


import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    LiltForQuestionAnswering,
			    LiltForSequenceClassification,
			    LiltForTokenClassification,
			    LiltModel,
			)
			from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST

class        a	:




							"""simple docstring"""


							def __init__(							self :						Optional[Any]					,			lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						List[str]=13					,			lowerCamelCase :						Tuple=7					,			lowerCamelCase :						int=True					,			lowerCamelCase :						Any=True					,			lowerCamelCase :						Optional[int]=True					,			lowerCamelCase :						Union[str, Any]=True					,			lowerCamelCase :						Optional[Any]=99					,			lowerCamelCase :						Optional[int]=24					,			lowerCamelCase :						Union[str, Any]=2					,			lowerCamelCase :						Any=6					,			lowerCamelCase :						Optional[int]=37					,			lowerCamelCase :						Union[str, Any]="gelu"					,			lowerCamelCase :						Dict=0.1					,			lowerCamelCase :						Optional[Any]=0.1					,			lowerCamelCase :						Dict=512					,			lowerCamelCase :						List[str]=16					,			lowerCamelCase :						Optional[int]=2					,			lowerCamelCase :						Union[str, Any]=0.02					,			lowerCamelCase :						Any=3					,			lowerCamelCase :						Optional[Any]=None					,			lowerCamelCase :						Optional[Any]=1000					,			)			->					Dict:
														__snake_case	:    Optional[Any]											=     parent
														__snake_case	:    Tuple											=     batch_size
														__snake_case	:    int											=     seq_length
														__snake_case	:    List[str]											=     is_training
														__snake_case	:    Tuple											=     use_input_mask
														__snake_case	:    Union[str, Any]											=     use_token_type_ids
														__snake_case	:    List[Any]											=     use_labels
														__snake_case	:    Union[str, Any]											=     vocab_size
														__snake_case	:    Tuple											=     hidden_size
														__snake_case	:    Tuple											=     num_hidden_layers
														__snake_case	:    Optional[int]											=     num_attention_heads
														__snake_case	:    Tuple											=     intermediate_size
														__snake_case	:    Tuple											=     hidden_act
														__snake_case	:    Optional[Any]											=     hidden_dropout_prob
														__snake_case	:    Optional[Any]											=     attention_probs_dropout_prob
														__snake_case	:    Any											=     max_position_embeddings
														__snake_case	:    Optional[int]											=     type_vocab_size
														__snake_case	:    List[Any]											=     type_sequence_label_size
														__snake_case	:    int											=     initializer_range
														__snake_case	:    str											=     num_labels
														__snake_case	:    List[Any]											=     scope
														__snake_case	:    str											=     range_bbox

							def 					__snake_case      (							self :						Optional[Any]       )			->					Optional[int]:
														__snake_case	:    int											=     ids_tensor([self.batch_size, self.seq_length]					,			self.vocab_size       )

														__snake_case	:    Union[str, Any]											=     ids_tensor([self.batch_size, self.seq_length, 4]					,			self.range_bbox       )
														# Ensure that bbox is legal
														for i in range(bbox.shape[0]       ):
																					for j in range(bbox.shape[1]       ):
																												if bbox[i, j, 3] < bbox[i, j, 1]:
																																			__snake_case	:    Optional[Any]											=     bbox[i, j, 3]
																																			__snake_case	:    str											=     bbox[i, j, 1]
																																			__snake_case	:    int											=     t
																												if bbox[i, j, 2] < bbox[i, j, 0]:
																																			__snake_case	:    Dict											=     bbox[i, j, 2]
																																			__snake_case	:    Union[str, Any]											=     bbox[i, j, 0]
																																			__snake_case	:    Optional[Any]											=     t

														__snake_case	:    Union[str, Any]											=     None
														if self.use_input_mask:
																					__snake_case	:    Optional[int]											=     ids_tensor([self.batch_size, self.seq_length]					,			vocab_size=2       )

														__snake_case	:    Union[str, Any]											=     None
														if self.use_token_type_ids:
																					__snake_case	:    Optional[int]											=     ids_tensor([self.batch_size, self.seq_length]					,			self.type_vocab_size       )

														__snake_case	:    List[Any]											=     None
														__snake_case	:    Optional[Any]											=     None
														if self.use_labels:
																					__snake_case	:    Optional[Any]											=     ids_tensor([self.batch_size]					,			self.type_sequence_label_size       )
																					__snake_case	:    List[Any]											=     ids_tensor([self.batch_size, self.seq_length]					,			self.num_labels       )

