



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 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					str              =						"\n".join(_lowerCamelCase    )
			Path(_lowerCamelCase    ).open("w"    ).writelines(_lowerCamelCase    )




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			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 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







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

									A							:					Optional[Any]              =						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
									A							:					Optional[int]              =						tmp_dir.joinpath("train.source" ).open().readlines()
									A							:					Union[str, Any]              =						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
									pack_data_dir(__lowerCamelCase	,	__lowerCamelCase	,	1_28	,	__lowerCamelCase )
									A							:					List[str]              =						{x.name for x in tmp_dir.iterdir()}
									A							:					Tuple              =						{x.name for x in save_dir.iterdir()}
									A							:					List[str]              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Any:
									if not FAIRSEQ_AVAILABLE:
												return
									A       , A       , A							:					Any              =						self._get_dataset(max_len=64 )
									A							:					List[Any]              =						64
									A							:					str              =						ds.make_dynamic_sampler(__lowerCamelCase	,	required_batch_size_multiple=__lowerCamelCase )
									A							:					Optional[Any]              =						[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
									A							:					Any              =						DataLoader(__lowerCamelCase	,	batch_sampler=__lowerCamelCase	,	collate_fn=ds.collate_fn	,	num_workers=2 )
									A							:					List[Any]              =						[]
									A							:					Union[str, Any]              =						[]
									for batch in data_loader:
												A							:					Union[str, Any]              =						batch["input_ids"].shape
												A							:					str              =						src_shape[0]
												assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
												A							:					int              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				str:
									A       , A       , A							:					Dict              =						self._get_dataset(max_len=5_12 )
									A							:					List[str]              =						2
									A							:					List[str]              =						ds.make_sortish_sampler(__lowerCamelCase	,	shuffle=__lowerCamelCase )

									A							:					Any              =						DataLoader(__lowerCamelCase	,	batch_size=__lowerCamelCase	,	collate_fn=ds.collate_fn	,	num_workers=2 )
									A							:					List[str]              =						DataLoader(__lowerCamelCase	,	batch_size=__lowerCamelCase	,	collate_fn=ds.collate_fn	,	num_workers=2	,	sampler=__lowerCamelCase )

									A							:					Optional[Any]              =						tokenizer.pad_token_id

									def count_pad_tokens(__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Dict="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 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Any=10_00	,	__lowerCamelCase      :			List[Any]=1_28 )      ->				Any:
									if os.getenv("USE_REAL_DATA"	,	__lowerCamelCase ):
												A							:					List[Any]              =						"examples/seq2seq/wmt_en_ro"
												A							:					Dict              =						max_len * 2 * 64
												if not Path(__lowerCamelCase ).joinpath("train.len" ).exists():
															save_len_file(__lowerCamelCase	,	__lowerCamelCase )
									else:
												A							:					Tuple              =						"examples/seq2seq/test_data/wmt_en_ro"
												A							:					List[str]              =						max_len * 4
												save_len_file(__lowerCamelCase	,	__lowerCamelCase )

									A							:					Any              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
									A							:					Tuple              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Any:
									A       , A       , A							:					Any              =						self._get_dataset()
									A							:					Dict              =						set(DistributedSortishSampler(__lowerCamelCase	,	2_56	,	num_replicas=2	,	rank=0	,	add_extra_examples=__lowerCamelCase ) )
									A							:					Union[str, Any]              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			List[str] )      ->				Optional[Any]:
									A							:					List[str]              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	use_fast=__lowerCamelCase )
									if tok_name == MBART_TINY:
												A							:					str              =						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"	,	)
												A							:					int              =						train_dataset.dataset_kwargs
												assert "src_lang" in kwargs and "tgt_lang" in kwargs
									else:
												A							:					Dict              =						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	,	)
												A							:					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(__lowerCamelCase ) == 1 if tok_name == BART_TINY else len(__lowerCamelCase ) == 0




import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE           =  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE           =  {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__SCREAMING_SNAKE_CASE           =  {
    """allenai/led-base-16384""": 16384,
}

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  VOCAB_FILES_NAMES
						a__   =  PRETRAINED_VOCAB_FILES_MAP
						a__   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__   =  LEDTokenizer
						a__   =  ["input_ids", "attention_mask"]







						def __init__(	self      :			int	,	__lowerCamelCase      :			List[str]=None	,	__lowerCamelCase      :			Optional[int]=None	,	__lowerCamelCase      :			Optional[int]=None	,	__lowerCamelCase      :			str="replace"	,	__lowerCamelCase      :			Optional[int]="<s>"	,	__lowerCamelCase      :			str="</s>"	,	__lowerCamelCase      :			Any="</s>"	,	__lowerCamelCase      :			Dict="<s>"	,	__lowerCamelCase      :			Optional[int]="<unk>"	,	__lowerCamelCase      :			Union[str, Any]="<pad>"	,	__lowerCamelCase      :			Dict="<mask>"	,	__lowerCamelCase      :			Union[str, Any]=False	,	__lowerCamelCase      :			str=True	,	**__lowerCamelCase      :			Union[str, Any]	,	)      ->				Optional[int]:
									super().__init__(
									    __lowerCamelCase	,	__lowerCamelCase	,	tokenizer_file=__lowerCamelCase	,	errors=__lowerCamelCase	,	bos_token=__lowerCamelCase	,	eos_token=__lowerCamelCase	,	sep_token=__lowerCamelCase	,	cls_token=__lowerCamelCase	,	unk_token=__lowerCamelCase	,	pad_token=__lowerCamelCase	,	mask_token=__lowerCamelCase	,	add_prefix_space=__lowerCamelCase	,	trim_offsets=__lowerCamelCase	,	**__lowerCamelCase	,	)

