
'''simple docstring'''





import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY

@is_pipeline_test
class     a (					unittest.TestCase     ):
				_snake_case		     =			MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				_snake_case		     =			TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




				def 						__snake_case				(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					Any   ):







										if isinstance(model.config,     lowercase__   ):
																snake_case,       snake_case							:  str			=     list(tokenizer.lang_code_to_id.keys()   )[:2]
																snake_case							:  str			=     TranslationPipeline(model=lowercase__,     tokenizer=lowercase__,     src_lang=lowercase__,     tgt_lang=lowercase__   )
										else:
																snake_case							:  Tuple			=     TranslationPipeline(model=lowercase__,     tokenizer=lowercase__   )
										return translator, ["Some string", "Some other text"]




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Optional[Any]   ):
										snake_case							:  str			=     translator('''Some string'''   )
										self.assertEqual(lowercase__,     [{'''translation_text''': ANY(lowercase__   )}]   )

										snake_case							:  Tuple			=     translator(['''Some string''']   )
										self.assertEqual(lowercase__,     [{'''translation_text''': ANY(lowercase__   )}]   )

										snake_case							:  Tuple			=     translator(['''Some string''', '''other string''']   )
										self.assertEqual(lowercase__,     [{'''translation_text''': ANY(lowercase__   )}, {'''translation_text''': ANY(lowercase__   )}]   )




				@require_torch
				def 						__snake_case				(				self						:					Optional[int]   ):
										snake_case							:  str			=     pipeline('''translation_en_to_ro''',     model='''patrickvonplaten/t5-tiny-random''',     framework='''pt'''   )
										snake_case							:  Tuple			=     translator('''This is a test string''',     max_length=20   )
										self.assertEqual(
										    lowercase__,     [
										        {
										            '''translation_text''': (
										                '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
										                ''' Beide Beide'''
										            )
										        }
										    ],     )




				@require_tf
				def 						__snake_case				(				self						:					str   ):
										snake_case							:  List[Any]			=     pipeline('''translation_en_to_ro''',     model='''patrickvonplaten/t5-tiny-random''',     framework='''tf'''   )
										snake_case							:  str			=     translator('''This is a test string''',     max_length=20   )
										self.assertEqual(
										    lowercase__,     [
										        {
										            '''translation_text''': (
										                '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
										                ''' Beide Beide'''
										            )
										        }
										    ],     )




				@require_torch
				def 						__snake_case				(				self						:					Optional[int]   ):
										snake_case							:  Any			=     pipeline('''translation_en_to_de''',     model='''patrickvonplaten/t5-tiny-random''',     framework='''pt'''   )
										snake_case							:  Tuple			=     translator('''This is a test string''',     max_length=20   )
										self.assertEqual(
										    lowercase__,     [
										        {
										            '''translation_text''': (
										                '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
										                ''' urine urine urine urine urine urine urine'''
										            )
										        }
										    ],     )




				@require_tf
				def 						__snake_case				(				self						:					str   ):
										snake_case							:  Optional[int]			=     pipeline('''translation_en_to_de''',     model='''patrickvonplaten/t5-tiny-random''',     framework='''tf'''   )
										snake_case							:  List[Any]			=     translator('''This is a test string''',     max_length=20   )
										self.assertEqual(
										    lowercase__,     [
										        {
										            '''translation_text''': (
										                '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
										                ''' urine urine urine urine urine urine urine'''
										            )
										        }
										    ],     )




class     a (					unittest.TestCase     ):




				@require_torch
				@slow
				def 						__snake_case				(				self						:					Optional[int]   ):







										with self.assertRaises(lowercase__   ):
																pipeline(task='''translation_cn_to_ar'''   )

										# but we do for this one
										snake_case							:  Any			=     pipeline(task='''translation_en_to_de'''   )
										self.assertEqual(translator._preprocess_params['''src_lang'''],     '''en'''   )
										self.assertEqual(translator._preprocess_params['''tgt_lang'''],     '''de'''   )




				@require_torch
				@slow
				def 						__snake_case				(				self						:					str   ):
										snake_case							:  Dict			=     MBartForConditionalGeneration.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''   )
										snake_case							:  int			=     MBartaaTokenizerFast.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''   )

										snake_case							:  Union[str, Any]			=     pipeline(task='''translation''',     model=lowercase__,     tokenizer=lowercase__   )
										# Missing src_lang, tgt_lang
										with self.assertRaises(lowercase__   ):
																translator('''This is a test'''   )

										snake_case							:  Dict			=     translator('''This is a test''',     src_lang='''en_XX''',     tgt_lang='''ar_AR'''   )
										self.assertEqual(lowercase__,     [{'''translation_text''': '''هذا إختبار'''}]   )

										snake_case							:  List[str]			=     translator('''This is a test''',     src_lang='''en_XX''',     tgt_lang='''hi_IN'''   )
										self.assertEqual(lowercase__,     [{'''translation_text''': '''यह एक परीक्षण है'''}]   )

