

import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)




def        _lowercase  (					a_					:       List[str]       ,a_					:       Optional[Any]=False       ,a_					:       Optional[Any]=False       ,a_					:       Any="data2vec."					)					->						Optional[int]:




					'''simple docstring'''







					__magic_name__				=     'backbone.' if is_semantic else ''

					__magic_name__				=     []
					for i in range(config.num_hidden_layers					):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append(
										    (F'{prefix}blocks.{i}.norm1.weight', F'{hf_prefix}encoder.layer.{i}.layernorm_before.weight')					)
										rename_keys.append((F'{prefix}blocks.{i}.norm1.bias', F'{hf_prefix}encoder.layer.{i}.layernorm_before.bias')					)
										rename_keys.append(
										    (F'{prefix}blocks.{i}.attn.proj.weight', F'{hf_prefix}encoder.layer.{i}.attention.output.dense.weight')					)
										rename_keys.append(
										    (F'{prefix}blocks.{i}.attn.proj.bias', F'{hf_prefix}encoder.layer.{i}.attention.output.dense.bias')					)
										rename_keys.append(
										    (F'{prefix}blocks.{i}.norm2.weight', F'{hf_prefix}encoder.layer.{i}.layernorm_after.weight')					)
										rename_keys.append((F'{prefix}blocks.{i}.norm2.bias', F'{hf_prefix}encoder.layer.{i}.layernorm_after.bias')					)
										rename_keys.append(
										    (F'{prefix}blocks.{i}.mlp.fc1.weight', F'{hf_prefix}encoder.layer.{i}.intermediate.dense.weight')					)
										rename_keys.append(
										    (F'{prefix}blocks.{i}.mlp.fc1.bias', F'{hf_prefix}encoder.layer.{i}.intermediate.dense.bias')					)
										rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.weight', F'{hf_prefix}encoder.layer.{i}.output.dense.weight')					)
										rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.bias', F'{hf_prefix}encoder.layer.{i}.output.dense.bias')					)

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        (F'{prefix}cls_token', F'{hf_prefix}embeddings.cls_token'),
					        (F'{prefix}patch_embed.proj.weight', F'{hf_prefix}embeddings.patch_embeddings.projection.weight'),
					        (F'{prefix}patch_embed.proj.bias', F'{hf_prefix}embeddings.patch_embeddings.projection.bias'),
					    ]					)

					if has_lm_head:
										# mask token + shared relative position bias + layernorm
										rename_keys.extend(
										    [
										        ('mask_token', F'{hf_prefix}embeddings.mask_token'),
										        (
										            'rel_pos_bias.relative_position_bias_table',
										            F'{hf_prefix}encoder.relative_position_bias.relative_position_bias_table',
										        ),
										        (
										            'rel_pos_bias.relative_position_index',
										            F'{hf_prefix}encoder.relative_position_bias.relative_position_index',
										        ),
										        ('norm.weight', 'layernorm.weight'),
										        ('norm.bias', 'layernorm.bias'),
										    ]					)
					elif is_semantic:
										# semantic segmentation classification heads
										rename_keys.extend(
										    [
										        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
										        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
										        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
										        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
										    ]					)
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ('fc_norm.weight', F'{hf_prefix}pooler.layernorm.weight'),
										        ('fc_norm.bias', F'{hf_prefix}pooler.layernorm.bias'),
										        ('head.weight', 'classifier.weight'),
										        ('head.bias', 'classifier.bias'),
										    ]					)

					return rename_keys




def        _lowercase  (					a_					:       Tuple       ,a_					:       Tuple       ,a_					:       Dict=False       ,a_					:       List[Any]=False       ,a_					:       Tuple="data2vec_vision."					)					->						List[str]:




					'''simple docstring'''







					for i in range(config.num_hidden_layers					):
										__magic_name__				=     'backbone.' if is_semantic else ''
										# queries, keys and values
										__magic_name__				=     state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'					)
										__magic_name__				=     state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'					)
										__magic_name__				=     state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'					)

										__magic_name__				=     in_proj_weight[
										    : config.hidden_size, :
										]
										__magic_name__				=     q_bias
										__magic_name__				=     in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__magic_name__				=     in_proj_weight[
										    -config.hidden_size :, :
										]
										__magic_name__				=     v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										__magic_name__				=     state_dict.pop(F'{prefix}blocks.{i}.gamma_1'					)
										__magic_name__				=     state_dict.pop(F'{prefix}blocks.{i}.gamma_2'					)

										__magic_name__				=     gamma_a
										__magic_name__				=     gamma_a

										# relative_position bias table + index
										if not has_lm_head:
															# each layer has its own relative position bias
															__magic_name__				=     state_dict.pop(F'{prefix}blocks.{i}.attn.relative_position_bias_table'					)
															__magic_name__				=     state_dict.pop(F'{prefix}blocks.{i}.attn.relative_position_index'					)

