




import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
a_      						=   logging.get_logger(__name__)

a_      						=   [
    (
        "protein1",
        "MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA",
    ),
    ("protein2", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"),
    ("protein3", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"),
    ("protein4", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"),
]

a_      						=   {
    "esm1b_t33_650M_UR50S": esm_module.pretrained.esm1b_t33_650M_UR50S,
    "esm1v_t33_650M_UR90S_1": esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    "esm1v_t33_650M_UR90S_2": esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    "esm1v_t33_650M_UR90S_3": esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    "esm1v_t33_650M_UR90S_4": esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    "esm1v_t33_650M_UR90S_5": esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    "esm2_t48_15B_UR50D": esm_module.pretrained.esm2_t48_15B_UR50D,
    "esm2_t36_3B_UR50D": esm_module.pretrained.esm2_t36_3B_UR50D,
    "esm2_t33_650M_UR50D": esm_module.pretrained.esm2_t33_650M_UR50D,
    "esm2_t30_150M_UR50D": esm_module.pretrained.esm2_t30_150M_UR50D,
    "esm2_t12_35M_UR50D": esm_module.pretrained.esm2_t12_35M_UR50D,
    "esm2_t6_8M_UR50D": esm_module.pretrained.esm2_t6_8M_UR50D,
    "esmfold_v1": esmfold_va,
}

a_      						=   list("ARNDCQEGHILKMFPSTWYV")

a_      						=   restypes + ["X"]
a_      						=   restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]




def     __SCREAMING_SNAKE_CASE      (     )				->		Any:

    """simple docstring"""


    with TemporaryDirectory() as tempdir:
        __UpperCamelCase     						=  '''\n'''.join(lowercase_					)
        __UpperCamelCase     						=  Path(lowercase_					) / '''vocab.txt'''
        vocab_file.write_text(lowercase_					)
        __UpperCamelCase     						=  EsmTokenizer(vocab_file=str(lowercase_					)					)
    __UpperCamelCase     						=  0  # Overlaps with 'A' but that seems to be what they want
    return hf_tokenizer




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		Any:

    """simple docstring"""


    __UpperCamelCase     						=  our_module.load_state_dict(original_module.state_dict()					)
    if status.missing_keys:
        raise ValueError(F"Missing keys: {status.missing_keys}"					)
    if status.unexpected_keys:
        raise ValueError(F"Unexpected keys: {status.unexpected_keys}"					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_					)				->		Dict:

    """simple docstring"""


    if model.startswith('''esmfold'''					):
        __UpperCamelCase     						=  MODEL_MAPPING[model]()
    else:
        __UpperCamelCase						,					__UpperCamelCase     						=  MODEL_MAPPING[model]()
    esm.eval()  # disable dropout

    if model.startswith('''esmfold'''					):
        __UpperCamelCase     						=  esm.esm.embed_dim
        __UpperCamelCase     						=  esm.esm.num_layers
        __UpperCamelCase     						=  esm.esm.attention_heads
        __UpperCamelCase     						=  4 * embed_dim
        __UpperCamelCase     						=  esm.esm.token_dropout
        __UpperCamelCase     						=  False  # This code path does not exist in ESM-2
        __UpperCamelCase     						=  '''rotary'''
        __UpperCamelCase     						=  True
        __UpperCamelCase     						=  EsmFoldConfig()
        for key, val in esm.cfg.items():
            if hasattr(lowercase_    ,   lowercase_					) and key != "trunk":
                setattr(lowercase_    ,   lowercase_    ,   lowercase_					)
        for key, val in esm.cfg.trunk.items():
            if hasattr(esmfold_config.trunk    ,   lowercase_					) and key != "structure_module":
                setattr(esmfold_config.trunk    ,   lowercase_    ,   lowercase_					)
        for key, val in esm.cfg.trunk.structure_module.items():
            if hasattr(esmfold_config.trunk.structure_module    ,   lowercase_					):
                setattr(esmfold_config.trunk.structure_module    ,   lowercase_    ,   lowercase_					)
    elif hasattr(lowercase_    ,   '''args'''					):
        # Indicates an ESM-1b or ESM-1v model
        __UpperCamelCase     						=  esm.args.embed_dim
        __UpperCamelCase     						=  esm.args.layers
        __UpperCamelCase     						=  esm.args.attention_heads
        __UpperCamelCase     						=  esm.args.ffn_embed_dim
        __UpperCamelCase     						=  esm.args.token_dropout
        __UpperCamelCase     						=  True if esm.emb_layer_norm_before else False
        __UpperCamelCase     						=  '''absolute'''
        __UpperCamelCase     						=  False
        __UpperCamelCase     						=  None
    else:
        # Indicates an ESM-2 model
        __UpperCamelCase     						=  esm.embed_dim
        __UpperCamelCase     						=  esm.num_layers
        __UpperCamelCase     						=  esm.attention_heads
        __UpperCamelCase     						=  4 * embed_dim  # This is hardcoded in ESM-2
        __UpperCamelCase     						=  esm.token_dropout
        __UpperCamelCase     						=  False  # This code path does not exist in ESM-2
        __UpperCamelCase     						=  '''rotary'''
        __UpperCamelCase     						=  False
        __UpperCamelCase     						=  None

