
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,
)


SCREAMING_SNAKE_CASE							:Optional[int]											=       {
    'vit_b32': {
        'embed_dim': 512,
        'image_resolution': 768,
        'context_length': 16,
        'vocab_size': 4_9408,
        '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': 4_9408,
        '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': 4_9408,
        'vision_layers': 24,
        'vision_width': 1024,
        'vision_patch_size': 14,
        'transformer_width': 768,
        'transformer_heads': 12,
        'transformer_layers': 12,
    },
}


def 		UpperCAmelCase    (			a_	,     a_=""	,     a_="/"		)	->			Optional[int]:







				"""simple docstring"""



				__A         =				[]

				for k, v in params.items():
								__A         =				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_		)	->			List[str]:







				"""simple docstring"""



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


def 		UpperCAmelCase    (			a_	,     a_		)	->			List[Any]:







				"""simple docstring"""



				__A    ,							__A    ,							__A         =				pt_attn_layer.in_proj_weight.chunk(3	,     dim=0		)
				__A    ,							__A    ,							__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 		UpperCAmelCase    (			a_	,     a_		)	->			Any:







				"""simple docstring"""



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


def 		UpperCAmelCase    (			a_	,     a_		)	->			int:







				"""simple docstring"""



				__A         =				pt_linear.weight
				__A         =				pt_linear.bias


def 		UpperCAmelCase    (			a_	,     a_		)	->			Dict:







				"""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 		UpperCAmelCase    (			a_	,     a_		)	->			str:







				"""simple docstring"""



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


def 		UpperCAmelCase    (			a_	,     a_		)	->			Union[str, Any]:







				"""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 		UpperCAmelCase    (			a_	,     a_		)	->			Dict:







				"""simple docstring"""



				__A         =				pt_model.text_projection.data.T

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


def 		UpperCAmelCase    (			a_	,     a_		)	->			Tuple:







				"""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 		UpperCAmelCase    (			a_	,     a_		)	->			Tuple:







				"""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(a_		)
				__A         =				nn.Parameter(a_		)


def 		UpperCAmelCase    (			a_	,     a_		)	->			int:







				"""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(a_		)		)

				# 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(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_	,     a_		)	->			Optional[int]:







				"""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(a_		)

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

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


def 		UpperCAmelCase    (			a_		)	->			int:







				"""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(a_		)
												__A         =				params[base + "out.weight"].shape[-1]
												__A         =				params[base + "out.weight"].reshape(a_	,     a_		).T
												__A         =				params[base + "out.bias"]
								else:
												__A         =				v
				return new_params


def 		UpperCAmelCase    (			a_	,     a_		)	->			Tuple:







				"""simple docstring"""



				__A         =				CLIP(**a_		)
				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(a_		)
				new_torch_params.update(a_		)
				__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_(a_		)
								else:
												__A         =				param

				return torch_clip_params, torch_model, attn_params


@torch.no_grad()
def 		UpperCAmelCase    (			a_	,     a_	,     a_	,     a_	,     a_=None		)	->			Optional[Any]:







				"""simple docstring"""



				__A         =				Repository(a_	,     clone_from=F'''google/{pytorch_dump_folder_path}'''		)
				repo.git_pull()

				if config_path is not None:
								__A         =				OwlViTConfig.from_pretrained(a_		)
				else:
								__A         =				OwlViTConfig()

				__A         =				OwlViTModel(a_		).eval()
				__A         =				OwlViTForObjectDetection(a_		).eval()

				copy_text_model_and_projection(a_	,     a_		)
				copy_vision_model_and_projection(a_	,     a_		)
				__A         =				pt_backbone.logit_scale
				copy_flax_attn_params(a_	,     a_		)

				__A         =				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
				__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=1_6		)

				# Initialize processor
				__A         =				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__":
				SCREAMING_SNAKE_CASE							:Union[str, 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.'
				)
				SCREAMING_SNAKE_CASE							:Optional[Any]											=       parser.parse_args()

				# Initialize PyToch clip model
				SCREAMING_SNAKE_CASE							:Dict											=       args.owlvit_version
				if model_name == "clip_b16":
								SCREAMING_SNAKE_CASE							:Dict											=       CONFIGS['vit_b16']
				elif model_name == "clip_b32":
								SCREAMING_SNAKE_CASE							:Dict											=       CONFIGS['vit_b32']
				elif model_name == "clip_l14":
								SCREAMING_SNAKE_CASE							:Tuple											=       CONFIGS['vit_l14']

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


from typing import Any





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Union[str, Any]				):
					__A         =				[]
					__A         =				0






	def __str__( self      :				Tuple				):
					__A         =				"<" + str(self.stack				)[1:-1] + ">"
					return printed






	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				Any				):
					self.stack.append(A				)
					__A         =				self.length + 1






	def        UpperCamelCase_						( self      :				Any				):
					self.rotate(1				)
					__A         =				self.stack[self.length - 1]
					__A         =				self.stack[:-1]
					self.rotate(self.length - 1				)
					__A         =				self.length - 1
					return dequeued






	def        UpperCamelCase_						( self      :				Tuple							,A      :				int				):
					for _ in range(A				):
									__A         =				self.stack[0]
									__A         =				self.stack[1:]
									self.put(A				)
									__A         =				self.length - 1






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					__A         =				self.get()
					self.put(A				)
					self.rotate(self.length - 1				)
					return front






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					return self.length
