


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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






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





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''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 			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(_a	)
											snake_case_          =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
											snake_case_          =   max(len(tokenizer.encode(_a	)	) for a in ARTICLES	)
											snake_case_          =   max(len(tokenizer.encode(_a	)	) 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_          =   "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
											snake_case_          =   SeqaSeqDataset(
											    _a				,       data_dir=_a				,       type_path='train'				,       max_source_length=_a				,       max_target_length=_a				,       src_lang=_a				,       tgt_lang=_a				,       )
											snake_case_          =   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
																		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(_a	)
											snake_case_          =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
											snake_case_          =   max(len(tokenizer.encode(_a	)	) for a in ARTICLES	)
											snake_case_          =   max(len(tokenizer.encode(_a	)	) for a in SUMMARIES	)
											snake_case_          =   4
											snake_case_          =   LegacySeqaSeqDataset(
											    _a				,       data_dir=_a				,       type_path='train'				,       max_source_length=20				,       max_target_length=_a				,       )
											snake_case_          =   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    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(_a				,       _a				,       128				,       _a	)
											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(_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    a							( self	):
											if not FAIRSEQ_AVAILABLE:
																		return
											snake_case_          =   self._get_dataset(max_len=64	)
											snake_case_          =   64
											snake_case_          =   ds.make_dynamic_sampler(_a				,       required_batch_size_multiple=_a	)
											snake_case_          =   [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
											snake_case_          =   DataLoader(_a				,       batch_sampler=_a				,       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(_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    a							( self	):
											snake_case_          =   self._get_dataset(max_len=512	)
											snake_case_          =   2
											snake_case_          =   ds.make_sortish_sampler(_a				,       shuffle=_a	)

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

											snake_case_          =   tokenizer.pad_token_id

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

											snake_case_          =   AutoTokenizer.from_pretrained(_a	)
											snake_case_          =   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    a							( self	):
											snake_case_          =   self._get_dataset()
											snake_case_          =   set(DistributedSortishSampler(_a				,       256				,       num_replicas=2				,       rank=0				,       add_extra_examples=_a	)	)
											snake_case_          =   set(DistributedSortishSampler(_a				,       256				,       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    a							( self				,       snake_case	):
											snake_case_          =   AutoTokenizer.from_pretrained(_a				,       use_fast=_a	)
											if tok_name == MBART_TINY:
																		snake_case_          =   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'				,       )
																		snake_case_          =   train_dataset.dataset_kwargs
																		assert "src_lang" in kwargs and "tgt_lang" in kwargs
											else:
																		snake_case_          =   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				,       )
																		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(_a	) == 1 if tok_name == BART_TINY else len(_a	) == 0







import os
import sys
import unittest


_UpperCAmelCase		:       str						    =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


_UpperCAmelCase		:       List[Any]						    =	os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
_UpperCAmelCase		:       Dict						    =	os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")







class 			lowercase    (	unittest.TestCase      ):




				def    a							( self	):
											snake_case_          =   get_test_to_tester_mapping(snake_case	)
											snake_case_          =   get_test_to_tester_mapping(snake_case	)

											snake_case_          =   {'BertModelTest': 'BertModelTester'}

											snake_case_          =   {
											    'BlipModelTest': 'BlipModelTester',
											    'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
											    'BlipTextModelTest': 'BlipTextModelTester',
											    'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
											    'BlipVQAModelTest': 'BlipVQAModelTester',
											    'BlipVisionModelTest': 'BlipVisionModelTester',
											}

											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)
											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)




				def    a							( self	):
											snake_case_          =   get_model_to_test_mapping(snake_case	)
											snake_case_          =   get_model_to_test_mapping(snake_case	)

											snake_case_          =   {
											    'BertForMaskedLM': ['BertModelTest'],
											    'BertForMultipleChoice': ['BertModelTest'],
											    'BertForNextSentencePrediction': ['BertModelTest'],
											    'BertForPreTraining': ['BertModelTest'],
											    'BertForQuestionAnswering': ['BertModelTest'],
											    'BertForSequenceClassification': ['BertModelTest'],
											    'BertForTokenClassification': ['BertModelTest'],
											    'BertLMHeadModel': ['BertModelTest'],
											    'BertModel': ['BertModelTest'],
											}

											snake_case_          =   {
											    'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
											    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
											    'BlipForQuestionAnswering': ['BlipVQAModelTest'],
											    'BlipModel': ['BlipModelTest'],
											    'BlipTextModel': ['BlipTextModelTest'],
											    'BlipVisionModel': ['BlipVisionModelTest'],
											}

											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)
											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)




				def    a							( self	):
											snake_case_          =   get_model_to_tester_mapping(snake_case	)
											snake_case_          =   get_model_to_tester_mapping(snake_case	)

											snake_case_          =   {
											    'BertForMaskedLM': ['BertModelTester'],
											    'BertForMultipleChoice': ['BertModelTester'],
											    'BertForNextSentencePrediction': ['BertModelTester'],
											    'BertForPreTraining': ['BertModelTester'],
											    'BertForQuestionAnswering': ['BertModelTester'],
											    'BertForSequenceClassification': ['BertModelTester'],
											    'BertForTokenClassification': ['BertModelTester'],
											    'BertLMHeadModel': ['BertModelTester'],
											    'BertModel': ['BertModelTester'],
											}

											snake_case_          =   {
											    'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
											    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
											    'BlipForQuestionAnswering': ['BlipVQAModelTester'],
											    'BlipModel': ['BlipModelTester'],
											    'BlipTextModel': ['BlipTextModelTester'],
											    'BlipVisionModel': ['BlipVisionModelTester'],
											}

											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)
											self.assertEqual(get_test_info.to_json(snake_case	)				,       snake_case	)




