

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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class    __UpperCAmelCase	(			lowerCamelCase__		):
   UpperCamelCase  	=	42
   UpperCamelCase  	=	42






def      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     Optional[int]=0.999							,					UpperCAmelCase				:     Tuple="cosine"							,					)    ->		List[Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(UpperCAmelCase				:     List[str]     ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(UpperCAmelCase				:     int     ):
      return math.exp(t * -12.0     )

  else:
    raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''     )

  UpperCAmelCase			:				Dict										=					[]
  for i in range(UpperCAmelCase     ):
    UpperCAmelCase			:				Any										=					i / num_diffusion_timesteps
    UpperCAmelCase			:				List[Any]										=					(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(UpperCAmelCase     ) / alpha_bar_fn(UpperCAmelCase     )							,					UpperCAmelCase     )     )
  return torch.tensor(UpperCAmelCase							,					dtype=torch.floataa     )






class    __UpperCAmelCase	(			lowerCamelCase__     ,    lowerCamelCase__		):
   UpperCamelCase  	=	1



   @register_to_config
   def __init__(				self				:	str,						__A				:	int = 1_0_0_0,						__A				:	float = 0.0_0_0_1,						__A				:	float = 0.0_2,						__A				:	str = "linear",						__A				:	float = 0.0,						__A				:	Optional[np.ndarray] = None,						__A				:	bool = True,						):
     if trained_betas is not None:
       UpperCAmelCase			:				int										=					torch.from_numpy(__A		)
     elif beta_schedule == "linear":
       UpperCAmelCase			:				str										=					torch.linspace(__A,						__A,						__A,						dtype=torch.floataa		)
     elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       UpperCAmelCase			:				List[str]										=					(
           torch.linspace(beta_start**0.5,						beta_end**0.5,						__A,						dtype=torch.floataa		) ** 2
       )
     elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       UpperCAmelCase			:				Tuple										=					betas_for_alpha_bar(__A		)
     elif beta_schedule == "sigmoid":
       # GeoDiff sigmoid schedule
       UpperCAmelCase			:				Any										=					torch.linspace(-6,						6,						__A		)
       UpperCAmelCase			:				Tuple										=					torch.sigmoid(__A		) * (beta_end - beta_start) + beta_start
     else:
       raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''		)

     UpperCAmelCase			:				Optional[int]										=					1.0 - self.betas
     UpperCAmelCase			:				int										=					torch.cumprod(self.alphas,						dim=0		)
     UpperCAmelCase			:				int										=					torch.tensor(1.0		)

     UpperCAmelCase			:				Union[str, Any]										=					torch.tensor(1.0		)

     # standard deviation of the initial noise distribution
     UpperCAmelCase			:				str										=					1.0

     # setable values
     UpperCAmelCase			:				int										=					None
     UpperCAmelCase			:				Dict										=					torch.from_numpy(np.arange(0,						__A		)[::-1].copy()		)

     UpperCAmelCase			:				List[str]										=					eta



   def 			__magic_name__       (				self				:	List[str],						__A				:	torch.FloatTensor,						__A				:	Optional[int] = None		):
     return sample



   def 			__magic_name__       (				self				:	List[str],						__A				:	int,						__A				:	int = 1_0,						__A				:	int = 1_0,						__A				:	Union[str, torch.device] = None,						):
     UpperCAmelCase			:				Union[str, Any]										=					min(self.config.num_train_timesteps,						__A		)
     UpperCAmelCase			:				Optional[int]										=					num_inference_steps

     UpperCAmelCase			:				Union[str, Any]										=					[]

     UpperCAmelCase			:				Any										=					{}
     for j in range(0,						num_inference_steps - jump_length,						__A		):
       UpperCAmelCase			:				Tuple										=					jump_n_sample - 1

     UpperCAmelCase			:				str										=					num_inference_steps
     while t >= 1:
       UpperCAmelCase			:				Dict										=					t - 1
       timesteps.append(__A		)

       if jumps.get(__A,						0		) > 0:
         UpperCAmelCase			:				Dict										=					jumps[t] - 1
         for _ in range(__A		):
           UpperCAmelCase			:				Any										=					t + 1
           timesteps.append(__A		)

     UpperCAmelCase			:				Optional[Any]										=					np.array(__A		) * (self.config.num_train_timesteps // self.num_inference_steps)
     UpperCAmelCase			:				Dict										=					torch.from_numpy(__A		).to(__A		)



   def 			__magic_name__       (				self				:	List[Any],						__A				:	Any		):
     UpperCAmelCase			:				int										=					t - self.config.num_train_timesteps // self.num_inference_steps

     UpperCAmelCase			:				Optional[Any]										=					self.alphas_cumprod[t]
     UpperCAmelCase			:				Union[str, Any]										=					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
     UpperCAmelCase			:				List[Any]										=					1 - alpha_prod_t
     UpperCAmelCase			:				str										=					1 - alpha_prod_t_prev

     # For t > 0, compute predicted variance βt (see formula (6) and (7) from
     # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
     # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
     # variance to pred_sample
     # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
     # without eta.
     # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
     UpperCAmelCase			:				Union[str, Any]										=					(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

     return variance



   def 			__magic_name__       (				self				:	Tuple,						__A				:	torch.FloatTensor,						__A				:	int,						__A				:	torch.FloatTensor,						__A				:	torch.FloatTensor,						__A				:	torch.FloatTensor,						__A				:	Optional[torch.Generator] = None,						__A				:	bool = True,						):
     UpperCAmelCase			:				Union[str, Any]										=					timestep
     UpperCAmelCase			:				str										=					timestep - self.config.num_train_timesteps // self.num_inference_steps

     # 1. compute alphas, betas
     UpperCAmelCase			:				List[Any]										=					self.alphas_cumprod[t]
     UpperCAmelCase			:				str										=					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
     UpperCAmelCase			:				Any										=					1 - alpha_prod_t

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     UpperCAmelCase			:				int										=					(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
       UpperCAmelCase			:				Tuple										=					torch.clamp(__A,						-1,						1		)

     # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
     # substitute formula (7) in the algorithm coming from DDPM paper
     # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
     # DDIM schedule gives the same results as DDPM with eta = 1.0
     # Noise is being reused in 7. and 8., but no impact on quality has
     # been observed.

     # 5. Add noise
     UpperCAmelCase			:				Optional[Any]										=					model_output.device
     UpperCAmelCase			:				Optional[Any]										=					randn_tensor(model_output.shape,						generator=__A,						device=__A,						dtype=model_output.dtype		)
     UpperCAmelCase			:				List[str]										=					self.eta * self._get_variance(__A		) ** 0.5

     UpperCAmelCase			:				int										=					0
     if t > 0 and self.eta > 0:
       UpperCAmelCase			:				List[str]										=					std_dev_t * noise

     # 6. compute "direction pointing to x_t" of formula (12)
     # from https://arxiv.org/pdf/2010.02502.pdf
     UpperCAmelCase			:				Any										=					(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

     # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     UpperCAmelCase			:				str										=					alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

     # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
     UpperCAmelCase			:				Union[str, Any]										=					(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

     # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
     UpperCAmelCase			:				Optional[Any]										=					mask * prev_known_part + (1.0 - mask) * prev_unknown_part

     if not return_dict:
       return (
           pred_prev_sample,
           pred_original_sample,
       )

     return RePaintSchedulerOutput(prev_sample=__A,						pred_original_sample=__A		)



   def 			__magic_name__       (				self				:	Dict,						__A				:	Dict,						__A				:	Tuple,						__A				:	Tuple=None		):
     UpperCAmelCase			:				Optional[int]										=					self.config.num_train_timesteps // self.num_inference_steps

     for i in range(__A		):
       UpperCAmelCase			:				Optional[Any]										=					self.betas[timestep + i]
       if sample.device.type == "mps":
         # randn does not work reproducibly on mps
         UpperCAmelCase			:				List[str]										=					randn_tensor(sample.shape,						dtype=sample.dtype,						generator=__A		)
         UpperCAmelCase			:				Optional[int]										=					noise.to(sample.device		)
       else:
         UpperCAmelCase			:				Union[str, Any]										=					randn_tensor(sample.shape,						generator=__A,						device=sample.device,						dtype=sample.dtype		)

       # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
       UpperCAmelCase			:				Optional[int]										=					(1 - beta) ** 0.5 * sample + beta**0.5 * noise

     return sample



   def 			__magic_name__       (				self				:	Optional[Any],						__A				:	torch.FloatTensor,						__A				:	torch.FloatTensor,						__A				:	torch.IntTensor,						):
     raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''		)





   def __len__(				self				:	str		):
     return self.config.num_train_timesteps








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,
)


_lowerCamelCase							:  List[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      a__	(						UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     Dict=""							,					UpperCAmelCase				:     Optional[Any]="/"     )    ->		Optional[int]:
  UpperCAmelCase			:				str										=					[]

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

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

def      a__	(						UpperCAmelCase				:     Optional[int]     )    ->		Tuple:
  return jax.tree_util.tree_map(lambda UpperCAmelCase     : x.astype(jnp.floataa     ) if x.dtype == jnp.bfloataa else x							,					UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     Union[str, Any]							,					UpperCAmelCase				:     List[Any]     )    ->		str:
  UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				List[Any]										=					pt_attn_layer.in_proj_weight.chunk(3							,					dim=0     )
  UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				Tuple										=					pt_attn_layer.in_proj_bias.chunk(3							,					dim=0     )

  UpperCAmelCase			:				Optional[Any]										=					pt_attn_layer.out_proj.weight
  UpperCAmelCase			:				Tuple										=					pt_attn_layer.out_proj.bias

  UpperCAmelCase			:				List[str]										=					q_proj
  UpperCAmelCase			:				Optional[int]										=					q_proj_bias

  UpperCAmelCase			:				Any										=					k_proj
  UpperCAmelCase			:				Optional[Any]										=					k_proj_bias

  UpperCAmelCase			:				Optional[Any]										=					v_proj
  UpperCAmelCase			:				Optional[Any]										=					v_proj_bias

  UpperCAmelCase			:				str										=					out_proj_weights
  UpperCAmelCase			:				List[str]										=					out_proj_bias

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Dict     )    ->		Optional[int]:
  copy_linear(hf_mlp.fca							,					pt_mlp.c_fc     )
  copy_linear(hf_mlp.fca							,					pt_mlp.c_proj     )

def      a__	(						UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     str     )    ->		Dict:
  UpperCAmelCase			:				Dict										=					pt_linear.weight
  UpperCAmelCase			:				List[Any]										=					pt_linear.bias

def      a__	(						UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     int     )    ->		Tuple:
  # 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      a__	(						UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     Union[str, Any]     )    ->		int:
  for hf_layer, pt_layer in zip(UpperCAmelCase							,					UpperCAmelCase     ):
    copy_layer(UpperCAmelCase							,					UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     int							,					UpperCAmelCase				:     Dict     )    ->		Union[str, Any]:
  # copy  embeds
  UpperCAmelCase			:				List[Any]										=					pt_model.token_embedding.weight
  UpperCAmelCase			:				Any										=					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__	(						UpperCAmelCase				:     int							,					UpperCAmelCase				:     Tuple     )    ->		str:
  # copy projection
  UpperCAmelCase			:				int										=					pt_model.text_projection.data.T

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

def      a__	(						UpperCAmelCase				:     int							,					UpperCAmelCase				:     List[str]     )    ->		List[Any]:
  # copy projection
  UpperCAmelCase			:				List[Any]										=					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
  UpperCAmelCase			:				Dict										=					pt_model.visual.conva.weight.data
  UpperCAmelCase			:				Any										=					pt_model.visual.class_embedding
  UpperCAmelCase			:				str										=					pt_model.visual.positional_embedding.data

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

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Any     )    ->		Union[str, Any]:
  UpperCAmelCase			:				str										=					flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']     )

  UpperCAmelCase			:				str										=					torch.from_numpy(flax_class_token_params['''scale''']     )
  UpperCAmelCase			:				Union[str, Any]										=					torch.from_numpy(flax_class_token_params['''bias''']     )
  UpperCAmelCase			:				Union[str, Any]										=					nn.Parameter(UpperCAmelCase     )
  UpperCAmelCase			:				int										=					nn.Parameter(UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Optional[Any]     )    ->		List[Any]:
  UpperCAmelCase			:				int										=					hf_model.state_dict()
  UpperCAmelCase			:				Any										=					{}

  # Rename class prediction head flax params to pytorch HF
  UpperCAmelCase			:				Optional[Any]										=					flatten_nested_dict(flax_params['''class_head''']     )

  for flax_key, v in flax_class_params.items():
    UpperCAmelCase			:				List[str]										=					flax_key.replace('''/'''							,					'''.'''     )
    UpperCAmelCase			:				Dict										=					torch_key.replace('''.kernel'''							,					'''.weight'''     )
    UpperCAmelCase			:				Optional[int]										=					torch_key.replace('''Dense_0'''							,					'''dense0'''     )
    UpperCAmelCase			:				List[Any]										=					'''class_head.''' + torch_key

    if "weight" in torch_key and v.ndim == 2:
      UpperCAmelCase			:				Union[str, Any]										=					v.T

    UpperCAmelCase			:				List[str]										=					nn.Parameter(torch.from_numpy(UpperCAmelCase     )     )

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

  for flax_key, v in flax_box_params.items():
    UpperCAmelCase			:				Any										=					flax_key.replace('''/'''							,					'''.'''     )
    UpperCAmelCase			:				Any										=					torch_key.replace('''.kernel'''							,					'''.weight'''     )
    UpperCAmelCase			:				List[Any]										=					torch_key.replace('''_'''							,					''''''     ).lower()
    UpperCAmelCase			:				int										=					'''box_head.''' + torch_key

    if "weight" in torch_key and v.ndim == 2:
      UpperCAmelCase			:				List[str]										=					v.T

    UpperCAmelCase			:				int										=					nn.Parameter(torch.from_numpy(UpperCAmelCase     )     )

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

def      a__	(						UpperCAmelCase				:     int							,					UpperCAmelCase				:     List[Any]     )    ->		List[Any]:
  for k, v in flax_attn_params.items():
    if k.startswith('''transformer'''     ):
      UpperCAmelCase			:				Optional[int]										=					k.replace('''transformer.resblocks'''							,					'''text_model.encoder.layers'''     )
    else:
      UpperCAmelCase			:				Tuple										=					k.replace('''visual.transformer.resblocks'''							,					'''vision_model.encoder.layers'''     )

    UpperCAmelCase			:				List[Any]										=					torch_key.replace('''attn'''							,					'''self_attn'''     )
    UpperCAmelCase			:				Union[str, Any]										=					torch_key.replace('''key'''							,					'''k_proj'''     )
    UpperCAmelCase			:				int										=					torch_key.replace('''value'''							,					'''v_proj'''     )
    UpperCAmelCase			:				Dict										=					torch_key.replace('''query'''							,					'''q_proj'''     )
    UpperCAmelCase			:				int										=					torch_key.replace('''out'''							,					'''out_proj'''     )

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

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

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

    # Copy flax CLIP attn params to HF PyTorch params
    UpperCAmelCase			:				List[str]										=					torch.from_numpy(UpperCAmelCase     )
    hf_backbone.state_dict()[torch_key].copy_(UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     Union[str, Any]     )    ->		List[str]:
  UpperCAmelCase			:				Optional[Any]										=					{}
  UpperCAmelCase			:				Dict										=					[]

