



import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


UpperCAmelCase__       =	logging.getLogger(__name__)







class       __lowerCAmelCase   :



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



						"""simple docstring"""






						_UpperCAmelCase			=       False



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  Optional[int]			,		A       :  Any			,		A       :  int			,		A       :  str)       ->   List[str]:



						"""simple docstring"""






						if not self.initialized:
									_UpperCAmelCase			=       RagRetriever(
									    A			,		question_encoder_tokenizer=A			,		generator_tokenizer=A			,		index=A			,		init_retrieval=A			,		)
									_UpperCAmelCase			=       True



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   List[str]:



						"""simple docstring"""






						self.retriever.index.init_index()



			def    _lowerCamelCase	(						self       :  Tuple			,		A       :  List[str]			,		A       :  Any)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase	,					_UpperCAmelCase			=       self.retriever._main_retrieve(A			,		A)
						return doc_ids, retrieved_doc_embeds







class       __lowerCAmelCase   (      A						):



			def __init__(						self       :  Tuple			,		A       :  List[str]			,		A       :  str			,		A       :  List[str]			,		A       :  Tuple			,		A       :  str=None)       ->   List[Any]:



						"""simple docstring"""






						if index is not None and index.is_initialized() and len(A) > 0:
									raise ValueError(
									    'When using Ray for distributed fine-tuning, '
									    'you\'ll need to provide the paths instead, '
									    'as the dataset and the index are loaded '
									    'separately. More info in examples/rag/use_own_knowledge_dataset.py ')
						super().__init__(
						    A			,		question_encoder_tokenizer=A			,		generator_tokenizer=A			,		index=A			,		init_retrieval=A			,		)
						_UpperCAmelCase			=       retrieval_workers
						if len(self.retrieval_workers) > 0:
									ray.get(
									    [
									        worker.create_rag_retriever.remote(A			,		A			,		A			,		A)
									        for worker in self.retrieval_workers
									    ])



			def    _lowerCamelCase	(						self       :  str)       ->   int:



						"""simple docstring"""






						logger.info('initializing retrieval')

						if len(self.retrieval_workers) > 0:
									ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
						else:
									# Non-distributed training. Load index into this same process.
									self.index.init_index()



			def    _lowerCamelCase	(						self       :  Optional[Any]			,		A       :  Any			,		A       :  Optional[Any])       ->   List[Any]:



						"""simple docstring"""






						if len(self.retrieval_workers) > 0:
									# Select a random retrieval actor.
									_UpperCAmelCase			=       self.retrieval_workers[random.randint(0			,		len(self.retrieval_workers) - 1)]
									_UpperCAmelCase	,					_UpperCAmelCase			=       ray.get(random_worker.retrieve.remote(A			,		A))
						else:
									_UpperCAmelCase	,					_UpperCAmelCase			=       self._main_retrieve(A			,		A)
						return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A)



			@classmethod
			def    _lowerCamelCase	(						cls       :  Dict			,		A       :  Tuple			,		A       :  List[Any]=None			,		**A       :  List[Any])       ->   str:



						"""simple docstring"""






						return super(A			,		cls).get_tokenizers(A			,		A			,		**A)



			@classmethod
			def    _lowerCamelCase	(						cls       :  Optional[int]			,		A       :  Dict			,		A       :  List[str]			,		A       :  List[str]=None			,		**A       :  Any)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       kwargs.pop('config'			,		A) or RagConfig.from_pretrained(A			,		**A)
						_UpperCAmelCase			=       RagTokenizer.from_pretrained(A			,		config=A)
						_UpperCAmelCase			=       rag_tokenizer.question_encoder
						_UpperCAmelCase			=       rag_tokenizer.generator
						if indexed_dataset is not None:
									_UpperCAmelCase			=       'custom'
									_UpperCAmelCase			=       CustomHFIndex(config.retrieval_vector_size			,		A)
						else:
									_UpperCAmelCase			=       cls._build_index(A)
						return cls(
						    A			,		question_encoder_tokenizer=A			,		generator_tokenizer=A			,		retrieval_workers=A			,		index=A			,		)





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


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{
    "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       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''decision_transformer'''
			UpperCamelCase        =					['''past_key_values''']
			UpperCamelCase        =					{
			    '''max_position_embeddings''': '''n_positions''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}



			def __init__(						self       :  Any			,		A       :  Optional[int]=17			,		A       :  List[str]=4			,		A       :  int=1_28			,		A       :  Union[str, Any]=40_96			,		A       :  Any=True			,		A       :  Any=1			,		A       :  List[Any]=10_24			,		A       :  List[Any]=3			,		A       :  Tuple=1			,		A       :  Any=None			,		A       :  Optional[int]="relu"			,		A       :  Union[str, Any]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=1E-5			,		A       :  List[Any]=0.0_2			,		A       :  Tuple=True			,		A       :  Union[str, Any]=True			,		A       :  str=5_02_56			,		A       :  Union[str, Any]=5_02_56			,		A       :  List[Any]=False			,		A       :  Optional[int]=False			,		**A       :  int			,		)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       state_dim
						_UpperCAmelCase			=       act_dim
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       max_ep_len
						_UpperCAmelCase			=       action_tanh
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       n_positions
						_UpperCAmelCase			=       n_layer
						_UpperCAmelCase			=       n_head
						_UpperCAmelCase			=       n_inner
						_UpperCAmelCase			=       activation_function
						_UpperCAmelCase			=       resid_pdrop
						_UpperCAmelCase			=       embd_pdrop
						_UpperCAmelCase			=       attn_pdrop
						_UpperCAmelCase			=       layer_norm_epsilon
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       scale_attn_weights
						_UpperCAmelCase			=       use_cache
						_UpperCAmelCase			=       scale_attn_by_inverse_layer_idx
						_UpperCAmelCase			=       reorder_and_upcast_attn

						_UpperCAmelCase			=       bos_token_id
						_UpperCAmelCase			=       eos_token_id

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