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__  :  Dict											=     {
    '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   a_						( lowerCamelCase ,      lowerCamelCase="" ,      lowerCamelCase="/"	):
				UpperCAmelCase__					=							[]

				for k, v in params.items():
								UpperCAmelCase__					=							parent_key + sep + k if parent_key else k

								if isinstance(SCREAMING_SNAKE_CASE_ ,      collections.MutableMapping	):
												items.extend(flatten_nested_dict(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_ ,      sep=SCREAMING_SNAKE_CASE_	).items()	)
								else:
												items.append((new_key, v)	)
				return dict(SCREAMING_SNAKE_CASE_	)



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



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__					=							pt_attn_layer.in_proj_weight.chunk(3 ,      dim=0	)
				UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__					=							pt_attn_layer.in_proj_bias.chunk(3 ,      dim=0	)

				UpperCAmelCase__					=							pt_attn_layer.out_proj.weight
				UpperCAmelCase__					=							pt_attn_layer.out_proj.bias

				UpperCAmelCase__					=							q_proj
				UpperCAmelCase__					=							q_proj_bias

				UpperCAmelCase__					=							k_proj
				UpperCAmelCase__					=							k_proj_bias

				UpperCAmelCase__					=							v_proj
				UpperCAmelCase__					=							v_proj_bias

				UpperCAmelCase__					=							out_proj_weights
				UpperCAmelCase__					=							out_proj_bias



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				copy_linear(hf_mlp.fca ,      pt_mlp.c_fc	)
				copy_linear(hf_mlp.fca ,      pt_mlp.c_proj	)



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							pt_linear.weight
				UpperCAmelCase__					=							pt_linear.bias



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				# 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   a_						( lowerCamelCase ,      lowerCamelCase	):
				for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	):
								copy_layer(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	)



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				# copy  embeds
				UpperCAmelCase__					=							pt_model.token_embedding.weight
				UpperCAmelCase__					=							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   a_						( lowerCamelCase ,      lowerCamelCase	):
				# copy projection
				UpperCAmelCase__					=							pt_model.text_projection.data.T

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



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				# copy projection
				UpperCAmelCase__					=							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__					=							pt_model.visual.conva.weight.data
				UpperCAmelCase__					=							pt_model.visual.class_embedding
				UpperCAmelCase__					=							pt_model.visual.positional_embedding.data

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



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							flatten_nested_dict(flax_params['backbone']['merged_class_token']	)

				UpperCAmelCase__					=							torch.from_numpy(flax_class_token_params['scale']	)
				UpperCAmelCase__					=							torch.from_numpy(flax_class_token_params['bias']	)
				UpperCAmelCase__					=							nn.Parameter(SCREAMING_SNAKE_CASE_	)
				UpperCAmelCase__					=							nn.Parameter(SCREAMING_SNAKE_CASE_	)



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							hf_model.state_dict()
				UpperCAmelCase__					=							{}

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

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

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

								UpperCAmelCase__					=							nn.Parameter(torch.from_numpy(SCREAMING_SNAKE_CASE_	)	)

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

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

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

								UpperCAmelCase__					=							nn.Parameter(torch.from_numpy(SCREAMING_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_(SCREAMING_SNAKE_CASE_	)



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

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

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

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

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

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



def   a_						( lowerCamelCase	):
				UpperCAmelCase__					=							{}
				UpperCAmelCase__					=							[]

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

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



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							CLIP(**SCREAMING_SNAKE_CASE_	)
				torch_model.eval()
				UpperCAmelCase__					=							torch_model.state_dict()

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

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

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

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

								UpperCAmelCase__					=							v

				UpperCAmelCase__					=							_convert_attn_layers(SCREAMING_SNAKE_CASE_	)
				new_torch_params.update(SCREAMING_SNAKE_CASE_	)
				UpperCAmelCase__					=							{}

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

				return torch_clip_params, torch_model, attn_params



@torch.no_grad()
def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase=None	):
				UpperCAmelCase__					=							Repository(SCREAMING_SNAKE_CASE_ ,      clone_from=f'''google/{pytorch_dump_folder_path}'''	)
				repo.git_pull()

				if config_path is not None:
								UpperCAmelCase__					=							OwlViTConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
				else:
								UpperCAmelCase__					=							OwlViTConfig()

				UpperCAmelCase__					=							OwlViTModel(SCREAMING_SNAKE_CASE_	).eval()
				UpperCAmelCase__					=							OwlViTForObjectDetection(SCREAMING_SNAKE_CASE_	).eval()

				copy_text_model_and_projection(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	)
				copy_vision_model_and_projection(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	)
				UpperCAmelCase__					=							pt_backbone.logit_scale
				copy_flax_attn_params(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	)

				UpperCAmelCase__					=							hf_backbone
				copy_class_merge_token(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	)
				copy_class_box_heads(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	)

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

				# Initialize image processor
				UpperCAmelCase__					=							OwlViTImageProcessor(
				    size=config.vision_config.image_size ,      crop_size=config.vision_config.image_size	)
				# Initialize tokenizer
				UpperCAmelCase__					=							CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32' ,      pad_token='!' ,      model_max_length=1_6	)

				# Initialize processor
				UpperCAmelCase__					=							OwlViTProcessor(image_processor=SCREAMING_SNAKE_CASE_ ,      tokenizer=SCREAMING_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__":
						lowerCAmelCase__  :  Tuple											=     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__  :  Optional[Any]											=     parser.parse_args()

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

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






from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase__  :  List[str]											=     '\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


lowerCAmelCase__  :  List[str]											=     '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


lowerCAmelCase__  :  Dict											=     '\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,   _KWARGS_DESCRIPTION    )
class     snake_case	(			datasets.Metric    ):




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									return datasets.MetricInfo(
									    description=_DESCRIPTION   ,citation=_CITATION   ,inputs_description=_KWARGS_DESCRIPTION   ,features=datasets.Features(
									        {
									            'predictions': datasets.Sequence(datasets.Value('int32'      )      ),
									            'references': datasets.Sequence(datasets.Value('int32'      )      ),
									        }
									        if self.config_name == 'multilabel'
									        else {
									            'predictions': datasets.Value('int32'      ),
									            'references': datasets.Value('int32'      ),
									        }      )   ,reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']   ,)







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     List[str]   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     Tuple=None      ):
									return {
									    "accuracy": float(
									        accuracy_score(lowerCamelCase__   ,lowerCamelCase__   ,normalize=lowerCamelCase__   ,sample_weight=lowerCamelCase__      )      )
									}



