

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


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





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					'\n'.join(UpperCamelCase__   )
							Path(UpperCamelCase__   ).open('w'   ).writelines(UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							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       ( lowercase_    ):




				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]   ,     )
				@slow
				def 					a							(      self   ,     snake_case ):
											snake_case_           =					AutoTokenizer.from_pretrained(snake_case )
											snake_case_           =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
											snake_case_           =					max(len(tokenizer.encode(snake_case ) ) for a in ARTICLES )
											snake_case_           =					max(len(tokenizer.encode(snake_case ) ) for a in SUMMARIES )
											snake_case_           =					4
											snake_case_           =					8
											assert max_len_target > max_src_len  # Will be truncated
											assert max_len_source > max_src_len  # Will be truncated
											snake_case_       ,					snake_case_           =					'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
											snake_case_           =					SeqaSeqDataset(
											    snake_case   ,     data_dir=snake_case   ,     type_path='train'   ,     max_source_length=snake_case   ,     max_target_length=snake_case   ,     src_lang=snake_case   ,     tgt_lang=snake_case   ,     )
											snake_case_           =					DataLoader(snake_case   ,     batch_size=2   ,     collate_fn=train_dataset.collate_fn )
											for batch in dataloader:
																		assert isinstance(snake_case   ,     snake_case )
																		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
																		snake_case_           =					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   ,     snake_case ):
											snake_case_           =					AutoTokenizer.from_pretrained(snake_case )
											snake_case_           =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
											snake_case_           =					max(len(tokenizer.encode(snake_case ) ) for a in ARTICLES )
											snake_case_           =					max(len(tokenizer.encode(snake_case ) ) for a in SUMMARIES )
											snake_case_           =					4
											snake_case_           =					LegacySeqaSeqDataset(
											    snake_case   ,     data_dir=snake_case   ,     type_path='train'   ,     max_source_length=20   ,     max_target_length=snake_case   ,     )
											snake_case_           =					DataLoader(snake_case   ,     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 ):
											snake_case_           =					AutoTokenizer.from_pretrained('facebook/mbart-large-cc25' )

											snake_case_           =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
											snake_case_           =					tmp_dir.joinpath('train.source' ).open().readlines()
											snake_case_           =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
											pack_data_dir(snake_case   ,     snake_case   ,     128   ,     snake_case )
											snake_case_           =					{x.name for x in tmp_dir.iterdir()}
											snake_case_           =					{x.name for x in save_dir.iterdir()}
											snake_case_           =					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(snake_case ) < len(snake_case )
											assert len(snake_case ) == 1
											assert len(packed_examples[0] ) == sum(len(snake_case ) for x in orig_examples )
											assert orig_paths == new_paths




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




				def 					a							(      self ):
											snake_case_       ,					snake_case_       ,					snake_case_           =					self._get_dataset(max_len=512 )
											snake_case_           =					2
											snake_case_           =					ds.make_sortish_sampler(snake_case   ,     shuffle=snake_case )

											snake_case_           =					DataLoader(snake_case   ,     batch_size=snake_case   ,     collate_fn=ds.collate_fn   ,     num_workers=2 )
											snake_case_           =					DataLoader(snake_case   ,     batch_size=snake_case   ,     collate_fn=ds.collate_fn   ,     num_workers=2   ,     sampler=snake_case )

											snake_case_           =					tokenizer.pad_token_id

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

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




				def 					a							(      self   ,     snake_case=1000   ,     snake_case=128 ):
											if os.getenv('USE_REAL_DATA'   ,     snake_case ):
																		snake_case_           =					'examples/seq2seq/wmt_en_ro'
																		snake_case_           =					max_len * 2 * 64
																		if not Path(snake_case ).joinpath('train.len' ).exists():
																									save_len_file(snake_case   ,     snake_case )
											else:
																		snake_case_           =					'examples/seq2seq/test_data/wmt_en_ro'
																		snake_case_           =					max_len * 4
																		save_len_file(snake_case   ,     snake_case )

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




				def 					a							(      self ):
											snake_case_       ,					snake_case_       ,					snake_case_           =					self._get_dataset()
											snake_case_           =					set(DistributedSortishSampler(snake_case   ,     256   ,     num_replicas=2   ,     rank=0   ,     add_extra_examples=snake_case ) )
											snake_case_           =					set(DistributedSortishSampler(snake_case   ,     256   ,     num_replicas=2   ,     rank=1   ,     add_extra_examples=snake_case ) )
											assert idsa.intersection(snake_case ) == set()




				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]   ,     )
				def 					a							(      self   ,     snake_case ):
											snake_case_           =					AutoTokenizer.from_pretrained(snake_case   ,     use_fast=snake_case )
											if tok_name == MBART_TINY:
																		snake_case_           =					SeqaSeqDataset(
																		    snake_case   ,     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'   ,     )
																		snake_case_           =					train_dataset.dataset_kwargs
																		assert "src_lang" in kwargs and "tgt_lang" in kwargs
											else:
																		snake_case_           =					SeqaSeqDataset(
																		    snake_case   ,     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   ,     )
																		snake_case_           =					train_dataset.dataset_kwargs
																		assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
																		assert len(snake_case ) == 1 if tok_name == BART_TINY else len(snake_case ) == 0







def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					(
							    first_str_length if first_str_length > second_str_length else second_str_length
							)
							snake_case_           =					[]
							for char_count in range(UpperCamelCase__   ):
														if char_count < first_str_length:
																					output_list.append(first_str[char_count]   )
														if char_count < second_str_length:
																					output_list.append(second_str[char_count]   )
							return "".join(UpperCamelCase__   )


if __name__ == "__main__":
					print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)




