

import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


lowerCAmelCase							:  Dict            =						"""bert-base-cased"""
lowerCAmelCase							:  Dict            =						"""google/pegasus-xsum"""
lowerCAmelCase							:  List[Any]            =						[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
lowerCAmelCase							:  int            =						["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
lowerCAmelCase							:  Dict            =						"""patrickvonplaten/t5-tiny-random"""
lowerCAmelCase							:  str            =						"""sshleifer/bart-tiny-random"""
lowerCAmelCase							:  Optional[Any]            =						"""sshleifer/tiny-mbart"""
lowerCAmelCase							:  Dict            =						"""sshleifer/tiny-marian-en-de"""







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"\n".join(_UpperCAmelCase   )
						Path(_UpperCAmelCase   ).open("w"   ).writelines(_UpperCAmelCase   )







def       A_				(   _UpperCAmelCase   ):
						for split in ["train", "val", "test"]:
												_dump_articles(os.path.join(_UpperCAmelCase    ,     f"{split}.source"   )    ,     _UpperCAmelCase   )
												_dump_articles(os.path.join(_UpperCAmelCase    ,     f"{split}.target"   )    ,     _UpperCAmelCase   )
						return tmp_dir



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]       ,						)
			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	AutoTokenizer.from_pretrained(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
									SCREAMING_SNAKE_CASE_:			Dict  		=	max(len(tokenizer.encode(lowerCAmelCase__)) for a in ARTICLES)
									SCREAMING_SNAKE_CASE_:			Dict  		=	max(len(tokenizer.encode(lowerCAmelCase__)) for a in SUMMARIES)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	4
									SCREAMING_SNAKE_CASE_:			Tuple  		=	8
									assert max_len_target > max_src_len  # Will be truncated
									assert max_len_source > max_src_len  # Will be truncated
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Tuple  		=	"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
									SCREAMING_SNAKE_CASE_:			Any  		=	SeqaSeqDataset(
									    lowerCAmelCase__       ,						data_dir=lowerCAmelCase__       ,						type_path="train"       ,						max_source_length=lowerCAmelCase__       ,						max_target_length=lowerCAmelCase__       ,						src_lang=lowerCAmelCase__       ,						tgt_lang=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			Dict  		=	DataLoader(lowerCAmelCase__       ,						batch_size=2       ,						collate_fn=train_dataset.collate_fn)
									for batch in dataloader:
															assert isinstance(lowerCAmelCase__       ,						lowerCAmelCase__)
															assert batch["attention_mask"].shape == batch["input_ids"].shape
															# show that articles were trimmed.
															assert batch["input_ids"].shape[1] == max_src_len
															# show that targets are the same len
															assert batch["labels"].shape[1] == max_tgt_len
															if tok_name != MBART_TINY:
																					continue
															# check language codes in correct place
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	shift_tokens_right(batch["labels"]       ,						tokenizer.pad_token_id)
															assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
															assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
															assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
															assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

															break  # No need to test every batch



			@parameterized.expand([BART_TINY, BERT_BASE_CASED])
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					Dict):
									SCREAMING_SNAKE_CASE_:			Dict  		=	AutoTokenizer.from_pretrained(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	max(len(tokenizer.encode(lowerCAmelCase__)) for a in ARTICLES)
									SCREAMING_SNAKE_CASE_:			str  		=	max(len(tokenizer.encode(lowerCAmelCase__)) for a in SUMMARIES)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	4
									SCREAMING_SNAKE_CASE_:			Tuple  		=	LegacySeqaSeqDataset(
									    lowerCAmelCase__       ,						data_dir=lowerCAmelCase__       ,						type_path="train"       ,						max_source_length=20       ,						max_target_length=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			Dict  		=	DataLoader(lowerCAmelCase__       ,						batch_size=2       ,						collate_fn=train_dataset.collate_fn)
									for batch in dataloader:
															assert batch["attention_mask"].shape == batch["input_ids"].shape
															# show that articles were trimmed.
															assert batch["input_ids"].shape[1] == max_len_source
															assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
															# show that targets were truncated
															assert batch["labels"].shape[1] == trunc_target  # Truncated
															assert max_len_target > trunc_target  # Truncated
															break  # No need to test every batch



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									SCREAMING_SNAKE_CASE_:			str  		=	AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tmp_dir.joinpath("train.source").open().readlines()
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
									pack_data_dir(lowerCAmelCase__       ,						lowerCAmelCase__       ,						128       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	{x.name for x in tmp_dir.iterdir()}
									SCREAMING_SNAKE_CASE_:			List[str]  		=	{x.name for x in save_dir.iterdir()}
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	save_dir.joinpath("train.source").open().readlines()
									# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
									# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
									assert len(lowerCAmelCase__) < len(lowerCAmelCase__)
									assert len(lowerCAmelCase__) == 1
									assert len(packed_examples[0]) == sum(len(lowerCAmelCase__) for x in orig_examples)
									assert orig_paths == new_paths



			@pytest.mark.skipif(not FAIRSEQ_AVAILABLE       ,						reason="This test requires fairseq")
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									if not FAIRSEQ_AVAILABLE:
															return
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self._get_dataset(max_len=64)
									SCREAMING_SNAKE_CASE_:			str  		=	64
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	ds.make_dynamic_sampler(lowerCAmelCase__       ,						required_batch_size_multiple=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[len(lowerCAmelCase__) for x in batch_sampler]
									assert len(set(lowerCAmelCase__)) > 1  # it's not dynamic batch size if every batch is the same length
									assert sum(lowerCAmelCase__) == len(lowerCAmelCase__)  # no dropped or added examples
									SCREAMING_SNAKE_CASE_:			int  		=	DataLoader(lowerCAmelCase__       ,						batch_sampler=lowerCAmelCase__       ,						collate_fn=ds.collate_fn       ,						num_workers=2)
									SCREAMING_SNAKE_CASE_:			Dict  		=	[]
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[]
									for batch in data_loader:
															SCREAMING_SNAKE_CASE_:			Dict  		=	batch["input_ids"].shape
															SCREAMING_SNAKE_CASE_:			str  		=	src_shape[0]
															assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
															SCREAMING_SNAKE_CASE_:			Any  		=	np.product(batch["input_ids"].shape)
															num_src_per_batch.append(lowerCAmelCase__)
															if num_src_tokens > (max_tokens * 1.1):
																					failures.append(lowerCAmelCase__)
									assert num_src_per_batch[0] == max(lowerCAmelCase__)
									if failures:
															raise AssertionError(F"too many tokens in {len(lowerCAmelCase__)} batches")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Dict  		=	self._get_dataset(max_len=512)
									SCREAMING_SNAKE_CASE_:			Dict  		=	2
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	ds.make_sortish_sampler(lowerCAmelCase__       ,						shuffle=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	DataLoader(lowerCAmelCase__       ,						batch_size=lowerCAmelCase__       ,						collate_fn=ds.collate_fn       ,						num_workers=2)
									SCREAMING_SNAKE_CASE_:			int  		=	DataLoader(lowerCAmelCase__       ,						batch_size=lowerCAmelCase__       ,						collate_fn=ds.collate_fn       ,						num_workers=2       ,						sampler=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tokenizer.pad_token_id

									def count_pad_tokens(lowerCAmelCase__  :					Union[str, Any]       ,						lowerCAmelCase__  :					Optional[Any]="input_ids"):
															return [batch[k].eq(lowerCAmelCase__).sum().item() for batch in data_loader]

									assert sum(count_pad_tokens(lowerCAmelCase__       ,						k="labels")) < sum(count_pad_tokens(lowerCAmelCase__       ,						k="labels"))
									assert sum(count_pad_tokens(lowerCAmelCase__)) < sum(count_pad_tokens(lowerCAmelCase__))
									assert len(lowerCAmelCase__) == len(lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Any=1000       ,						lowerCAmelCase__  :					Tuple=128):
									if os.getenv("USE_REAL_DATA"       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	"examples/seq2seq/wmt_en_ro"
															SCREAMING_SNAKE_CASE_:			str  		=	max_len * 2 * 64
															if not Path(lowerCAmelCase__).joinpath("train.len").exists():
																					save_len_file(lowerCAmelCase__       ,						lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			str  		=	"examples/seq2seq/test_data/wmt_en_ro"
															SCREAMING_SNAKE_CASE_:			Any  		=	max_len * 4
															save_len_file(lowerCAmelCase__       ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	AutoTokenizer.from_pretrained(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	SeqaSeqDataset(
									    lowerCAmelCase__       ,						data_dir=lowerCAmelCase__       ,						type_path="train"       ,						max_source_length=lowerCAmelCase__       ,						max_target_length=lowerCAmelCase__       ,						n_obs=lowerCAmelCase__       ,						)
									return ds, max_tokens, tokenizer



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	self._get_dataset()
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	set(DistributedSortishSampler(lowerCAmelCase__       ,						256       ,						num_replicas=2       ,						rank=0       ,						add_extra_examples=lowerCAmelCase__))
									SCREAMING_SNAKE_CASE_:			int  		=	set(DistributedSortishSampler(lowerCAmelCase__       ,						256       ,						num_replicas=2       ,						rank=1       ,						add_extra_examples=lowerCAmelCase__))
									assert idsa.intersection(lowerCAmelCase__) == set()




			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]       ,						)
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int       ,						lowerCAmelCase__  :					int):
									SCREAMING_SNAKE_CASE_:			Any  		=	AutoTokenizer.from_pretrained(lowerCAmelCase__       ,						use_fast=lowerCAmelCase__)
									if tok_name == MBART_TINY:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	SeqaSeqDataset(
															    lowerCAmelCase__       ,						data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())       ,						type_path="train"       ,						max_source_length=4       ,						max_target_length=8       ,						src_lang="EN"       ,						tgt_lang="FR"       ,						)
															SCREAMING_SNAKE_CASE_:			Any  		=	train_dataset.dataset_kwargs
															assert "src_lang" in kwargs and "tgt_lang" in kwargs
									else:
															SCREAMING_SNAKE_CASE_:			str  		=	SeqaSeqDataset(
															    lowerCAmelCase__       ,						data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())       ,						type_path="train"       ,						max_source_length=4       ,						max_target_length=8       ,						)
															SCREAMING_SNAKE_CASE_:			Dict  		=	train_dataset.dataset_kwargs
															assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
															assert len(lowerCAmelCase__) == 1 if tok_name == BART_TINY else len(lowerCAmelCase__) == 0









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


lowerCAmelCase							:  Tuple            =						{
    """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__":
						lowerCAmelCase							:  Optional[int]            =						"""hopper-medium-v2"""
						lowerCAmelCase							:  Optional[int]            =						gym.make(env_name)

						lowerCAmelCase							:  Optional[int]            =						ValueGuidedRLPipeline.from_pretrained(
						    """bglick13/hopper-medium-v2-value-function-hor32""",
						    env=env,
						)

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

																		# execute action in environment
																		lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase							:  Dict            =						env.step(denorm_actions)
																		lowerCAmelCase							:  Tuple            =						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())

																		lowerCAmelCase							:  Tuple            =						next_observation
						except KeyboardInterrupt:
												pass

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