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

      processed_attn_layers.append(UpperCAmelCase     )
      UpperCAmelCase			:				List[str]										=					params[base + '''out.weight'''].shape[-1]
      UpperCAmelCase			:				Tuple										=					params[base + '''out.weight'''].reshape(UpperCAmelCase							,					UpperCAmelCase     ).T
      UpperCAmelCase			:				Tuple										=					params[base + '''out.bias''']
    else:
      UpperCAmelCase			:				Optional[int]										=					v
  return new_params

def      a__	(						UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     Optional[Any]     )    ->		int:
  UpperCAmelCase			:				Optional[int]										=					CLIP(**UpperCAmelCase     )
  torch_model.eval()
  UpperCAmelCase			:				Optional[Any]										=					torch_model.state_dict()

  UpperCAmelCase			:				List[str]										=					flatten_nested_dict(flax_params['''backbone''']['''clip''']     )
  UpperCAmelCase			:				Any										=					{}

  for flax_key, v in flax_clip_params.items():
    UpperCAmelCase			:				Optional[int]										=					flax_key.replace('''/'''							,					'''.'''     )
    UpperCAmelCase			:				Dict										=					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'''     )
    ):
      UpperCAmelCase			:				List[str]										=					torch_key[5:]

    UpperCAmelCase			:				Dict										=					torch_key.replace('''text_projection.kernel'''							,					'''text_projection'''     )
    UpperCAmelCase			:				Tuple										=					torch_key.replace('''visual.proj.kernel'''							,					'''visual.proj'''     )
    UpperCAmelCase			:				Dict										=					torch_key.replace('''.scale'''							,					'''.weight'''     )
    UpperCAmelCase			:				str										=					torch_key.replace('''.kernel'''							,					'''.weight'''     )

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

    UpperCAmelCase			:				Union[str, Any]										=					v

  UpperCAmelCase			:				Dict										=					_convert_attn_layers(UpperCAmelCase     )
  new_torch_params.update(UpperCAmelCase     )
  UpperCAmelCase			:				int										=					{}

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

  return torch_clip_params, torch_model, attn_params

@torch.no_grad()
def      a__	(						UpperCAmelCase				:     str							,					UpperCAmelCase				:     Union[str, Any]							,					UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     Optional[Any]=None     )    ->		Optional[int]:
  UpperCAmelCase			:				Optional[int]										=					Repository(UpperCAmelCase							,					clone_from=f'''google/{pytorch_dump_folder_path}'''     )
  repo.git_pull()

  if config_path is not None:
    UpperCAmelCase			:				str										=					OwlViTConfig.from_pretrained(UpperCAmelCase     )
  else:
    UpperCAmelCase			:				List[str]										=					OwlViTConfig()

  UpperCAmelCase			:				List[str]										=					OwlViTModel(UpperCAmelCase     ).eval()
  UpperCAmelCase			:				Optional[int]										=					OwlViTForObjectDetection(UpperCAmelCase     ).eval()

  copy_text_model_and_projection(UpperCAmelCase							,					UpperCAmelCase     )
  copy_vision_model_and_projection(UpperCAmelCase							,					UpperCAmelCase     )
  UpperCAmelCase			:				Tuple										=					pt_backbone.logit_scale
  copy_flax_attn_params(UpperCAmelCase							,					UpperCAmelCase     )

  UpperCAmelCase			:				int										=					hf_backbone
  copy_class_merge_token(UpperCAmelCase							,					UpperCAmelCase     )
  copy_class_box_heads(UpperCAmelCase							,					UpperCAmelCase     )

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

  # Initialize image processor
  UpperCAmelCase			:				List[str]										=					OwlViTImageProcessor(
      size=config.vision_config.image_size							,					crop_size=config.vision_config.image_size     )
  # Initialize tokenizer
  UpperCAmelCase			:				Union[str, Any]										=					CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''							,					pad_token='''!'''							,					model_max_length=16     )

  # Initialize processor
  UpperCAmelCase			:				str										=					OwlViTProcessor(image_processor=UpperCAmelCase							,					tokenizer=UpperCAmelCase     )
  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__":
     _lowerCamelCase							:  List[str]     =				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."
     )
     _lowerCamelCase							:  List[Any]     =				parser.parse_args()

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

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

     # Convert CLIP backbone
     _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase							:  Optional[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)





