pipe(
														    prompt_ids=UpperCAmelCase  ,  image=UpperCAmelCase  ,  params=UpperCAmelCase  ,  prng_seed=UpperCAmelCase  ,  num_inference_steps=50  ,  jit=UpperCAmelCase  ,  ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														_snake_case       =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
														_snake_case       =  images[0, 253:256, 253:256, -1]

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  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