




"""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 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ):
			__lowercase       :   str        =    '''\n'''.join(__lowerCamelCase     )
			Path(__lowerCamelCase     ).open('''w'''     ).writelines(__lowerCamelCase     )







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

					__lowercase       :   str        =    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )    )
					__lowercase       :   Any        =    tmp_dir.joinpath('''train.source'''    ).open().readlines()
					__lowercase       :   str        =    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )    )
					pack_data_dir(UpperCamelCase_							,		UpperCamelCase_							,		1_28							,		UpperCamelCase_    )
					__lowercase       :   str        =    {x.name for x in tmp_dir.iterdir()}
					__lowercase       :   Union[str, Any]        =    {x.name for x in save_dir.iterdir()}
					__lowercase       :   Dict        =    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(UpperCamelCase_    ) < len(UpperCamelCase_    )
					assert len(UpperCamelCase_    ) == 1
					assert len(packed_examples[0]    ) == sum(len(UpperCamelCase_    ) for x in orig_examples    )
					assert orig_paths == new_paths
		@pytest.mark.skipif(not FAIRSEQ_AVAILABLE							,		reason='''This test requires fairseq'''    )
		def      _lowerCamelCase				(		self    )		->    Union[str, Any]:
					if not FAIRSEQ_AVAILABLE:
								return
					__lowercase       :   Tuple        =    self._get_dataset(max_len=64    )
					__lowercase       :   Dict        =    64
					__lowercase       :   Union[str, Any]        =    ds.make_dynamic_sampler(UpperCamelCase_							,		required_batch_size_multiple=UpperCamelCase_    )
					__lowercase       :   Optional[int]        =    [len(UpperCamelCase_    ) for x in batch_sampler]
					assert len(set(UpperCamelCase_    )    ) > 1  # it's not dynamic batch size if every batch is the same length
					assert sum(UpperCamelCase_    ) == len(UpperCamelCase_    )  # no dropped or added examples
					__lowercase       :   int        =    DataLoader(UpperCamelCase_							,		batch_sampler=UpperCamelCase_							,		collate_fn=ds.collate_fn							,		num_workers=2    )
					__lowercase       :   Any        =    []
					__lowercase       :   Any        =    []
					for batch in data_loader:
								__lowercase       :   Optional[int]        =    batch['''input_ids'''].shape
								__lowercase       :   str        =    src_shape[0]
								assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
								__lowercase       :   int        =    np.product(batch['''input_ids'''].shape    )
								num_src_per_batch.append(UpperCamelCase_    )
								if num_src_tokens > (max_tokens * 1.1):
											failures.append(UpperCamelCase_    )
					assert num_src_per_batch[0] == max(UpperCamelCase_    )
					if failures:
								raise AssertionError(F"""too many tokens in {len(UpperCamelCase_    )} batches"""    )
		def      _lowerCamelCase				(		self    )		->    Optional[Any]:
					__lowercase       :   Optional[int]        =    self._get_dataset(max_len=5_12    )
					__lowercase       :   Tuple        =    2
					__lowercase       :   Dict        =    ds.make_sortish_sampler(UpperCamelCase_							,		shuffle=UpperCamelCase_    )

					__lowercase       :   int        =    DataLoader(UpperCamelCase_							,		batch_size=UpperCamelCase_							,		collate_fn=ds.collate_fn							,		num_workers=2    )
					__lowercase       :   int        =    DataLoader(UpperCamelCase_							,		batch_size=UpperCamelCase_							,		collate_fn=ds.collate_fn							,		num_workers=2							,		sampler=UpperCamelCase_    )

					__lowercase       :   int        =    tokenizer.pad_token_id

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

					assert sum(count_pad_tokens(UpperCamelCase_							,		k='''labels'''    )    ) < sum(count_pad_tokens(UpperCamelCase_							,		k='''labels'''    )    )
					assert sum(count_pad_tokens(UpperCamelCase_    )    ) < sum(count_pad_tokens(UpperCamelCase_    )    )
					assert len(UpperCamelCase_    ) == len(UpperCamelCase_    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_=10_00							,		UpperCamelCase_=1_28    )		->    List[str]:
					if os.getenv('''USE_REAL_DATA'''							,		UpperCamelCase_    ):
								__lowercase       :   Dict        =    '''examples/seq2seq/wmt_en_ro'''
								__lowercase       :   Tuple        =    max_len * 2 * 64
								if not Path(UpperCamelCase_    ).joinpath('''train.len'''    ).exists():
											save_len_file(UpperCamelCase_							,		UpperCamelCase_    )
					else:
								__lowercase       :   int        =    '''examples/seq2seq/test_data/wmt_en_ro'''
								__lowercase       :   Tuple        =    max_len * 4
								save_len_file(UpperCamelCase_							,		UpperCamelCase_    )