    if is_folding_model:
        __UpperCamelCase     						=  esm.esm.alphabet
    __UpperCamelCase     						=  tuple(alphabet.all_toks					)
    __UpperCamelCase     						=  alphabet.mask_idx
    __UpperCamelCase     						=  alphabet.padding_idx

    if is_folding_model:
        __UpperCamelCase     						=  esm.esm
    else:
        __UpperCamelCase     						=  esm

    __UpperCamelCase     						=  EsmConfig(
        vocab_size=original_esm_model.embed_tokens.num_embeddings    ,   mask_token_id=lowercase_    ,   hidden_size=lowercase_    ,   num_hidden_layers=lowercase_    ,   num_attention_heads=lowercase_    ,   intermediate_size=lowercase_    ,   max_position_embeddings=10_26    ,   layer_norm_eps=1E-5    ,   attention_probs_dropout_prob=0.0    ,   hidden_dropout_prob=0.0    ,   pad_token_id=lowercase_    ,   emb_layer_norm_before=lowercase_    ,   token_dropout=lowercase_    ,   position_embedding_type=lowercase_    ,   is_folding_model=lowercase_    ,   esmfold_config=lowercase_    ,   vocab_list=lowercase_    ,   )
    if classification_head:
        __UpperCamelCase     						=  esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
    print('''Our ESM config:'''    ,   lowercase_					)

    if model.startswith('''esmfold'''					):
        __UpperCamelCase     						=  EsmForProteinFolding
    elif classification_head:
        __UpperCamelCase     						=  EsmForSequenceClassification
    else:
        __UpperCamelCase     						=  EsmForMaskedLM
    __UpperCamelCase     						=  model_class(lowercase_					)
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    __UpperCamelCase     						=  original_esm_model.embed_tokens.weight
    if position_embedding_type == "absolute":
        __UpperCamelCase     						=  original_esm_model.embed_positions.weight

    if config.emb_layer_norm_before:
        __UpperCamelCase     						=  original_esm_model.emb_layer_norm_before.weight
        __UpperCamelCase     						=  original_esm_model.emb_layer_norm_before.bias

    __UpperCamelCase     						=  original_esm_model.emb_layer_norm_after.weight
    __UpperCamelCase     						=  original_esm_model.emb_layer_norm_after.bias

    for i in range(config.num_hidden_layers					):
        # Encoder: start of layer
        __UpperCamelCase     						=  model.esm.encoder.layer[i]
        # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
        __UpperCamelCase     						=  original_esm_model.layers[i]

        # self attention
        __UpperCamelCase     						=  layer.attention.self
        assert (
            esm_layer.self_attn.k_proj.weight.data.shape
            == esm_layer.self_attn.q_proj.weight.data.shape
            == esm_layer.self_attn.v_proj.weight.data.shape
            == torch.Size((config.hidden_size, config.hidden_size)					)
        )

