






"""simple docstring"""



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,
)


_UpperCamelCase						:    Union[str, 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     :    Tuple						,   __snake_case     :    List[str]=""						,   __snake_case     :    List[str]="/"       ):





  '''simple docstring'''





  lowercase				       =			[]

  for k, v in params.items():
    lowercase				       =			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[int]       ):





  '''simple docstring'''





  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     :    str						,   __snake_case     :    str       ):





  '''simple docstring'''





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

  lowercase				       =			pt_attn_layer.out_proj.weight
  lowercase				       =			pt_attn_layer.out_proj.bias

  lowercase				       =			q_proj
  lowercase				       =			q_proj_bias

  lowercase				       =			k_proj
  lowercase				       =			k_proj_bias

  lowercase				       =			v_proj
  lowercase				       =			v_proj_bias

  lowercase				       =			out_proj_weights
  lowercase				       =			out_proj_bias
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Tuple       ):





  '''simple docstring'''





  copy_linear(hf_mlp.fca						,   pt_mlp.c_fc       )
  copy_linear(hf_mlp.fca						,   pt_mlp.c_proj       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int						,   __snake_case     :    Any       ):





  '''simple docstring'''





  lowercase				       =			pt_linear.weight
  lowercase				       =			pt_linear.bias
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]						,   __snake_case     :    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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  for hf_layer, pt_layer in zip(__snake_case						,   __snake_case       ):
    copy_layer(__snake_case						,   __snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict						,   __snake_case     :    Union[str, Any]       ):





  '''simple docstring'''





  lowercase				       =			pt_model.token_embedding.weight
  lowercase				       =			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     :    Optional[int]						,   __snake_case     :    List[Any]       ):





  '''simple docstring'''





  lowercase				       =			pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model						,   __snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict						,   __snake_case     :    Tuple       ):





  '''simple docstring'''





  lowercase				       =			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
  lowercase				       =			pt_model.visual.conva.weight.data
  lowercase				       =			pt_model.visual.class_embedding
  lowercase				       =			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     :    Tuple						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  lowercase				       =			flatten_nested_dict(flax_params['backbone']['merged_class_token']       )

  lowercase				       =			torch.from_numpy(flax_class_token_params['scale']       )
  lowercase				       =			torch.from_numpy(flax_class_token_params['bias']       )
  lowercase				       =			nn.Parameter(__snake_case       )
  lowercase				       =			nn.Parameter(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Any       ):





  '''simple docstring'''





  lowercase				       =			hf_model.state_dict()
  lowercase				       =			{}

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

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

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

    lowercase				       =			nn.Parameter(torch.from_numpy(__snake_case       )       )

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

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

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

    lowercase				       =			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     :    List[Any]						,   __snake_case     :    Union[str, Any]       ):





  '''simple docstring'''





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

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

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

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

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

    # Copy flax CLIP attn params to HF PyTorch params
    lowercase				       =			torch.from_numpy(__snake_case       )
    hf_backbone.state_dict()[torch_key].copy_(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple       ):





  '''simple docstring'''





  lowercase				       =			{}
  lowercase				       =			[]

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

      processed_attn_layers.append(__snake_case       )
      lowercase				       =			params[base + 'out.weight'].shape[-1]
      lowercase				       =			params[base + 'out.weight'].reshape(__snake_case						,   __snake_case       ).T
      lowercase				       =			params[base + 'out.bias']
    else:
      lowercase				       =			v
  return new_params
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]						,   __snake_case     :    Optional[Any]       ):





  '''simple docstring'''





  lowercase				       =			CLIP(**__snake_case       )
  torch_model.eval()
  lowercase				       =			torch_model.state_dict()

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

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

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

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

    lowercase				       =			v

  lowercase				       =			_convert_attn_layers(__snake_case       )
  new_torch_params.update(__snake_case       )
  lowercase				       =			{}

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

  return torch_clip_params, torch_model, attn_params
@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[str]						,   __snake_case     :    Tuple						,   __snake_case     :    Optional[int]						,   __snake_case     :    Tuple						,   __snake_case     :    Tuple=None       ):





  '''simple docstring'''





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

  if config_path is not None:
    lowercase				       =			OwlViTConfig.from_pretrained(__snake_case       )
  else:
    lowercase				       =			OwlViTConfig()

  lowercase				       =			OwlViTModel(__snake_case       ).eval()
  lowercase				       =			OwlViTForObjectDetection(__snake_case       ).eval()

  copy_text_model_and_projection(__snake_case						,   __snake_case       )
  copy_vision_model_and_projection(__snake_case						,   __snake_case       )
  lowercase				       =			pt_backbone.logit_scale
  copy_flax_attn_params(__snake_case						,   __snake_case       )

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

  # Initialize processor
  lowercase				       =			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__":
    _UpperCamelCase						:    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.'
    )
    _UpperCamelCase						:    Tuple		=						parser.parse_args()

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

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

    # Convert CLIP backbone
    _UpperCamelCase,						_UpperCamelCase,						_UpperCamelCase						:    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)







'''simple docstring'''




class      UpperCamelCase_					:





	def __init__(    self       )     ->     None:
								UpperCAmelCase	:						int					=						[0, 1]






	def 					_lowercase(    self						,						A       )     ->     list:
								if (difference := index - (len(self.sequence       ) - 2)) >= 1:
															for _ in range(A       ):
																						self.sequence.append(self.sequence[-1] + self.sequence[-2]       )
								return self.sequence[:index]




def  __lowerCamelCase    (   ) ->     None:
							print(
							    """Fibonacci Series Using Dynamic Programming\n"""      ,							"""Enter the index of the Fibonacci number you want to calculate """      ,							"""in the prompt below. (To exit enter exit or Ctrl-C)\n"""      ,							sep=""""""      ,							)
							UpperCAmelCase	:						Tuple					=						Fibonacci()

							while True:
														UpperCAmelCase	:						str					=						input(""">> """						)
														if prompt in {"exit", "quit"}:
																					break

														try:
																					UpperCAmelCase	:						int					=						int(_lowercase						)
														except ValueError:
																					print("""Enter a number or 'exit'"""						)
																					continue

														print(fibonacci.get(_lowercase						)						)


if __name__ == "__main__":
							main()

