



'''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  ,     __A  ,     __A  ,     __A  ,     __A  ,     __A):



     """simple docstring"""



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

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

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

     _a     =  model(
         states=_UpperCamelCase  ,     actions=_UpperCamelCase  ,     rewards=_UpperCamelCase  ,     returns_to_go=_UpperCamelCase  ,     timesteps=_UpperCamelCase  ,     attention_mask=_UpperCamelCase  ,     return_dict=_UpperCamelCase  ,     )

     return action_preds[0, -1]


# build the environment

lowercase_			=  gym.make("Hopper-v3")
lowercase_			=  env.observation_space.shape[0]
lowercase_			=  env.action_space.shape[0]
lowercase_			=  1_000
lowercase_			=  "cuda"
lowercase_			=  1_000.0  # normalization for rewards/returns
lowercase_			=  3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
lowercase_			=  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,
    ]
)
lowercase_			=  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,
    ]
)
lowercase_			=  torch.from_numpy(state_mean).to(device=device)
lowercase_			=  torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
lowercase_			=  DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
lowercase_			=  model.to(device)
model.eval()

for ep in range(10):
  lowercase_	, lowercase_			=  0, 0
  lowercase_			=  env.reset()
  lowercase_			=  torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
  lowercase_			=  torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
  lowercase_			=  torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
  lowercase_			=  torch.zeros(0, device=device, dtype=torch.floataa)

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

    lowercase_			=  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),
    )
    lowercase_			=  action
    lowercase_			=  action.detach().cpu().numpy()

    lowercase_	, lowercase_	, lowercase_	, lowercase_			=  env.step(action)

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

    lowercase_			=  target_return[0, -1] - (reward / scale)
    lowercase_			=  torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    lowercase_			=  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 os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
  raise Exception("requires fairseq >= 0.9.0")


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

lowercase_			=  "Hello world! cécé herlolip"




def lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



     _a  ,   _a     =  os.path.split(__A)
     _a     =  DataaVecTextModel.from_pretrained(
         __A  ,     checkpoint_file=__A)
     dataavec.eval()  # disable dropout
     _a     =  dataavec.models[0]
     _a     =  dataavec_model.encoder.sentence_encoder
     _a     =  DataaVecTextConfig(
         vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings  ,     hidden_size=dataavec_model.args.encoder_embed_dim  ,     num_hidden_layers=dataavec_model.args.encoder_layers  ,     num_attention_heads=dataavec_model.args.encoder_attention_heads  ,     intermediate_size=dataavec_model.args.encoder_ffn_embed_dim  ,     max_position_embeddings=514  ,     type_vocab_size=1  ,     layer_norm_eps=1e-5  ,     )
     if classification_head:
          _a     =  dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our BERT config:'''  ,     __A)

     _a     =  DataaVecTextForSequenceClassification(__A) if classification_head else DataaVecTextForMaskedLM(__A)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     _a     =  dataavec_sent_encoder.embed_tokens.weight
     _a     =  dataavec_sent_encoder.embed_positions.weight
     _a     =  torch.zeros_like(
         model.dataavec_text.embeddings.token_type_embeddings.weight)  # just zero them out b/c data2vec doesn't use them.
     _a     =  dataavec_sent_encoder.layernorm_embedding.weight
     _a     =  dataavec_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers):
          # Encoder: start of layer
          _a     =  model.dataavec_text.encoder.layer[i]
          _a     =  dataavec_sent_encoder.layers[i]

          # self attention
          _a     =  layer.attention.self
          assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)), (
              "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
          )
          assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)), (
              "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
          )
          assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)), (
              "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
          )

          _a     =  dataavec_layer.self_attn.q_proj.weight
          _a     =  dataavec_layer.self_attn.q_proj.bias
          _a     =  dataavec_layer.self_attn.k_proj.weight
          _a     =  dataavec_layer.self_attn.k_proj.bias
          _a     =  dataavec_layer.self_attn.v_proj.weight
          _a     =  dataavec_layer.self_attn.v_proj.bias

          # self-attention output
          _a     =  layer.attention.output
          assert (
              self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
          ), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
          _a     =  dataavec_layer.self_attn.out_proj.weight
          _a     =  dataavec_layer.self_attn.out_proj.bias
          _a     =  dataavec_layer.self_attn_layer_norm.weight
          _a     =  dataavec_layer.self_attn_layer_norm.bias

          # intermediate
          _a     =  layer.intermediate
          assert (
              intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
          ), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
          _a     =  dataavec_layer.fca.weight
          _a     =  dataavec_layer.fca.bias

          # output
          _a     =  layer.output
          assert (
              bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
          ), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
          _a     =  dataavec_layer.fca.weight
          _a     =  dataavec_layer.fca.bias
          _a     =  dataavec_layer.final_layer_norm.weight
          _a     =  dataavec_layer.final_layer_norm.bias
          # end of layer

     if classification_head:
          _a     =  dataavec.model.classification_heads['''mnli'''].dense.weight
          _a     =  dataavec.model.classification_heads['''mnli'''].dense.bias
          _a     =  dataavec.model.classification_heads['''mnli'''].out_proj.weight
          _a     =  dataavec.model.classification_heads['''mnli'''].out_proj.bias
     else:
          # LM Head
          _a     =  dataavec_model.encoder.lm_head.dense.weight
          _a     =  dataavec_model.encoder.lm_head.dense.bias
          _a     =  dataavec_model.encoder.lm_head.layer_norm.weight
          _a     =  dataavec_model.encoder.lm_head.layer_norm.bias
          _a     =  dataavec_model.encoder.lm_head.weight
          _a     =  dataavec_model.encoder.lm_head.bias

     # Let's check that we get the same results.
     _a     =  dataavec.encode(__A).unsqueeze(0)  # batch of size 1

     _a     =  model(__A)[0]
     if classification_head:
          _a     =  dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(__A))
     else:
          _a     =  dataavec_model(__A)[0]
     print(our_output.shape  ,     their_output.shape)
     _a     =  torch.max(torch.abs(our_output - their_output)).item()
     print(F'''max_absolute_diff = {max_absolute_diff}''')  # ~ 1e-7
     _a     =  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''')

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


if __name__ == "__main__":
  lowercase_			=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, 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."
  )
  lowercase_			=  parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )



