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 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    List[Any]:




       '''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,      _A,      _A=						model(
           states=lowerCAmelCase_							,       actions=lowerCAmelCase_							,       rewards=lowerCAmelCase_							,       returns_to_go=lowerCAmelCase_							,       timesteps=lowerCAmelCase_							,       attention_mask=lowerCAmelCase_							,       return_dict=lowerCAmelCase_							,       )

       return action_preds[0, -1]


# build the environment

UpperCAmelCase_    		=				gym.make('''Hopper-v3''')
UpperCAmelCase_    		=				env.observation_space.shape[0]
UpperCAmelCase_    		=				env.action_space.shape[0]
UpperCAmelCase_    		=				1_000
UpperCAmelCase_    		=				'''cuda'''
UpperCAmelCase_    		=				10_00.0  # normalization for rewards/returns
UpperCAmelCase_    		=				3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCAmelCase_    		=				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,
    ]
)
UpperCAmelCase_    		=				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,
    ]
)
UpperCAmelCase_    		=				torch.from_numpy(state_mean).to(device=device)
UpperCAmelCase_    		=				torch.from_numpy(state_std).to(device=device)

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

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

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

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

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

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

            UpperCAmelCase_    		=				target_return[0, -1] - (reward / scale)
            UpperCAmelCase_    		=				torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
            UpperCAmelCase_    		=				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
import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


UpperCAmelCase_    		=				logging.get_logger(__name__)


UpperCAmelCase_    		=				{
    '''suno/bark-small''': '''https://huggingface.co/suno/bark-small/resolve/main/config.json''',
    '''suno/bark''': '''https://huggingface.co/suno/bark/resolve/main/config.json''',
}

UpperCAmelCase_    		=				'''
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
'''





class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  List[str]		="""bark_module"""
     _SCREAMING_SNAKE_CASE  :  str		=["""past_key_values"""]

     _SCREAMING_SNAKE_CASE  :  Tuple		={
         """num_attention_heads""": """num_heads""",
         """num_hidden_layers""": """num_layers""",
         """vocab_size""": """input_vocab_size""",
         """window_size""": """block_size""",
     }



     def __init__(       self					,   lowerCAmelCase__=1024					,   lowerCAmelCase__=10048					,   lowerCAmelCase__=10048					,   lowerCAmelCase__=12					,   lowerCAmelCase__=12					,   lowerCAmelCase__=768					,   lowerCAmelCase__=0.0					,   lowerCAmelCase__=True					,   lowerCAmelCase__=0.02					,   lowerCAmelCase__=True					,   **lowerCAmelCase__					,   ):
            _A=						block_size
            _A=						input_vocab_size
            _A=						output_vocab_size
            _A=						num_layers
            _A=						num_heads
            _A=						hidden_size
            _A=						dropout
            _A=						bias
            _A=						use_cache
            _A=						initializer_range

            super().__init__(**lowerCAmelCase__							)





     @classmethod
     def       a__	(       cls					,   lowerCAmelCase__					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = "main"					,   **lowerCAmelCase__					,   ):
            _A=						cache_dir
            _A=						force_download
            _A=						local_files_only
            _A=						revision

            cls._set_token_in_kwargs(lowerCAmelCase__					,   lowerCAmelCase__							)

            _A,      _A=						cls.get_config_dict(lowerCAmelCase__					,   **lowerCAmelCase__							)

            # get the config dict if we are loading from Bark
            if config_dict.get('model_type'							) == "bark":
                   _A=						config_dict[f"{cls.model_type}_config"]

            if "model_type" in config_dict and hasattr(cls					,   'model_type'							) and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                       f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."							)

            return cls.from_dict(lowerCAmelCase__					,   **lowerCAmelCase__							)





@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkSemanticConfig"""      ,		model="""BarkSemanticModel"""      )      ,		"""
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""      ,		)
class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  Union[str, Any]		="""semantic"""





@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkCoarseConfig"""      ,		model="""BarkCoarseModel"""      )      ,		"""
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""      ,		)
class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  str		="""coarse_acoustics"""





@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkFineConfig"""      ,		model="""BarkFineModel"""      )      ,		"""
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""      ,		)
class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  str		="""fine_acoustics"""



     def __init__(       self					,   lowerCAmelCase__=True					,   lowerCAmelCase__=8					,   lowerCAmelCase__=1					,   **lowerCAmelCase__							):
            _A=						n_codes_total
            _A=						n_codes_given

            super().__init__(tie_word_embeddings=lowerCAmelCase__					,   **lowerCAmelCase__							)





class   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  Optional[Any]		="""bark"""
     _SCREAMING_SNAKE_CASE  :  Any		=True



     def __init__(       self					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__ = None					,   lowerCAmelCase__=0.02					,   **lowerCAmelCase__					,   ):
            if semantic_config is None:
                   _A=						{}
                   logger.info('semantic_config is None. initializing the semantic model with default values.'							)

            if coarse_acoustics_config is None:
                   _A=						{}
                   logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'							)

            if fine_acoustics_config is None:
                   _A=						{}
                   logger.info('fine_acoustics_config is None. initializing the fine model with default values.'							)

            if codec_config is None:
                   _A=						{}
                   logger.info('codec_config is None. initializing the codec model with default values.'							)

            _A=						BarkSemanticConfig(**lowerCAmelCase__							)
            _A=						BarkCoarseConfig(**lowerCAmelCase__							)
            _A=						BarkFineConfig(**lowerCAmelCase__							)
            _A=						AutoConfig.for_model(**lowerCAmelCase__							)

            _A=						initializer_range

            super().__init__(**lowerCAmelCase__							)



     @classmethod
     def       a__	(       cls					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   **lowerCAmelCase__					,   ):
            return cls(
                semantic_config=semantic_config.to_dict()					,   coarse_acoustics_config=coarse_acoustics_config.to_dict()					,   fine_acoustics_config=fine_acoustics_config.to_dict()					,   codec_config=codec_config.to_dict()					,   **lowerCAmelCase__					,   )





     def       a__	(       self							):
            _A=						copy.deepcopy(self.__dict__							)

            _A=						self.semantic_config.to_dict()
            _A=						self.coarse_acoustics_config.to_dict()
            _A=						self.fine_acoustics_config.to_dict()
            _A=						self.codec_config.to_dict()

            _A=						self.__class__.model_type
            return output