


'''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 _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A  ,			A  ,			A	)	->	List[Any]:






       """simple docstring"""







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

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

              # pad all tokens to sequence length
              lowercase__				      =			torch.cat(
                  [torch.zeros(model.config.max_length - states.shape[1]	), torch.ones(states.shape[1]	)]	)
              lowercase__				      =			attention_mask.to(dtype=torch.long  ,			device=states.device	).reshape(1  ,			-1	)
              lowercase__				      =			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	)
              lowercase__				      =			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	)
              lowercase__				      =			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	)
              lowercase__				      =			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:
              lowercase__				      =			None

       lowercase__      ,lowercase__      ,lowercase__				      =			model(
           states=SCREAMING_SNAKE_CASE_  ,			actions=SCREAMING_SNAKE_CASE_  ,			rewards=SCREAMING_SNAKE_CASE_  ,			returns_to_go=SCREAMING_SNAKE_CASE_  ,			timesteps=SCREAMING_SNAKE_CASE_  ,			attention_mask=SCREAMING_SNAKE_CASE_  ,			return_dict=SCREAMING_SNAKE_CASE_  ,			)

       return action_preds[0, -1]


# build the environment

lowerCamelCase							:       Any  						=     gym.make('Hopper-v3')
lowerCamelCase							:       Any  						=     env.observation_space.shape[0]
lowerCamelCase							:       Optional[Any]  						=     env.action_space.shape[0]
lowerCamelCase							:       Optional[Any]  						=     1_000
lowerCamelCase							:       Any  						=     'cuda'
lowerCamelCase							:       Optional[Any]  						=     1_000.0  # normalization for rewards/returns
lowerCamelCase							:       int  						=     3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
lowerCamelCase							:       Tuple  						=     np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
lowerCamelCase							:       Tuple  						=     np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
lowerCamelCase							:       str  						=     torch.from_numpy(state_mean).to(device=device)
lowerCamelCase							:       Tuple  						=     torch.from_numpy(state_std).to(device=device)

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

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

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

        lowerCamelCase							:       Tuple  						=     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),
        )
        lowerCamelCase							:       List[Any]  						=     action
        lowerCamelCase							:       List[str]  						=     action.detach().cpu().numpy()

        lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase							:       str  						=     env.step(action)

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

        lowerCamelCase							:       Any  						=     target_return[0, -1] - (reward / scale)
        lowerCamelCase							:       Tuple  						=     torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
        lowerCamelCase							:       Tuple  						=     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








'''simple docstring'''

import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase							:       Dict  						=     logging.get_logger(__name__)

def _SCREAMING_SNAKE_CASE				(A	)	->	Optional[Any]:






       """simple docstring"""







       lowercase__				      =			MobileNetVaConfig(layer_norm_eps=0.001	)

       if "quant" in model_name:
              raise ValueError('''Quantized models are not supported.'''	)

       lowercase__				      =			re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''  ,			A	)
       if matches:
              lowercase__				      =			float(matches[1]	)
              lowercase__				      =			int(matches[2]	)

       if model_name.startswith('''deeplabv3_'''	):
              lowercase__				      =			8
              lowercase__				      =			21
              lowercase__				      =			'''pascal-voc-id2label.json'''
       else:
              # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
              # of the usual 1000. The first class (index 0) is "background".
              lowercase__				      =			1_001
              lowercase__				      =			'''imagenet-1k-id2label.json'''

       lowercase__				      =			'''huggingface/label-files'''
       lowercase__				      =			json.load(open(hf_hub_download(A  ,			A  ,			repo_type='''dataset'''	)  ,			'''r'''	)	)

       if config.num_labels == 1_001:
              lowercase__				      =			{int(A	) + 1: v for k, v in idalabel.items()}
              lowercase__				      =			'''background'''
       else:
              lowercase__				      =			{int(A	): v for k, v in idalabel.items()}

       lowercase__				      =			idalabel
       lowercase__				      =			{v: k for k, v in idalabel.items()}

       return config

def _SCREAMING_SNAKE_CASE				()	->	List[Any]:






       """simple docstring"""







       lowercase__				      =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       lowercase__				      =			Image.open(requests.get(A  ,			stream=A	).raw	)
       return im

@torch.no_grad()
def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A=False	)	->	Any:






       """simple docstring"""







       lowercase__				      =			get_mobilenet_va_config(A	)

       # Load 🤗 model
       if model_name.startswith('''deeplabv3_'''	):
              lowercase__				      =			MobileNetVaForSemanticSegmentation(A	).eval()
       else:
              lowercase__				      =			MobileNetVaForImageClassification(A	).eval()

       # Load weights from TensorFlow checkpoint
       load_tf_weights_in_mobilenet_va(A  ,			A  ,			A	)

       # Check outputs on an image, prepared by MobileNetV2ImageProcessor
       lowercase__				      =			MobileNetVaImageProcessor(
           crop_size={'''width''': config.image_size, '''height''': config.image_size}  ,			size={'''shortest_edge''': config.image_size + 32}  ,			)
       lowercase__				      =			image_processor(images=prepare_img()  ,			return_tensors='''pt'''	)
       lowercase__				      =			model(**A	)
       lowercase__				      =			outputs.logits

       if model_name.startswith('''deeplabv3_'''	):
              assert logits.shape == (1, 21, 65, 65)

              if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                     lowercase__				      =			torch.tensor(
                         [
                             [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
                             [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
                             [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
                         ]	)

              else:
                     raise ValueError(f"Unknown model name: {model_name}"	)

              assert torch.allclose(logits[0, :3, :3, :3]  ,			A  ,			atol=1E-4	)
       else:
              assert logits.shape == (1, 1_001)

              if model_name == "mobilenet_v2_1.4_224":
                     lowercase__				      =			torch.tensor([0.0_181, -1.0_015, 0.4_688]	)
              elif model_name == "mobilenet_v2_1.0_224":
                     lowercase__				      =			torch.tensor([0.2_445, -1.1_993, 0.1_905]	)
              elif model_name == "mobilenet_v2_0.75_160":
                     lowercase__				      =			torch.tensor([0.2_482, 0.4_136, 0.6_669]	)
              elif model_name == "mobilenet_v2_0.35_96":
                     lowercase__				      =			torch.tensor([0.1_451, -0.4_624, 0.7_192]	)
              else:
                     lowercase__				      =			None

              if expected_logits is not None:
                     assert torch.allclose(logits[0, :3]  ,			A  ,			atol=1E-4	)

       Path(A	).mkdir(exist_ok=A	)
       print(f"Saving model {model_name} to {pytorch_dump_folder_path}"	)
       model.save_pretrained(A	)
       print(f"Saving image processor to {pytorch_dump_folder_path}"	)
       image_processor.save_pretrained(A	)

       if push_to_hub:
              print('''Pushing to the hub...'''	)
              lowercase__				      =			'''google/''' + model_name
              image_processor.push_to_hub(A	)
              model.push_to_hub(A	)


if __name__ == "__main__":
 lowerCamelCase							:       int  						=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--model_name',
     default='mobilenet_v2_1.0_224',
     type=str,
     help='Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<depth>_<size>\'.',
 )
 parser.add_argument(
     '--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
 )
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
 )

 lowerCamelCase							:       int  						=     parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )




