



'''simple docstring'''





import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			torch.FloatTensor
 __lowerCamelCase       :			Optional[torch.FloatTensor]		=			None





def lowerCAmelCase    (__A    ,      __A=0.9_99    ,      __A="cosine"    ,      ):



     """simple docstring"""



     if alpha_transform_type == "cosine":

          def alpha_bar_fn(__A):
               return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(__A):
               return math.exp(t * -12.0)

     else:
          raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''')

     _a           =    []
     for i in range(__A):
          _a           =    i / num_diffusion_timesteps
          _a           =    (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(__A) / alpha_bar_fn(__A)    ,      __A))
     return torch.tensor(__A    ,      dtype=torch.floataa)






class __A     (    A  ,  A		):



 '''simple docstring'''







 @register_to_config
 def __init__(self					,  A = 1_000					,  A = "fixed_small_log"					,  A = True					,  A = 1.0					,  A = "epsilon"					,  A = "squaredcos_cap_v2"					,  )       ->	Optional[int]:


      """simple docstring"""



      if beta_schedule != "squaredcos_cap_v2":
           raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''			)

      _a           =    betas_for_alpha_bar(A			)

      _a           =    1.0 - self.betas
      _a           =    torch.cumprod(self.alphas					,  dim=0			)
      _a           =    torch.tensor(1.0			)

      # standard deviation of the initial noise distribution
      _a           =    1.0

      # setable values
      _a           =    None
      _a           =    torch.from_numpy(np.arange(0					,  A			)[::-1].copy()			)

      _a           =    variance_type



 def    a__    (self					,  A					,  A = None			)       ->	torch.FloatTensor:


      """simple docstring"""



      return sample



 def    a__    (self					,  A					,  A = None			)       ->	int:


      """simple docstring"""



      _a           =    num_inference_steps
      _a           =    (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
      _a           =    (np.arange(0					,  A			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
      _a           =    torch.from_numpy(A			).to(A			)



 def    a__    (self					,  A					,  A=None					,  A=None					,  A=None			)       ->	Optional[Any]:


      """simple docstring"""



      if prev_timestep is None:
           _a           =    t - 1

      _a           =    self.alphas_cumprod[t]
      _a           =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      _a           =    1 - alpha_prod_t
      _a           =    1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
           _a           =    self.betas[t]
      else:
           _a           =    1 - alpha_prod_t / alpha_prod_t_prev

      # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
      # and sample from it to get previous sample
      # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
      _a           =    beta_prod_t_prev / beta_prod_t * beta

      if variance_type is None:
           _a           =    self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small_log":
           _a           =    torch.log(torch.clamp(A					,  min=1E-20			)			)
           _a           =    torch.exp(0.5 * variance			)
      elif variance_type == "learned_range":
           # NOTE difference with DDPM scheduler
           _a           =    variance.log()
           _a           =    beta.log()

           _a           =    (predicted_variance + 1) / 2
           _a           =    frac * max_log + (1 - frac) * min_log

      return variance



 def    a__    (self					,  A					,  A					,  A					,  A = None					,  A=None					,  A = True					,  )       ->	Union[UnCLIPSchedulerOutput, Tuple]:


      """simple docstring"""



      _a           =    timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
           _a    ,       _a           =    torch.split(A					,  sample.shape[1]					,  dim=1			)
      else:
           _a           =    None

      # 1. compute alphas, betas
      if prev_timestep is None:
           _a           =    t - 1

      _a           =    self.alphas_cumprod[t]
      _a           =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      _a           =    1 - alpha_prod_t
      _a           =    1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
           _a           =    self.betas[t]
           _a           =    self.alphas[t]
      else:
           _a           =    1 - alpha_prod_t / alpha_prod_t_prev
           _a           =    1 - beta

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if self.config.prediction_type == "epsilon":
           _a           =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
           _a           =    model_output
      else:
           raise ValueError(
               f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
               ''' for the UnCLIPScheduler.'''			)

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
           _a           =    torch.clamp(
               A					,  -self.config.clip_sample_range					,  self.config.clip_sample_range			)

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      _a           =    (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
      _a           =    alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      _a           =    pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      _a           =    0
      if t > 0:
           _a           =    randn_tensor(
               model_output.shape					,  dtype=model_output.dtype					,  generator=A					,  device=model_output.device			)

           _a           =    self._get_variance(
               A					,  predicted_variance=A					,  prev_timestep=A					,  )

           if self.variance_type == "fixed_small_log":
                _a           =    variance
           elif self.variance_type == "learned_range":
                _a           =    (0.5 * variance).exp()
           else:
                raise ValueError(
                    f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
                    ''' for the UnCLIPScheduler.'''			)

           _a           =    variance * variance_noise

      _a           =    pred_prev_sample + variance

      if not return_dict:
           return (pred_prev_sample,)

      return UnCLIPSchedulerOutput(prev_sample=A					,  pred_original_sample=A			)



 def    a__    (self					,  A					,  A					,  A					,  )       ->	torch.FloatTensor:


      """simple docstring"""



      _a           =    self.alphas_cumprod.to(device=original_samples.device					,  dtype=original_samples.dtype			)
      _a           =    timesteps.to(original_samples.device			)

      _a           =    alphas_cumprod[timesteps] ** 0.5
      _a           =    sqrt_alpha_prod.flatten()
      while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
           _a           =    sqrt_alpha_prod.unsqueeze(-1			)

      _a           =    (1 - alphas_cumprod[timesteps]) ** 0.5
      _a           =    sqrt_one_minus_alpha_prod.flatten()
      while len(sqrt_one_minus_alpha_prod.shape			) < len(original_samples.shape			):
           _a           =    sqrt_one_minus_alpha_prod.unsqueeze(-1			)

      _a           =    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples








'''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					,  A					,  A=13					,  A=7					,  A=6					,  A=17					,  A=23					,  A=11					,  A=True					,  )       ->	Tuple:


      """simple docstring"""



      _a           =    parent
      _a           =    batch_size
      _a           =    seq_length
      _a           =    act_dim
      _a           =    state_dim
      _a           =    hidden_size
      _a           =    max_length
      _a           =    is_training



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, 1)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, 1)			)
      _a           =    ids_tensor((self.batch_size, self.seq_length)					,  vocab_size=1_000			)
      _a           =    random_attention_mask((self.batch_size, self.seq_length)			)

      _a           =    self.get_config()

      return (
          config,
          states,
          actions,
          rewards,
          returns_to_go,
          timesteps,
          attention_mask,
      )



 def    a__    (self			)       ->	str:


      """simple docstring"""



      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    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	List[Any]:


      """simple docstring"""



      _a           =    DecisionTransformerModel(config=A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  A					,  A					,  A					,  A					,  A			)

      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    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.prepare_config_and_inputs()
      (
          (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       
      )           =    config_and_inputs
      _a           =    {
          '''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     (    A  ,  A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[Any]		=			(DecisionTransformerModel,) if is_torch_available() else ()
 __lowerCamelCase       :			List[str]		=			()
 __lowerCamelCase       :			Tuple		=			{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

 # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
 __lowerCamelCase       :			str		=			False

 # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
 __lowerCamelCase       :			List[str]		=			False
 __lowerCamelCase       :			List[str]		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			str		=			False
 __lowerCamelCase       :			Dict		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			Dict		=			False
 __lowerCamelCase       :			List[str]		=			False



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    DecisionTransformerModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A					,  hidden_size=37			)



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A			)



 @slow
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a           =    DecisionTransformerModel.from_pretrained(A			)
           self.assertIsNotNone(A			)



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a           =    model_class(A			)
           _a           =    inspect.signature(model.forward			)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _a           =    [*signature.parameters.keys()]

           _a           =    [
               '''states''',
               '''actions''',
               '''rewards''',
               '''returns_to_go''',
               '''timesteps''',
               '''attention_mask''',
           ]

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






@require_torch
class __A     (    unittest.TestCase		):



 '''simple docstring'''







 @slow
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    2  # number of steps of autoregressive prediction we will perform
      _a           =    10  # defined by the RL environment, may be normalized
      _a           =    DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''			)
      _a           =    model.to(A			)
      _a           =    model.config
      torch.manual_seed(0			)
      _a           =    torch.randn(1					,  1					,  config.state_dim			).to(device=A					,  dtype=torch.floataa			)  # env.reset()

      _a           =    torch.tensor(
          [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]					,  device=A			)

      _a           =    torch.tensor(A					,  device=A					,  dtype=torch.floataa			).reshape(1					,  1					,  1			)
      _a           =    state
      _a           =    torch.zeros(1					,  0					,  config.act_dim					,  device=A					,  dtype=torch.floataa			)
      _a           =    torch.zeros(1					,  0					,  device=A					,  dtype=torch.floataa			)
      _a           =    torch.tensor(0					,  device=A					,  dtype=torch.long			).reshape(1					,  1			)

      for step in range(A			):
           _a           =    torch.cat([actions, torch.zeros(1					,  1					,  config.act_dim					,  device=A			)]					,  dim=1			)
           _a           =    torch.cat([rewards, torch.zeros(1					,  1					,  device=A			)]					,  dim=1			)

           _a           =    torch.ones(1					,  states.shape[1]			).to(dtype=torch.long					,  device=states.device			)

           with torch.no_grad():
                _a    ,       _a    ,       _a           =    model(
                    states=A					,  actions=A					,  rewards=A					,  returns_to_go=A					,  timesteps=A					,  attention_mask=A					,  return_dict=A					,  )

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

           _a           =    action_pred[0, -1]
           _a           =    torch.cat([states, state]					,  dim=1			)
           _a           =    returns_to_go[0, -1] - reward
           _a           =    torch.cat([returns_to_go, pred_return.reshape(1					,  1					,  1			)]					,  dim=1			)
           _a           =    torch.cat(
               [timesteps, torch.ones((1, 1)					,  device=A					,  dtype=torch.long			) * (step + 1)]					,  dim=1			)



