"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase			:			Optional[Any]										=						logging.get_logger(__name__)






def 					snake_case__       (		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   List[Any]=False     ):
			"""simple docstring"""
			lowerCamelCase__		:		str		     =[]

			# fmt: off
			# stem:
			rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')     )
			rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')     )

			rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')     )
			rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')     )

			# backbone
			rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')     )
			rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')     )
			rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')     )

			for stage_idx in range(len(config.backbone_config.depths     )     ):
						for layer_idx in range(config.backbone_config.depths[stage_idx]     ):
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''')     )
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''')     )
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''')     )
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''')     )
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''')     )
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''')     )
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''')     )
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''')     )
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''')     )

						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''')     )
						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''')     )
						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''')     )

			# transformer encoder
			for i in range(config.num_hidden_layers     ):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')     )
						rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')     )
						rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')     )
						rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')     )
						rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')     )
						rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')     )
						rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')     )
						rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')     )
						rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')     )
						rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')     )

			if base_model:
						# layernorm + pooler
						rename_keys.extend(
						    [
						        ('''norm.weight''', '''layernorm.weight'''),
						        ('''norm.bias''', '''layernorm.bias'''),
						        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
						        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
						    ]     )

						# if just the base model, we should remove "vit" from all keys that start with "vit"
						lowerCamelCase__		:		int		     =[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''     ) else pair for pair in rename_keys]
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ('''norm.weight''', '''vit.layernorm.weight'''),
						        ('''norm.bias''', '''vit.layernorm.bias'''),
						        ('''head.weight''', '''classifier.weight'''),
						        ('''head.bias''', '''classifier.bias'''),
						    ]     )
			# fmt: on

			return rename_keys






def 					snake_case__       (		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   List[Any]=False     ):
			"""simple docstring"""

			for i in range(config.num_hidden_layers     ):
						if base_model:
									lowerCamelCase__		:		int		     =''''''
						else:
									lowerCamelCase__		:		Dict		     ='''vit.'''
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						lowerCamelCase__		:		Union[str, Any]		     =state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''     )
						lowerCamelCase__		:		Union[str, Any]		     =state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''     )
						# next, add query, keys and values (in that order) to the state dict
						lowerCamelCase__		:		List[str]		     =in_proj_weight[
						    : config.hidden_size, :
						]
						lowerCamelCase__		:		int		     =in_proj_bias[: config.hidden_size]
						lowerCamelCase__		:		Any		     =in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						lowerCamelCase__		:		Tuple		     =in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						lowerCamelCase__		:		Any		     =in_proj_weight[
						    -config.hidden_size :, :
						]
						lowerCamelCase__		:		Optional[Any]		     =in_proj_bias[-config.hidden_size :]






def 					snake_case__       (		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =['''head.weight''', '''head.bias''']
			for k in ignore_keys:
						state_dict.pop(a__							,		a__     )






def 					snake_case__       (		__lowerCamelCase    :   List[Any]							,		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   List[Any]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Dict		     =dct.pop(a__     )
			lowerCamelCase__		:		List[Any]		     =val






def 					snake_case__       (		):
			"""simple docstring"""
			lowerCamelCase__		:		Dict		     ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase__		:		Union[str, Any]		     =Image.open(requests.get(a__							,		stream=a__     ).raw     )
			return im






@torch.no_grad()
def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Optional[int]=False     ):
			"""simple docstring"""
			lowerCamelCase__		:		int		     =BitConfig(
			    global_padding='''same'''							,		layer_type='''bottleneck'''							,		depths=(3, 4, 9)							,		out_features=['''stage3''']							,		embedding_dynamic_padding=a__							,		)
			lowerCamelCase__		:		Dict		     =ViTHybridConfig(backbone_config=a__							,		image_size=384							,		num_labels=1000     )
			lowerCamelCase__		:		Optional[int]		     =False

			# load original model from timm
			lowerCamelCase__		:		Optional[Any]		     =timm.create_model(a__							,		pretrained=a__     )
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			lowerCamelCase__		:		List[str]		     =timm_model.state_dict()
			if base_model:
						remove_classification_head_(a__     )
			lowerCamelCase__		:		Dict		     =create_rename_keys(a__							,		a__     )
			for src, dest in rename_keys:
						rename_key(a__							,		a__							,		a__     )
			read_in_q_k_v(a__							,		a__							,		a__     )

			lowerCamelCase__		:		int		     ='''huggingface/label-files'''
			lowerCamelCase__		:		List[Any]		     ='''imagenet-1k-id2label.json'''
			lowerCamelCase__		:		Optional[int]		     =json.load(open(hf_hub_download(a__							,		a__							,		repo_type='''dataset'''     )							,		'''r'''     )     )
			lowerCamelCase__		:		Dict		     ={int(a__     ): v for k, v in idalabel.items()}
			lowerCamelCase__		:		Any		     =idalabel
			lowerCamelCase__		:		Optional[Any]		     ={v: k for k, v in idalabel.items()}

