
"""simple docstring"""

import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 	_lowercase       (    __UpperCAmelCase    ,	__UpperCAmelCase    ,	__UpperCAmelCase		):




	@register_to_config
	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ = False		,			)		->							Optional[Any]:
			super().__init__()

			lowerCamelCase					:      Any          =   nn.Embedding(UpperCAmelCase_		,			UpperCAmelCase_ )

			lowerCamelCase					:      Optional[int]          =   nn.Embedding(UpperCAmelCase_		,			UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   False

			lowerCamelCase					:      List[Any]          =   nn.Dropout(p=UpperCAmelCase_ )

			lowerCamelCase					:      List[str]          =   TaConfig(
			    vocab_size=UpperCAmelCase_		,			d_model=UpperCAmelCase_		,			num_heads=UpperCAmelCase_		,			d_kv=UpperCAmelCase_		,			d_ff=UpperCAmelCase_		,			dropout_rate=UpperCAmelCase_		,			feed_forward_proj=UpperCAmelCase_		,			is_decoder=UpperCAmelCase_		,			is_encoder_decoder=UpperCAmelCase_		,			)

			lowerCamelCase					:      List[Any]          =   nn.ModuleList()
			for lyr_num in range(UpperCAmelCase_ ):
					lowerCamelCase					:      int          =   TaBlock(UpperCAmelCase_ )
					self.encoders.append(UpperCAmelCase_ )

			lowerCamelCase					:      int          =   TaLayerNorm(UpperCAmelCase_ )
			lowerCamelCase					:      int          =   nn.Dropout(p=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Optional[int]:
			lowerCamelCase					:      Optional[int]          =   self.token_embedder(UpperCAmelCase_ )

			lowerCamelCase					:      Union[str, Any]          =   encoder_input_tokens.shape[1]
			lowerCamelCase					:      List[str]          =   torch.arange(UpperCAmelCase_		,			device=encoder_input_tokens.device )
			x += self.position_encoding(UpperCAmelCase_ )

			lowerCamelCase					:      List[str]          =   self.dropout_pre(UpperCAmelCase_ )

			# inverted the attention mask
			lowerCamelCase					:      Any          =   encoder_input_tokens.size()
			lowerCamelCase					:      List[Any]          =   self.get_extended_attention_mask(UpperCAmelCase_		,			UpperCAmelCase_ )

			for lyr in self.encoders:
					lowerCamelCase					:      Dict          =   lyr(UpperCAmelCase_		,			UpperCAmelCase_ )[0]
			lowerCamelCase					:      int          =   self.layer_norm(UpperCAmelCase_ )

			return self.dropout_post(UpperCAmelCase_ ), encoder_inputs_mask








"""simple docstring"""

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


_A						=			logging.get_logger(__name__)

_A						=			{
    '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		):
	lowercase_				=     'trajectory_transformer'
	lowercase_				=     ['past_key_values']
	lowercase_				=     {
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}




	def __init__(			self		,			UpperCAmelCase_=100		,			UpperCAmelCase_=5		,			UpperCAmelCase_=1		,			UpperCAmelCase_=1		,			UpperCAmelCase_=249		,			UpperCAmelCase_=6		,			UpperCAmelCase_=17		,			UpperCAmelCase_=25		,			UpperCAmelCase_=4		,			UpperCAmelCase_=4		,			UpperCAmelCase_=128		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.0006		,			UpperCAmelCase_=512		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=1E-1_2		,			UpperCAmelCase_=1		,			UpperCAmelCase_=True		,			UpperCAmelCase_=1		,			UpperCAmelCase_=50256		,			UpperCAmelCase_=50256		,			**UpperCAmelCase_		,			)		->							List[Any]:
			lowerCamelCase					:      int          =   vocab_size
			lowerCamelCase					:      List[str]          =   action_weight
			lowerCamelCase					:      List[Any]          =   reward_weight
			lowerCamelCase					:      List[str]          =   value_weight
			lowerCamelCase					:      Tuple          =   max_position_embeddings
			lowerCamelCase					:      List[str]          =   block_size
			lowerCamelCase					:      Any          =   action_dim
			lowerCamelCase					:      List[Any]          =   observation_dim
			lowerCamelCase					:      Any          =   transition_dim
			lowerCamelCase					:      int          =   learning_rate
			lowerCamelCase					:      Union[str, Any]          =   n_layer
			lowerCamelCase					:      Tuple          =   n_head
			lowerCamelCase					:      Any          =   n_embd
			lowerCamelCase					:      Union[str, Any]          =   embd_pdrop
			lowerCamelCase					:      Optional[int]          =   attn_pdrop
			lowerCamelCase					:      int          =   resid_pdrop
			lowerCamelCase					:      Optional[int]          =   initializer_range
			lowerCamelCase					:      List[str]          =   layer_norm_eps
			lowerCamelCase					:      Any          =   kaiming_initializer_range
			lowerCamelCase					:      str          =   use_cache
			super().__init__(pad_token_id=UpperCAmelCase_		,			bos_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_		,			**UpperCAmelCase_ )






