"""simple docstring"""
from __future__ import annotations

from statistics import mean





def  lowerCAmelCase__     (      _UpperCamelCase							:					list[int]       )       ->					list[int]:





						"""simple docstring"""






						snake_case             =      2
						snake_case             =      list(_UpperCamelCase       )
						snake_case             =      [0] * len(_UpperCamelCase       )
						snake_case             =      0
						while True:
												snake_case             =      True
												for i, burst_time in enumerate(_UpperCamelCase       ):
																		if rem_burst_times[i] > 0:
																								snake_case             =      False
																								if rem_burst_times[i] > quantum:
																														t += quantum
																														rem_burst_times[i] -= quantum
																								else:
																														t += rem_burst_times[i]
																														snake_case             =      t - burst_time
																														snake_case             =      0
												if done is True:
																		return waiting_times





def  lowerCAmelCase__     (      _UpperCamelCase							:					list[int]			,      _UpperCamelCase							:					list[int]       )       ->					list[int]:





						"""simple docstring"""






						return [burst + waiting for burst, waiting in zip(_UpperCamelCase			,      _UpperCamelCase       )]


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__       =	[3, 5, 7]
					SCREAMING_SNAKE_CASE__       =	calculate_waiting_times(burst_times)
					SCREAMING_SNAKE_CASE__       =	calculate_turn_around_times(burst_times, waiting_times)
					print("Process ID \tBurst Time \tWaiting Time \tTurnaround Time")
					for i, burst_time in enumerate(burst_times):
										print(
										    f"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
										    f"""{turn_around_times[i]}"""
										)
					print(f"""\nAverage waiting time = {mean(waiting_times):.5f}""")
					print(f"""Average turn around time = {mean(turn_around_times):.5f}""")





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)