									A							:					Union[str, Any]              =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
									if pre_tok_state.get("add_prefix_space"	,	__lowerCamelCase ) != add_prefix_space:
												A							:					Any              =						getattr(__lowerCamelCase	,	pre_tok_state.pop("type" ) )
												A							:					Any              =						add_prefix_space
												A							:					Tuple              =						pre_tok_class(**__lowerCamelCase )

									A							:					str              =						add_prefix_space

									# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
									A							:					List[str]              =						"post_processor"
									A							:					Union[str, Any]              =						getattr(self.backend_tokenizer	,	__lowerCamelCase	,	__lowerCamelCase )
									if tokenizer_component_instance:
												A							:					Dict              =						json.loads(tokenizer_component_instance.__getstate__() )

												# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
												if "sep" in state:
															A							:					Union[str, Any]              =						tuple(state["sep"] )
												if "cls" in state:
															A							:					str              =						tuple(state["cls"] )

												A							:					int              =						False

												if state.get("add_prefix_space"	,	__lowerCamelCase ) != add_prefix_space:
															A							:					List[Any]              =						add_prefix_space
															A							:					Dict              =						True

												if state.get("trim_offsets"	,	__lowerCamelCase ) != trim_offsets:
															A							:					Dict              =						trim_offsets
															A							:					str              =						True

												if changes_to_apply:
															A							:					int              =						getattr(__lowerCamelCase	,	state.pop("type" ) )
															A							:					Dict              =						component_class(**__lowerCamelCase )
															setattr(self.backend_tokenizer	,	__lowerCamelCase	,	__lowerCamelCase )







						@property
						# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				str:
									if self._mask_token is None:
												if self.verbose:
															logger.error("Using mask_token, but it is not set yet." )
												return None
									return str(self._mask_token )







						@mask_token.setter
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Any )      ->				Dict:
									A							:					Tuple              =						AddedToken(__lowerCamelCase	,	lstrip=__lowerCamelCase	,	rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase	,	__lowerCamelCase ) else value
									A							:					Tuple              =						value







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	*__lowerCamelCase      :			Optional[Any]	,	**__lowerCamelCase      :			List[str] )      ->				BatchEncoding:
									A							:					List[str]              =						kwargs.get("is_split_into_words"	,	__lowerCamelCase )

									if is_split_into_words and not self.add_prefix_space:
												raise ValueError(
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs." )

									return super()._batch_encode_plus(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	*__lowerCamelCase      :			str	,	**__lowerCamelCase      :			Optional[Any] )      ->				BatchEncoding:
									A							:					List[str]              =						kwargs.get("is_split_into_words"	,	__lowerCamelCase )

									if is_split_into_words and not self.add_prefix_space:
												raise ValueError(
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs." )

									return super()._encode_plus(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			Optional[str] = None )      ->				Tuple[str]:
									A							:					Optional[Any]              =						self._tokenizer.model.save(__lowerCamelCase	,	name=__lowerCamelCase )
									return tuple(__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			List[Any]=None )      ->				List[str]:
									A							:					Optional[int]              =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
												return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			List[int]	,	__lowerCamelCase      :			Optional[List[int]] = None )      ->				List[int]:
									A							:					str              =						[self.sep_token_id]
									A							:					int              =						[self.cls_token_id]

									if token_ids_a is None:
												return len(cls + token_ids_a + sep ) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Union[Dict[str, EncodedInput], BatchEncoding]	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			Optional[bool] = None	,	)      ->				dict:
									A							:					Dict              =						super()._pad(
									    encoded_inputs=__lowerCamelCase	,	max_length=__lowerCamelCase	,	padding_strategy=__lowerCamelCase	,	pad_to_multiple_of=__lowerCamelCase	,	return_attention_mask=__lowerCamelCase	,	)

									# Load from model defaults
									if return_attention_mask is None:
												A							:					List[Any]              =						"attention_mask" in self.model_input_names

									if return_attention_mask and "global_attention_mask" in encoded_inputs:
												A							:					Optional[int]              =						encoded_inputs[self.model_input_names[0]]
												# `global_attention_mask` need to have the same length as other (sequential) inputs.
												A							:					Tuple              =						len(encoded_inputs["global_attention_mask"] ) != len(__lowerCamelCase )

												if needs_to_be_padded:
															A							:					Any              =						len(__lowerCamelCase ) - len(encoded_inputs["global_attention_mask"] )

															if self.padding_side == "right":
																		# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																		A							:					Tuple              =						(
																		    encoded_inputs["global_attention_mask"] + [-1] * difference
																		)
															elif self.padding_side == "left":
																		A							:					Tuple              =						[-1] * difference + encoded_inputs[
																		    "global_attention_mask"
																		]
															else:
																		raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )

									return encoded_inputs