import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


lowercase__    :       Optional[Any]						=			{
    '''vit_b32''': {
        '''embed_dim''': 5_1_2,
        '''image_resolution''': 7_6_8,
        '''context_length''': 1_6,
        '''vocab_size''': 4_9_4_0_8,
        '''vision_layers''': 1_2,
        '''vision_width''': 7_6_8,
        '''vision_patch_size''': 3_2,
        '''transformer_width''': 5_1_2,
        '''transformer_heads''': 8,
        '''transformer_layers''': 1_2,
    },
    '''vit_b16''': {
        '''embed_dim''': 5_1_2,
        '''image_resolution''': 7_6_8,
        '''context_length''': 1_6,
        '''vocab_size''': 4_9_4_0_8,
        '''vision_layers''': 1_2,
        '''vision_width''': 7_6_8,
        '''vision_patch_size''': 1_6,
        '''transformer_width''': 5_1_2,
        '''transformer_heads''': 8,
        '''transformer_layers''': 1_2,
    },
    '''vit_l14''': {
        '''embed_dim''': 7_6_8,
        '''image_resolution''': 8_4_0,
        '''context_length''': 1_6,
        '''vocab_size''': 4_9_4_0_8,
        '''vision_layers''': 2_4,
        '''vision_width''': 1_0_2_4,
        '''vision_patch_size''': 1_4,
        '''transformer_width''': 7_6_8,
        '''transformer_heads''': 1_2,
        '''transformer_layers''': 1_2,
    },
}

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__=""     ,						snake_case__="/"							)  ->			str:
							lowerCAmelCase				=	[]

							for k, v in params.items():
														lowerCAmelCase				=	parent_key + sep + k if parent_key else k

														if isinstance(snake_case__     ,						collections.MutableMapping							):
																					items.extend(flatten_nested_dict(snake_case__     ,						snake_case__     ,						sep=snake_case__							).items()							)
														else:
																					items.append((new_key, v)							)
							return dict(snake_case__							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			Optional[Any]:
							return jax.tree_util.tree_map(lambda snake_case__							: x.astype(jnp.floataa							) if x.dtype == jnp.bfloataa else x     ,						snake_case__							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			int:
							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	pt_attn_layer.in_proj_weight.chunk(3     ,						dim=0							)
							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	pt_attn_layer.in_proj_bias.chunk(3     ,						dim=0							)

							lowerCAmelCase				=	pt_attn_layer.out_proj.weight
							lowerCAmelCase				=	pt_attn_layer.out_proj.bias

							lowerCAmelCase				=	q_proj
							lowerCAmelCase				=	q_proj_bias

							lowerCAmelCase				=	k_proj
							lowerCAmelCase				=	k_proj_bias

							lowerCAmelCase				=	v_proj
							lowerCAmelCase				=	v_proj_bias

							lowerCAmelCase				=	out_proj_weights
							lowerCAmelCase				=	out_proj_bias

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			int:
							copy_linear(hf_mlp.fca     ,						pt_mlp.c_fc							)
							copy_linear(hf_mlp.fca     ,						pt_mlp.c_proj							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			int:
							lowerCAmelCase				=	pt_linear.weight
							lowerCAmelCase				=	pt_linear.bias

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Optional[int]:
							# copy layer norms
							copy_linear(hf_layer.layer_norma     ,						pt_layer.ln_a							)
							copy_linear(hf_layer.layer_norma     ,						pt_layer.ln_a							)

							# copy MLP
							copy_mlp(hf_layer.mlp     ,						pt_layer.mlp							)

							# copy attn
							copy_attn_layer(hf_layer.self_attn     ,						pt_layer.attn							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Any:
							for hf_layer, pt_layer in zip(snake_case__     ,						snake_case__							):
														copy_layer(snake_case__     ,						snake_case__							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			int:
							# copy  embeds
							lowerCAmelCase				=	pt_model.token_embedding.weight
							lowerCAmelCase				=	pt_model.positional_embedding