					__lowercase       :   str        =    AutoTokenizer.from_pretrained(UpperCamelCase_    )
					__lowercase       :   int        =    SeqaSeqDataset(
					    UpperCamelCase_							,		data_dir=UpperCamelCase_							,		type_path='''train'''							,		max_source_length=UpperCamelCase_							,		max_target_length=UpperCamelCase_							,		n_obs=UpperCamelCase_							,		)
					return ds, max_tokens, tokenizer
		def      _lowerCamelCase				(		self    )		->    Tuple:
					__lowercase       :   Tuple        =    self._get_dataset()
					__lowercase       :   Optional[int]        =    set(DistributedSortishSampler(UpperCamelCase_							,		2_56							,		num_replicas=2							,		rank=0							,		add_extra_examples=UpperCamelCase_    )    )
					__lowercase       :   List[str]        =    set(DistributedSortishSampler(UpperCamelCase_							,		2_56							,		num_replicas=2							,		rank=1							,		add_extra_examples=UpperCamelCase_    )    )
					assert idsa.intersection(UpperCamelCase_    ) == set()
		@parameterized.expand(
		    [
		        MBART_TINY,
		        MARIAN_TINY,
		        T5_TINY,
		        BART_TINY,
		        PEGASUS_XSUM,
		    ]							,		)
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    int:
					__lowercase       :   Tuple        =    AutoTokenizer.from_pretrained(UpperCamelCase_							,		use_fast=UpperCamelCase_    )
					if tok_name == MBART_TINY:
								__lowercase       :   Optional[int]        =    SeqaSeqDataset(
								    UpperCamelCase_							,		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       :   Union[str, Any]        =    train_dataset.dataset_kwargs
								assert "src_lang" in kwargs and "tgt_lang" in kwargs
					else:
								__lowercase       :   Tuple        =    SeqaSeqDataset(
								    UpperCamelCase_							,		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       :   Optional[int]        =    train_dataset.dataset_kwargs
								assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
								assert len(UpperCamelCase_    ) == 1 if tok_name == BART_TINY else len(UpperCamelCase_    ) == 0








"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							['image_processor', 'tokenizer']
					_a   			=							'BlipImageProcessor'
					_a   			=							'AutoTokenizer'



					def __init__(       self							:  Tuple,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict     )->					str:
								lowerCamelCase__		:		Any			=False
								super().__init__(lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=self.image_processor



					def __call__(       self							:  Union[str, Any],  lowerCamelCase							:  ImageInput = None,  lowerCamelCase							:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Union[bool, str, PaddingStrategy] = False,  lowerCamelCase							:  Union[bool, str, TruncationStrategy] = None,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  int = 0,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Optional[Union[str, TensorType]] = None,  **lowerCamelCase							:  List[str],  )->					BatchEncoding:
								if images is None and text is None:
											raise ValueError('''You have to specify either images or text.'''     )

								# Get only text
								if images is None:
											lowerCamelCase__		:		str			=self.tokenizer
											lowerCamelCase__		:		str			=self.tokenizer(
											    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
											return text_encoding

								# add pixel_values
								lowerCamelCase__		:		Optional[int]			=self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase     )

								if text is not None:
											lowerCamelCase__		:		Union[str, Any]			=self.tokenizer(
											    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
								else:
											lowerCamelCase__		:		Optional[Any]			=None

								if text_encoding is not None:
											encoding_image_processor.update(lowerCamelCase     )

								return encoding_image_processor



					def 		snake_case       (       self							:  str,  *lowerCamelCase							:  Any,  **lowerCamelCase							:  List[str]     )->					Union[str, Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Dict,  *lowerCamelCase							:  str,  **lowerCamelCase							:  str     )->					Union[str, Any]:
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase     )




					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def 		snake_case       (       self							:  List[str]     )->					List[str]:
								lowerCamelCase__		:		Union[str, Any]			=self.tokenizer.model_input_names
								lowerCamelCase__		:		List[str]			=self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )




