import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__lowercase										= logging.get_logger(__name__)


__lowercase										= {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
__lowercase										= {
    "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",
    },
}

__lowercase										= {
    "allenai/led-base-16384": 1_6384,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def lowerCamelCase  (       ):
				'''simple docstring'''







				__UpperCamelCase					:Tuple        =		(
				    list(range(ord('''!'''     )  ,      ord('''~'''     ) + 1     )     ) + list(range(ord('''¡'''     )  ,      ord('''¬'''     ) + 1     )     ) + list(range(ord('''®'''     )  ,      ord('''ÿ'''     ) + 1     )     )
				)
				__UpperCamelCase					:Dict        =		bs[:]
				__UpperCamelCase					:List[Any]        =		0
				for b in range(2**8     ):
								if b not in bs:
												bs.append(lowerCamelCase__     )
												cs.append(2**8 + n     )
												n += 1
				__UpperCamelCase					:str        =		[chr(lowerCamelCase__     ) for n in cs]
				return dict(zip(lowerCamelCase__  ,      lowerCamelCase__     )     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
				'''simple docstring'''







				__UpperCamelCase					:Tuple        =		set()
				__UpperCamelCase					:Dict        =		word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)     )
								__UpperCamelCase					:Optional[Any]        =		char
				return pairs


class      lowerCamelCase_					(       a_			):






				'''simple docstring'''






				a__     :						Optional[Any]      =				VOCAB_FILES_NAMES
				a__     :						Union[str, Any]      =				PRETRAINED_VOCAB_FILES_MAP
				a__     :						Tuple      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a__     :						Union[str, Any]      =				["input_ids", "attention_mask"]







				def __init__(     self			,			__lowercase			,			__lowercase			,			__lowercase="replace"			,			__lowercase="<s>"			,			__lowercase="</s>"			,			__lowercase="</s>"			,			__lowercase="<s>"			,			__lowercase="<unk>"			,			__lowercase="<pad>"			,			__lowercase="<mask>"			,			__lowercase=False			,			**__lowercase			,			)	->     Tuple:
								__UpperCamelCase					:List[Any]        =		AddedToken(__lowercase			,			lstrip=__lowercase			,			rstrip=__lowercase) if isinstance(__lowercase			,			__lowercase) else bos_token
								__UpperCamelCase					:List[Any]        =		AddedToken(__lowercase			,			lstrip=__lowercase			,			rstrip=__lowercase) if isinstance(__lowercase			,			__lowercase) else eos_token
								__UpperCamelCase					:Optional[Any]        =		AddedToken(__lowercase			,			lstrip=__lowercase			,			rstrip=__lowercase) if isinstance(__lowercase			,			__lowercase) else sep_token
								__UpperCamelCase					:int        =		AddedToken(__lowercase			,			lstrip=__lowercase			,			rstrip=__lowercase) if isinstance(__lowercase			,			__lowercase) else cls_token
								__UpperCamelCase					:List[str]        =		AddedToken(__lowercase			,			lstrip=__lowercase			,			rstrip=__lowercase) if isinstance(__lowercase			,			__lowercase) else unk_token
								__UpperCamelCase					:Tuple        =		AddedToken(__lowercase			,			lstrip=__lowercase			,			rstrip=__lowercase) if isinstance(__lowercase			,			__lowercase) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								__UpperCamelCase					:List[Any]        =		AddedToken(__lowercase			,			lstrip=__lowercase			,			rstrip=__lowercase) if isinstance(__lowercase			,			__lowercase) else mask_token

								super().__init__(
								    errors=__lowercase			,			bos_token=__lowercase			,			eos_token=__lowercase			,			unk_token=__lowercase			,			sep_token=__lowercase			,			cls_token=__lowercase			,			pad_token=__lowercase			,			mask_token=__lowercase			,			add_prefix_space=__lowercase			,			**__lowercase			,			)

								with open(__lowercase			,			encoding='''utf-8''') as vocab_handle:
												__UpperCamelCase					:Optional[Any]        =		json.load(__lowercase)
								__UpperCamelCase					:Dict        =		{v: k for k, v in self.encoder.items()}
								__UpperCamelCase					:Optional[int]        =		errors  # how to handle errors in decoding
								__UpperCamelCase					:Optional[Any]        =		bytes_to_unicode()
								__UpperCamelCase					:List[Any]        =		{v: k for k, v in self.byte_encoder.items()}
								with open(__lowercase			,			encoding='''utf-8''') as merges_handle:
												__UpperCamelCase					:Any        =		merges_handle.read().split('''\n''')[1:-1]
								__UpperCamelCase					:Dict        =		[tuple(merge.split()) for merge in bpe_merges]
								__UpperCamelCase					:Union[str, Any]        =		dict(zip(__lowercase			,			range(len(__lowercase))))
								__UpperCamelCase					:Tuple        =		{}
								__UpperCamelCase					:Any        =		add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								__UpperCamelCase					:Optional[Any]        =		re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''')







				@property
				# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
				def       UpperCamelCase__       (     self)	->     Tuple:
								return len(self.encoder)







				def       UpperCamelCase__       (     self)	->     Optional[int]:
								return dict(self.encoder			,			**self.added_tokens_encoder)







				def       UpperCamelCase__       (     self			,			__lowercase)	->     List[str]:







								if token in self.cache:
												return self.cache[token]
								__UpperCamelCase					:Optional[Any]        =		tuple(__lowercase)
								__UpperCamelCase					:Union[str, Any]        =		get_pairs(__lowercase)

								if not pairs:
												return token

								while True:
												__UpperCamelCase					:str        =		min(__lowercase			,			key=lambda __lowercase: self.bpe_ranks.get(__lowercase			,			float('''inf''')))
												if bigram not in self.bpe_ranks:
																break
												__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		bigram
												__UpperCamelCase					:List[Any]        =		[]
												__UpperCamelCase					:int        =		0
												while i < len(__lowercase):
																try:
																				__UpperCamelCase					:str        =		word.index(__lowercase			,			__lowercase)
																except ValueError:
																				new_word.extend(word[i:])
																				break
																else:
																				new_word.extend(word[i:j])
																				__UpperCamelCase					:Optional[Any]        =		j

																if word[i] == first and i < len(__lowercase) - 1 and word[i + 1] == second:
																				new_word.append(first + second)
																				i += 2
																else:
																				new_word.append(word[i])
																				i += 1
												__UpperCamelCase					:int        =		tuple(__lowercase)
												__UpperCamelCase					:str        =		new_word
												if len(__lowercase) == 1:
																break
												else:
																__UpperCamelCase					:Any        =		get_pairs(__lowercase)
								__UpperCamelCase					:List[str]        =		''' '''.join(__lowercase)
								__UpperCamelCase					:List[Any]        =		word
								return word







				def       UpperCamelCase__       (     self			,			__lowercase)	->     List[str]:
								__UpperCamelCase					:Optional[int]        =		[]
								for token in re.findall(self.pat			,			__lowercase):
												__UpperCamelCase					:List[str]        =		''''''.join(
												    self.byte_encoder[b] for b in token.encode('''utf-8'''))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
												bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowercase).split(''' '''))
								return bpe_tokens







				def       UpperCamelCase__       (     self			,			__lowercase)	->     Optional[Any]:
								return self.encoder.get(__lowercase			,			self.encoder.get(self.unk_token))







				def       UpperCamelCase__       (     self			,			__lowercase)	->     Optional[int]:
								return self.decoder.get(__lowercase)







				def       UpperCamelCase__       (     self			,			__lowercase)	->     Tuple:
								__UpperCamelCase					:str        =		''''''.join(__lowercase)
								__UpperCamelCase					:int        =		bytearray([self.byte_decoder[c] for c in text]).decode('''utf-8'''			,			errors=self.errors)
								return text







				def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     Tuple[str]:







								if not os.path.isdir(__lowercase):
												logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
												return
								__UpperCamelCase					:Optional[int]        =		os.path.join(
								    __lowercase			,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
								__UpperCamelCase					:Any        =		os.path.join(
								    __lowercase			,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''])

								with open(__lowercase			,			'''w'''			,			encoding='''utf-8''') as f:
												f.write(json.dumps(self.encoder			,			indent=2			,			sort_keys=__lowercase			,			ensure_ascii=__lowercase) + '''\n''')

