






'''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		:		Optional[Any]								=			{
    'vit_b32': {
        'embed_dim': 512,
        'image_resolution': 768,
        'context_length': 16,
        'vocab_size': 49_408,
        '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': 49_408,
        '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': 49_408,
        'vision_layers': 24,
        'vision_width': 1_024,
        'vision_patch_size': 14,
        'transformer_width': 768,
        'transformer_heads': 12,
        'transformer_layers': 12,
    },
}
def __UpperCAmelCase (			A			:	int	,   A			:	Union[str, Any]=""	,   A			:	Union[str, Any]="/"						)  -> List[Any]:
			UpperCAmelCase_     :   int       =   []

			for k, v in params.items():
						UpperCAmelCase_     :   Optional[int]       =   parent_key + sep + k if parent_key else k

						if isinstance(A	,   collections.MutableMapping						):
									items.extend(flatten_nested_dict(A	,   A	,   sep=A						).items()						)
						else:
									items.append((new_key, v)						)
			return dict(A						)
def __UpperCAmelCase (			A			:	Dict						)  -> Union[str, Any]:
			return jax.tree_util.tree_map(lambda A						: x.astype(jnp.floataa						) if x.dtype == jnp.bfloataa else x	,   A						)
def __UpperCAmelCase (			A			:	Union[str, Any]	,   A			:	Any						)  -> Optional[Any]:
			UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_     :   Dict       =   pt_attn_layer.in_proj_weight.chunk(3	,   dim=0						)
			UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_     :   Dict       =   pt_attn_layer.in_proj_bias.chunk(3	,   dim=0						)

			UpperCAmelCase_     :   Union[str, Any]       =   pt_attn_layer.out_proj.weight
			UpperCAmelCase_     :   Union[str, Any]       =   pt_attn_layer.out_proj.bias

			UpperCAmelCase_     :   Union[str, Any]       =   q_proj
			UpperCAmelCase_     :   List[Any]       =   q_proj_bias

			UpperCAmelCase_     :   Any       =   k_proj
			UpperCAmelCase_     :   List[str]       =   k_proj_bias

			UpperCAmelCase_     :   Dict       =   v_proj
			UpperCAmelCase_     :   Optional[int]       =   v_proj_bias

			UpperCAmelCase_     :   str       =   out_proj_weights
			UpperCAmelCase_     :   Any       =   out_proj_bias
def __UpperCAmelCase (			A			:	List[str]	,   A			:	List[Any]						)  -> Optional[int]:
			copy_linear(hf_mlp.fca	,   pt_mlp.c_fc						)
			copy_linear(hf_mlp.fca	,   pt_mlp.c_proj						)
def __UpperCAmelCase (			A			:	Any	,   A			:	Tuple						)  -> str:
			UpperCAmelCase_     :   List[Any]       =   pt_linear.weight
			UpperCAmelCase_     :   Optional[Any]       =   pt_linear.bias
def __UpperCAmelCase (			A			:	List[str]	,   A			:	List[str]						)  -> List[Any]:
			# 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 __UpperCAmelCase (			A			:	int	,   A			:	Optional[int]						)  -> int:
			for hf_layer, pt_layer in zip(A	,   A						):
						copy_layer(A	,   A						)
def __UpperCAmelCase (			A			:	Any	,   A			:	List[Any]						)  -> Union[str, Any]:
			# copy  embeds
			UpperCAmelCase_     :   Tuple       =   pt_model.token_embedding.weight
			UpperCAmelCase_     :   int       =   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 __UpperCAmelCase (			A			:	Any	,   A			:	List[Any]						)  -> Tuple:
			# copy projection
			UpperCAmelCase_     :   Any       =   pt_model.text_projection.data.T

			# copy text encoder
			copy_encoder(hf_model.text_model	,   A						)
def __UpperCAmelCase (			A			:	Tuple	,   A			:	List[str]						)  -> List[str]:
			# copy projection
			UpperCAmelCase_     :   str       =   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_     :   Dict       =   pt_model.visual.class_embedding
			UpperCAmelCase_     :   Tuple       =   pt_model.visual.positional_embedding.data

			# copy encoder
			copy_layers(hf_model.vision_model.encoder.layers	,   pt_model.visual.transformer.resblocks						)
def __UpperCAmelCase (			A			:	int	,   A			:	List[str]						)  -> Any:
			UpperCAmelCase_     :   Optional[int]       =   flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']						)

			UpperCAmelCase_     :   str       =   torch.from_numpy(flax_class_token_params['''scale''']						)
			UpperCAmelCase_     :   List[Any]       =   torch.from_numpy(flax_class_token_params['''bias''']						)
			UpperCAmelCase_     :   str       =   nn.Parameter(A						)
			UpperCAmelCase_     :   str       =   nn.Parameter(A						)
def __UpperCAmelCase (			A			:	List[str]	,   A			:	Any						)  -> Tuple:
			UpperCAmelCase_     :   Union[str, Any]       =   hf_model.state_dict()
			UpperCAmelCase_     :   Any       =   {}

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

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

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

						UpperCAmelCase_     :   Union[str, Any]       =   nn.Parameter(torch.from_numpy(A						)						)

			# 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_     :   Union[str, Any]       =   flax_key.replace('''/'''	,   '''.'''						)
						UpperCAmelCase_     :   Tuple       =   torch_key.replace('''.kernel'''	,   '''.weight'''						)
						UpperCAmelCase_     :   Dict       =   torch_key.replace('''_'''	,   ''''''						).lower()
						UpperCAmelCase_     :   Any       =   '''box_head.''' + torch_key

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

						UpperCAmelCase_     :   Optional[int]       =   nn.Parameter(torch.from_numpy(A						)						)

			# Copy flax params to PyTorch params
			for name, param in new_params.items():
						if name in pt_params.keys():
									pt_params[name].copy_(A						)
def __UpperCAmelCase (			A			:	Dict	,   A			:	Optional[int]						)  -> int:
			for k, v in flax_attn_params.items():
						if k.startswith('''transformer'''						):
									UpperCAmelCase_     :   Tuple       =   k.replace('''transformer.resblocks'''	,   '''text_model.encoder.layers'''						)
						else:
									UpperCAmelCase_     :   Dict       =   k.replace('''visual.transformer.resblocks'''	,   '''vision_model.encoder.layers'''						)

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

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

						if "weight" in torch_key and "out" in torch_key:
									UpperCAmelCase_     :   Optional[Any]       =   (v.shape[0] * v.shape[1], v.shape[2])
									UpperCAmelCase_     :   Union[str, Any]       =   v.reshape(A						).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_     :   Tuple       =   v.reshape(A						).T

						# Copy flax CLIP attn params to HF PyTorch params
						UpperCAmelCase_     :   Union[str, Any]       =   torch.from_numpy(A						)
						hf_backbone.state_dict()[torch_key].copy_(A						)
def __UpperCAmelCase (			A			:	Union[str, Any]						)  -> List[Any]:
			UpperCAmelCase_     :   Any       =   {}
			UpperCAmelCase_     :   List[Any]       =   []

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

									processed_attn_layers.append(A						)
									UpperCAmelCase_     :   Optional[int]       =   params[base + '''out.weight'''].shape[-1]
									UpperCAmelCase_     :   List[str]       =   params[base + '''out.weight'''].reshape(A	,   A						).T
									UpperCAmelCase_     :   Tuple       =   params[base + '''out.bias''']
						else:
									UpperCAmelCase_     :   int       =   v
			return new_params
def __UpperCAmelCase (			A			:	str	,   A			:	int						)  -> Any:
			UpperCAmelCase_     :   List[str]       =   CLIP(**A						)
			torch_model.eval()
			UpperCAmelCase_     :   str       =   torch_model.state_dict()

			UpperCAmelCase_     :   int       =   flatten_nested_dict(flax_params['''backbone''']['''clip''']						)
			UpperCAmelCase_     :   int       =   {}

			for flax_key, v in flax_clip_params.items():
						UpperCAmelCase_     :   int       =   flax_key.replace('''/'''	,   '''.'''						)
						UpperCAmelCase_     :   Tuple       =   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_     :   Union[str, Any]       =   torch_key[5:]

						UpperCAmelCase_     :   Optional[Any]       =   torch_key.replace('''text_projection.kernel'''	,   '''text_projection'''						)
						UpperCAmelCase_     :   Optional[Any]       =   torch_key.replace('''visual.proj.kernel'''	,   '''visual.proj'''						)
						UpperCAmelCase_     :   Any       =   torch_key.replace('''.scale'''	,   '''.weight'''						)
						UpperCAmelCase_     :   List[Any]       =   torch_key.replace('''.kernel'''	,   '''.weight'''						)

						if "conv" in torch_key or "downsample.0.weight" in torch_key:
									UpperCAmelCase_     :   Union[str, Any]       =   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_     :   Optional[int]       =   v.T

