

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__             =		{
    """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        lowerCamelCase_  (				UpperCAmelCase_				:  Tuple				,							UpperCAmelCase_				:  str=""				,							UpperCAmelCase_				:  List[Any]="/"						)    ->					Any:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    []

	for k, v in params.items():
		_UpperCamelCase :		Dict								=    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        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]						)    ->					int:




	'''simple docstring'''




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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Tuple				,							UpperCAmelCase_				:  Dict						)    ->					Optional[int]:




	'''simple docstring'''




	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		int								=    pt_attn_layer.in_proj_weight.chunk(3				,							dim=0						)
	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		List[str]								=    pt_attn_layer.in_proj_bias.chunk(3				,							dim=0						)

	_UpperCamelCase :		Tuple								=    pt_attn_layer.out_proj.weight
	_UpperCamelCase :		Dict								=    pt_attn_layer.out_proj.bias

	_UpperCamelCase :		Optional[Any]								=    q_proj
	_UpperCamelCase :		List[str]								=    q_proj_bias

	_UpperCamelCase :		List[str]								=    k_proj
	_UpperCamelCase :		int								=    k_proj_bias

	_UpperCamelCase :		Tuple								=    v_proj
	_UpperCamelCase :		Optional[Any]								=    v_proj_bias

	_UpperCamelCase :		Tuple								=    out_proj_weights
	_UpperCamelCase :		Tuple								=    out_proj_bias






def        lowerCamelCase_  (				UpperCAmelCase_				:  Union[str, Any]				,							UpperCAmelCase_				:  int						)    ->					Dict:




	'''simple docstring'''




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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  Optional[int]						)    ->					Any:




	'''simple docstring'''




	_UpperCamelCase :		List[str]								=    pt_linear.weight
	_UpperCamelCase :		int								=    pt_linear.bias






def        lowerCamelCase_  (				UpperCAmelCase_				:  List[str]				,							UpperCAmelCase_				:  List[Any]						)    ->					List[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        lowerCamelCase_  (				UpperCAmelCase_				:  Tuple				,							UpperCAmelCase_				:  Dict						)    ->					Optional[int]:




	'''simple docstring'''




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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					List[Any]:




	'''simple docstring'''




	_UpperCamelCase :		Optional[int]								=    pt_model.token_embedding.weight
	_UpperCamelCase :		List[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        lowerCamelCase_  (				UpperCAmelCase_				:  Tuple				,							UpperCAmelCase_				:  Any						)    ->					Optional[int]:




	'''simple docstring'''




	_UpperCamelCase :		List[str]								=    pt_model.text_projection.data.T

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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  Dict						)    ->					Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase :		Optional[int]								=    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 :		Tuple								=    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        lowerCamelCase_  (				UpperCAmelCase_				:  Tuple				,							UpperCAmelCase_				:  Tuple						)    ->					List[str]:




	'''simple docstring'''




	_UpperCamelCase :		int								=    flatten_nested_dict(flax_params['backbone']['merged_class_token']						)

	_UpperCamelCase :		Tuple								=    torch.from_numpy(flax_class_token_params['scale']						)
	_UpperCamelCase :		Optional[int]								=    torch.from_numpy(flax_class_token_params['bias']						)
	_UpperCamelCase :		Union[str, Any]								=    nn.Parameter(UpperCAmelCase_						)
	_UpperCamelCase :		Dict								=    nn.Parameter(UpperCAmelCase_						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Union[str, Any]				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					Dict:




	'''simple docstring'''




	_UpperCamelCase :		Optional[int]								=    hf_model.state_dict()
	_UpperCamelCase :		Optional[int]								=    {}

	# 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 :		List[Any]								=    flax_key.replace('/'				,							'.'						)
		_UpperCamelCase :		Union[str, Any]								=    torch_key.replace('.kernel'				,							'.weight'						)
		_UpperCamelCase :		Optional[Any]								=    torch_key.replace('Dense_0'				,							'dense0'						)
		_UpperCamelCase :		Optional[int]								=    '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(UpperCAmelCase_						)						)

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

	for flax_key, v in flax_box_params.items():
		_UpperCamelCase :		Optional[Any]								=    flax_key.replace('/'				,							'.'						)
		_UpperCamelCase :		Union[str, Any]								=    torch_key.replace('.kernel'				,							'.weight'						)
		_UpperCamelCase :		Tuple								=    torch_key.replace('_'				,							''						).lower()
		_UpperCamelCase :		int								=    'box_head.' + torch_key