															__magic_name__				=     table
															__magic_name__				=     index




def        _lowercase  (					)					->						List[str]:




					'''simple docstring'''







					__magic_name__				=     argparse.ArgumentParser(
					    'Convert Data2VecVision to HF for image classification and pretraining'       ,add_help=__A					)
					parser.add_argument('--hf_checkpoint_name'       ,type=__A					)
					parser.add_argument('--input_size'       ,default=2_2_4       ,type=__A       ,help='images input size'					)
					parser.add_argument('--beit_checkpoint'       ,default=''       ,help='beit checkpoint'					)

					return parser.parse_args()




def        _lowercase  (					a_					:       Optional[int]       ,a_					:       str       ,a_					:       Union[str, Any]					)					->						List[Any]:




					'''simple docstring'''







					def load_state_dict(a_					:       Tuple       ,a_					:       int       ,a_					:       Optional[int]=""       ,a_					:       List[Any]="relative_position_index"					):
										__magic_name__				=     []
										__magic_name__				=     []
										__magic_name__				=     []
										# copy state_dict so _load_from_state_dict can modify it
										__magic_name__				=     getattr(__A       ,'_metadata'       ,__A					)
										__magic_name__				=     state_dict.copy()
										if metadata is not None:
															__magic_name__				=     metadata

										def load(a_					:       str       ,a_					:       int=""					):
															__magic_name__				=     {} if metadata is None else metadata.get(prefix[:-1]       ,{}					)
															module._load_from_state_dict(
															    __A       ,__A       ,__A       ,__A       ,__A       ,__A       ,__A					)
															for name, child in module._modules.items():
																				if child is not None:
																									load(__A       ,prefix + name + '.'					)

										load(__A       ,prefix=__A					)

										__magic_name__				=     []
										__magic_name__				=     []
										for key in missing_keys:
															__magic_name__				=     True
															for ignore_key in ignore_missing.split('|'					):
																				if ignore_key in key:
																									__magic_name__				=     False
																									break
															if keep_flag:
																				warn_missing_keys.append(__A					)
															else:
																				ignore_missing_keys.append(__A					)

										__magic_name__				=     warn_missing_keys

										if len(__A					) > 0:
															print(
															    'Weights of {} not initialized from pretrained model: {}'.format(
															        model.__class__.__name__       ,__A					)					)
										if len(__A					) > 0:
															print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__       ,__A					)					)
										if len(__A					) > 0:
															print(
															    'Ignored weights of {} not initialized from pretrained model: {}'.format(
															        model.__class__.__name__       ,__A					)					)
										if len(__A					) > 0:
															print('\n'.join(__A					)					)

					__magic_name__				=     {
					    'pretrained': False,
					    'use_shared_rel_pos_bias': True,
					    'use_abs_pos_emb': False,
					    'init_values': 0.1,
					}

					if is_finetuned:
										model_kwargs.update(
										    {
										        'num_classes': 1_0_0_0,
										        'use_mean_pooling': True,
										        'init_scale': 0.001,
										        'use_rel_pos_bias': True,
										    }					)

					__magic_name__				=     create_model(
					    'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'       ,**__A       ,)
					__magic_name__				=     model.patch_embed.patch_size
					__magic_name__				=     (args.input_size // patch_size[0], args.input_size // patch_size[1])
					__magic_name__				=     torch.load(args.beit_checkpoint       ,map_location='cpu'					)

					print(F'Load ckpt from {args.beit_checkpoint}'					)
					__magic_name__				=     None
					for model_key in ("model", "module"):
										if model_key in checkpoint:
															__magic_name__				=     checkpoint[model_key]
															print(F'Load state_dict by model_key = {model_key}'					)
															break

					__magic_name__				=     list(checkpoint_model.keys()					)
					for key in all_keys:
										if "relative_position_index" in key:
															checkpoint_model.pop(__A					)

										if "relative_position_bias_table" in key:
															__magic_name__				=     checkpoint_model[key]
															__magic_name__,						__magic_name__				=     rel_pos_bias.size()
															__magic_name__,						__magic_name__				=     model.state_dict()[key].size()
															__magic_name__				=     model.patch_embed.patch_shape
															if dst_patch_shape[0] != dst_patch_shape[1]:
																				raise NotImplementedError()

					load_state_dict(__A       ,__A       ,prefix=''					)

					return model




def        _lowercase  (					)					->						Optional[int]:




					'''simple docstring'''







					__magic_name__				=     get_args()

					__magic_name__				=     'ft1k' in args.hf_checkpoint_name
					__magic_name__				=     'large' in args.hf_checkpoint_name

					if is_finetuned:
										# To convert Beit's data2vec_vision to HF you need to copy
										# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
										# into this folder.
										import modeling_finetune  # noqa: F401
					else:
										# To convert Beit's data2vec_vision to HF you need to copy
										# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
										# into this folder
										# IMPORTANT: Note that for now we've only converted the down-stream
										# model and not the full pretrained model. This means for the integration
										# test you need to add a `return x` after the following line:
										# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
										# to make the integration test pass.
										import modeling_cyclical  # noqa: F401

					# 1. Create model config
					__magic_name__				=     DataaVecVisionConfig()
					if is_finetuned:
										__magic_name__				=     True
										__magic_name__				=     False
										__magic_name__				=     True
										__magic_name__				=     1_0_0_0

										__magic_name__				=     'huggingface/label-files'
										__magic_name__				=     'imagenet-1k-id2label.json'
										__magic_name__				=     json.load(open(hf_hub_download(__A       ,__A       ,repo_type='dataset'					)       ,'r'					)					)
										__magic_name__				=     {int(__A					): v for k, v in idalabel.items()}
										__magic_name__				=     idalabel
										__magic_name__				=     {v: k for k, v in idalabel.items()}
					else:
										__magic_name__				=     False
										__magic_name__				=     True
										__magic_name__				=     False

					if is_large:
										__magic_name__				=     1_0_2_4
										__magic_name__				=     4_0_9_6
										__magic_name__				=     2_4
										__magic_name__				=     1_6

					# 2. Load Beit model
					__magic_name__				=     load_beit_model(__A       ,__A       ,__A					)
					orig_model.eval()

					# 3. Forward Beit model
					__magic_name__				=     BeitImageProcessor(size=config.image_size       ,do_center_crop=__A					)
					__magic_name__				=     Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'					)
					__magic_name__				=     image_processor(images=__A       ,return_tensors='pt'					)
					__magic_name__				=     encoding['pixel_values']

					__magic_name__				=     (pixel_values,) if is_finetuned else (pixel_values, None)
					with torch.no_grad():
										__magic_name__				=     orig_model(*__A					)

					# 4. Load HF Data2VecVision model
					if is_finetuned:
										__magic_name__				=     DataaVecVisionForImageClassification(__A					)
										hf_model.eval()
										__magic_name__				=     False
										__magic_name__				=     'data2vec_vision.'
					else:
										__magic_name__				=     DataaVecVisionModel(__A					)
										hf_model.eval()
										__magic_name__				=     True
										__magic_name__				=     ''

					__magic_name__				=     create_rename_keys(__A       ,hf_prefix=__A       ,has_lm_head=__A					)
					__magic_name__				=     orig_model.state_dict()
					for src, dest in rename_keys:
										__magic_name__				=     state_dict.pop(__A					)
										__magic_name__				=     val

					read_in_q_k_v(__A       ,__A       ,hf_prefix=__A       ,has_lm_head=__A					)
					__magic_name__,						__magic_name__				=     hf_model.load_state_dict(__A       ,strict=__A					)
					print('HF missing'       ,__A					)
					print('HF unexpected_keys'       ,__A					)

					# 5. Forward HF Data2VecVision model
					with torch.no_grad():
										__magic_name__				=     hf_model(__A					)

					__magic_name__				=     hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

					# 6. Compare
					__magic_name__				=     torch.max(torch.abs(hf_output - orig_model_output					)					).item()

					print(F'max_absolute_diff = {max_absolute_diff}'					)
					__magic_name__				=     torch.allclose(__A       ,__A       ,atol=1e-3					)
					print('Do both models output the same tensors?'       ,'🔥' if success else '💩'					)
					if not success:
										raise Exception('Something went wRoNg'					)

					# 7. Save
					print(F'Saving to {args.hf_checkpoint_name}'					)
					hf_model.save_pretrained(args.hf_checkpoint_name					)
					image_processor.save_pretrained(args.hf_checkpoint_name					)


if __name__ == "__main__":
						main()
						# Run the following to convert checkpoints
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./pretrained_base.pt \
						#          --hf_checkpoint_name "./data2vec-vision-base"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./finetuned_base.pt \
						#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./pretrained_large.pt \
						#          --hf_checkpoint_name "./data2vec-vision-large"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./finetuned_large.pt \
						#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"












"""simple docstring"""



import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.




def     lowerCamelCase_					(	__A	:		Tuple      ,						__A	:		Optional[int]      ,						__A	:		str      ,						__A	:		Optional[Any]      ,						__A	:		Optional[int]      ,						__A	:		Union[str, Any]      ):
					# we don't care about the past rewards in this model

					lowerCamelCase__					=states.reshape(1      ,						-1      ,						model.config.state_dim      )
					lowerCamelCase__					=actions.reshape(1      ,						-1      ,						model.config.act_dim      )
					lowerCamelCase__					=returns_to_go.reshape(1      ,						-1      ,						1      )
					lowerCamelCase__					=timesteps.reshape(1      ,						-1      )

					if model.config.max_length is not None:
										lowerCamelCase__					=states[:, -model.config.max_length :]
										lowerCamelCase__					=actions[:, -model.config.max_length :]
										lowerCamelCase__					=returns_to_go[:, -model.config.max_length :]
										lowerCamelCase__					=timesteps[:, -model.config.max_length :]

