



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': 512,
        'image_resolution': 768,
        'context_length': 16,
        'vocab_size': 49408,
        'vision_layers': 12,
        'vision_width': 768,
        'vision_patch_size': 32,
        'transformer_width': 512,
        'transformer_heads': 8,
        'transformer_layers': 12,
    },
    'vit_b16': {
        'embed_dim': 512,
        'image_resolution': 768,
        'context_length': 16,
        'vocab_size': 49408,
        'vision_layers': 12,
        'vision_width': 768,
        'vision_patch_size': 16,
        'transformer_width': 512,
        'transformer_heads': 8,
        'transformer_layers': 12,
    },
    'vit_l14': {
        'embed_dim': 768,
        'image_resolution': 840,
        'context_length': 16,
        'vocab_size': 49408,
        'vision_layers': 24,
        'vision_width': 1024,
        'vision_patch_size': 14,
        'transformer_width': 768,
        'transformer_heads': 12,
        'transformer_layers': 12,
    },
}






def 					a		(   A :					Optional[Any]				,  A :					Dict=""				,  A :					int="/" )	-> Optional[Any]:
					"""simple docstring"""





					a        =     []

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

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






def 					a		(   A :					Tuple )	-> Any:
					"""simple docstring"""





					return jax.tree_util.tree_map(lambda A : x.astype(jnp.floataa ) if x.dtype == jnp.bfloataa else x				,  _SCREAMING_SNAKE_CASE )






def 					a		(   A :					Any				,  A :					Optional[Any] )	-> Tuple:
					"""simple docstring"""





					a        =     pt_attn_layer.in_proj_weight.chunk(3				,  dim=0 )
					a        =     pt_attn_layer.in_proj_bias.chunk(3				,  dim=0 )

					a        =     pt_attn_layer.out_proj.weight
					a        =     pt_attn_layer.out_proj.bias

					a        =     q_proj
					a        =     q_proj_bias

					a        =     k_proj
					a        =     k_proj_bias

					a        =     v_proj
					a        =     v_proj_bias

					a        =     out_proj_weights
					a        =     out_proj_bias






def 					a		(   A :					Optional[int]				,  A :					Union[str, Any] )	-> List[str]:
					"""simple docstring"""





					copy_linear(hf_mlp.fca				,  pt_mlp.c_fc )
					copy_linear(hf_mlp.fca				,  pt_mlp.c_proj )






def 					a		(   A :					List[str]				,  A :					Optional[int] )	-> int:
					"""simple docstring"""





					a        =     pt_linear.weight
					a        =     pt_linear.bias






def 					a		(   A :					Optional[Any]				,  A :					List[str] )	-> str:
					"""simple docstring"""





					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 					a		(   A :					Any				,  A :					Dict )	-> Any:
					"""simple docstring"""





					for hf_layer, pt_layer in zip(_SCREAMING_SNAKE_CASE				,  _SCREAMING_SNAKE_CASE ):
										copy_layer(_SCREAMING_SNAKE_CASE				,  _SCREAMING_SNAKE_CASE )






def 					a		(   A :					Dict				,  A :					Dict )	-> List[str]:
					"""simple docstring"""





					a        =     pt_model.token_embedding.weight
					a        =     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 					a		(   A :					str				,  A :					Optional[Any] )	-> Tuple:
					"""simple docstring"""





					a        =     pt_model.text_projection.data.T

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






def 					a		(   A :					List[str]				,  A :					Union[str, Any] )	-> List[str]:
					"""simple docstring"""





					a        =     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
					a        =     pt_model.visual.conva.weight.data
					a        =     pt_model.visual.class_embedding
					a        =     pt_model.visual.positional_embedding.data

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






def 					a		(   A :					List[str]				,  A :					List[Any] )	-> List[str]:
					"""simple docstring"""





					a        =     flatten_nested_dict(flax_params["backbone"]["merged_class_token"] )

					a        =     torch.from_numpy(flax_class_token_params["scale"] )
					a        =     torch.from_numpy(flax_class_token_params["bias"] )
					a        =     nn.Parameter(_SCREAMING_SNAKE_CASE )
					a        =     nn.Parameter(_SCREAMING_SNAKE_CASE )






def 					a		(   A :					Optional[int]				,  A :					List[Any] )	-> List[Any]:
					"""simple docstring"""





					a        =     hf_model.state_dict()
					a        =     {}

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

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

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

										a        =     nn.Parameter(torch.from_numpy(_SCREAMING_SNAKE_CASE ) )

