from sklearn.metrics import roc_auc_score

import datasets


lowerCamelCase_				:		List[Any]       						=       """
This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model's predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.

This metric has three separate use cases:
    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
"""

lowerCamelCase_				:		str       						=       """
Args:
- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples,)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples, n_classes)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:
    - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).
    - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.
- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.
- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:
    - `'ovr'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == 'macro'`, because class imbalance affects the composition of each of the 'rest' groupings.
    - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.
- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in
    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in
    `prediction_scores` is used. Defaults to `None`.
Returns:
    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.
Examples:
    Example 1:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\")
        >>> refs = [1, 0, 1, 1, 0, 0]
        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)
        >>> print(round(results['roc_auc'], 2))
        0.78

    Example 2:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")
        >>> refs = [1, 0, 1, 2, 2, 0]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')
        >>> print(round(results['roc_auc'], 2))
        0.85

    Example 3:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")
        >>> refs = [[1, 1, 0],
        ...         [1, 1, 0],
        ...         [0, 1, 0],
        ...         [0, 0, 1],
        ...         [0, 1, 1],
        ...         [1, 0, 1]]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)
        >>> print([round(res, 2) for res in results['roc_auc']])
        [0.83, 0.38, 0.94]
"""

lowerCamelCase_				:		Optional[Any]       						=       """\
@article{doi:10.1177/0272989X8900900307,
author = {Donna Katzman McClish},
title ={Analyzing a Portion of the ROC Curve},
journal = {Medical Decision Making},
volume = {9},
number = {3},
pages = {190-195},
year = {1989},
doi = {10.1177/0272989X8900900307},
    note ={PMID: 2668680},
URL = {https://doi.org/10.1177/0272989X8900900307},
eprint = {https://doi.org/10.1177/0272989X8900900307}
}


@article{10.1023/A:1010920819831,
author = {Hand, David J. and Till, Robert J.},
title = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},
year = {2001},
issue_date = {November 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {45},
number = {2},
issn = {0885-6125},
url = {https://doi.org/10.1023/A:1010920819831},
doi = {10.1023/A:1010920819831},
journal = {Mach. Learn.},
month = {oct},
pages = {171–186},
numpages = {16},
keywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}
}


@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,						_KWARGS_DESCRIPTION		)
class 						a__							(   datasets.Metric		):



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								return datasets.MetricInfo(
								    description=_DESCRIPTION      ,						citation=_CITATION      ,						inputs_description=_KWARGS_DESCRIPTION      ,						features=datasets.Features(
								        {
								            'prediction_scores': datasets.Sequence(datasets.Value('float'    )    ),
								            'references': datasets.Value('int32'    ),
								        }
								        if self.config_name == 'multiclass'
								        else {
								            'references': datasets.Sequence(datasets.Value('int32'    )    ),
								            'prediction_scores': datasets.Sequence(datasets.Value('float'    )    ),
								        }
								        if self.config_name == 'multilabel'
								        else {
								            'references': datasets.Value('int32'    ),
								            'prediction_scores': datasets.Value('float'    ),
								        }    )      ,						reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html']      ,						)







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase="macro"      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None      ,						UpperCAmelCase="raise"      ,						UpperCAmelCase=None      ,						) ->    Any:
								return {
								    "roc_auc": roc_auc_score(
								        UpperCAmelCase      ,						UpperCAmelCase      ,						average=UpperCAmelCase      ,						sample_weight=UpperCAmelCase      ,						max_fpr=UpperCAmelCase      ,						multi_class=UpperCAmelCase      ,						labels=UpperCAmelCase      ,						)
								}












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,
)


__snake_case			:			int			   =   {
    """vit_b32""": {
        """embed_dim""": 5_12,
        """image_resolution""": 7_68,
        """context_length""": 16,
        """vocab_size""": 4_94_08,
        """vision_layers""": 12,
        """vision_width""": 7_68,
        """vision_patch_size""": 32,
        """transformer_width""": 5_12,
        """transformer_heads""": 8,
        """transformer_layers""": 12,
    },
    """vit_b16""": {
        """embed_dim""": 5_12,
        """image_resolution""": 7_68,
        """context_length""": 16,
        """vocab_size""": 4_94_08,
        """vision_layers""": 12,
        """vision_width""": 7_68,
        """vision_patch_size""": 16,
        """transformer_width""": 5_12,
        """transformer_heads""": 8,
        """transformer_layers""": 12,
    },
    """vit_l14""": {
        """embed_dim""": 7_68,
        """image_resolution""": 8_40,
        """context_length""": 16,
        """vocab_size""": 4_94_08,
        """vision_layers""": 24,
        """vision_width""": 10_24,
        """vision_patch_size""": 14,
        """transformer_width""": 7_68,
        """transformer_heads""": 12,
        """transformer_layers""": 12,
    },
}







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    Any=""			,				UpperCamelCase_				:    List[str]="/"						)     ->      List[str]:

						"""simple docstring"""



						lowerCAmelCase__			  =  []

						for k, v in params.items():
												lowerCAmelCase__			  =  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 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple						)     ->      Optional[int]:

