
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


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




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Tuple    ,							SCREAMING_SNAKE_CASE       :      Union[str, Any]			)     ->						List[str]:
				__lowercase								=					"""\n""".join(_lowerCAmelCase			)
				Path(_lowerCAmelCase			).open('w'			).writelines(_lowerCAmelCase			)




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      List[str]			)     ->						List[str]:






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





							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]     , )
							@slow
							def 			a__ ( self  :   Dict     , _UpperCAmelCase  :   Dict				)						->		List[Any]:



											"""simple docstring"""


											__lowercase								=					AutoTokenizer.from_pretrained(lowerCamelCase__				)
											__lowercase								=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
											__lowercase								=					max(len(tokenizer.encode(lowerCamelCase__				)				) for a in ARTICLES				)
											__lowercase								=					max(len(tokenizer.encode(lowerCamelCase__				)				) for a in SUMMARIES				)
											__lowercase								=					4
											__lowercase								=					8
											assert max_len_target > max_src_len  # Will be truncated
											assert max_len_source > max_src_len  # Will be truncated
											__lowercase								=					"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
											__lowercase								=					SeqaSeqDataset(
											    lowerCamelCase__     , data_dir=lowerCamelCase__     , type_path='train'     , max_source_length=lowerCamelCase__     , max_target_length=lowerCamelCase__     , src_lang=lowerCamelCase__     , tgt_lang=lowerCamelCase__     , )
											__lowercase								=					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
															__lowercase								=					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 			a__ ( self  :   Tuple     , _UpperCAmelCase  :   Optional[int]				)						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								=					AutoTokenizer.from_pretrained(lowerCamelCase__				)
											__lowercase								=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
											__lowercase								=					max(len(tokenizer.encode(lowerCamelCase__				)				) for a in ARTICLES				)
											__lowercase								=					max(len(tokenizer.encode(lowerCamelCase__				)				) for a in SUMMARIES				)
											__lowercase								=					4
											__lowercase								=					LegacySeqaSeqDataset(
											    lowerCamelCase__     , data_dir=lowerCamelCase__     , type_path='train'     , max_source_length=20     , max_target_length=lowerCamelCase__     , )
											__lowercase								=					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 			a__ ( self  :   List[Any]				)						->		Dict:



											"""simple docstring"""


											__lowercase								=					AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'				)

											__lowercase								=					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
											__lowercase								=					tmp_dir.joinpath('train.source'				).open().readlines()
											__lowercase								=					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
											pack_data_dir(lowerCamelCase__     , lowerCamelCase__     , 1_28     , lowerCamelCase__				)
											__lowercase								=					{x.name for x in tmp_dir.iterdir()}
											__lowercase								=					{x.name for x in save_dir.iterdir()}
											__lowercase								=					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 			a__ ( self  :   Dict				)						->		Any:



											"""simple docstring"""


											if not FAIRSEQ_AVAILABLE:
															return
											__lowercase								=					self._get_dataset(max_len=64				)
											__lowercase								=					64
											__lowercase								=					ds.make_dynamic_sampler(lowerCamelCase__     , required_batch_size_multiple=lowerCamelCase__				)
											__lowercase								=					[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
											__lowercase								=					DataLoader(lowerCamelCase__     , batch_sampler=lowerCamelCase__     , collate_fn=ds.collate_fn     , num_workers=2				)
											__lowercase								=					[]
											__lowercase								=					[]
											for batch in data_loader:
															__lowercase								=					batch["""input_ids"""].shape
															__lowercase								=					src_shape[0]
															assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
															__lowercase								=					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 			a__ ( self  :   Union[str, Any]				)						->		List[Any]:



											"""simple docstring"""


											__lowercase								=					self._get_dataset(max_len=5_12				)
											__lowercase								=					2
											__lowercase								=					ds.make_sortish_sampler(lowerCamelCase__     , shuffle=lowerCamelCase__				)

											__lowercase								=					DataLoader(lowerCamelCase__     , batch_size=lowerCamelCase__     , collate_fn=ds.collate_fn     , num_workers=2				)
											__lowercase								=					DataLoader(lowerCamelCase__     , batch_size=lowerCamelCase__     , collate_fn=ds.collate_fn     , num_workers=2     , sampler=lowerCamelCase__				)

											__lowercase								=					tokenizer.pad_token_id

											def count_pad_tokens(_UpperCAmelCase  :   Dict     , _UpperCAmelCase  :   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 			a__ ( self  :   int     , _UpperCAmelCase  :   Optional[int]=10_00     , _UpperCAmelCase  :   Union[str, Any]=1_28				)						->		List[Any]:



											"""simple docstring"""


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

											__lowercase								=					AutoTokenizer.from_pretrained(lowerCamelCase__				)
											__lowercase								=					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 			a__ ( self  :   List[Any]				)						->		Optional[int]:



											"""simple docstring"""


											__lowercase								=					self._get_dataset()
											__lowercase								=					set(DistributedSortishSampler(lowerCamelCase__     , 2_56     , num_replicas=2     , rank=0     , add_extra_examples=lowerCamelCase__				)				)
											__lowercase								=					set(DistributedSortishSampler(lowerCamelCase__     , 2_56     , 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 			a__ ( self  :   List[str]     , _UpperCAmelCase  :   Any				)						->		List[str]:



											"""simple docstring"""


											__lowercase								=					AutoTokenizer.from_pretrained(lowerCamelCase__     , use_fast=lowerCamelCase__				)
											if tok_name == MBART_TINY:
															__lowercase								=					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'     , )
															__lowercase								=					train_dataset.dataset_kwargs
															assert "src_lang" in kwargs and "tgt_lang" in kwargs
											else:
															__lowercase								=					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     , )
															__lowercase								=					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










from ..utils import DummyObject, requires_backends






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: List[str]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Any					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)


