






'''simple docstring'''

import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


UpperCamelCase      					="scheduler_config.json"




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Optional[Any]			  =    1
 __a      :    Dict			  =    2
 __a      :    List[str]			  =    3
 __a      :    int			  =    4
 __a      :    List[str]			  =    5






@dataclass
class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    jnp.ndarray






class      A     :






 """simple docstring"""





 __a      :    Any			  =    SCHEDULER_CONFIG_NAME
 __a      :    Optional[int]			  =    ['''dtype''']
 __a      :    List[Any]			  =    []
 __a      :    Dict			  =    True
 @classmethod
 def 	_UpperCAmelCase							(				cls      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase=False      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_     ,      UpperCamelCase_					: List[str]            =				cls.load_config(
           pretrained_model_name_or_path=__lowerCAmelCase      ,  subfolder=__lowerCAmelCase      ,  return_unused_kwargs=__lowerCAmelCase      ,  **__lowerCAmelCase      ,  )
       UpperCamelCase_     ,      UpperCamelCase_					: int            =				cls.from_config(__lowerCAmelCase      ,  return_unused_kwargs=__lowerCAmelCase      ,  **__lowerCAmelCase			)

       if hasattr(__lowerCAmelCase      ,  """create_state"""			) and getattr(__lowerCAmelCase      ,  """has_state"""      ,  __lowerCAmelCase			):
             UpperCamelCase_					: Dict            =				scheduler.create_state()

       if return_unused_kwargs:
             return scheduler, state, unused_kwargs

       return scheduler, state
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = False      ,  **__lowerCAmelCase			):
       self.save_config(save_directory=__lowerCAmelCase      ,  push_to_hub=__lowerCAmelCase      ,  **__lowerCAmelCase			)
 @property
 def 	_UpperCAmelCase							(				self			):
       return self._get_compatibles()





 @classmethod
 def 	_UpperCAmelCase							(				cls			):
       UpperCamelCase_					: str            =				list(set([cls.__name__] + cls._compatibles			)			)
       UpperCamelCase_					: Optional[Any]            =				importlib.import_module(__name__.split("""."""			)[0]			)
       UpperCamelCase_					: int            =				[
           getattr(__lowerCAmelCase      ,  __lowerCAmelCase			) for c in compatible_classes_str if hasattr(__lowerCAmelCase      ,  __lowerCAmelCase			)
       ]
       return compatible_classes






def 				snake_case       (  a_				:			jnp.ndarray     , a_				:			Tuple[int]						)							->					jnp.ndarray:





      """simple docstring"""

      assert len(a_						) >= x.ndim
      return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(a_						) - x.ndim)						)     , a_						)



def 				snake_case       (  a_				:			int     , a_				:			Optional[int]=0.999     , a_				:			str=jnp.floataa						)							->					jnp.ndarray:





      """simple docstring"""

      def alpha_bar(a_				:			List[Any]						):
            return math.cos((time_step + 0.008) / 1.008 * math.pi / 2						) ** 2

      UpperCamelCase_					: int            =				[]
      for i in range(a_						):
            UpperCamelCase_					: Dict            =				i / num_diffusion_timesteps
            UpperCamelCase_					: Any            =				(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar(a_						) / alpha_bar(a_						)     , a_						)						)
      return jnp.array(a_     , dtype=a_						)




@flax.struct.dataclass
class      A     :






 """simple docstring"""





 __a      :    jnp.ndarray
 __a      :    jnp.ndarray
 __a      :    jnp.ndarray
 @classmethod
 def 	_UpperCAmelCase							(				cls      ,  __lowerCAmelCase			):
       UpperCamelCase_					: List[Any]            =				scheduler.config

       if config.trained_betas is not None:
             UpperCamelCase_					: Optional[int]            =				jnp.asarray(config.trained_betas      ,  dtype=scheduler.dtype			)
       elif config.beta_schedule == "linear":
             UpperCamelCase_					: int            =				jnp.linspace(config.beta_start      ,  config.beta_end      ,  config.num_train_timesteps      ,  dtype=scheduler.dtype			)
       elif config.beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             UpperCamelCase_					: List[Any]            =				(
                 jnp.linspace(
                     config.beta_start**0.5      ,  config.beta_end**0.5      ,  config.num_train_timesteps      ,  dtype=scheduler.dtype			)
                 ** 2
             )
       elif config.beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCamelCase_					: Any            =				betas_for_alpha_bar(config.num_train_timesteps      ,  dtype=scheduler.dtype			)
       else:
             raise NotImplementedError(
                 F"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"			)

       UpperCamelCase_					: Dict            =				1.0 - betas

       UpperCamelCase_					: Union[str, Any]            =				jnp.cumprod(__lowerCAmelCase      ,  axis=0			)

       return cls(
           alphas=__lowerCAmelCase      ,  betas=__lowerCAmelCase      ,  alphas_cumprod=__lowerCAmelCase      ,  )






def 				snake_case       (  a_				:			CommonSchedulerState     , a_				:			jnp.ndarray     , a_				:			jnp.ndarray     , a_				:			jnp.ndarray						)							->					Dict:





      """simple docstring"""

      UpperCamelCase_					: Dict            =				state.alphas_cumprod

      UpperCamelCase_					: List[Any]            =				alphas_cumprod[timesteps] ** 0.5
      UpperCamelCase_					: int            =				sqrt_alpha_prod.flatten()
      UpperCamelCase_					: List[Any]            =				broadcast_to_shape_from_left(a_     , original_samples.shape						)

      UpperCamelCase_					: Any            =				(1 - alphas_cumprod[timesteps]) ** 0.5
      UpperCamelCase_					: Dict            =				sqrt_one_minus_alpha_prod.flatten()
      UpperCamelCase_					: Optional[Any]            =				broadcast_to_shape_from_left(a_     , original_samples.shape						)

      return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 				snake_case       (  a_				:			CommonSchedulerState     , a_				:			jnp.ndarray     , a_				:			jnp.ndarray     , a_				:			jnp.ndarray						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_     ,      UpperCamelCase_					: Tuple            =				get_sqrt_alpha_prod(a_     , a_     , a_     , a_						)
      UpperCamelCase_					: int            =				sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples



def 				snake_case       (  a_				:			CommonSchedulerState     , a_				:			jnp.ndarray     , a_				:			jnp.ndarray     , a_				:			jnp.ndarray						)							->					Optional[int]:





      """simple docstring"""

      UpperCamelCase_     ,      UpperCamelCase_					: List[str]            =				get_sqrt_alpha_prod(a_     , a_     , a_     , a_						)
      UpperCamelCase_					: Dict            =				sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
      return velocity














'''simple docstring'''

import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import DecisionTransformerModel
      from transformers.models.decision_transformer.modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      )




class      A     :






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=13      ,  __lowerCAmelCase=7      ,  __lowerCAmelCase=6      ,  __lowerCAmelCase=17      ,  __lowerCAmelCase=23      ,  __lowerCAmelCase=11      ,  __lowerCAmelCase=True      ,  ):
       UpperCamelCase_					: Tuple            =				parent
       UpperCamelCase_					: List[Any]            =				batch_size
       UpperCamelCase_					: List[str]            =				seq_length
       UpperCamelCase_					: Dict            =				act_dim
       UpperCamelCase_					: Optional[int]            =				state_dim
       UpperCamelCase_					: Dict            =				hidden_size
       UpperCamelCase_					: str            =				max_length
       UpperCamelCase_					: int            =				is_training
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[str]            =				floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
       UpperCamelCase_					: Dict            =				floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
       UpperCamelCase_					: Union[str, Any]            =				floats_tensor((self.batch_size, self.seq_length, 1)			)
       UpperCamelCase_					: List[str]            =				floats_tensor((self.batch_size, self.seq_length, 1)			)
       UpperCamelCase_					: Optional[Any]            =				ids_tensor((self.batch_size, self.seq_length)      ,  vocab_size=10_00			)
       UpperCamelCase_					: Optional[Any]            =				random_attention_mask((self.batch_size, self.seq_length)			)

       UpperCamelCase_					: Tuple            =				self.get_config()

       return (
           config,
           states,
           actions,
           rewards,
           returns_to_go,
           timesteps,
           attention_mask,
       )
 def 	_UpperCAmelCase							(				self			):
       return DecisionTransformerConfig(
           batch_size=self.batch_size      ,  seq_length=self.seq_length      ,  act_dim=self.act_dim      ,  state_dim=self.state_dim      ,  hidden_size=self.hidden_size      ,  max_length=self.max_length      ,  )
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  ):
       UpperCamelCase_					: Tuple            =				DecisionTransformerModel(config=__lowerCAmelCase			)
       model.to(__lowerCAmelCase			)
       model.eval()
       UpperCamelCase_					: str            =				model(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)

       self.parent.assertEqual(result.state_preds.shape      ,  states.shape			)
       self.parent.assertEqual(result.action_preds.shape      ,  actions.shape			)
       self.parent.assertEqual(result.return_preds.shape      ,  returns_to_go.shape			)
       self.parent.assertEqual(
           result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length * 3, self.hidden_size)			)  # seq length *3 as there are 3 modelities: states, returns and actions





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      
       )					: str            =				config_and_inputs
       UpperCamelCase_					: int            =				{
           """states""": states,
           """actions""": actions,
           """rewards""": rewards,
           """returns_to_go""": returns_to_go,
           """timesteps""": timesteps,
           """attention_mask""": attention_mask,
       }
       return config, inputs_dict






@require_torch
class      A     (   SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, unittest.TestCase    ):






 """simple docstring"""





 __a      :    Tuple			  =    (DecisionTransformerModel,) if is_torch_available() else ()
 __a      :    Union[str, Any]			  =    ()
 __a      :    int			  =    {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

 # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
 __a      :    Optional[Any]			  =    False

 # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
 __a      :    int			  =    False
 __a      :    Tuple			  =    False
 __a      :    Dict			  =    False
 __a      :    Union[str, Any]			  =    False
 __a      :    int			  =    False
 __a      :    List[Any]			  =    False
 __a      :    List[str]			  =    False
 __a      :    Dict			  =    False
 __a      :    Optional[Any]			  =    False
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				DecisionTransformerModelTester(self			)
       UpperCamelCase_					: Dict            =				ConfigTester(self      ,  config_class=__lowerCAmelCase      ,  hidden_size=37			)
 def 	_UpperCAmelCase							(				self			):
       self.config_tester.run_common_tests()
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[int]            =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCAmelCase			)
 @slow
 def 	_UpperCAmelCase							(				self			):
       for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_					: Union[str, Any]            =				DecisionTransformerModel.from_pretrained(__lowerCAmelCase			)
             self.assertIsNotNone(__lowerCAmelCase			)





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_     ,      UpperCamelCase_					: str            =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             UpperCamelCase_					: Union[str, Any]            =				model_class(__lowerCAmelCase			)
             UpperCamelCase_					: Optional[int]            =				inspect.signature(model.forward			)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase_					: Any            =				[*signature.parameters.keys()]

             UpperCamelCase_					: List[str]            =				[
                 """states""",
                 """actions""",
                 """rewards""",
                 """returns_to_go""",
                 """timesteps""",
                 """attention_mask""",
             ]

             self.assertListEqual(arg_names[: len(__lowerCAmelCase			)]      ,  __lowerCAmelCase			)






@require_torch
class      A     (   unittest.TestCase    ):






 """simple docstring"""





 @slow
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Tuple            =				2  # number of steps of autoregressive prediction we will perform
       UpperCamelCase_					: Dict            =				10  # defined by the RL environment, may be normalized
       UpperCamelCase_					: Any            =				DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""			)
       UpperCamelCase_					: Tuple            =				model.to(__lowerCAmelCase			)
       UpperCamelCase_					: List[str]            =				model.config
       torch.manual_seed(0			)
       UpperCamelCase_					: str            =				torch.randn(1      ,  1      ,  config.state_dim			).to(device=__lowerCAmelCase      ,  dtype=torch.floataa			)  # env.reset()

       UpperCamelCase_					: List[str]            =				torch.tensor(
           [[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]]      ,  device=__lowerCAmelCase			)

       UpperCamelCase_					: Tuple            =				torch.tensor(__lowerCAmelCase      ,  device=__lowerCAmelCase      ,  dtype=torch.floataa			).reshape(1      ,  1      ,  1			)
       UpperCamelCase_					: Optional[int]            =				state
       UpperCamelCase_					: int            =				torch.zeros(1      ,  0      ,  config.act_dim      ,  device=__lowerCAmelCase      ,  dtype=torch.floataa			)
       UpperCamelCase_					: Optional[int]            =				torch.zeros(1      ,  0      ,  device=__lowerCAmelCase      ,  dtype=torch.floataa			)
       UpperCamelCase_					: Optional[Any]            =				torch.tensor(0      ,  device=__lowerCAmelCase      ,  dtype=torch.long			).reshape(1      ,  1			)

       for step in range(__lowerCAmelCase			):
             UpperCamelCase_					: List[Any]            =				torch.cat([actions, torch.zeros(1      ,  1      ,  config.act_dim      ,  device=__lowerCAmelCase			)]      ,  dim=1			)
             UpperCamelCase_					: Optional[int]            =				torch.cat([rewards, torch.zeros(1      ,  1      ,  device=__lowerCAmelCase			)]      ,  dim=1			)

             UpperCamelCase_					: Union[str, Any]            =				torch.ones(1      ,  states.shape[1]			).to(dtype=torch.long      ,  device=states.device			)

             with torch.no_grad():
                   UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				model(
                       states=__lowerCAmelCase      ,  actions=__lowerCAmelCase      ,  rewards=__lowerCAmelCase      ,  returns_to_go=__lowerCAmelCase      ,  timesteps=__lowerCAmelCase      ,  attention_mask=__lowerCAmelCase      ,  return_dict=__lowerCAmelCase      ,  )

             self.assertEqual(action_pred.shape      ,  actions.shape			)
             self.assertTrue(torch.allclose(action_pred[0, -1]      ,  expected_outputs[step]      ,  atol=1E-4			)			)
             UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Union[str, Any]            =				(  # env.step(action)
                 torch.randn(1      ,  1      ,  config.state_dim			).to(device=__lowerCAmelCase      ,  dtype=torch.floataa			),
                 1.0,
                 False,
                 {},
             )

             UpperCamelCase_					: Any            =				action_pred[0, -1]
             UpperCamelCase_					: int            =				torch.cat([states, state]      ,  dim=1			)
             UpperCamelCase_					: str            =				returns_to_go[0, -1] - reward
             UpperCamelCase_					: int            =				torch.cat([returns_to_go, pred_return.reshape(1      ,  1      ,  1			)]      ,  dim=1			)
             UpperCamelCase_					: int            =				torch.cat(
                 [timesteps, torch.ones((1, 1)      ,  device=__lowerCAmelCase      ,  dtype=torch.long			) * (step + 1)]      ,  dim=1			)