								__UpperCamelCase					:List[Any]        =		0
								with open(__lowercase			,			'''w'''			,			encoding='''utf-8''') as writer:
												writer.write('''#version: 0.2\n''')
												for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,			key=lambda __lowercase: kv[1]):
																if index != token_index:
																				logger.warning(
																				    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																				    ''' Please check that the tokenizer is not corrupted!''')
																				__UpperCamelCase					:int        =		token_index
																writer.write(''' '''.join(__lowercase) + '''\n''')
																index += 1

								return vocab_file, merge_file







				def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[int]:







								if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__UpperCamelCase					:str        =		[self.cls_token_id]
								__UpperCamelCase					:Any        =		[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep







				def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None			,			__lowercase = False)	->     List[int]:







								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__lowercase			,			token_ids_a=__lowercase			,			already_has_special_tokens=__lowercase)

								if token_ids_a is None:
												return [1] + ([0] * len(__lowercase)) + [1]
								return [1] + ([0] * len(__lowercase)) + [1, 1] + ([0] * len(__lowercase)) + [1]







				def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[int]:
								__UpperCamelCase					:int        =		[self.sep_token_id]
								__UpperCamelCase					:List[str]        =		[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       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase=False			,			**__lowercase)	->     List[str]:
								__UpperCamelCase					:Any        =		kwargs.pop('''add_prefix_space'''			,			self.add_prefix_space)
								if (is_split_into_words or add_prefix_space) and (len(__lowercase) > 0 and not text[0].isspace()):
												__UpperCamelCase					:Union[str, Any]        =		''' ''' + text
								return (text, kwargs)







				def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None			,			__lowercase = PaddingStrategy.DO_NOT_PAD			,			__lowercase = None			,			__lowercase = None			,			)	->     dict:
								__UpperCamelCase					:Optional[int]        =		super()._pad(
								    encoded_inputs=__lowercase			,			max_length=__lowercase			,			padding_strategy=__lowercase			,			pad_to_multiple_of=__lowercase			,			return_attention_mask=__lowercase			,			)

								# Load from model defaults
								if return_attention_mask is None:
												__UpperCamelCase					:List[str]        =		'''attention_mask''' in self.model_input_names

								if return_attention_mask and "global_attention_mask" in encoded_inputs:
												__UpperCamelCase					:str        =		encoded_inputs[self.model_input_names[0]]
												# `global_attention_mask` need to have the same length as other (sequential) inputs.
												__UpperCamelCase					:Optional[Any]        =		len(encoded_inputs['''global_attention_mask''']) != len(__lowercase)

												if needs_to_be_padded:
																__UpperCamelCase					:int        =		len(__lowercase) - 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`
																				__UpperCamelCase					:List[Any]        =		(
																				    encoded_inputs['''global_attention_mask'''] + [-1] * difference
																				)
																elif self.padding_side == "left":
																				__UpperCamelCase					:Optional[int]        =		[-1] * difference + encoded_inputs[
																				    '''global_attention_mask'''
																				]
																else:
																				raise ValueError('''Invalid padding strategy:''' + str(self.padding_side))

								return encoded_inputs






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										= '''bert-base-cased'''
__lowercase										= '''google/pegasus-xsum'''
__lowercase										= [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__lowercase										= ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__lowercase										= '''patrickvonplaten/t5-tiny-random'''
__lowercase										= '''sshleifer/bart-tiny-random'''
__lowercase										= '''sshleifer/tiny-mbart'''
__lowercase										= '''sshleifer/tiny-marian-en-de'''