										# pad all tokens to sequence length
										lowerCamelCase__					=torch.cat(
										    [torch.zeros(model.config.max_length - states.shape[1]      ), torch.ones(states.shape[1]      )]      )
										lowerCamelCase__					=attention_mask.to(dtype=torch.long      ,						device=states.device      ).reshape(1      ,						-1      )
										lowerCamelCase__					=torch.cat(
										    [
										        torch.zeros(
										            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)      ,						device=states.device      ,						),
										        states,
										    ]      ,						dim=1      ,						).to(dtype=torch.floataa      )
										lowerCamelCase__					=torch.cat(
										    [
										        torch.zeros(
										            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)      ,						device=actions.device      ,						),
										        actions,
										    ]      ,						dim=1      ,						).to(dtype=torch.floataa      )
										lowerCamelCase__					=torch.cat(
										    [
										        torch.zeros(
										            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)      ,						device=returns_to_go.device      ,						),
										        returns_to_go,
										    ]      ,						dim=1      ,						).to(dtype=torch.floataa      )
										lowerCamelCase__					=torch.cat(
										    [
										        torch.zeros(
										            (timesteps.shape[0], model.config.max_length - timesteps.shape[1])      ,						device=timesteps.device      ),
										        timesteps,
										    ]      ,						dim=1      ,						).to(dtype=torch.long      )
					else:
										lowerCamelCase__					=None

					lowerCamelCase__					,  lowerCamelCase__					,  lowerCamelCase__					=model(
					    states=__A      ,						actions=__A      ,						rewards=__A      ,						returns_to_go=__A      ,						timesteps=__A      ,						attention_mask=__A      ,						return_dict=__A      ,						)

					return action_preds[0, -1]


# build the environment

_snake_case							:   List[str]	  =       gym.make("Hopper-v3")
_snake_case							:   Optional[int]	  =       env.observation_space.shape[0]
_snake_case							:   Tuple	  =       env.action_space.shape[0]
_snake_case							:   Union[str, Any]	  =       1_000
_snake_case							:   Dict	  =       "cuda"
_snake_case							:   Dict	  =       1000.0  # normalization for rewards/returns
_snake_case							:   Optional[int]	  =       3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_snake_case							:   Optional[Any]	  =       np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
_snake_case							:   Tuple	  =       np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
_snake_case							:   int	  =       torch.from_numpy(state_mean).to(device=device)
_snake_case							:   int	  =       torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_snake_case							:   List[Any]	  =       DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
_snake_case							:   List[str]	  =       model.to(device)
model.eval()

for ep in range(10):
						_snake_case  ,    _snake_case							:   List[str]	  =       0, 0
						_snake_case							:   Any	  =       env.reset()
						_snake_case							:   int	  =       torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
						_snake_case							:   Optional[Any]	  =       torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
						_snake_case							:   Tuple	  =       torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
						_snake_case							:   Tuple	  =       torch.zeros(0, device=device, dtype=torch.floataa)

						_snake_case							:   Tuple	  =       torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
						for t in range(max_ep_len):
												env.render()
												# add padding
												_snake_case							:   List[Any]	  =       torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
												_snake_case							:   str	  =       torch.cat([rewards, torch.zeros(1, device=device)])

												_snake_case							:   List[str]	  =       get_action(
												    model,
												    (states.to(dtype=torch.floataa) - state_mean) / state_std,
												    actions.to(dtype=torch.floataa),
												    rewards.to(dtype=torch.floataa),
												    target_return.to(dtype=torch.floataa),
												    timesteps.to(dtype=torch.long),
												)
												_snake_case							:   List[str]	  =       action
												_snake_case							:   Optional[int]	  =       action.detach().cpu().numpy()

												_snake_case  ,    _snake_case  ,    _snake_case  ,    _snake_case							:   List[str]	  =       env.step(action)

												_snake_case							:   Optional[int]	  =       torch.from_numpy(state).to(device=device).reshape(1, state_dim)
												_snake_case							:   Optional[int]	  =       torch.cat([states, cur_state], dim=0)
												_snake_case							:   Tuple	  =       reward

												_snake_case							:   List[str]	  =       target_return[0, -1] - (reward / scale)
												_snake_case							:   Optional[int]	  =       torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
												_snake_case							:   Any	  =       torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

												episode_return += reward
												episode_length += 1

												if done:
																		break