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						lowerCamelCase__		:		Dict		     =ViTHybridModel(a__     ).eval()
			else:
						lowerCamelCase__		:		int		     =ViTHybridForImageClassification(a__     ).eval()
			model.load_state_dict(a__     )

			# create image processor
			lowerCamelCase__		:		Dict		     =create_transform(**resolve_data_config({}							,		model=a__     )     )
			lowerCamelCase__		:		Any		     =transform.transforms

			lowerCamelCase__		:		Dict		     ={
			    '''bilinear''': PILImageResampling.BILINEAR,
			    '''bicubic''': PILImageResampling.BICUBIC,
			    '''nearest''': PILImageResampling.NEAREST,
			}

			lowerCamelCase__		:		List[Any]		     =ViTHybridImageProcessor(
			    do_resize=a__							,		size={'''shortest_edge''': timm_transforms[0].size}							,		resample=pillow_resamplings[timm_transforms[0].interpolation.value]							,		do_center_crop=a__							,		crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}							,		do_normalize=a__							,		image_mean=timm_transforms[-1].mean.tolist()							,		image_std=timm_transforms[-1].std.tolist()							,		)

			lowerCamelCase__		:		Tuple		     =prepare_img()
			lowerCamelCase__		:		str		     =transform(a__     ).unsqueeze(0     )
			lowerCamelCase__		:		str		     =processor(a__							,		return_tensors='''pt'''     ).pixel_values

			# verify pixel values
			assert torch.allclose(a__							,		a__     )

			# verify logits
			with torch.no_grad():
						lowerCamelCase__		:		List[str]		     =model(a__     )
						lowerCamelCase__		:		Optional[int]		     =outputs.logits

			print('''Predicted class:'''							,		logits.argmax(-1     ).item()     )
			if base_model:
						lowerCamelCase__		:		int		     =timm_model.forward_features(a__     )
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(a__							,		outputs.pooler_output							,		atol=1e-3     )
			else:
						lowerCamelCase__		:		Tuple		     =timm_model(a__     )
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(a__							,		outputs.logits							,		atol=1e-3     )
			print('''Looks ok!'''     )

			if pytorch_dump_folder_path is not None:
						Path(a__     ).mkdir(exist_ok=a__     )
						print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}'''     )
						model.save_pretrained(a__     )
						print(f'''Saving processor to {pytorch_dump_folder_path}'''     )
						processor.save_pretrained(a__     )

			if push_to_hub:
						print(f'''Pushing model and processor to the hub {vit_name}'''     )
						model.push_to_hub(f'''ybelkada/{vit_name}'''     )
						processor.push_to_hub(f'''ybelkada/{vit_name}'''     )


if __name__ == "__main__":
		_lowercase			:			int										=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--vit_name",
		    default="vit_base_r50_s16_384",
		    type=str,
		    help="Name of the hybrid ViT timm model you\'d like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
		)
		parser.add_argument(
		    "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
		)

		_lowercase			:			Tuple										=						parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""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 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





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



					def 		snake_case	(	self							:  str					)-> Any:
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[Any]		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-canny''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[int]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[int]		     =controlnet_params

								lowerCamelCase__		:		Dict		     ='''bird'''
								lowerCamelCase__		:		List[str]		     =jax.device_count()
								lowerCamelCase__		:		Optional[Any]		     =pipe.prepare_text_inputs([prompts] * num_samples					)

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

								lowerCamelCase__		:		Optional[int]		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Dict		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		Tuple		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		Optional[int]		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =pipe(
								    prompt_ids=lowerCamelCase,  image=lowerCamelCase,  params=lowerCamelCase,  prng_seed=lowerCamelCase,  num_inference_steps=50,  jit=lowerCamelCase,  ).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

								lowerCamelCase__		:		Optional[Any]		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Dict		     =jnp.array(
								    [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]					)
								print(F'''output_slice: {output_slice}'''					)
								assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




					def 		snake_case	(	self							:  Optional[int]					)-> Optional[int]:
								lowerCamelCase__     ,       lowerCamelCase__		:		Dict		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-openpose''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		List[Any]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[Any]		     =controlnet_params

								lowerCamelCase__		:		int		     ='''Chef in the kitchen'''
								lowerCamelCase__		:		Optional[Any]		     =jax.device_count()
								lowerCamelCase__		:		Any		     =pipe.prepare_text_inputs([prompts] * num_samples					)

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

								lowerCamelCase__		:		Tuple		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Optional[Any]		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		int		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		int		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =pipe(
								    prompt_ids=lowerCamelCase,  image=lowerCamelCase,  params=lowerCamelCase,  prng_seed=lowerCamelCase,  num_inference_steps=50,  jit=lowerCamelCase,  ).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

								lowerCamelCase__		:		Dict		     =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
								lowerCamelCase__		:		List[str]		     =images[0, 253:256, 253:256, -1]

								lowerCamelCase__		:		int		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Any		     =jnp.array(
								    [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]					)
								print(F'''output_slice: {output_slice}'''					)
								assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




