
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,
)


lowercase__				:				Optional[Any]						=      {
    "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 						A_			(				snake_case				: Dict			,   snake_case				: Optional[Any]=""			,   snake_case				: List[str]="/"			)   ->     Optional[int]:



						'''simple docstring'''

						__UpperCamelCase  	=      []

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

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




def 						A_			(				snake_case				: List[Any]			)   ->     str:



						'''simple docstring'''

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




def 						A_			(				snake_case				: Optional[int]			,   snake_case				: int			)   ->     Optional[int]:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_attn_layer.in_proj_weight.chunk(3			,   dim=0			)
						__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_			(				snake_case				: Any			,   snake_case				: Union[str, Any]			)   ->     Optional[int]:



						'''simple docstring'''

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




def 						A_			(				snake_case				: str			,   snake_case				: Optional[int]			)   ->     int:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_linear.weight
						__UpperCamelCase  	=      pt_linear.bias




def 						A_			(				snake_case				: List[Any]			,   snake_case				: Optional[int]			)   ->     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 						A_			(				snake_case				: Optional[int]			,   snake_case				: Tuple			)   ->     Tuple:



						'''simple docstring'''

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




def 						A_			(				snake_case				: List[Any]			,   snake_case				: Union[str, Any]			)   ->     List[Any]:



						'''simple docstring'''

						__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_			(				snake_case				: Optional[Any]			,   snake_case				: List[Any]			)   ->     Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.text_projection.data.T

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




def 						A_			(				snake_case				: List[str]			,   snake_case				: Optional[Any]			)   ->     int:



						'''simple docstring'''

						__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_			(				snake_case				: List[str]			,   snake_case				: Dict			)   ->     List[str]:



						'''simple docstring'''

						__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(snake_case			)
						__UpperCamelCase  	=      nn.Parameter(snake_case			)




def 						A_			(				snake_case				: Dict			,   snake_case				: Dict			)   ->     List[Any]:



						'''simple docstring'''

						__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(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(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_(snake_case			)




def 						A_			(				snake_case				: int			,   snake_case				: Union[str, Any]			)   ->     Union[str, Any]:



						'''simple docstring'''

						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(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(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(snake_case			).T

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




def 						A_			(				snake_case				: Union[str, Any]			)   ->     Tuple:



						'''simple docstring'''

						__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(snake_case			)
																		__UpperCamelCase  	=      params[base + '''out.weight'''].shape[-1]
																		__UpperCamelCase  	=      params[base + '''out.weight'''].reshape(snake_case			,   snake_case			).T
																		__UpperCamelCase  	=      params[base + '''out.bias''']
												else:
																		__UpperCamelCase  	=      v
						return new_params




def 						A_			(				snake_case				: Dict			,   snake_case				: Dict			)   ->     List[Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      CLIP(**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(snake_case			)
						new_torch_params.update(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_(snake_case			)
												else:
																		__UpperCamelCase  	=      param

						return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def 						A_			(				snake_case				: Tuple			,   snake_case				: Optional[int]			,   snake_case				: Optional[int]			,   snake_case				: List[str]			,   snake_case				: Tuple=None			)   ->     Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      Repository(snake_case			,   clone_from=f"google/{pytorch_dump_folder_path}"			)
						repo.git_pull()

						if config_path is not None:
												__UpperCamelCase  	=      OwlViTConfig.from_pretrained(snake_case			)
						else:
												__UpperCamelCase  	=      OwlViTConfig()

						__UpperCamelCase  	=      OwlViTModel(snake_case			).eval()
						__UpperCamelCase  	=      OwlViTForObjectDetection(snake_case			).eval()

						copy_text_model_and_projection(snake_case			,   snake_case			)
						copy_vision_model_and_projection(snake_case			,   snake_case			)
						__UpperCamelCase  	=      pt_backbone.logit_scale
						copy_flax_attn_params(snake_case			,   snake_case			)

						__UpperCamelCase  	=      hf_backbone
						copy_class_merge_token(snake_case			,   snake_case			)
						copy_class_box_heads(snake_case			,   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=16			)

						# Initialize processor
						__UpperCamelCase  	=      OwlViTProcessor(image_processor=snake_case			,   tokenizer=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__":
				lowercase__				:				Optional[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."
				)
				lowercase__				:				Dict						=      parser.parse_args()

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

				# Load from checkpoint and convert params to float-32
				lowercase__				:				Any						=      checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
				lowercase__				:				Dict						=      jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
				del variables

				# Convert CLIP backbone
				lowercase__		,	lowercase__		,	lowercase__				:				List[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)












import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		str:







							"""simple docstring"""





							_a  :    Union[str, Any]			=						ElectraConfig.from_json_file(UpperCAmelCase     )
							print(F'Building PyTorch model from configuration: {config}'     )

							if discriminator_or_generator == "discriminator":
														_a  :    int			=						ElectraForPreTraining(UpperCAmelCase     )
							elif discriminator_or_generator == "generator":
														_a  :    Any			=						ElectraForMaskedLM(UpperCAmelCase     )
							else:
														raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''     )

							# Load weights from tf checkpoint
							load_tf_weights_in_electra(
							    UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	discriminator_or_generator=UpperCAmelCase     )

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}'     )
							torch.save(model.state_dict()    ,	UpperCAmelCase     )


if __name__ == "__main__":
						__lowerCamelCase						= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
						)
						parser.add_argument(
						    '--config_file',
						    default=None,
						    type=str,
						    required=True,
						    help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
						)
						parser.add_argument(
						    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						parser.add_argument(
						    '--discriminator_or_generator',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '
						        '\'generator\'.'
						    ),
						)
						__lowerCamelCase						= parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
						)