



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,
)


UpperCAmelCase								=   {
    """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_ (		__a       :			Any	,						__a       :			Union[str, Any]=""	,						__a       :			Tuple="/"						):

				"""simple docstring"""
				a__			 =						[]

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

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

def      A_ (		__a       :			Dict						):

				"""simple docstring"""
				return jax.tree_util.tree_map(lambda __a						: x.astype(jnp.floataa						) if x.dtype == jnp.bfloataa else x	,						__a						)

def      A_ (		__a       :			Tuple	,						__a       :			List[str]						):

				"""simple docstring"""
				a__ ,     a__ ,     a__			 =						pt_attn_layer.in_proj_weight.chunk(3	,						dim=0						)
				a__ ,     a__ ,     a__			 =						pt_attn_layer.in_proj_bias.chunk(3	,						dim=0						)

				a__			 =						pt_attn_layer.out_proj.weight
				a__			 =						pt_attn_layer.out_proj.bias

				a__			 =						q_proj
				a__			 =						q_proj_bias

				a__			 =						k_proj
				a__			 =						k_proj_bias

				a__			 =						v_proj
				a__			 =						v_proj_bias

				a__			 =						out_proj_weights
				a__			 =						out_proj_bias

def      A_ (		__a       :			Any	,						__a       :			Union[str, Any]						):

				"""simple docstring"""
				copy_linear(hf_mlp.fca	,						pt_mlp.c_fc						)
				copy_linear(hf_mlp.fca	,						pt_mlp.c_proj						)

def      A_ (		__a       :			Any	,						__a       :			int						):

				"""simple docstring"""
				a__			 =						pt_linear.weight
				a__			 =						pt_linear.bias

def      A_ (		__a       :			Optional[Any]	,						__a       :			List[Any]						):

				"""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_ (		__a       :			Tuple	,						__a       :			Optional[Any]						):

				"""simple docstring"""
				for hf_layer, pt_layer in zip(__a	,						__a						):
								copy_layer(__a	,						__a						)

def      A_ (		__a       :			Union[str, Any]	,						__a       :			Optional[Any]						):

				"""simple docstring"""
				a__			 =						pt_model.token_embedding.weight
				a__			 =						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_ (		__a       :			str	,						__a       :			List[str]						):

				"""simple docstring"""
				a__			 =						pt_model.text_projection.data.T

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

def      A_ (		__a       :			Tuple	,						__a       :			Optional[Any]						):

				"""simple docstring"""
				a__			 =						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
				a__			 =						pt_model.visual.conva.weight.data
				a__			 =						pt_model.visual.class_embedding
				a__			 =						pt_model.visual.positional_embedding.data

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

def      A_ (		__a       :			List[Any]	,						__a       :			Dict						):

				"""simple docstring"""
				a__			 =						flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""]						)

				a__			 =						torch.from_numpy(flax_class_token_params["""scale"""]						)
				a__			 =						torch.from_numpy(flax_class_token_params["""bias"""]						)
				a__			 =						nn.Parameter(__a						)
				a__			 =						nn.Parameter(__a						)

def      A_ (		__a       :			Any	,						__a       :			str						):

				"""simple docstring"""
				a__			 =						hf_model.state_dict()
				a__			 =						{}

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

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

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

								a__			 =						nn.Parameter(torch.from_numpy(__a						)						)

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

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

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

								a__			 =						nn.Parameter(torch.from_numpy(__a						)						)

				# Copy flax params to PyTorch params
				for name, param in new_params.items():
								if name in pt_params.keys():
												pt_params[name].copy_(__a						)

def      A_ (		__a       :			Union[str, Any]	,						__a       :			Optional[Any]						):

				"""simple docstring"""
				for k, v in flax_attn_params.items():
								if k.startswith("""transformer"""						):
												a__			 =						k.replace("""transformer.resblocks"""	,						"""text_model.encoder.layers"""						)
								else:
												a__			 =						k.replace("""visual.transformer.resblocks"""	,						"""vision_model.encoder.layers"""						)