						"""simple docstring"""



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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    Union[str, Any]						)     ->      List[Any]:

						"""simple docstring"""



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

						lowerCAmelCase__			  =  pt_attn_layer.out_proj.weight
						lowerCAmelCase__			  =  pt_attn_layer.out_proj.bias

						lowerCAmelCase__			  =  q_proj
						lowerCAmelCase__			  =  q_proj_bias

						lowerCAmelCase__			  =  k_proj
						lowerCAmelCase__			  =  k_proj_bias

						lowerCAmelCase__			  =  v_proj
						lowerCAmelCase__			  =  v_proj_bias

						lowerCAmelCase__			  =  out_proj_weights
						lowerCAmelCase__			  =  out_proj_bias







def 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple			,				UpperCamelCase_				:    Union[str, Any]						)     ->      Union[str, Any]:

						"""simple docstring"""



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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Tuple						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  pt_linear.weight
						lowerCAmelCase__			  =  pt_linear.bias







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    Optional[Any]						)     ->      Tuple:

						"""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      (			UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    List[str]						)     ->      Optional[int]:

						"""simple docstring"""



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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    Union[str, Any]						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  pt_model.token_embedding.weight
						lowerCAmelCase__			  =  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      (			UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    Dict						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  pt_model.text_projection.data.T

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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    str						)     ->      Tuple:

						"""simple docstring"""



						lowerCAmelCase__			  =  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
						lowerCAmelCase__			  =  pt_model.visual.conva.weight.data
						lowerCAmelCase__			  =  pt_model.visual.class_embedding
						lowerCAmelCase__			  =  pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers			,				pt_model.visual.transformer.resblocks						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    Any						)     ->      List[str]:

						"""simple docstring"""



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

						lowerCAmelCase__			  =  torch.from_numpy(flax_class_token_params['scale']						)
						lowerCAmelCase__			  =  torch.from_numpy(flax_class_token_params['bias']						)
						lowerCAmelCase__			  =  nn.Parameter(UpperCamelCase_						)
						lowerCAmelCase__			  =  nn.Parameter(UpperCamelCase_						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    str			,				UpperCamelCase_				:    Union[str, Any]						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  hf_model.state_dict()
						lowerCAmelCase__			  =  {}

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

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

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

												lowerCAmelCase__			  =  nn.Parameter(torch.from_numpy(UpperCamelCase_						)						)

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

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

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

												lowerCAmelCase__			  =  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 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple			,				UpperCamelCase_				:    List[str]						)     ->      str:

						"""simple docstring"""



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

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

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

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

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

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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any						)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  {}
						lowerCAmelCase__			  =  []

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

																		processed_attn_layers.append(UpperCamelCase_						)
																		lowerCAmelCase__			  =  params[base + 'out.weight'].shape[-1]
																		lowerCAmelCase__			  =  params[base + 'out.weight'].reshape(UpperCamelCase_			,				UpperCamelCase_						).T
																		lowerCAmelCase__			  =  params[base + 'out.bias']
												else:
																		lowerCAmelCase__			  =  v
						return new_params







def 		_UpperCamelCase      (			UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    int						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  CLIP(**UpperCamelCase_						)
						torch_model.eval()
						lowerCAmelCase__			  =  torch_model.state_dict()

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

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

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

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

												lowerCAmelCase__			  =  v

						lowerCAmelCase__			  =  _convert_attn_layers(UpperCamelCase_						)
						new_torch_params.update(UpperCamelCase_						)
						lowerCAmelCase__			  =  {}

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

						return torch_clip_params, torch_model, attn_params







@torch.no_grad()
def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    List[Any]=None						)     ->      Any:

						"""simple docstring"""



						lowerCAmelCase__			  =  Repository(UpperCamelCase_			,				clone_from=F"google/{pytorch_dump_folder_path}"						)
						repo.git_pull()

						if config_path is not None:
												lowerCAmelCase__			  =  OwlViTConfig.from_pretrained(UpperCamelCase_						)
						else:
												lowerCAmelCase__			  =  OwlViTConfig()

						lowerCAmelCase__			  =  OwlViTModel(UpperCamelCase_						).eval()
						lowerCAmelCase__			  =  OwlViTForObjectDetection(UpperCamelCase_						).eval()

						copy_text_model_and_projection(UpperCamelCase_			,				UpperCamelCase_						)
						copy_vision_model_and_projection(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  pt_backbone.logit_scale
						copy_flax_attn_params(UpperCamelCase_			,				UpperCamelCase_						)

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

						# Initialize processor
						lowerCAmelCase__			  =  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__":
					__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."""
					)
					__snake_case			:			Any			   =   parser.parse_args()

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

					# Load from checkpoint and convert params to float-32
					__snake_case			:			List[str]			   =   checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["""optimizer"""]["""target"""]
					__snake_case			:			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
					__snake_case   ,       __snake_case   ,       __snake_case			:			Tuple			   =   convert_clip_backbone(flax_params, torch_config)

					convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)





