
'''simple docstring'''


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__lowercase							:   List[Any]					=     {
    '''n_samples''': 64,
    '''horizon''': 32,
    '''num_inference_steps''': 20,
    '''n_guide_steps''': 2,  # can set to 0 for faster sampling, does not use value network
    '''scale_grad_by_std''': True,
    '''scale''': 0.1,
    '''eta''': 0.0,
    '''t_grad_cutoff''': 2,
    '''device''': '''cpu''',
}


if __name__ == "__main__":
					__lowercase							:   str					=     '''hopper-medium-v2'''
					__lowercase							:   Optional[int]					=     gym.make(env_name)

					__lowercase							:   Tuple					=     ValueGuidedRLPipeline.from_pretrained(
					    '''bglick13/hopper-medium-v2-value-function-hor32''',
					    env=env,
					)

					env.seed(0)
					__lowercase							:   Optional[int]					=     env.reset()
					__lowercase							:   Any					=     0
					__lowercase							:   str					=     0
					__lowercase							:   List[str]					=     1000
					__lowercase							:   List[str]					=     [obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															__lowercase							:   Union[str, Any]					=     pipeline(obs, planning_horizon=32)

															# execute action in environment
															__lowercase				,		__lowercase				,		__lowercase				,		__lowercase							:   Tuple					=     env.step(denorm_actions)
															__lowercase							:   Any					=     env.get_normalized_score(total_reward)

															# update return
															total_reward += reward
															total_score += score
															print(
															    f'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
															    f' {total_score}'
															)

															# save observations for rendering
															rollout.append(next_observation.copy())

															__lowercase							:   int					=     next_observation
					except KeyboardInterrupt:
										pass

					print(f'Total reward: {total_reward}')




'''simple docstring'''


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 	__lowercase				(  _lowercase    ):

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		tempfile.mkdtemp()
								lowerCamelCase_					:      Optional[int]     =		5

								# Realm tok
								lowerCamelCase_					:      Any     =		[
								    '''[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_					:      Optional[int]     =		os.path.join(self.tmpdirname			,   '''realm_tokenizer'''      )
								os.makedirs(A			,   exist_ok=A      )
								lowerCamelCase_					:      str     =		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_					:      List[Any]     =		os.path.join(self.tmpdirname			,   '''realm_block_records'''      )
								os.makedirs(A			,   exist_ok=A      )

			def        UpperCAmelCase__ (self      ):
								return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname			,   '''realm_tokenizer'''      )      )

			def        UpperCAmelCase__ (self      ):
								shutil.rmtree(self.tmpdirname      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      int     =		RealmConfig(num_block_records=self.num_block_records      )
								return config

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		Dataset.from_dict(
								    {
								        '''id''': ['''0''', '''1'''],
								        '''question''': ['''foo''', '''bar'''],
								        '''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
								    }      )
								return dataset

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Dict     =		RealmRetriever(
								    block_records=self.get_dummy_block_records()			,   tokenizer=self.get_tokenizer()			,   )
								return retriever

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		self.get_config()
								lowerCamelCase_					:      List[Any]     =		self.get_dummy_retriever()
								lowerCamelCase_					:      Dict     =		retriever.tokenizer

								lowerCamelCase_					:      List[Any]     =		np.array([0, 3]			,   dtype='''long'''      )
								lowerCamelCase_					:      int     =		tokenizer(['''Test question''']      ).input_ids
								lowerCamelCase_					:      Any     =		tokenizer(
								    ['''the fourth''']			,   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_					:      Tuple     =		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, 1_0)      )
								self.assertEqual(concat_inputs.attention_mask.shape			,   (2, 1_0)      )
								self.assertEqual(concat_inputs.token_type_ids.shape			,   (2, 1_0)      )
								self.assertEqual(concat_inputs.special_tokens_mask.shape			,   (2, 1_0)      )
								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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      str     =		self.get_config()
								lowerCamelCase_					:      Optional[Any]     =		self.get_dummy_retriever()
								lowerCamelCase_					:      Optional[int]     =		retriever.tokenizer

								lowerCamelCase_					:      int     =		np.array([0, 3, 5]			,   dtype='''long'''      )
								lowerCamelCase_					:      List[Any]     =		tokenizer(['''Test question''']      ).input_ids
								lowerCamelCase_					:      int     =		tokenizer(
								    ['''the fourth''', '''longer longer''']			,   add_special_tokens=A			,   return_token_type_ids=A			,   return_attention_mask=A			,   ).input_ids
								lowerCamelCase_					:      Tuple     =		config.reader_seq_len

								lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		self.get_dummy_retriever()
								retriever.save_pretrained(os.path.join(self.tmpdirname			,   '''realm_block_records'''      )      )

								# Test local path
								lowerCamelCase_					:      str     =		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_					:      Dict     =		os.path.join(
													    os.path.join(self.tmpdirname			,   '''realm_block_records'''      )			,   _REALM_BLOCK_RECORDS_FILENAME      )
													lowerCamelCase_					:      str     =		RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa'''      )

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