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

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

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

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

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

def      A_ (		__a       :			Optional[int]						):

				"""simple docstring"""
				a__			 =						{}
				a__			 =						[]

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

												processed_attn_layers.append(__a						)
												a__			 =						params[base + """out.weight"""].shape[-1]
												a__			 =						params[base + """out.weight"""].reshape(__a	,						__a						).T
												a__			 =						params[base + """out.bias"""]
								else:
												a__			 =						v
				return new_params

def      A_ (		__a       :			Any	,						__a       :			Union[str, Any]						):

				"""simple docstring"""
				a__			 =						CLIP(**__a						)
				torch_model.eval()
				a__			 =						torch_model.state_dict()

				a__			 =						flatten_nested_dict(flax_params["""backbone"""]["""clip"""]						)
				a__			 =						{}

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

								a__			 =						torch_key.replace("""text_projection.kernel"""	,						"""text_projection"""						)
								a__			 =						torch_key.replace("""visual.proj.kernel"""	,						"""visual.proj"""						)
								a__			 =						torch_key.replace(""".scale"""	,						""".weight"""						)
								a__			 =						torch_key.replace(""".kernel"""	,						""".weight"""						)

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

								a__			 =						v

				a__			 =						_convert_attn_layers(__a						)
				new_torch_params.update(__a						)
				a__			 =						{}

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

				return torch_clip_params, torch_model, attn_params

@torch.no_grad()
def      A_ (		__a       :			Any	,						__a       :			Union[str, Any]	,						__a       :			Union[str, Any]	,						__a       :			List[str]	,						__a       :			List[str]=None						):

				"""simple docstring"""
				a__			 =						Repository(__a	,						clone_from=F'''google/{pytorch_dump_folder_path}'''						)
				repo.git_pull()

				if config_path is not None:
								a__			 =						OwlViTConfig.from_pretrained(__a						)
				else:
								a__			 =						OwlViTConfig()

				a__			 =						OwlViTModel(__a						).eval()
				a__			 =						OwlViTForObjectDetection(__a						).eval()

				copy_text_model_and_projection(__a	,						__a						)
				copy_vision_model_and_projection(__a	,						__a						)
				a__			 =						pt_backbone.logit_scale
				copy_flax_attn_params(__a	,						__a						)

				a__			 =						hf_backbone
				copy_class_merge_token(__a	,						__a						)
				copy_class_box_heads(__a	,						__a						)

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

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

				# Initialize processor
				a__			 =						OwlViTProcessor(image_processor=__a	,						tokenizer=__a						)
				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__":
				UpperCAmelCase								=   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."""
				)
				UpperCAmelCase								=   parser.parse_args()

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

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

				# Convert CLIP backbone
				UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase								=   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 torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class 	__snake_case (				utils.Parser):







							'''simple docstring'''






							UpperCamelCase__			: str             =   "halfcheetah-medium-expert-v2"
							UpperCamelCase__			: str             =   "config.offline"




def      A_ (		__a       :			List[Any]	,						__a       :			Union[str, Any]	,						__a       :			Optional[Any]	,						__a       :			Dict	,						__a       :			Union[str, Any]						):

				"""simple docstring"""
				a__ ,     a__			 =						utils.load_model(__a	,						__a	,						__a	,						epoch=__a	,						device=__a						)
				a__			 =						TrajectoryTransformerModel(gpt.config						)

				trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()						)
				a__			 =						gpt.pos_emb
				trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()						)
				trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()						)
				trajectory_transformer.head.load_state_dict(gpt.head.state_dict()						)

				for i, block in enumerate(gpt.blocks						):
								trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()						)
								trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()						)
								trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()						)

								trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()						)
								trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()						)
								trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()						)
								trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()						)

				torch.save(trajectory_transformer.state_dict()	,						"""pytorch_model.bin"""						)


if __name__ == "__main__":
	UpperCAmelCase								=   Parser().parse_args("""plan""")
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)

