


"""simple docstring"""
import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
SCREAMING_SNAKE_CASE					:       Optional[int]							   =							"""stas/tiny-wmt19-en-ru"""



class      _UpperCAmelCase					(				__snake_case,							unittest.TestCase							):






		'''simple docstring'''

		lowerCamelCase__           =FSMTTokenizer
		lowerCamelCase__           =False



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			super().setUp()

			# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
			__snake_case      :		Optional[int]         =							[
			    '''l''',
			    '''o''',
			    '''w''',
			    '''e''',
			    '''r''',
			    '''s''',
			    '''t''',
			    '''i''',
			    '''d''',
			    '''n''',
			    '''w</w>''',
			    '''r</w>''',
			    '''t</w>''',
			    '''lo''',
			    '''low''',
			    '''er</w>''',
			    '''low</w>''',
			    '''lowest</w>''',
			    '''newer</w>''',
			    '''wider</w>''',
			    '''<unk>''',
			]
			__snake_case      :		List[str]         =							dict(zip(a_							,			range(len(a_   )   )   )   )
			__snake_case      :		Optional[int]         =							['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

			__snake_case      :		Optional[int]         =							['''en''', '''ru''']
			__snake_case      :		int         =							{
			    '''langs''': self.langs,
			    '''src_vocab_size''': 10,
			    '''tgt_vocab_size''': 20,
			}

			__snake_case      :		List[str]         =							os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES['''src_vocab_file''']   )
			__snake_case      :		Optional[Any]         =							os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES['''tgt_vocab_file''']   )
			__snake_case      :		List[Any]         =							os.path.join(self.tmpdirname							,			'''tokenizer_config.json'''   )
			__snake_case      :		Optional[int]         =							os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES['''merges_file''']   )
			with open(self.src_vocab_file							,			'''w'''   ) as fp:
				fp.write(json.dumps(a_   )   )
			with open(self.tgt_vocab_file							,			'''w'''   ) as fp:
				fp.write(json.dumps(a_   )   )
			with open(self.merges_file							,			'''w'''   ) as fp:
				fp.write('''\n'''.join(a_   )   )
			with open(a_							,			'''w'''   ) as fp:
				fp.write(json.dumps(a_   )   )



		@cached_property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''   )



		@cached_property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							FSMTTokenizer.from_pretrained(a_   )
			self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]							,			['''en''', '''ru''']   )
			self.assertEqual(tokenizer.src_vocab_size							,			21   )
			self.assertEqual(tokenizer.tgt_vocab_size							,			21   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		int         =							FSMTTokenizer(self.langs							,			self.src_vocab_file							,			self.tgt_vocab_file							,			self.merges_file   )

			__snake_case      :		List[str]         =							'''lower'''
			__snake_case      :		Optional[int]         =							['''low''', '''er</w>''']
			__snake_case      :		Dict         =							tokenizer.tokenize(a_   )
			self.assertListEqual(a_							,			a_   )

			__snake_case      :		Union[str, Any]         =							tokens + ['''<unk>''']
			__snake_case      :		int         =							[14, 15, 20]
			self.assertListEqual(tokenizer.convert_tokens_to_ids(a_   )							,			a_   )



		@slow
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		str         =							self.tokenizer_ru_en

			__snake_case      :		str         =							tokenizer.encode('''sequence builders'''							,			add_special_tokens=a_   )
			__snake_case      :		List[str]         =							tokenizer.encode('''multi-sequence build'''							,			add_special_tokens=a_   )

			__snake_case      :		str         =							tokenizer.build_inputs_with_special_tokens(a_   )
			__snake_case      :		str         =							tokenizer.build_inputs_with_special_tokens(a_							,			a_   )

			assert encoded_sentence == text + [2]
			assert encoded_pair == text + [2] + text_a + [2]



		@slow
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							self.tokenizer_en_ru
			__snake_case      :		List[Any]         =							self.tokenizer_ru_en

			__snake_case      :		Tuple         =							[
			    [
			        '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
			        [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
			    ],
			    ['''This is it. No more. I\'m done!''', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
			]

			# if data needs to be recreated or added, run:
			# import torch
			# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
			# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

			for src_text, tgt_input_ids in targets:
				__snake_case      :		Optional[int]         =							tokenizer_enc.encode(a_							,			return_tensors=a_   )
				self.assertListEqual(a_							,			a_   )

				# and decode backward, using the reversed languages model
				__snake_case      :		int         =							tokenizer_dec.decode(a_							,			skip_special_tokens=a_   )
				self.assertEqual(a_							,			a_   )



		@slow
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''							,			do_lower_case=a_   )
			__snake_case      :		str         =							tokenizer.tokenize('''USA is United States of America'''   )
			__snake_case      :		List[Any]         =							['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
			self.assertListEqual(a_							,			a_   )



		@unittest.skip('''FSMTConfig.__init__  requires non-optional args'''   )
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			pass



		@unittest.skip('''FSMTConfig.__init__  requires non-optional args'''   )
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			pass









"""simple docstring"""
import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.






def 						lowercase				(	_snake_case		: Tuple	,  _snake_case		: Tuple	,  _snake_case		: Optional[Any]	,  _snake_case		: str	,  _snake_case		: int	,  _snake_case		: List[str]	)	->Optional[int]:
	"""simple docstring"""





