

import functools
import operator

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


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """sew-d"""
 def __init__(							self      ,							A_=32      ,							A_=768      ,							A_=12      ,							A_=12      ,							A_=3072      ,							A_=2      ,							A_=512      ,							A_=256      ,							A_=True      ,							A_=True      ,							A_=("p2c", "c2p")      ,							A_="layer_norm"      ,							A_="gelu_python"      ,							A_=0.1      ,							A_=0.1      ,							A_=0.1      ,							A_=0.0      ,							A_=0.1      ,							A_=0.02      ,							A_=1e-7      ,							A_=1e-5      ,							A_="group"      ,							A_="gelu"      ,							A_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)      ,							A_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)      ,							A_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)      ,							A_=False      ,							A_=128      ,							A_=16      ,							A_=True      ,							A_=0.05      ,							A_=10      ,							A_=2      ,							A_=0.0      ,							A_=10      ,							A_=0      ,							A_="mean"      ,							A_=False      ,							A_=False      ,							A_=256      ,							A_=0      ,							A_=1      ,							A_=2      ,							**A_      ,							)					->Optional[Any]:



       '''simple docstring'''





       super().__init__(**A_      ,							pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_					)
       __lowerCAmelCase					:						Tuple					=					hidden_size
       __lowerCAmelCase					:						List[Any]					=					feat_extract_norm
       __lowerCAmelCase					:						List[Any]					=					feat_extract_activation
       __lowerCAmelCase					:						Union[str, Any]					=					list(A_					)
       __lowerCAmelCase					:						List[str]					=					list(A_					)
       __lowerCAmelCase					:						Tuple					=					list(A_					)
       __lowerCAmelCase					:						Optional[int]					=					conv_bias
       __lowerCAmelCase					:						Any					=					num_conv_pos_embeddings
       __lowerCAmelCase					:						str					=					num_conv_pos_embedding_groups
       __lowerCAmelCase					:						int					=					len(self.conv_dim					)
       __lowerCAmelCase					:						Dict					=					num_hidden_layers
       __lowerCAmelCase					:						str					=					intermediate_size
       __lowerCAmelCase					:						Union[str, Any]					=					squeeze_factor
       __lowerCAmelCase					:						Any					=					max_position_embeddings
       __lowerCAmelCase					:						Dict					=					position_buckets
       __lowerCAmelCase					:						Tuple					=					share_att_key
       __lowerCAmelCase					:						Union[str, Any]					=					relative_attention
       __lowerCAmelCase					:						Any					=					norm_rel_ebd
       __lowerCAmelCase					:						Optional[Any]					=					list(A_					)
       __lowerCAmelCase					:						Dict					=					hidden_act
       __lowerCAmelCase					:						Dict					=					num_attention_heads
       __lowerCAmelCase					:						Tuple					=					hidden_dropout
       __lowerCAmelCase					:						Any					=					attention_dropout
       __lowerCAmelCase					:						str					=					activation_dropout
       __lowerCAmelCase					:						Any					=					feat_proj_dropout
       __lowerCAmelCase					:						List[Any]					=					final_dropout
       __lowerCAmelCase					:						Optional[int]					=					layer_norm_eps
       __lowerCAmelCase					:						List[str]					=					feature_layer_norm_eps
       __lowerCAmelCase					:						int					=					initializer_range
       __lowerCAmelCase					:						int					=					vocab_size

       if (
           (len(self.conv_stride					) != self.num_feat_extract_layers)
           or (len(self.conv_kernel					) != self.num_feat_extract_layers)
           or (len(self.conv_dim					) != self.num_feat_extract_layers)
       ):
             raise ValueError(
                 '''Configuration for convolutional layers is incorrect.'''
                 '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
                 f"""but is `len(config.conv_dim) = {len(self.conv_dim					)}`, `len(config.conv_stride)"""
                 f"""= {len(self.conv_stride					)}`, `len(config.conv_kernel) = {len(self.conv_kernel					)}`."""					)

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       __lowerCAmelCase					:						List[str]					=					apply_spec_augment
       __lowerCAmelCase					:						Optional[Any]					=					mask_time_prob
       __lowerCAmelCase					:						int					=					mask_time_length
       __lowerCAmelCase					:						Union[str, Any]					=					mask_time_min_masks
       __lowerCAmelCase					:						int					=					mask_feature_prob
       __lowerCAmelCase					:						Dict					=					mask_feature_length
       __lowerCAmelCase					:						Dict					=					mask_feature_min_masks

       # ctc loss
       __lowerCAmelCase					:						str					=					ctc_loss_reduction
       __lowerCAmelCase					:						Dict					=					ctc_zero_infinity

       # sequence classification
       __lowerCAmelCase					:						Optional[int]					=					use_weighted_layer_sum
       __lowerCAmelCase					:						Optional[Any]					=					classifier_proj_size





 @property
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       return functools.reduce(operator.mul      ,							self.conv_stride      ,							1					)





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


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "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   __lowercase      (_UpperCAmelCase ):
 _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      ,							A_=100      ,							A_=5      ,							A_=1      ,							A_=1      ,							A_=249      ,							A_=6      ,							A_=17      ,							A_=25      ,							A_=4      ,							A_=4      ,							A_=128      ,							A_=0.1      ,							A_=0.1      ,							A_=0.1      ,							A_=0.0_006      ,							A_=512      ,							A_=0.02      ,							A_=1e-12      ,							A_=1      ,							A_=True      ,							A_=1      ,							A_=5_0256      ,							A_=5_0256      ,							**A_      ,							)					->int:



       '''simple docstring'''





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


