


import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				tempfile.mkdtemp()
     lowerCamelCase :					Tuple    =				5

     # Realm tok
     lowerCamelCase :					int    =				[
         "[UNK]",
         "[CLS]",
         "[SEP]",
         "[PAD]",
         "[MASK]",
         "test",
         "question",
         "this",
         "is",
         "the",
         "first",
         "second",
         "third",
         "fourth",
         "fifth",
         "record",
         "want",
         "##want",
         "##ed",
         "wa",
         "un",
         "runn",
         "##ing",
         ",",
         "low",
         "lowest",
     ]
     lowerCamelCase :					str    =				os.path.join(self.tmpdirname						,				"realm_tokenizer" )
     os.makedirs(__A						,				exist_ok=__A )
     lowerCamelCase :					Optional[Any]    =				os.path.join(__A						,				VOCAB_FILES_NAMES["vocab_file"] )
     with open(self.vocab_file						,				"w"						,				encoding="utf-8" ) as vocab_writer:
       vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

     lowerCamelCase :					Optional[int]    =				os.path.join(self.tmpdirname						,				"realm_block_records" )
     os.makedirs(__A						,				exist_ok=__A )






   def _snake_case    (		self ):






     """simple docstring"""





     return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname						,				"realm_tokenizer" ) )






   def _snake_case    (		self ):






     """simple docstring"""





     shutil.rmtree(self.tmpdirname )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				RealmConfig(num_block_records=self.num_block_records )
     return config






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				Dataset.from_dict(
         {
             "id": ["0", "1"],
             "question": ["foo", "bar"],
             "answers": [["Foo", "Bar"], ["Bar"]],
         } )
     return dataset






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				np.array(
         [
             B"This is the first record",
             B"This is the second record",
             B"This is the third record",
             B"This is the fourth record",
             B"This is the fifth record",
             B"This is a longer longer longer record",
         ]						,				dtype=__A						,				)
     return block_records






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				RealmRetriever(
         block_records=self.get_dummy_block_records()						,				tokenizer=self.get_tokenizer()						,				)
     return retriever






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				self.get_config()
     lowerCamelCase :					List[Any]    =				self.get_dummy_retriever()
     lowerCamelCase :					List[Any]    =				retriever.tokenizer

     lowerCamelCase :					Tuple    =				np.array([0, 3]						,				dtype="long" )
     lowerCamelCase :					Union[str, Any]    =				tokenizer(["Test question"] ).input_ids
     lowerCamelCase :					Optional[Any]    =				tokenizer(
         ["the fourth"]						,				add_special_tokens=__A						,				return_token_type_ids=__A						,				return_attention_mask=__A						,				).input_ids
     lowerCamelCase :					int    =				config.reader_seq_len

     lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase :					List[str]    =				retriever(
         __A						,				__A						,				answer_ids=__A						,				max_length=__A						,				return_tensors="np" )

     self.assertEqual(len(__A )						,				2 )
     self.assertEqual(len(__A )						,				2 )
     self.assertEqual(len(__A )						,				2 )
     self.assertEqual(concat_inputs.input_ids.shape						,				(2, 10) )
     self.assertEqual(concat_inputs.attention_mask.shape						,				(2, 10) )
     self.assertEqual(concat_inputs.token_type_ids.shape						,				(2, 10) )
     self.assertEqual(concat_inputs.special_tokens_mask.shape						,				(2, 10) )
     self.assertEqual(
         tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] )						,				["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "first", "record", "[SEP]"]						,				)
     self.assertEqual(
         tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] )						,				["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "fourth", "record", "[SEP]"]						,				)






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				self.get_config()
     lowerCamelCase :					Union[str, Any]    =				self.get_dummy_retriever()
     lowerCamelCase :					Optional[Any]    =				retriever.tokenizer

     lowerCamelCase :					str    =				np.array([0, 3, 5]						,				dtype="long" )
     lowerCamelCase :					List[Any]    =				tokenizer(["Test question"] ).input_ids
     lowerCamelCase :					Dict    =				tokenizer(
         ["the fourth", "longer longer"]						,				add_special_tokens=__A						,				return_token_type_ids=__A						,				return_attention_mask=__A						,				).input_ids
     lowerCamelCase :					Any    =				config.reader_seq_len

     lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase :					Optional[int]    =				retriever(
         __A						,				__A						,				answer_ids=__A						,				max_length=__A						,				return_tensors="np" )

     self.assertEqual([False, True, True]						,				__A )
     self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]						,				__A )
     self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]						,				__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				self.get_dummy_retriever()
     retriever.save_pretrained(os.path.join(self.tmpdirname						,				"realm_block_records" ) )

     # Test local path
     lowerCamelCase :					Optional[int]    =				retriever.from_pretrained(os.path.join(self.tmpdirname						,				"realm_block_records" ) )
     self.assertEqual(retriever.block_records[0]						,				B"This is the first record" )

     # Test mocked remote path
     with patch("transformers.models.realm.retrieval_realm.hf_hub_download" ) as mock_hf_hub_download:
       lowerCamelCase :					List[Any]    =				os.path.join(
           os.path.join(self.tmpdirname						,				"realm_block_records" )						,				_REALM_BLOCK_RECORDS_FILENAME )
       lowerCamelCase :					Optional[Any]    =				RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa" )

     self.assertEqual(retriever.block_records[0]						,				B"This is the first record" )







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 )