										# src_lang, tgt_lang can be defined at pipeline call time
										snake_case							:  int			=     pipeline(task='''translation''',     model=lowercase__,     tokenizer=lowercase__,     src_lang='''en_XX''',     tgt_lang='''ar_AR'''   )
										snake_case							:  Optional[Any]			=     translator('''This is a test'''   )
										self.assertEqual(lowercase__,     [{'''translation_text''': '''هذا إختبار'''}]   )




				@require_torch
				def 						__snake_case				(				self						:					Union[str, Any]   ):
										snake_case							:  Dict			=     '''patrickvonplaten/t5-tiny-random'''
										snake_case							:  Dict			=     pipeline(task='''translation_cn_to_ar''',     model=lowercase__   )
										self.assertEqual(translator._preprocess_params['''src_lang'''],     '''cn'''   )
										self.assertEqual(translator._preprocess_params['''tgt_lang'''],     '''ar'''   )




				@require_torch
				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case							:  List[str]			=     '''patrickvonplaten/t5-tiny-random'''
										with pytest.warns(lowercase__,     match=R'''.*translation_en_to_de.*'''   ):
																snake_case							:  Union[str, Any]			=     pipeline(task='''translation''',     model=lowercase__   )
										self.assertEqual(translator.task,     '''translation_en_to_de'''   )
										self.assertEqual(translator._preprocess_params['''src_lang'''],     '''en'''   )
										self.assertEqual(translator._preprocess_params['''tgt_lang'''],     '''de'''   )




				@require_torch
				def 						__snake_case				(				self						:					int   ):







										with self.assertRaises(lowercase__   ):
																pipeline(task='''translation'''   )



"""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     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					# we don't care about the past rewards in this model

					SCREAMING_SNAKE_CASE           =      states.reshape(1,	-1,	model.config.state_dim     )
					SCREAMING_SNAKE_CASE           =      actions.reshape(1,	-1,	model.config.act_dim     )
					SCREAMING_SNAKE_CASE           =      returns_to_go.reshape(1,	-1,	1     )
					SCREAMING_SNAKE_CASE           =      timesteps.reshape(1,	-1     )

					if model.config.max_length is not None:
										SCREAMING_SNAKE_CASE           =      states[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =      actions[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =      returns_to_go[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =      timesteps[:, -model.config.max_length :]

										# pad all tokens to sequence length
										SCREAMING_SNAKE_CASE           =      torch.cat(
										    [torch.zeros(model.config.max_length - states.shape[1]     ), torch.ones(states.shape[1]     )]     )
										SCREAMING_SNAKE_CASE           =      attention_mask.to(dtype=torch.long,	device=states.device     ).reshape(1,	-1     )
										SCREAMING_SNAKE_CASE           =      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     )
										SCREAMING_SNAKE_CASE           =      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     )
										SCREAMING_SNAKE_CASE           =      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     )
										SCREAMING_SNAKE_CASE           =      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:
										SCREAMING_SNAKE_CASE           =      None

					SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      model(
					    states=SCREAMING_SNAKE_CASE_,	actions=SCREAMING_SNAKE_CASE_,	rewards=SCREAMING_SNAKE_CASE_,	returns_to_go=SCREAMING_SNAKE_CASE_,	timesteps=SCREAMING_SNAKE_CASE_,	attention_mask=SCREAMING_SNAKE_CASE_,	return_dict=SCREAMING_SNAKE_CASE_,	)

					return action_preds[0, -1]


# build the environment

snake_case					  =						gym.make('Hopper-v3')
snake_case					  =						env.observation_space.shape[0]
snake_case					  =						env.action_space.shape[0]
snake_case					  =						1_0_0_0
snake_case					  =						'cuda'
snake_case					  =						1_0_0_0.0  # normalization for rewards/returns
snake_case					  =						3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case					  =						np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
snake_case					  =						np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
snake_case					  =						torch.from_numpy(state_mean).to(device=device)
snake_case					  =						torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
snake_case					  =						DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
snake_case					  =						model.to(device)
model.eval()

for ep in range(1_0):
	snake_case  ,     snake_case					  =						0, 0
	snake_case					  =						env.reset()
	snake_case					  =						torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
	snake_case					  =						torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
	snake_case					  =						torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
	snake_case					  =						torch.zeros(0, device=device, dtype=torch.floataa)

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

		snake_case					  =						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),
		)
		snake_case					  =						action
		snake_case					  =						action.detach().cpu().numpy()

		snake_case  ,     snake_case  ,     snake_case  ,     snake_case					  =						env.step(action)

		snake_case					  =						torch.from_numpy(state).to(device=device).reshape(1, state_dim)
		snake_case					  =						torch.cat([states, cur_state], dim=0)
		snake_case					  =						reward

		snake_case					  =						target_return[0, -1] - (reward / scale)
		snake_case					  =						torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
		snake_case					  =						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
