
"""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,
)


snake_case			       =		{
    '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   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_="",					SCREAMING_SNAKE_CASE_="/"      ):
					SCREAMING_SNAKE_CASE           =     []

					for k, v in params.items():
										SCREAMING_SNAKE_CASE           =     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   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					return jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE_      : x.astype(jnp.floataa      ) if x.dtype == jnp.bfloataa else x,					SCREAMING_SNAKE_CASE_      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     pt_attn_layer.in_proj_weight.chunk(3,					dim=0      )
					SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     pt_attn_layer.in_proj_bias.chunk(3,					dim=0      )

					SCREAMING_SNAKE_CASE           =     pt_attn_layer.out_proj.weight
					SCREAMING_SNAKE_CASE           =     pt_attn_layer.out_proj.bias

					SCREAMING_SNAKE_CASE           =     q_proj
					SCREAMING_SNAKE_CASE           =     q_proj_bias

					SCREAMING_SNAKE_CASE           =     k_proj
					SCREAMING_SNAKE_CASE           =     k_proj_bias

					SCREAMING_SNAKE_CASE           =     v_proj
					SCREAMING_SNAKE_CASE           =     v_proj_bias

					SCREAMING_SNAKE_CASE           =     out_proj_weights
					SCREAMING_SNAKE_CASE           =     out_proj_bias

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					copy_linear(hf_mlp.fca,					pt_mlp.c_fc      )
					copy_linear(hf_mlp.fca,					pt_mlp.c_proj      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     pt_linear.weight
					SCREAMING_SNAKE_CASE           =     pt_linear.bias

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					# 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_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
										copy_layer(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					# copy  embeds
					SCREAMING_SNAKE_CASE           =     pt_model.token_embedding.weight
					SCREAMING_SNAKE_CASE           =     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_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					# copy projection
					SCREAMING_SNAKE_CASE           =     pt_model.text_projection.data.T

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

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					# copy projection
					SCREAMING_SNAKE_CASE           =     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
					SCREAMING_SNAKE_CASE           =     pt_model.visual.conva.weight.data
					SCREAMING_SNAKE_CASE           =     pt_model.visual.class_embedding
					SCREAMING_SNAKE_CASE           =     pt_model.visual.positional_embedding.data

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

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     flatten_nested_dict(flax_params['backbone']['merged_class_token']      )

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

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     hf_model.state_dict()
					SCREAMING_SNAKE_CASE           =     {}

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

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

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

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

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

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

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

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

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

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

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

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

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

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     {}
					SCREAMING_SNAKE_CASE           =     []

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

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

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     CLIP(**SCREAMING_SNAKE_CASE_      )
					torch_model.eval()
					SCREAMING_SNAKE_CASE           =     torch_model.state_dict()

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

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

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

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

										SCREAMING_SNAKE_CASE           =     v

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

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

					return torch_clip_params, torch_model, attn_params

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

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

					SCREAMING_SNAKE_CASE           =     OwlViTModel(SCREAMING_SNAKE_CASE_      ).eval()
					SCREAMING_SNAKE_CASE           =     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_      )
					SCREAMING_SNAKE_CASE           =     pt_backbone.logit_scale
					copy_flax_attn_params(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )

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

					# Initialize processor
					SCREAMING_SNAKE_CASE           =     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__":
	snake_case			       =		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			       =		parser.parse_args()

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

	# Load from checkpoint and convert params to float-32
	snake_case			       =		checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['optimizer']['target']
	snake_case			       =		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			       =		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 abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


snake_case			       =		TypeVar('T', bound='TaskTemplate')




@dataclass(frozen=__magic_name__				)
class 					UpperCamelCase     (	abc.ABC				):

				"""simple docstring"""



				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				ClassVar[Features]
				UpperCAmelCase_    :				ClassVar[Features]


				def 							A (    self		,     lowercase__      )   ->   T:
									"""simple docstring"""







									return copy.deepcopy(self      )


				@property
				def 							A (    self      )   ->   Features:
									"""simple docstring"""







									return Features(**self.input_schema		,     **self.label_schema      )


				@property
				@abc.abstractmethod
				def 							A (    self      )   ->   Dict[str, str]:
									"""simple docstring"""







									raise NotImplementedError




				@classmethod
				def 							A (    cls		,     lowercase__      )   ->   T:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     {f.name for f in dataclasses.fields(cls      )}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}      )