							# copy layer norm
							copy_linear(hf_encoder.final_layer_norm     ,						pt_model.ln_final							)

							# copy hidden layers
							copy_layers(hf_encoder.encoder.layers     ,						pt_model.transformer.resblocks							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Optional[int]:
							# copy projection
							lowerCAmelCase				=	pt_model.text_projection.data.T

							# copy text encoder
							copy_encoder(hf_model.text_model     ,						snake_case__							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Any:
							# copy projection
							lowerCAmelCase				=	pt_model.visual.proj.data.T

							# copy layer norms
							copy_linear(hf_model.vision_model.pre_layernorm     ,						pt_model.visual.ln_pre							)
							copy_linear(hf_model.vision_model.post_layernorm     ,						pt_model.visual.ln_post							)

							# copy embeds
							lowerCAmelCase				=	pt_model.visual.conva.weight.data
							lowerCAmelCase				=	pt_model.visual.class_embedding
							lowerCAmelCase				=	pt_model.visual.positional_embedding.data

							# copy encoder
							copy_layers(hf_model.vision_model.encoder.layers     ,						pt_model.visual.transformer.resblocks							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Any:
							lowerCAmelCase				=	flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']							)

							lowerCAmelCase				=	torch.from_numpy(flax_class_token_params['''scale''']							)
							lowerCAmelCase				=	torch.from_numpy(flax_class_token_params['''bias''']							)
							lowerCAmelCase				=	nn.Parameter(snake_case__							)
							lowerCAmelCase				=	nn.Parameter(snake_case__							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Dict:
							lowerCAmelCase				=	hf_model.state_dict()
							lowerCAmelCase				=	{}

							# Rename class prediction head flax params to pytorch HF
							lowerCAmelCase				=	flatten_nested_dict(flax_params['''class_head''']							)

							for flax_key, v in flax_class_params.items():
														lowerCAmelCase				=	flax_key.replace('''/'''     ,						'''.'''							)
														lowerCAmelCase				=	torch_key.replace('''.kernel'''     ,						'''.weight'''							)
														lowerCAmelCase				=	torch_key.replace('''Dense_0'''     ,						'''dense0'''							)
														lowerCAmelCase				=	'''class_head.''' + torch_key

														if "weight" in torch_key and v.ndim == 2:
																					lowerCAmelCase				=	v.T

														lowerCAmelCase				=	nn.Parameter(torch.from_numpy(snake_case__							)							)

							# Rename box prediction box flax params to pytorch HF
							lowerCAmelCase				=	flatten_nested_dict(flax_params['''obj_box_head''']							)

							for flax_key, v in flax_box_params.items():
														lowerCAmelCase				=	flax_key.replace('''/'''     ,						'''.'''							)
														lowerCAmelCase				=	torch_key.replace('''.kernel'''     ,						'''.weight'''							)
														lowerCAmelCase				=	torch_key.replace('''_'''     ,						''''''							).lower()
														lowerCAmelCase				=	'''box_head.''' + torch_key

														if "weight" in torch_key and v.ndim == 2:
																					lowerCAmelCase				=	v.T

														lowerCAmelCase				=	nn.Parameter(torch.from_numpy(snake_case__							)							)

							# Copy flax params to PyTorch params
							for name, param in new_params.items():
														if name in pt_params.keys():
																					pt_params[name].copy_(snake_case__							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Dict:
							for k, v in flax_attn_params.items():
														if k.startswith('''transformer'''							):
																					lowerCAmelCase				=	k.replace('''transformer.resblocks'''     ,						'''text_model.encoder.layers'''							)
														else:
																					lowerCAmelCase				=	k.replace('''visual.transformer.resblocks'''     ,						'''vision_model.encoder.layers'''							)

														lowerCAmelCase				=	torch_key.replace('''attn'''     ,						'''self_attn'''							)
														lowerCAmelCase				=	torch_key.replace('''key'''     ,						'''k_proj'''							)
														lowerCAmelCase				=	torch_key.replace('''value'''     ,						'''v_proj'''							)
														lowerCAmelCase				=	torch_key.replace('''query'''     ,						'''q_proj'''							)
														lowerCAmelCase				=	torch_key.replace('''out'''     ,						'''out_proj'''							)

														if "bias" in torch_key and v.ndim == 2:
																					lowerCAmelCase				=	v.shape[0] * v.shape[1]
																					lowerCAmelCase				=	v.reshape(snake_case__							)

														if "weight" in torch_key and "out" in torch_key:
																					lowerCAmelCase				=	(v.shape[0] * v.shape[1], v.shape[2])
																					lowerCAmelCase				=	v.reshape(snake_case__							).T

														if "weight" in torch_key and "out" not in torch_key:
																					lowerCAmelCase				=	(v.shape[0], v.shape[1] * v.shape[2])
																					lowerCAmelCase				=	v.reshape(snake_case__							).T

														# Copy flax CLIP attn params to HF PyTorch params
														lowerCAmelCase				=	torch.from_numpy(snake_case__							)
														hf_backbone.state_dict()[torch_key].copy_(snake_case__							)

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			List[str]:
							lowerCAmelCase				=	{}
							lowerCAmelCase				=	[]

							for k, v in params.items():
														if "attn." in k:
																					lowerCAmelCase				=	k[: k.rindex('''attn.'''							) + 5]
																					if base in processed_attn_layers:
																												continue

																					processed_attn_layers.append(snake_case__							)
																					lowerCAmelCase				=	params[base + '''out.weight'''].shape[-1]
																					lowerCAmelCase				=	params[base + '''out.weight'''].reshape(snake_case__     ,						snake_case__							).T
																					lowerCAmelCase				=	params[base + '''out.bias''']
														else:
																					lowerCAmelCase				=	v
							return new_params

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			str:
							lowerCAmelCase				=	CLIP(**snake_case__							)
							torch_model.eval()
							lowerCAmelCase				=	torch_model.state_dict()

							lowerCAmelCase				=	flatten_nested_dict(flax_params['''backbone''']['''clip''']							)
							lowerCAmelCase				=	{}

							for flax_key, v in flax_clip_params.items():
														lowerCAmelCase				=	flax_key.replace('''/'''     ,						'''.'''							)
														lowerCAmelCase				=	torch_key.replace('''text.token_embedding.embedding'''     ,						'''token_embedding.kernel'''							)

														if (
														    torch_key.startswith('''text.transformer'''							)
														    or torch_key.startswith('''text.text_projection'''							)
														    or torch_key.startswith('''text.ln_final'''							)
														    or torch_key.startswith('''text.positional_embedding'''							)
														):
																					lowerCAmelCase				=	torch_key[5:]

														lowerCAmelCase				=	torch_key.replace('''text_projection.kernel'''     ,						'''text_projection'''							)
														lowerCAmelCase				=	torch_key.replace('''visual.proj.kernel'''     ,						'''visual.proj'''							)
														lowerCAmelCase				=	torch_key.replace('''.scale'''     ,						'''.weight'''							)
														lowerCAmelCase				=	torch_key.replace('''.kernel'''     ,						'''.weight'''							)

														if "conv" in torch_key or "downsample.0.weight" in torch_key:
																					lowerCAmelCase				=	v.transpose(3     ,						2     ,						0     ,						1							)

														elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
																					# Fully connected layers are transposed, embeddings are not
																					lowerCAmelCase				=	v.T

														lowerCAmelCase				=	v

							lowerCAmelCase				=	_convert_attn_layers(snake_case__							)
							new_torch_params.update(snake_case__							)
							lowerCAmelCase				=	{}

