



'''simple docstring'''

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__lowerCAmelCase									=   logging.get_logger(__name__)

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    str   				=					['input_values', 'padding_mask']







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       int = 1	,_UpperCAmelCase					:       int = 24000	,_UpperCAmelCase					:       float = 0.0	,_UpperCAmelCase					:       float = None	,_UpperCAmelCase					:       float = None	,**_UpperCAmelCase					:       str	,):
									super().__init__(feature_size=_UpperCAmelCase	,sampling_rate=_UpperCAmelCase	,padding_value=_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      Optional[Any]        =							chunk_length_s
									_a     :      str        =							overlap







							@property
							def 							__lowercase			(							self					:       Union[str, Any]		):
									if self.chunk_length_s is None:
											return None
									else:
											return int(self.chunk_length_s * self.sampling_rate		)







							@property
							def 							__lowercase			(							self					:       Optional[int]		):
									if self.chunk_length_s is None or self.overlap is None:
											return None
									else:
											return max(1	,int((1.0 - self.overlap) * self.chunk_length		)		)







							def __call__(							self					:       Tuple	,_UpperCAmelCase					:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,_UpperCAmelCase					:       Optional[Union[bool, str, PaddingStrategy]] = None	,_UpperCAmelCase					:       Optional[bool] = False	,_UpperCAmelCase					:       Optional[int] = None	,_UpperCAmelCase					:       Optional[Union[str, TensorType]] = None	,_UpperCAmelCase					:       Optional[int] = None	,):
									if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
													raise ValueError(
													    F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
													    F""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
													    F""" {self.sampling_rate} and not {sampling_rate}."""		)
									else:
											logger.warning(
											    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
											    'Failing to do so can result in silent errors that might be hard to debug.'		)

									if padding and truncation:
											raise ValueError('Both padding and truncation were set. Make sure you only set one.'		)
									elif padding is None:
											# by default let's pad the inputs
											_a     :      Union[str, Any]        =							True

									_a     :      Optional[int]        =							bool(
									    isinstance(_UpperCAmelCase	,(list, tuple)		) and (isinstance(raw_audio[0]	,(np.ndarray, tuple, list)		))		)

									if is_batched:
											_a     :      Tuple        =							[np.asarray(_UpperCAmelCase	,dtype=np.floataa		).T for audio in raw_audio]
									elif not is_batched and not isinstance(_UpperCAmelCase	,np.ndarray		):
											_a     :      List[str]        =							np.asarray(_UpperCAmelCase	,dtype=np.floataa		)
									elif isinstance(_UpperCAmelCase	,np.ndarray		) and raw_audio.dtype is np.dtype(np.floataa		):
											_a     :      Optional[int]        =							raw_audio.astype(np.floataa		)

									# always return batch
									if not is_batched:
											_a     :      int        =							[np.asarray(_UpperCAmelCase		).T]

									# verify inputs are valid
									for idx, example in enumerate(_UpperCAmelCase		):
											if example.ndim > 2:
													raise ValueError(F"""Expected input shape (channels, length) but got shape {example.shape}"""		)
											if self.feature_size == 1 and example.ndim != 1:
													raise ValueError(F"""Expected mono audio but example has {example.shape[-1]} channels"""		)
											if self.feature_size == 2 and example.shape[-1] != 2:
													raise ValueError(F"""Expected stereo audio but example has {example.shape[-1]} channels"""		)

									_a     :      Tuple        =							None
									_a     :      str        =							BatchFeature({'input_values': raw_audio}		)
									if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
											if truncation:
													_a     :      Optional[Any]        =							min(array.shape[0] for array in raw_audio		)
													_a     :      Optional[int]        =							int(np.floor(max_length / self.chunk_stride		)		)
													_a     :      int        =							(nb_step - 1) * self.chunk_stride + self.chunk_length
											elif padding:
													_a     :      str        =							max(array.shape[0] for array in raw_audio		)
													_a     :      Optional[int]        =							int(np.ceil(max_length / self.chunk_stride		)		)
													_a     :      Optional[Any]        =							(nb_step - 1) * self.chunk_stride + self.chunk_length
													_a     :      str        =							'max_length'
											else:
													_a     :      Dict        =							input_values

        # normal padding on batch
									if padded_inputs is None:
											_a     :      Tuple        =							self.pad(
											    _UpperCAmelCase	,max_length=_UpperCAmelCase	,truncation=_UpperCAmelCase	,padding=_UpperCAmelCase	,return_attention_mask=_UpperCAmelCase	,)
											if padding:
													_a     :      Optional[Any]        =							padded_inputs.pop('attention_mask'		)

									_a     :      Any        =							[]
									for example in padded_inputs.pop('input_values'		):
											if self.feature_size == 1:
													_a     :      Optional[int]        =							example[..., None]
											input_values.append(example.T		)

									_a     :      Optional[Any]        =							input_values
									if return_tensors is not None:
											_a     :      str        =							padded_inputs.convert_to_tensors(_UpperCAmelCase		)

									return padded_inputs








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


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      str     		=		"decision_transformer"
   __A :      Union[str, Any]     		=		["past_key_values"]
   __A :      Optional[int]     		=		{
       "max_position_embeddings": "n_positions",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }






   def __init__(		self						,				__A=17						,				__A=4						,				__A=128						,				__A=4096						,				__A=True						,				__A=1						,				__A=1024						,				__A=3						,				__A=1						,				__A=None						,				__A="relu"						,				__A=0.1						,				__A=0.1						,				__A=0.1						,				__A=1e-5						,				__A=0.02						,				__A=True						,				__A=True						,				__A=5_0256						,				__A=5_0256						,				__A=False						,				__A=False						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				state_dim
     lowerCamelCase :					Tuple    =				act_dim
     lowerCamelCase :					List[str]    =				hidden_size
     lowerCamelCase :					Optional[Any]    =				max_ep_len
     lowerCamelCase :					Union[str, Any]    =				action_tanh
     lowerCamelCase :					int    =				vocab_size
     lowerCamelCase :					List[Any]    =				n_positions
     lowerCamelCase :					Dict    =				n_layer
     lowerCamelCase :					int    =				n_head
     lowerCamelCase :					List[Any]    =				n_inner
     lowerCamelCase :					Any    =				activation_function
     lowerCamelCase :					Optional[int]    =				resid_pdrop
     lowerCamelCase :					str    =				embd_pdrop
     lowerCamelCase :					Tuple    =				attn_pdrop
     lowerCamelCase :					List[Any]    =				layer_norm_epsilon
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					Optional[int]    =				scale_attn_weights
     lowerCamelCase :					List[Any]    =				use_cache
     lowerCamelCase :					Tuple    =				scale_attn_by_inverse_layer_idx
     lowerCamelCase :					Optional[int]    =				reorder_and_upcast_attn

     lowerCamelCase :					Dict    =				bos_token_id
     lowerCamelCase :					Any    =				eos_token_id

     super().__init__(bos_token_id=__A						,				eos_token_id=__A						,				**__A )