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:str        =		'''\n'''.join(SCREAMING_SNAKE_CASE     )
    Path(SCREAMING_SNAKE_CASE     ).open('''w'''     ).writelines(SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(SCREAMING_SNAKE_CASE  ,      f"""{split}.source"""     )  ,      SCREAMING_SNAKE_CASE     )
        _dump_articles(os.path.join(SCREAMING_SNAKE_CASE  ,      f"""{split}.target"""     )  ,      SCREAMING_SNAKE_CASE     )
    return tmp_dir


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






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

        __UpperCamelCase					:Union[str, Any]        =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        __UpperCamelCase					:str        =		tmp_dir.joinpath('''train.source''').open().readlines()
        __UpperCamelCase					:int        =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        pack_data_dir(__lowercase			,			__lowercase			,			128			,			__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		{x.name for x in tmp_dir.iterdir()}
        __UpperCamelCase					:int        =		{x.name for x in save_dir.iterdir()}
        __UpperCamelCase					:Optional[int]        =		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(__lowercase) < len(__lowercase)
        assert len(__lowercase) == 1
        assert len(packed_examples[0]) == sum(len(__lowercase) for x in orig_examples)
        assert orig_paths == new_paths







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







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		self._get_dataset(max_len=512)
        __UpperCamelCase					:Any        =		2
        __UpperCamelCase					:List[Any]        =		ds.make_sortish_sampler(__lowercase			,			shuffle=__lowercase)

        __UpperCamelCase					:List[Any]        =		DataLoader(__lowercase			,			batch_size=__lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2)
        __UpperCamelCase					:Tuple        =		DataLoader(__lowercase			,			batch_size=__lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2			,			sampler=__lowercase)

        __UpperCamelCase					:int        =		tokenizer.pad_token_id

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

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







    def       UpperCamelCase__       (     self			,			__lowercase=1_000			,			__lowercase=128)	->     List[Any]:
        if os.getenv('''USE_REAL_DATA'''			,			__lowercase):
            __UpperCamelCase					:Optional[Any]        =		'''examples/seq2seq/wmt_en_ro'''
            __UpperCamelCase					:Dict        =		max_len * 2 * 64
            if not Path(__lowercase).joinpath('''train.len''').exists():
                save_len_file(__lowercase			,			__lowercase)
        else:
            __UpperCamelCase					:Union[str, Any]        =		'''examples/seq2seq/test_data/wmt_en_ro'''
            __UpperCamelCase					:Optional[int]        =		max_len * 4
            save_len_file(__lowercase			,			__lowercase)

        __UpperCamelCase					:str        =		AutoTokenizer.from_pretrained(__lowercase)
        __UpperCamelCase					:List[Any]        =		SeqaSeqDataset(
            __lowercase			,			data_dir=__lowercase			,			type_path='''train'''			,			max_source_length=__lowercase			,			max_target_length=__lowercase			,			n_obs=__lowercase			,			)
        return ds, max_tokens, tokenizer







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		self._get_dataset()
        __UpperCamelCase					:List[str]        =		set(DistributedSortishSampler(__lowercase			,			256			,			num_replicas=2			,			rank=0			,			add_extra_examples=__lowercase))
        __UpperCamelCase					:Tuple        =		set(DistributedSortishSampler(__lowercase			,			256			,			num_replicas=2			,			rank=1			,			add_extra_examples=__lowercase))
        assert idsa.intersection(__lowercase) == set()







    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]			,			)
    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[Any]:
        __UpperCamelCase					:List[Any]        =		AutoTokenizer.from_pretrained(__lowercase			,			use_fast=__lowercase)
        if tok_name == MBART_TINY:
            __UpperCamelCase					:Optional[Any]        =		SeqaSeqDataset(
                __lowercase			,			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'''			,			)
            __UpperCamelCase					:Tuple        =		train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
            __UpperCamelCase					:Tuple        =		SeqaSeqDataset(
                __lowercase			,			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			,			)
            __UpperCamelCase					: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(__lowercase) == 1 if tok_name == BART_TINY else len(__lowercase) == 0