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

		_UpperCamelCase :		Optional[Any]								=    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        lowerCamelCase_  (				UpperCAmelCase_				:  Tuple				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					Union[str, Any]:




	'''simple docstring'''




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

		_UpperCamelCase :		List[str]								=    torch_key.replace('attn'				,							'self_attn'						)
		_UpperCamelCase :		Dict								=    torch_key.replace('key'				,							'k_proj'						)
		_UpperCamelCase :		Any								=    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 :		Any								=    v.shape[0] * v.shape[1]
			_UpperCamelCase :		Tuple								=    v.reshape(UpperCAmelCase_						)

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

		if "weight" in torch_key and "out" not in torch_key:
			_UpperCamelCase :		Optional[Any]								=    (v.shape[0], v.shape[1] * v.shape[2])
			_UpperCamelCase :		Tuple								=    v.reshape(UpperCAmelCase_						).T

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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict						)    ->					List[Any]:




	'''simple docstring'''




	_UpperCamelCase :		List[str]								=    {}
	_UpperCamelCase :		List[str]								=    []

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

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






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  str						)    ->					Tuple:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    CLIP(**UpperCAmelCase_						)
	torch_model.eval()
	_UpperCamelCase :		str								=    torch_model.state_dict()

	_UpperCamelCase :		str								=    flatten_nested_dict(flax_params['backbone']['clip']						)
	_UpperCamelCase :		Optional[int]								=    {}

	for flax_key, v in flax_clip_params.items():
		_UpperCamelCase :		Any								=    flax_key.replace('/'				,							'.'						)
		_UpperCamelCase :		int								=    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 :		Optional[int]								=    torch_key[5:]

		_UpperCamelCase :		Optional[int]								=    torch_key.replace('text_projection.kernel'				,							'text_projection'						)
		_UpperCamelCase :		Optional[int]								=    torch_key.replace('visual.proj.kernel'				,							'visual.proj'						)
		_UpperCamelCase :		Dict								=    torch_key.replace('.scale'				,							'.weight'						)
		_UpperCamelCase :		List[str]								=    torch_key.replace('.kernel'				,							'.weight'						)

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

		_UpperCamelCase :		Dict								=    v

	_UpperCamelCase :		int								=    _convert_attn_layers(UpperCAmelCase_						)
	new_torch_params.update(UpperCAmelCase_						)
	_UpperCamelCase :		List[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 :		str								=    torch.from_numpy(new_torch_params[name]						)
			torch_clip_params[name].copy_(UpperCAmelCase_						)
		else:
			_UpperCamelCase :		str								=    param

	return torch_clip_params, torch_model, attn_params






@torch.no_grad()
def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  Union[str, Any]				,							UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  Optional[int]=None						)    ->					str:




	'''simple docstring'''




	_UpperCamelCase :		Optional[Any]								=    Repository(UpperCAmelCase_				,							clone_from=F'''google/{pytorch_dump_folder_path}'''						)
	repo.git_pull()

	if config_path is not None:
		_UpperCamelCase :		int								=    OwlViTConfig.from_pretrained(UpperCAmelCase_						)
	else:
		_UpperCamelCase :		Optional[Any]								=    OwlViTConfig()

	_UpperCamelCase :		List[str]								=    OwlViTModel(UpperCAmelCase_						).eval()
	_UpperCamelCase :		List[Any]								=    OwlViTForObjectDetection(UpperCAmelCase_						).eval()

	copy_text_model_and_projection(UpperCAmelCase_				,							UpperCAmelCase_						)
	copy_vision_model_and_projection(UpperCAmelCase_				,							UpperCAmelCase_						)
	_UpperCamelCase :		int								=    pt_backbone.logit_scale
	copy_flax_attn_params(UpperCAmelCase_				,							UpperCAmelCase_						)

	_UpperCamelCase :		str								=    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 :		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 :		List[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__             =		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__             =		parser.parse_args()

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

							# Load from checkpoint and convert params to float-32
							lowerCAmelCase__             =		checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["""optimizer"""]["""target"""]
							lowerCAmelCase__             =		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__             =		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'''

import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


a__         =     logging.get_logger(__name__)





class __magic_name__(						__lowerCAmelCase      ):






						def __init__(   self						:						List[str]					,   *__UpperCamelCase						:						Tuple					,   **__UpperCamelCase						:						Tuple       ):



									'''simple docstring'''






									warnings.warn(
									    """The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
									    """ use ViTImageProcessor instead."""					,   __UpperCamelCase					,   )
									super().__init__(*__UpperCamelCase					,   **__UpperCamelCase       )