							# Copy flax CLIP backbone params to PyTorch params
							for name, param in new_torch_params.items():
														if name in torch_clip_params.keys():
																					lowerCAmelCase				=	torch.from_numpy(new_torch_params[name]							)
																					torch_clip_params[name].copy_(snake_case__							)
														else:
																					lowerCAmelCase				=	param

							return torch_clip_params, torch_model, attn_params

@torch.no_grad()
def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__=None							)  ->			Optional[Any]:
							lowerCAmelCase				=	Repository(snake_case__     ,						clone_from=f"google/{pytorch_dump_folder_path}"							)
							repo.git_pull()

							if config_path is not None:
														lowerCAmelCase				=	OwlViTConfig.from_pretrained(snake_case__							)
							else:
														lowerCAmelCase				=	OwlViTConfig()

							lowerCAmelCase				=	OwlViTModel(snake_case__							).eval()
							lowerCAmelCase				=	OwlViTForObjectDetection(snake_case__							).eval()

							copy_text_model_and_projection(snake_case__     ,						snake_case__							)
							copy_vision_model_and_projection(snake_case__     ,						snake_case__							)
							lowerCAmelCase				=	pt_backbone.logit_scale
							copy_flax_attn_params(snake_case__     ,						snake_case__							)

							lowerCAmelCase				=	hf_backbone
							copy_class_merge_token(snake_case__     ,						snake_case__							)
							copy_class_box_heads(snake_case__     ,						snake_case__							)

							# Save HF model
							hf_model.save_pretrained(repo.local_dir							)

							# Initialize image processor
							lowerCAmelCase				=	OwlViTImageProcessor(
							    size=config.vision_config.image_size     ,						crop_size=config.vision_config.image_size							)
							# Initialize tokenizer
							lowerCAmelCase				=	CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''     ,						pad_token='''!'''     ,						model_max_length=1_6							)

							# Initialize processor
							lowerCAmelCase				=	OwlViTProcessor(image_processor=snake_case__     ,						tokenizer=snake_case__							)
							image_processor.save_pretrained(repo.local_dir							)
							processor.save_pretrained(repo.local_dir							)

							repo.git_add()
							repo.git_commit('''Upload model and processor'''							)
							repo.git_push()


if __name__ == "__main__":
	lowercase__    :       int						=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--owlvit_version''',
	    default=None,
	    type=str,
	    required=True,
	    help='''OWL-ViT model name [clip_b16, clip_b32, clip_l14].''',
	)
	parser.add_argument(
	    '''--owlvit_checkpoint''', default=None, type=str, required=True, help='''Path to flax model checkpoint.'''
	)
	parser.add_argument('''--hf_config''', default=None, type=str, required=True, help='''Path to HF model config.''')
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default='''hf_model''', type=str, help='''Path to the output PyTorch model.'''
	)
	lowercase__    :       str						=			parser.parse_args()

	# Initialize PyToch clip model
	lowercase__    :       Union[str, Any]						=			args.owlvit_version
	if model_name == "clip_b16":
		lowercase__    :       Union[str, Any]						=			CONFIGS['''vit_b16''']
	elif model_name == "clip_b32":
		lowercase__    :       Optional[Any]						=			CONFIGS['''vit_b32''']
	elif model_name == "clip_l14":
		lowercase__    :       str						=			CONFIGS['''vit_l14''']

	# Load from checkpoint and convert params to float-32
	lowercase__    :       List[str]						=			checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
	lowercase__    :       Dict						=			jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
	del variables

	# Convert CLIP backbone
	lowercase__			,					lowercase__			,					lowercase__    :       Any						=			convert_clip_backbone(flax_params, torch_config)

	convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)




import math

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			float:
							lowerCAmelCase				=	math.log(2     ,						1_0							) * number
							lowerCAmelCase				=	round((log_number - int(snake_case__							))     ,						1_5							)

							return difference

def      SCREAMING_SNAKE_CASE_  (		snake_case__ = 6_7_8_9_1_0							)  ->			int:
							lowerCAmelCase				=	9_0
							lowerCAmelCase				=	0

							lowerCAmelCase				=	math.log(1.23     ,						1_0							)
							lowerCAmelCase				=	math.log(1.24     ,						1_0							)
							lowerCAmelCase				=	0

							while position < number:
														lowerCAmelCase				=	log_difference(snake_case__							)

														if difference >= upper_limit:
																					power_iterator += 9_3

														elif difference < lower_limit:
																					power_iterator += 1_9_6

														else:
																					lowerCAmelCase				=	power_iterator
																					power_iterator += 1_9_6
																					position += 1

							return previous_power


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	print(f'{solution() = }')