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

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

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

										a        =     nn.Parameter(torch.from_numpy(_SCREAMING_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_(_SCREAMING_SNAKE_CASE )






def 					a		(   A :					Any				,  A :					Dict )	-> Any:
					"""simple docstring"""





					for k, v in flax_attn_params.items():
										if k.startswith("transformer" ):
															a        =     k.replace("transformer.resblocks"				,  "text_model.encoder.layers" )
										else:
															a        =     k.replace("visual.transformer.resblocks"				,  "vision_model.encoder.layers" )

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

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

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

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

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






def 					a		(   A :					Optional[Any] )	-> Dict:
					"""simple docstring"""





					a        =     {}
					a        =     []

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

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






def 					a		(   A :					Dict				,  A :					Union[str, Any] )	-> Optional[Any]:
					"""simple docstring"""





					a        =     CLIP(**_SCREAMING_SNAKE_CASE )
					torch_model.eval()
					a        =     torch_model.state_dict()

					a        =     flatten_nested_dict(flax_params["backbone"]["clip"] )
					a        =     {}

					for flax_key, v in flax_clip_params.items():
										a        =     flax_key.replace("/"				,  "." )
										a        =     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" )
										):
															a        =     torch_key[5:]

										a        =     torch_key.replace("text_projection.kernel"				,  "text_projection" )
										a        =     torch_key.replace("visual.proj.kernel"				,  "visual.proj" )
										a        =     torch_key.replace(".scale"				,  ".weight" )
										a        =     torch_key.replace(".kernel"				,  ".weight" )

										if "conv" in torch_key or "downsample.0.weight" in torch_key:
															a        =     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
															a        =     v.T

										a        =     v

					a        =     _convert_attn_layers(_SCREAMING_SNAKE_CASE )
					new_torch_params.update(_SCREAMING_SNAKE_CASE )
					a        =     {}

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

					return torch_clip_params, torch_model, attn_params






@torch.no_grad()
def 					a		(   A :					Any				,  A :					Optional[int]				,  A :					Optional[int]				,  A :					Tuple				,  A :					List[Any]=None )	-> Dict:
					"""simple docstring"""





					a        =     Repository(_SCREAMING_SNAKE_CASE				,  clone_from=f'''google/{pytorch_dump_folder_path}''' )
					repo.git_pull()

					if config_path is not None:
										a        =     OwlViTConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
					else:
										a        =     OwlViTConfig()

					a        =     OwlViTModel(_SCREAMING_SNAKE_CASE ).eval()
					a        =     OwlViTForObjectDetection(_SCREAMING_SNAKE_CASE ).eval()

					copy_text_model_and_projection(_SCREAMING_SNAKE_CASE				,  _SCREAMING_SNAKE_CASE )
					copy_vision_model_and_projection(_SCREAMING_SNAKE_CASE				,  _SCREAMING_SNAKE_CASE )
					a        =     pt_backbone.logit_scale
					copy_flax_attn_params(_SCREAMING_SNAKE_CASE				,  _SCREAMING_SNAKE_CASE )

					a        =     hf_backbone
					copy_class_merge_token(_SCREAMING_SNAKE_CASE				,  _SCREAMING_SNAKE_CASE )
					copy_class_box_heads(_SCREAMING_SNAKE_CASE				,  _SCREAMING_SNAKE_CASE )

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

					# Initialize image processor
					a        =     OwlViTImageProcessor(
					    size=config.vision_config.image_size				,  crop_size=config.vision_config.image_size )
					# Initialize tokenizer
					a        =     CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"				,  pad_token="!"				,  model_max_length=16 )

					# Initialize processor
					a        =     OwlViTProcessor(image_processor=_SCREAMING_SNAKE_CASE				,  tokenizer=_SCREAMING_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:				Optional[Any]       =	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:				List[Any]       =	parser.parse_args()

				# Initialize PyToch clip model
				_lowercase:				Tuple       =	args.owlvit_version
				if model_name == "clip_b16":
								_lowercase:				Tuple       =	CONFIGS['vit_b16']
				elif model_name == "clip_b32":
								_lowercase:				Optional[int]       =	CONFIGS['vit_b32']
				elif model_name == "clip_l14":
								_lowercase:				List[Any]       =	CONFIGS['vit_l14']

				# Load from checkpoint and convert params to float-32
				_lowercase:				Optional[Any]       =	checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['optimizer']['target']
				_lowercase:				List[Any]       =	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:				str       =	convert_clip_backbone(flax_params, torch_config)

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





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left



@flax.struct.dataclass
class 			_lowercase							:





	"""simple docstring"""
	# setable values
	__A					=							None
	__A					=							None
	__A					=							None




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls()




@dataclass
class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							42
	__A					=							42
	__A					=							None




class 			_lowercase							(     lowerCAmelCase,   lowerCAmelCase			):





	"""simple docstring"""




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return True




	@register_to_config
	def __init__(self	, lowerCamelCase_ = 2000	, lowerCamelCase_ = 0.15	, lowerCamelCase_ = 0.01	, lowerCamelCase_ = 1348.0	, lowerCamelCase_ = 1E-5	, lowerCamelCase_ = 1	, ):
						"""simple docstring"""

						pass




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     ScoreSdeVeSchedulerState.create()
						return self.set_sigmas(
						    lowerCamelCase_	, self.config.num_train_timesteps	, self.config.sigma_min	, self.config.sigma_max	, self.config.sampling_eps	, )




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = ()	, lowerCamelCase_ = None	):
						"""simple docstring"""

						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

						a        =     jnp.linspace(1	, lowerCamelCase_	, lowerCamelCase_	)
						return state.replace(timesteps=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, ):
						"""simple docstring"""

						a        =     sigma_min if sigma_min is not None else self.config.sigma_min
						a        =     sigma_max if sigma_max is not None else self.config.sigma_max
						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if state.timesteps is None:
											a        =     self.set_timesteps(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)

						a        =     jnp.exp(jnp.linspace(jnp.log(lowerCamelCase_	)	, jnp.log(lowerCamelCase_	)	, lowerCamelCase_	)	)
						a        =     jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]	)

						return state.replace(discrete_sigmas=lowerCamelCase_	, sigmas=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						return jnp.where(timesteps == 0	, jnp.zeros_like(lowerCamelCase_	)	, state.discrete_sigmas[timesteps - 1]	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						if state.timesteps is None:
											raise ValueError(
											    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"	)

						a        =     timestep * jnp.ones(
						    sample.shape[0]	, )
						a        =     (timestep * (len(state.timesteps	) - 1)).long()

						a        =     state.discrete_sigmas[timesteps]
						a        =     self.get_adjacent_sigma(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)
						a        =     jnp.zeros_like(lowerCamelCase_	)
						a        =     (sigma**2 - adjacent_sigma**2) ** 0.5

						# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
						# also equation 47 shows the analog from SDE models to ancestral sampling methods
						a        =     diffusion.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)
						a        =     sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						a        =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

						if not return_dict:
											return (prev_sample, prev_sample_mean, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, prev_sample_mean=lowerCamelCase_	, state=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						if state.timesteps is None:
											raise ValueError(
											    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"	)

						# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
						# sample noise for correction
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)

						# compute step size from the model_output, the noise, and the snr
						a        =     jnp.linalg.norm(lowerCamelCase_	)
						a        =     jnp.linalg.norm(lowerCamelCase_	)
						a        =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
						a        =     step_size * jnp.ones(sample.shape[0]	)

						# compute corrected sample: model_output term and noise term
						a        =     step_size.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     sample + step_size * model_output
						a        =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
											return (prev_sample, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, state=lowerCamelCase_	)




	def __len__(self	):
						"""simple docstring"""

						return self.config.num_train_timesteps