						UpperCAmelCase_     :   List[str]       =   v

			UpperCAmelCase_     :   Any       =   _convert_attn_layers(A						)
			new_torch_params.update(A						)
			UpperCAmelCase_     :   Any       =   {}

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

			return torch_clip_params, torch_model, attn_params
@torch.no_grad()
def __UpperCAmelCase (			A			:	Tuple	,   A			:	Any	,   A			:	Any	,   A			:	Dict	,   A			:	Any=None						)  -> Dict:
			UpperCAmelCase_     :   Optional[int]       =   Repository(A	,   clone_from=F"google/{pytorch_dump_folder_path}"						)
			repo.git_pull()

			if config_path is not None:
						UpperCAmelCase_     :   Union[str, Any]       =   OwlViTConfig.from_pretrained(A						)
			else:
						UpperCAmelCase_     :   Union[str, Any]       =   OwlViTConfig()

			UpperCAmelCase_     :   List[str]       =   OwlViTModel(A						).eval()
			UpperCAmelCase_     :   int       =   OwlViTForObjectDetection(A						).eval()

			copy_text_model_and_projection(A	,   A						)
			copy_vision_model_and_projection(A	,   A						)
			UpperCAmelCase_     :   Dict       =   pt_backbone.logit_scale
			copy_flax_attn_params(A	,   A						)

			UpperCAmelCase_     :   Dict       =   hf_backbone
			copy_class_merge_token(A	,   A						)
			copy_class_box_heads(A	,   A						)

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

			# Initialize image processor
			UpperCAmelCase_     :   Any       =   OwlViTImageProcessor(
			    size=config.vision_config.image_size	,   crop_size=config.vision_config.image_size						)
			# Initialize tokenizer
			UpperCAmelCase_     :   List[str]       =   CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''	,   pad_token='''!'''	,   model_max_length=1_6						)

			# Initialize processor
			UpperCAmelCase_     :   str       =   OwlViTProcessor(image_processor=A	,   tokenizer=A						)
			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		:		Dict								=			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		:		List[str]								=			parser.parse_args()

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

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





def __UpperCAmelCase (			A			:	list[list[int]]						)  -> bool:
			# must be
			UpperCAmelCase_     :   int       =   [list(A						) for row in matrix]
			if matrix and isinstance(A	,   A						):
						if isinstance(matrix[0]	,   A						):
									UpperCAmelCase_     :   Optional[Any]       =   0
									for row in matrix:
												if prev_len == 0:
															UpperCAmelCase_     :   List[str]       =   len(A						)
															UpperCAmelCase_     :   Any       =   True
												else:
															UpperCAmelCase_     :   Tuple       =   prev_len == len(A						)
						else:
									UpperCAmelCase_     :   Any       =   True
			else:
						UpperCAmelCase_     :   Dict       =   False

			return result
def __UpperCAmelCase (			A			:	list[list[int]]						)  -> None:
			if check_matrix(A						) and len(A						) > 0:
						UpperCAmelCase_     :   Dict       =   [list(A						) for row in a]
						UpperCAmelCase_     :   List[Any]       =   len(A						)
						if isinstance(a[0]	,   A						):
									UpperCAmelCase_     :   Any       =   len(a[0]						)
						else:
									for dat in a:
												print(A						)
									return

						# horizotal printing increasing
						for i in range(0	,   A						):
									print(a[0][i]						)
						# vertical printing down
						for i in range(1	,   A						):
									print(a[i][mat_col - 1]						)
						# horizotal printing decreasing
						if mat_row > 1:
									for i in range(mat_col - 2	,   -1	,   -1						):
												print(a[mat_row - 1][i]						)
        # vertical printing up
						for i in range(mat_row - 2	,   0	,   -1						):
									print(a[i][0]						)
						UpperCAmelCase_     :   Union[str, Any]       =   [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
						if len(A						) > 0:
									spiral_print_clockwise(A						)
						else:
									return
			else:
						print('''Not a valid matrix'''						)
						return
def __UpperCAmelCase (			A			:	list[list]						)  -> list[int]:
			if matrix:
						return list(matrix.pop(0						)						) + spiral_traversal(list(zip(*A						)						)[::-1]						)
			else:
						return []


# driver code
if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_UpperCamelCase		:		List[Any]								=			[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
						spiral_print_clockwise(a)


