
"""simple docstring"""





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


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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Any:


					"""simple docstring"""





					_UpperCamelCase									=    '''\n'''.join(__snake_case      )
					Path(__snake_case      ).open('''w'''      ).writelines(__snake_case      )



def  lowerCamelCase__						(     __snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(__snake_case,	F'''{split}.source'''      ),	__snake_case      )
										_dump_articles(os.path.join(__snake_case,	F'''{split}.target'''      ),	__snake_case      )
					return tmp_dir






class        _UpperCAmelCase(       lowerCamelCase      ):



		@parameterized.expand(
		    [
		        MBART_TINY,
		        MARIAN_TINY,
		        T5_TINY,
		        BART_TINY,
		        PEGASUS_XSUM,
		    ]							,						)
		@slow
		def 		UpperCAmelCase				(   self							,						__a)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a)
							_UpperCamelCase									=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
							_UpperCamelCase									=    max(len(tokenizer.encode(__a)) for a in ARTICLES)
							_UpperCamelCase									=    max(len(tokenizer.encode(__a)) for a in SUMMARIES)
							_UpperCamelCase									=    4
							_UpperCamelCase									=    8
							assert max_len_target > max_src_len  # Will be truncated
							assert max_len_source > max_src_len  # Will be truncated
							_UpperCamelCase					,    _UpperCamelCase									=    '''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
							_UpperCamelCase									=    SeqaSeqDataset(
							    __a							,						data_dir=__a							,						type_path='''train'''							,						max_source_length=__a							,						max_target_length=__a							,						src_lang=__a							,						tgt_lang=__a							,						)
							_UpperCamelCase									=    DataLoader(__a							,						batch_size=2							,						collate_fn=train_dataset.collate_fn)
							for batch in dataloader:
												assert isinstance(__a							,						__a)
												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
												_UpperCamelCase									=    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 		UpperCAmelCase				(   self							,						__a)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a)
							_UpperCamelCase									=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
							_UpperCamelCase									=    max(len(tokenizer.encode(__a)) for a in ARTICLES)
							_UpperCamelCase									=    max(len(tokenizer.encode(__a)) for a in SUMMARIES)
							_UpperCamelCase									=    4
							_UpperCamelCase									=    LegacySeqaSeqDataset(
							    __a							,						data_dir=__a							,						type_path='''train'''							,						max_source_length=20							,						max_target_length=__a							,						)
							_UpperCamelCase									=    DataLoader(__a							,						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 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''')

							_UpperCamelCase									=    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
							_UpperCamelCase									=    tmp_dir.joinpath('''train.source''').open().readlines()
							_UpperCamelCase									=    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
							pack_data_dir(__a							,						__a							,						1_28							,						__a)
							_UpperCamelCase									=    {x.name for x in tmp_dir.iterdir()}
							_UpperCamelCase									=    {x.name for x in save_dir.iterdir()}
							_UpperCamelCase									=    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(__a) < len(__a)
							assert len(__a) == 1
							assert len(packed_examples[0]) == sum(len(__a) for x in orig_examples)
							assert orig_paths == new_paths



		@pytest.mark.skipif(not FAIRSEQ_AVAILABLE							,						reason='''This test requires fairseq''')
		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							if not FAIRSEQ_AVAILABLE:
												return
							_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    self._get_dataset(max_len=64)
							_UpperCamelCase									=    64
							_UpperCamelCase									=    ds.make_dynamic_sampler(__a							,						required_batch_size_multiple=__a)
							_UpperCamelCase									=    [len(__a) for x in batch_sampler]
							assert len(set(__a)) > 1  # it's not dynamic batch size if every batch is the same length
							assert sum(__a) == len(__a)  # no dropped or added examples
							_UpperCamelCase									=    DataLoader(__a							,						batch_sampler=__a							,						collate_fn=ds.collate_fn							,						num_workers=2)
							_UpperCamelCase									=    []
							_UpperCamelCase									=    []
							for batch in data_loader:
												_UpperCamelCase									=    batch['''input_ids'''].shape
												_UpperCamelCase									=    src_shape[0]
												assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
												_UpperCamelCase									=    np.product(batch['''input_ids'''].shape)
												num_src_per_batch.append(__a)
												if num_src_tokens > (max_tokens * 1.1):
																	failures.append(__a)
							assert num_src_per_batch[0] == max(__a)
							if failures:
												raise AssertionError(F'''too many tokens in {len(__a)} batches''')



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    self._get_dataset(max_len=5_12)
							_UpperCamelCase									=    2
							_UpperCamelCase									=    ds.make_sortish_sampler(__a							,						shuffle=__a)

							_UpperCamelCase									=    DataLoader(__a							,						batch_size=__a							,						collate_fn=ds.collate_fn							,						num_workers=2)
							_UpperCamelCase									=    DataLoader(__a							,						batch_size=__a							,						collate_fn=ds.collate_fn							,						num_workers=2							,						sampler=__a)

							_UpperCamelCase									=    tokenizer.pad_token_id

							def count_pad_tokens(__a							,						__a="input_ids"):
												return [batch[k].eq(__a).sum().item() for batch in data_loader]

							assert sum(count_pad_tokens(__a							,						k='''labels''')) < sum(count_pad_tokens(__a							,						k='''labels'''))
							assert sum(count_pad_tokens(__a)) < sum(count_pad_tokens(__a))
							assert len(__a) == len(__a)



		def 		UpperCAmelCase				(   self							,						__a=10_00							,						__a=1_28)			->			int:






							'''simple docstring'''
							if os.getenv('''USE_REAL_DATA'''							,						__a):
												_UpperCamelCase									=    '''examples/seq2seq/wmt_en_ro'''
												_UpperCamelCase									=    max_len * 2 * 64
												if not Path(__a).joinpath('''train.len''').exists():
																	save_len_file(__a							,						__a)
							else:
												_UpperCamelCase									=    '''examples/seq2seq/test_data/wmt_en_ro'''
												_UpperCamelCase									=    max_len * 4
												save_len_file(__a							,						__a)

							_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a)
							_UpperCamelCase									=    SeqaSeqDataset(
							    __a							,						data_dir=__a							,						type_path='''train'''							,						max_source_length=__a							,						max_target_length=__a							,						n_obs=__a							,						)
							return ds, max_tokens, tokenizer



		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    self._get_dataset()
							_UpperCamelCase									=    set(DistributedSortishSampler(__a							,						2_56							,						num_replicas=2							,						rank=0							,						add_extra_examples=__a))
							_UpperCamelCase									=    set(DistributedSortishSampler(__a							,						2_56							,						num_replicas=2							,						rank=1							,						add_extra_examples=__a))
							assert idsa.intersection(__a) == set()




		@parameterized.expand(
		    [
		        MBART_TINY,
		        MARIAN_TINY,
		        T5_TINY,
		        BART_TINY,
		        PEGASUS_XSUM,
		    ]							,						)
		def 		UpperCAmelCase				(   self							,						__a)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a							,						use_fast=__a)
							if tok_name == MBART_TINY:
												_UpperCamelCase									=    SeqaSeqDataset(
												    __a							,						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'''							,						)
												_UpperCamelCase									=    train_dataset.dataset_kwargs
												assert "src_lang" in kwargs and "tgt_lang" in kwargs
							else:
												_UpperCamelCase									=    SeqaSeqDataset(
												    __a							,						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							,						)
												_UpperCamelCase									=    train_dataset.dataset_kwargs
												assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
												assert len(__a) == 1 if tok_name == BART_TINY else len(__a) == 0








"""simple docstring"""





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = (DDPMScheduler,)



		def 		UpperCAmelCase				(   self							,						**__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''num_train_timesteps''': 10_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''variance_type''': '''fixed_small''',
							    '''clip_sample''': True,
							}

							config.update(**__a)
							return config



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for timesteps in [1, 5, 1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]							,						[0.002, 0.02, 0.2, 2]):
												self.check_over_configs(beta_start=__a							,						beta_end=__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__a)



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=__a)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							self.check_over_configs(thresholding=__a)
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=__a							,						prediction_type=__a							,						sample_max_value=__a							,						)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=__a)



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							for t in [0, 5_00, 9_99]:
												self.check_over_forward(time_step=__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    len(__a)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter
							_UpperCamelCase									=    torch.manual_seed(0)

							for t in reversed(range(__a)):
												# 1. predict noise residual
												_UpperCamelCase									=    model(__a							,						__a)

												# 2. predict previous mean of sample x_t-1
												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a							,						generator=__a).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												_UpperCamelCase									=    pred_prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							assert abs(result_sum.item() - 258.9606) < 1e-2
							assert abs(result_mean.item() - 0.3372) < 1e-3



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config(prediction_type='''v_prediction''')
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    len(__a)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter
							_UpperCamelCase									=    torch.manual_seed(0)

							for t in reversed(range(__a)):
												# 1. predict noise residual
												_UpperCamelCase									=    model(__a							,						__a)

												# 2. predict previous mean of sample x_t-1
												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a							,						generator=__a).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												_UpperCamelCase									=    pred_prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							assert abs(result_sum.item() - 202.0296) < 1e-2
							assert abs(result_mean.item() - 0.2631) < 1e-3



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=__a)

							_UpperCamelCase									=    scheduler.timesteps

							for i, timestep in enumerate(__a):
												if i == len(__a) - 1:
																	_UpperCamelCase									=    -1
												else:
																	_UpperCamelCase									=    timesteps[i + 1]

												_UpperCamelCase									=    scheduler.previous_timestep(__a)
												_UpperCamelCase									=    prev_t.item()

												self.assertEqual(__a							,						__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 51, 0]

							with self.assertRaises(__a							,						msg='''`custom_timesteps` must be in descending order.'''):
												scheduler.set_timesteps(timesteps=__a)



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 1, 0]
							_UpperCamelCase									=    len(__a)

							with self.assertRaises(__a							,						msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
												scheduler.set_timesteps(num_inference_steps=__a							,						timesteps=__a)




		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    __a							,						msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''							,						):
												scheduler.set_timesteps(timesteps=__a)






