


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


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





def   _lowerCAmelCase				(   UpperCamelCase__:     Path							,					UpperCamelCase__:     list						) ->   Union[str, Any]:





	"""simple docstring"""







	A								=				"""\n""".join(UpperCamelCase__						)
	Path(UpperCamelCase__						).open("""w"""						).writelines(UpperCamelCase__						)





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]						) ->   Dict:





	"""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       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]				,   )
				@slow
				def      _UpperCAmelCase					(						self				,   a__   )	->				Optional[int]:
					A								=				AutoTokenizer.from_pretrained(a__   )
					A								=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
					A								=				max(len(tokenizer.encode(a__   )   ) for a in ARTICLES   )
					A								=				max(len(tokenizer.encode(a__   )   ) for a in SUMMARIES   )
					A								=				4
					A								=				8
					assert max_len_target > max_src_len  # Will be truncated
					assert max_len_source > max_src_len  # Will be truncated
					A					,			A								=				"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
					A								=				SeqaSeqDataset(
					    a__				,   data_dir=a__				,   type_path="""train"""				,   max_source_length=a__				,   max_target_length=a__				,   src_lang=a__				,   tgt_lang=a__				,   )
					A								=				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
						A								=				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__   )	->				Dict:
					A								=				AutoTokenizer.from_pretrained(a__   )
					A								=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
					A								=				max(len(tokenizer.encode(a__   )   ) for a in ARTICLES   )
					A								=				max(len(tokenizer.encode(a__   )   ) for a in SUMMARIES   )
					A								=				4
					A								=				LegacySeqaSeqDataset(
					    a__				,   data_dir=a__				,   type_path="""train"""				,   max_source_length=20				,   max_target_length=a__				,   )
					A								=				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   )	->				Union[str, Any]:
					A								=				AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""   )

					A								=				Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
					A								=				tmp_dir.joinpath("""train.source"""   ).open().readlines()
					A								=				Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
					pack_data_dir(a__				,   a__				,   128				,   a__   )
					A								=				{x.name for x in tmp_dir.iterdir()}
					A								=				{x.name for x in save_dir.iterdir()}
					A								=				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   )	->				Union[str, Any]:
					if not FAIRSEQ_AVAILABLE:
						return
					A					,			A					,			A								=				self._get_dataset(max_len=64   )
					A								=				64
					A								=				ds.make_dynamic_sampler(a__				,   required_batch_size_multiple=a__   )
					A								=				[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
					A								=				DataLoader(a__				,   batch_sampler=a__				,   collate_fn=ds.collate_fn				,   num_workers=2   )
					A								=				[]
					A								=				[]
					for batch in data_loader:
						A								=				batch["""input_ids"""].shape
						A								=				src_shape[0]
						assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
						A								=				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   )	->				Any:
					A					,			A					,			A								=				self._get_dataset(max_len=512   )
					A								=				2
					A								=				ds.make_sortish_sampler(a__				,   shuffle=a__   )

					A								=				DataLoader(a__				,   batch_size=a__				,   collate_fn=ds.collate_fn				,   num_workers=2   )
					A								=				DataLoader(a__				,   batch_size=a__				,   collate_fn=ds.collate_fn				,   num_workers=2				,   sampler=a__   )

					A								=				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__=1000				,   a__=128   )	->				Any:
					if os.getenv("""USE_REAL_DATA"""				,   a__   ):
						A								=				"""examples/seq2seq/wmt_en_ro"""
						A								=				max_len * 2 * 64
						if not Path(a__   ).joinpath("""train.len"""   ).exists():
							save_len_file(a__				,   a__   )
					else:
						A								=				"""examples/seq2seq/test_data/wmt_en_ro"""
						A								=				max_len * 4
						save_len_file(a__				,   a__   )

					A								=				AutoTokenizer.from_pretrained(a__   )
					A								=				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   )	->				Optional[Any]:
					A					,			A					,			A								=				self._get_dataset()
					A								=				set(DistributedSortishSampler(a__				,   256				,   num_replicas=2				,   rank=0				,   add_extra_examples=a__   )   )
					A								=				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      _UpperCAmelCase					(						self				,   a__   )	->				int:
					A								=				AutoTokenizer.from_pretrained(a__				,   use_fast=a__   )
					if tok_name == MBART_TINY:
						A								=				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"""				,   )
						A								=				train_dataset.dataset_kwargs
						assert "src_lang" in kwargs and "tgt_lang" in kwargs
					else:
						A								=				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				,   )
						A								=				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 warnings
from typing import List, Optional, Union

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






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= ['image_processor', 'tokenizer']
				lowerCAmelCase 		= 'ViltImageProcessor'
				lowerCAmelCase 		= ('BertTokenizer', 'BertTokenizerFast')

				def __init__(						self				,   a__=None				,   a__=None				,   **a__   )	->				Any:
					A								=				None
					if "feature_extractor" in kwargs:
						warnings.warn(
						    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
						    """ instead."""				,   a__				,   )
						A								=				kwargs.pop("""feature_extractor"""   )

					A								=				image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
						raise ValueError("""You need to specify an `image_processor`."""   )
					if tokenizer is None:
						raise ValueError("""You need to specify a `tokenizer`."""   )

					super().__init__(a__				,   a__   )
					A								=				self.image_processor

				def __call__(						self				,   a__				,   a__ = None				,   a__ = True				,   a__ = False				,   a__ = None				,   a__ = None				,   a__ = 0				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = True				,   a__ = None				,   **a__				,   )	->				BatchEncoding:
					A								=				self.tokenizer(
					    text=a__				,   add_special_tokens=a__				,   padding=a__				,   truncation=a__				,   max_length=a__				,   stride=a__				,   pad_to_multiple_of=a__				,   return_token_type_ids=a__				,   return_attention_mask=a__				,   return_overflowing_tokens=a__				,   return_special_tokens_mask=a__				,   return_offsets_mapping=a__				,   return_length=a__				,   verbose=a__				,   return_tensors=a__				,   **a__				,   )
					# add pixel_values + pixel_mask
					A								=				self.image_processor(a__				,   return_tensors=a__   )
					encoding.update(a__   )

					return encoding

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				Union[str, Any]:
					return self.tokenizer.batch_decode(*a__				,   **a__   )

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				Any:
					return self.tokenizer.decode(*a__				,   **a__   )

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				self.tokenizer.model_input_names
					A								=				self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )

				@property
				def      _UpperCAmelCase					(						self   )	->				str:
					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,   a__				,   )
					return self.image_processor_class

				@property
				def      _UpperCAmelCase					(						self   )	->				str:
					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,   a__				,   )
					return self.image_processor