        __UpperCamelCase     						=  esm_layer.self_attn.q_proj.weight
        __UpperCamelCase     						=  esm_layer.self_attn.q_proj.bias
        __UpperCamelCase     						=  esm_layer.self_attn.k_proj.weight
        __UpperCamelCase     						=  esm_layer.self_attn.k_proj.bias
        __UpperCamelCase     						=  esm_layer.self_attn.v_proj.weight
        __UpperCamelCase     						=  esm_layer.self_attn.v_proj.bias

        if getattr(esm_layer.self_attn    ,   '''rot_emb'''    ,   lowercase_					) is not None:
            # Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
            # During the training of ESM-2 the model was converted to float16 precision, which also converts
            # the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
            # If we recompute inv_freq without this loss of precision then we will get subtly different rotary
            # embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
            # we make sure the new model copies the data from the old inv_freq.
            __UpperCamelCase     						=  esm_layer.self_attn.rot_emb.inv_freq

        # LayerNorm changes for pre-activation
        __UpperCamelCase     						=  esm_layer.self_attn_layer_norm.weight
        __UpperCamelCase     						=  esm_layer.self_attn_layer_norm.bias
        __UpperCamelCase     						=  esm_layer.final_layer_norm.weight
        __UpperCamelCase     						=  esm_layer.final_layer_norm.bias

        # self-attention output
        __UpperCamelCase     						=  layer.attention.output
        assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
        __UpperCamelCase     						=  esm_layer.self_attn.out_proj.weight
        __UpperCamelCase     						=  esm_layer.self_attn.out_proj.bias

        # intermediate
        __UpperCamelCase     						=  layer.intermediate
        assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
        __UpperCamelCase     						=  esm_layer.fca.weight
        __UpperCamelCase     						=  esm_layer.fca.bias

        # output
        __UpperCamelCase     						=  layer.output
        assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
        __UpperCamelCase     						=  esm_layer.fca.weight
        __UpperCamelCase     						=  esm_layer.fca.bias
        # end of layer

    if is_folding_model:
        __UpperCamelCase     						=  esm.esm_s_combine.data
        __UpperCamelCase     						=  esm.afa_to_esm.data
        transfer_and_check_weights(esm.embedding    ,   model.embedding					)
        transfer_and_check_weights(esm.esm_s_mlp    ,   model.esm_s_mlp					)
        transfer_and_check_weights(esm.trunk    ,   model.trunk					)
        transfer_and_check_weights(esm.distogram_head    ,   model.distogram_head					)
        transfer_and_check_weights(esm.ptm_head    ,   model.ptm_head					)
        transfer_and_check_weights(esm.lm_head    ,   model.lm_head					)
        transfer_and_check_weights(esm.lddt_head    ,   model.lddt_head					)

    elif classification_head:
        __UpperCamelCase     						=  esm.esm.classification_heads['''mnli'''].dense.weight
        __UpperCamelCase     						=  esm.classification_heads['''mnli'''].dense.bias
        __UpperCamelCase     						=  esm.classification_heads['''mnli'''].out_proj.weight
        __UpperCamelCase     						=  esm.classification_heads['''mnli'''].out_proj.bias
    else:
        # LM Head
        __UpperCamelCase     						=  esm.lm_head.dense.weight
        __UpperCamelCase     						=  esm.lm_head.dense.bias
        __UpperCamelCase     						=  esm.lm_head.layer_norm.weight
        __UpperCamelCase     						=  esm.lm_head.layer_norm.bias
        __UpperCamelCase     						=  esm.lm_head.weight
        __UpperCamelCase     						=  esm.lm_head.bias

    # Contact prediction head
    transfer_and_check_weights(esm.contact_head    ,   model.esm.contact_head					)

    # Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
    if is_folding_model:
        # Folding models aren't trained on masked inputs and don't like mask tokens.
        __UpperCamelCase     						=  SAMPLE_DATA[:2]
    else:
        __UpperCamelCase     						=  SAMPLE_DATA

    if is_folding_model:
        __UpperCamelCase     						=  get_esmfold_tokenizer()
        __UpperCamelCase     						=  hf_tokenizer(
            [row[1] for row in sample_data]    ,   return_tensors='''pt'''    ,   padding=lowercase_    ,   add_special_tokens=lowercase_					)
        __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  esmfold_encode_sequences([row[1] for row in sample_data]					)
        __UpperCamelCase     						=  torch.all(hf_tokens['''input_ids'''] == esmfold_aas					) and torch.all(
            hf_tokens['''attention_mask'''] == esmfold_mask					)
    else:
        # Let's check that we get the same results.
        __UpperCamelCase     						=  alphabet.get_batch_converter()
        __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  batch_converter(lowercase_					)
        # Prepare tokenizer and make sure it matches
        with TemporaryDirectory() as tempdir:
            __UpperCamelCase     						=  '''\n'''.join(alphabet.all_toks					)
            __UpperCamelCase     						=  Path(lowercase_					) / '''vocab.txt'''
            vocab_file.write_text(lowercase_					)
            __UpperCamelCase     						=  EsmTokenizer(vocab_file=str(lowercase_					)					)

        __UpperCamelCase     						=  hf_tokenizer([row[1] for row in sample_data]    ,   return_tensors='''pt'''    ,   padding=lowercase_					)
        __UpperCamelCase     						=  torch.all(hf_tokens['''input_ids'''] == batch_tokens					)

    print('''Do both models tokenizers output the same tokens?'''    ,   '''🔥''' if success else '''💩'''					)
    if not success:
        raise Exception('''Tokenization does not match!'''					)

    with torch.no_grad():
        if is_folding_model:
            # Let's test the model in parts
            # ESMFold always converts the ESM stem to float16, which requires float16 ops
            # that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
            # ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
            # original and the converted model on the GPU at the same time.
            __UpperCamelCase     						=  esm.cuda().infer([row[1] for row in sample_data]					)
            __UpperCamelCase     						=  model.cuda()(
                input_ids=hf_tokens['''input_ids'''].cuda()    ,   attention_mask=hf_tokens['''attention_mask'''].cuda()					)
        else:
            __UpperCamelCase     						=  model(**lowercase_    ,   output_hidden_states=lowercase_					)
            __UpperCamelCase     						=  our_output['''logits''']
            if classification_head:
                __UpperCamelCase     						=  esm.model.classification_heads['''mnli'''](esm.extract_features(lowercase_					)					)
            else:
                __UpperCamelCase     						=  esm(hf_tokens['''input_ids''']    ,   repr_layers=list(range(9_99					)					)					)
                __UpperCamelCase     						=  their_output['''logits''']

        if is_folding_model:
            __UpperCamelCase     						=  torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']					)					).item()
            __UpperCamelCase     						=  torch.allclose(our_output['''positions''']    ,   their_output['''positions''']    ,   atol=1E-5					)
        else:
            __UpperCamelCase     						=  torch.max(torch.abs(our_output - their_output					)					).item()
            __UpperCamelCase     						=  torch.allclose(lowercase_    ,   lowercase_    ,   atol=1E-5					)

        print(F"max_absolute_diff = {max_absolute_diff}"					)  # ~ 1e-5
        print('''Do both models output the same tensors?'''    ,   '''🔥''' if success else '''💩'''					)

        if not success:
            raise Exception('''Something went wRoNg'''					)

        if not is_folding_model:
            # Let's check contact prediction too
            __UpperCamelCase     						=  model.predict_contacts(hf_tokens['''input_ids''']    ,   hf_tokens['''attention_mask''']					)
            __UpperCamelCase     						=  esm.predict_contacts(hf_tokens['''input_ids''']					)
            __UpperCamelCase     						=  torch.max(torch.abs(our_output - their_output					)					).item()
            __UpperCamelCase     						=  torch.allclose(lowercase_    ,   lowercase_    ,   atol=1E-5					)

            print('''Contact prediction testing:'''					)
            print(F"max_absolute_diff = {max_absolute_diff}"					)  # ~ 1e-5
            print('''Do both models output the same tensors?'''    ,   '''🔥''' if success else '''💩'''					)

            if not success:
                raise Exception('''Something went wRoNg'''					)

        pathlib.Path(lowercase_					).mkdir(parents=lowercase_    ,   exist_ok=lowercase_					)
        print(F"Saving model to {pytorch_dump_folder_path}"					)
        model.save_pretrained(lowercase_					)

        del esm  # Free up some memory before continuing

    print(F"Saving tokenizer to {pytorch_dump_folder_path}"					)
    hf_tokenizer.save_pretrained(lowercase_					)

    if push_to_repo:
        model.push_to_hub(repo_id=lowercase_    ,   use_auth_token=lowercase_					)
        hf_tokenizer.push_to_hub(repo_id=lowercase_    ,   use_auth_token=lowercase_					)


if __name__ == "__main__":
 a_      						=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_dump_folder_path", type=str, required=True, help="Path to the output PyTorch model."
 )
 parser.add_argument(
     "--classification_head", action="store_true", help="Whether to convert a final classification head."
 )
 parser.add_argument("--model", default=None, type=str, required=True, help="Name of model to convert.")
 parser.add_argument("--push_to_repo", type=str, help="Repo to upload to (including username!).")
 parser.add_argument("--auth_token", type=str, help="HuggingFace auth token.")
 a_      						=   parser.parse_args()
 convert_esm_checkpoint_to_pytorch(
     args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
 )














"""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__    (   snake_case    :      Any       , snake_case    :      Optional[int]       , snake_case    :      Tuple       , snake_case    :      Union[str, Any]       , snake_case    :      Dict       , snake_case    :      str							)->						Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				states.reshape(1       , -1       , model.config.state_dim							)
     UpperCAmelCase__		:  str				=				actions.reshape(1       , -1       , model.config.act_dim							)
     UpperCAmelCase__		:  Any				=				returns_to_go.reshape(1       , -1       , 1							)
     UpperCAmelCase__		:  Union[str, Any]				=				timesteps.reshape(1       , -1							)

     if model.config.max_length is not None:
          UpperCAmelCase__		:  Tuple				=				states[:, -model.config.max_length :]
          UpperCAmelCase__		:  int				=				actions[:, -model.config.max_length :]
          UpperCAmelCase__		:  Union[str, Any]				=				returns_to_go[:, -model.config.max_length :]
          UpperCAmelCase__		:  int				=				timesteps[:, -model.config.max_length :]

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

     UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  List[str]				=				model(
         states=snake_case       , actions=snake_case       , rewards=snake_case       , returns_to_go=snake_case       , timesteps=snake_case       , attention_mask=snake_case       , return_dict=snake_case       , )

     return action_preds[0, -1]


# build the environment

_lowerCAmelCase     :     Dict            =	gym.make("""Hopper-v3""")
_lowerCAmelCase     :     Any            =	env.observation_space.shape[0]
_lowerCAmelCase     :     List[Any]            =	env.action_space.shape[0]
_lowerCAmelCase     :     Any            =	1_000
_lowerCAmelCase     :     Union[str, Any]            =	"""cuda"""
_lowerCAmelCase     :     Optional[Any]            =	10_00.0  # normalization for rewards/returns
_lowerCAmelCase     :     Optional[int]            =	3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCAmelCase     :     Any            =	np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
_lowerCAmelCase     :     Optional[Any]            =	np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
_lowerCAmelCase     :     Any            =	torch.from_numpy(state_mean).to(device=device)
_lowerCAmelCase     :     List[Any]            =	torch.from_numpy(state_std).to(device=device)

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

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

 _lowerCAmelCase     :     Optional[int]            =	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     :     str            =	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[str]            =	action
  _lowerCAmelCase     :     Optional[int]            =	action.detach().cpu().numpy()

  _lowerCAmelCase       ,   _lowerCAmelCase       ,   _lowerCAmelCase       ,   _lowerCAmelCase     :     int            =	env.step(action)

  _lowerCAmelCase     :     Union[str, Any]            =	torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  _lowerCAmelCase     :     Optional[int]            =	torch.cat([states, cur_state], dim=0)
  _lowerCAmelCase     :     List[Any]            =	reward

  _lowerCAmelCase     :     List[str]            =	target_return[0, -1] - (reward / scale)
  _lowerCAmelCase     :     Optional[int]            =	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


