

'''simple docstring'''


from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase__						:       Tuple										=						logging.get_logger(__name__)

lowerCAmelCase__						:       Optional[Any]										=						{
    """CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
        """https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    """trajectory_transformer"""
						__UpperCAmelCase			     =    ["""past_key_values"""]
						__UpperCAmelCase			     =    {
						    """hidden_size""": """n_embd""",
						    """num_attention_heads""": """n_head""",
						    """num_hidden_layers""": """n_layer""",
						}







						def __init__(							self					:      List[str]			,			snake_case_					:      List[str]=1_0_0			,			snake_case_					:      Any=5			,			snake_case_					:      Any=1			,			snake_case_					:      Tuple=1			,			snake_case_					:      Optional[Any]=2_4_9			,			snake_case_					:      Optional[int]=6			,			snake_case_					:      Optional[int]=1_7			,			snake_case_					:      Any=2_5			,			snake_case_					:      List[str]=4			,			snake_case_					:      Optional[Any]=4			,			snake_case_					:      List[str]=1_2_8			,			snake_case_					:      int=0.1			,			snake_case_					:      Optional[Any]=0.1			,			snake_case_					:      List[str]=0.1			,			snake_case_					:      List[Any]=0.0_0_0_6			,			snake_case_					:      str=5_1_2			,			snake_case_					:      Tuple=0.0_2			,			snake_case_					:      Optional[int]=1e-12			,			snake_case_					:      Optional[int]=1			,			snake_case_					:      Union[str, Any]=True			,			snake_case_					:      Optional[int]=1			,			snake_case_					:      List[Any]=5_0_2_5_6			,			snake_case_					:      List[Any]=5_0_2_5_6			,			**snake_case_					:      Union[str, Any]			,			):



								'''simple docstring'''



								snake_case__						:       Any       						=  vocab_size
								snake_case__						:       int       						=  action_weight
								snake_case__						:       Any       						=  reward_weight
								snake_case__						:       str       						=  value_weight
								snake_case__						:       List[str]       						=  max_position_embeddings
								snake_case__						:       List[Any]       						=  block_size
								snake_case__						:       Optional[int]       						=  action_dim
								snake_case__						:       Optional[int]       						=  observation_dim
								snake_case__						:       Dict       						=  transition_dim
								snake_case__						:       Union[str, Any]       						=  learning_rate
								snake_case__						:       List[str]       						=  n_layer
								snake_case__						:       List[str]       						=  n_head
								snake_case__						:       Optional[Any]       						=  n_embd
								snake_case__						:       List[str]       						=  embd_pdrop
								snake_case__						:       Any       						=  attn_pdrop
								snake_case__						:       int       						=  resid_pdrop
								snake_case__						:       int       						=  initializer_range
								snake_case__						:       Any       						=  layer_norm_eps
								snake_case__						:       Dict       						=  kaiming_initializer_range
								snake_case__						:       Dict       						=  use_cache
								super().__init__(pad_token_id=snake_case_			,			bos_token_id=snake_case_			,			eos_token_id=snake_case_			,			**snake_case_  )






'''simple docstring'''


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
class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    None





class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    2







						@register_to_config
						def __init__(							self					:      int			,			snake_case_					:      float = 0.0_2			,			snake_case_					:      float = 1_0_0			,			snake_case_					:      float = 1.0_0_7			,			snake_case_					:      float = 8_0			,			snake_case_					:      float = 0.0_5			,			snake_case_					:      float = 5_0			,			):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  sigma_max

								# setable values
								snake_case__						:       int       						=  None
								snake_case__						:       np.IntTensor       						=  None
								snake_case__						:       torch.FloatTensor       						=  None  # sigma(t_i)







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      Optional[int] = None  ):



								'''simple docstring'''



								return sample







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      int			,			snake_case_					:      Union[str, torch.device] = None  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  num_inference_steps
								snake_case__						:       Tuple       						=  np.arange(0			,			self.num_inference_steps  )[::-1].copy()
								snake_case__						:       int       						=  torch.from_numpy(snake_case_  ).to(snake_case_  )
								snake_case__						:       List[str]       						=  [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								snake_case__						:       Union[str, Any]       						=  torch.tensor(snake_case_			,			dtype=torch.floataa			,			device=snake_case_  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      Optional[torch.Generator] = None  ):



								'''simple docstring'''



								if self.config.s_min <= sigma <= self.config.s_max:
										snake_case__						:       List[Any]       						=  min(self.config.s_churn / self.num_inference_steps			,			2**0.5 - 1  )
								else:
										snake_case__						:       List[Any]       						=  0

								# sample eps ~ N(0, S_noise^2 * I)
								snake_case__						:       Any       						=  self.config.s_noise * randn_tensor(sample.shape			,			generator=snake_case_  ).to(sample.device  )
								snake_case__						:       Tuple       						=  sigma + gamma * sigma
								snake_case__						:       Dict       						=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      float			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  sample_hat + sigma_hat * model_output
								snake_case__						:       List[Any]       						=  (sample_hat - pred_original_sample) / sigma_hat
								snake_case__						:       Any       						=  sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
										return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=snake_case_			,			derivative=snake_case_			,			pred_original_sample=snake_case_  )







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      float			,			snake_case_					:      float			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      torch.FloatTensor			,			snake_case_					:      bool = True			,			):



								'''simple docstring'''



								snake_case__						:       str       						=  sample_prev + sigma_prev * model_output
								snake_case__						:       List[Any]       						=  (sample_prev - pred_original_sample) / sigma_prev
								snake_case__						:       Any       						=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
										return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=snake_case_			,			derivative=snake_case_			,			pred_original_sample=snake_case_  )







						def       __magic_name__							(							self					:      str			,			snake_case_					:      List[Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								raise NotImplementedError()