	__snake_case      :		Dict         =							states.reshape(1	,  -1	,  model.config.state_dim	)
	__snake_case      :		List[str]         =							actions.reshape(1	,  -1	,  model.config.act_dim	)
	__snake_case      :		Union[str, Any]         =							returns_to_go.reshape(1	,  -1	,  1	)
	__snake_case      :		Any         =							timesteps.reshape(1	,  -1	)

	if model.config.max_length is not None:
		__snake_case      :		Union[str, Any]         =							states[:, -model.config.max_length :]
		__snake_case      :		Any         =							actions[:, -model.config.max_length :]
		__snake_case      :		Tuple         =							returns_to_go[:, -model.config.max_length :]
		__snake_case      :		Union[str, Any]         =							timesteps[:, -model.config.max_length :]

		# pad all tokens to sequence length
		__snake_case      :		Dict         =							torch.cat(
		    [torch.zeros(model.config.max_length - states.shape[1]	), torch.ones(states.shape[1]	)]	)
		__snake_case      :		str         =							attention_mask.to(dtype=torch.long	,  device=states.device	).reshape(1	,  -1	)
		__snake_case      :		Dict         =							torch.cat(
		    [
		        torch.zeros(
		            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)	,  device=states.device	,  ),
		        states,
		    ]	,  dim=1	,  ).to(dtype=torch.floataa	)
		__snake_case      :		Tuple         =							torch.cat(
		    [
		        torch.zeros(
		            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)	,  device=actions.device	,  ),
		        actions,
		    ]	,  dim=1	,  ).to(dtype=torch.floataa	)
		__snake_case      :		str         =							torch.cat(
		    [
		        torch.zeros(
		            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)	,  device=returns_to_go.device	,  ),
		        returns_to_go,
		    ]	,  dim=1	,  ).to(dtype=torch.floataa	)
		__snake_case      :		Union[str, Any]         =							torch.cat(
		    [
		        torch.zeros(
		            (timesteps.shape[0], model.config.max_length - timesteps.shape[1])	,  device=timesteps.device	),
		        timesteps,
		    ]	,  dim=1	,  ).to(dtype=torch.long	)
	else:
		__snake_case      :		int         =							None

	__snake_case      ,				__snake_case      ,				__snake_case      :		Optional[int]         =							model(
	    states=_snake_case	,  actions=_snake_case	,  rewards=_snake_case	,  returns_to_go=_snake_case	,  timesteps=_snake_case	,  attention_mask=_snake_case	,  return_dict=_snake_case	,  )

	return action_preds[0, -1]


# build the environment

SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							gym.make("""Hopper-v3""")
SCREAMING_SNAKE_CASE					:       List[str]							   =							env.observation_space.shape[0]
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							env.action_space.shape[0]
SCREAMING_SNAKE_CASE					:       Optional[int]							   =							1000
SCREAMING_SNAKE_CASE					:       Tuple							   =							"""cuda"""
SCREAMING_SNAKE_CASE					:       List[Any]							   =							1_000.0  # normalization for rewards/returns
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
SCREAMING_SNAKE_CASE					:       List[Any]							   =							np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
SCREAMING_SNAKE_CASE					:       List[str]							   =							torch.from_numpy(state_mean).to(device=device)
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
SCREAMING_SNAKE_CASE					:       List[Any]							   =							DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
SCREAMING_SNAKE_CASE					:       Optional[int]							   =							model.to(device)
model.eval()

for ep in range(10):
			SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE					:       str							   =							0, 0
			SCREAMING_SNAKE_CASE					:       int							   =							env.reset()
			SCREAMING_SNAKE_CASE					:       Optional[int]							   =							torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
			SCREAMING_SNAKE_CASE					:       str							   =							torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
			SCREAMING_SNAKE_CASE					:       Tuple							   =							torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
			SCREAMING_SNAKE_CASE					:       Optional[int]							   =							torch.zeros(0, device=device, dtype=torch.floataa)

			SCREAMING_SNAKE_CASE					:       int							   =							torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
			for t in range(max_ep_len):
						env.render()
						# add padding
						SCREAMING_SNAKE_CASE					:       str							   =							torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							torch.cat([rewards, torch.zeros(1, device=device)])

						SCREAMING_SNAKE_CASE					:       Dict							   =							get_action(
						    model,
						    (states.to(dtype=torch.floataa) - state_mean) / state_std,
						    actions.to(dtype=torch.floataa),
						    rewards.to(dtype=torch.floataa),
						    target_return.to(dtype=torch.floataa),
						    timesteps.to(dtype=torch.long),
						)
						SCREAMING_SNAKE_CASE					:       Optional[int]							   =							action
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							action.detach().cpu().numpy()

						SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE					:       List[str]							   =							env.step(action)

						SCREAMING_SNAKE_CASE					:       Dict							   =							torch.from_numpy(state).to(device=device).reshape(1, state_dim)
						SCREAMING_SNAKE_CASE					:       int							   =							torch.cat([states, cur_state], dim=0)
						SCREAMING_SNAKE_CASE					:       List[str]							   =							reward

						SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							target_return[0, -1] - (reward / scale)
						SCREAMING_SNAKE_CASE					:       Dict							   =							torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

						episode_return += reward
						episode_length += 1

						if done:
									break