														__snake_case	:    List[str]											=     self.get_config()

														return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels

							def 					__snake_case      (							self :						Union[str, Any]       )			->					str:
														return LiltConfig(
														    vocab_size=self.vocab_size					,			hidden_size=self.hidden_size					,			num_hidden_layers=self.num_hidden_layers					,			num_attention_heads=self.num_attention_heads					,			intermediate_size=self.intermediate_size					,			hidden_act=self.hidden_act					,			hidden_dropout_prob=self.hidden_dropout_prob					,			attention_probs_dropout_prob=self.attention_probs_dropout_prob					,			max_position_embeddings=self.max_position_embeddings					,			type_vocab_size=self.type_vocab_size					,			initializer_range=self.initializer_range					,			)

							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Dict					,			lowerCamelCase :						List[str]					,			lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						List[str]					,			lowerCamelCase :						Dict					,			)			->					List[str]:
														__snake_case	:    Dict											=     LiltModel(config=lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														__snake_case	:    Optional[Any]											=     model(lowerCamelCase					,			bbox=lowerCamelCase					,			attention_mask=lowerCamelCase					,			token_type_ids=lowerCamelCase       )
														__snake_case	:    int											=     model(lowerCamelCase					,			bbox=lowerCamelCase					,			token_type_ids=lowerCamelCase       )
														__snake_case	:    Dict											=     model(lowerCamelCase					,			bbox=lowerCamelCase       )

														self.parent.assertEqual(result.last_hidden_state.shape					,			(self.batch_size, self.seq_length, self.hidden_size)       )
														self.parent.assertEqual(result.pooler_output.shape					,			(self.batch_size, self.hidden_size)       )

							def 					__snake_case      (							self :						Union[str, Any]					,			lowerCamelCase :						str					,			lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Dict					,			)			->					str:
														__snake_case	:    List[Any]											=     self.num_labels
														__snake_case	:    List[str]											=     LiltForTokenClassification(config=lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														__snake_case	:    Optional[Any]											=     model(
														    lowerCamelCase					,			bbox=lowerCamelCase					,			attention_mask=lowerCamelCase					,			token_type_ids=lowerCamelCase					,			labels=lowerCamelCase       )
														self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.seq_length, self.num_labels)       )

							def 					__snake_case      (							self :						str					,			lowerCamelCase :						Dict					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						List[str]					,			lowerCamelCase :						Any					,			)			->					Any:
														__snake_case	:    Optional[int]											=     LiltForQuestionAnswering(config=lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														__snake_case	:    Optional[int]											=     model(
														    lowerCamelCase					,			bbox=lowerCamelCase					,			attention_mask=lowerCamelCase					,			token_type_ids=lowerCamelCase					,			start_positions=lowerCamelCase					,			end_positions=lowerCamelCase					,			)
														self.parent.assertEqual(result.start_logits.shape					,			(self.batch_size, self.seq_length)       )
														self.parent.assertEqual(result.end_logits.shape					,			(self.batch_size, self.seq_length)       )






							def 					__snake_case      (							self :						str       )			->					Union[str, Any]:
														__snake_case	:    List[str]											=     self.prepare_config_and_inputs()
														(
														    __snake_case    
														)	:    Optional[Any]											=     config_and_inputs
														__snake_case	:    Optional[Any]											=     {
														    "input_ids": input_ids,
														    "bbox": bbox,
														    "token_type_ids": token_type_ids,
														    "attention_mask": input_mask,
														}
														return config, inputs_dict

@require_torch
class        a	(_lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Tuple  						=						(
							    (
							        LiltModel,
							        LiltForSequenceClassification,
							        LiltForTokenClassification,
							        LiltForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							__UpperCAmelCase					:							List[str]  						=						(
							    {
							        "feature-extraction": LiltModel,
							        "question-answering": LiltForQuestionAnswering,
							        "text-classification": LiltForSequenceClassification,
							        "token-classification": LiltForTokenClassification,
							        "zero-shot": LiltForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__UpperCAmelCase					:							int  						=						False
							__UpperCAmelCase					:							str  						=						False

							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Optional[int]       )			->					List[Any]:
														return True

							def 					__snake_case      (							self :						str       )			->					List[str]:
														__snake_case	:    str											=     LiltModelTester(self       )
														__snake_case	:    int											=     ConfigTester(self					,			config_class=lowerCamelCase					,			hidden_size=37       )

							def 					__snake_case      (							self :						List[Any]       )			->					Any:
														self.config_tester.run_common_tests()

							def 					__snake_case      (							self :						str       )			->					Tuple:
														__snake_case	:    Any											=     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase       )

							def 					__snake_case      (							self :						List[str]       )			->					str:
														__snake_case	:    List[str]											=     self.model_tester.prepare_config_and_inputs()
														for type in ["absolute", "relative_key", "relative_key_query"]:
																					__snake_case	:    Dict											=     type
																					self.model_tester.create_and_check_model(*lowerCamelCase       )

							def 					__snake_case      (							self :						Union[str, Any]       )			->					Optional[Any]:
														__snake_case	:    Dict											=     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*lowerCamelCase       )

							def 					__snake_case      (							self :						Any       )			->					int:
														__snake_case	:    List[Any]											=     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*lowerCamelCase       )






							@slow
							def 					__snake_case      (							self :						Dict       )			->					int:
														for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					__snake_case	:    List[Any]											=     LiltModel.from_pretrained(lowerCamelCase       )
																					self.assertIsNotNone(lowerCamelCase       )

@require_torch
@slow
class        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Tuple       )			->					List[str]:
														__snake_case	:    Optional[int]											=     LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"       ).to(lowerCamelCase       )

														__snake_case	:    List[str]											=     torch.tensor([[1, 2]]					,			device=lowerCamelCase       )
														__snake_case	:    List[str]											=     torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]					,			device=lowerCamelCase       )

														# forward pass
														with torch.no_grad():
																					__snake_case	:    List[str]											=     model(input_ids=lowerCamelCase					,			bbox=lowerCamelCase       )

														__snake_case	:    List[str]											=     torch.Size([1, 2, 768]       )
														__snake_case	:    str											=     torch.tensor(
														    [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]					,			device=lowerCamelCase					,			)

														self.assertTrue(outputs.last_hidden_state.shape					,			lowerCamelCase       )
														self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]					,			lowerCamelCase					,			atol=1E-3       )       )









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


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







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    Union[str, Any]											=     "\n".join(__lowerCamelCase			)
							Path(__lowerCamelCase			).open("w"			).writelines(__lowerCamelCase			)







def        lowerCAmelCase_  (      __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        a	(_lowerCAmelCase   ):




							"""simple docstring"""


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

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

														__snake_case	:    Dict											=     DataLoader(lowerCamelCase					,			batch_size=lowerCamelCase					,			collate_fn=ds.collate_fn					,			num_workers=2       )
														__snake_case	:    List[Any]											=     DataLoader(lowerCamelCase					,			batch_size=lowerCamelCase					,			collate_fn=ds.collate_fn					,			num_workers=2					,			sampler=lowerCamelCase       )

														__snake_case	:    List[Any]											=     tokenizer.pad_token_id

														def count_pad_tokens(lowerCamelCase :						List[Any]					,			lowerCamelCase :						Optional[int]="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 					__snake_case      (							self :						Any					,			lowerCamelCase :						List[Any]=1000					,			lowerCamelCase :						Union[str, Any]=128       )			->					Any:
														if os.getenv("USE_REAL_DATA"					,			lowerCamelCase       ):
																					__snake_case	:    int											=     "examples/seq2seq/wmt_en_ro"
																					__snake_case	:    Union[str, Any]											=     max_len * 2 * 64
																					if not Path(lowerCamelCase       ).joinpath("train.len"       ).exists():
																												save_len_file(lowerCamelCase					,			lowerCamelCase       )
														else:
																					__snake_case	:    List[str]											=     "examples/seq2seq/test_data/wmt_en_ro"
																					__snake_case	:    List[Any]											=     max_len * 4
																					save_len_file(lowerCamelCase					,			lowerCamelCase       )

														__snake_case	:    Dict											=     AutoTokenizer.from_pretrained(lowerCamelCase       )
														__snake_case	:    int											=     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 					__snake_case      (							self :						Tuple       )			->					Dict:
														__snake_case				,    __snake_case				,    __snake_case	:    Any											=     self._get_dataset()
														__snake_case	:    Optional[Any]											=     set(DistributedSortishSampler(lowerCamelCase					,			256					,			num_replicas=2					,			rank=0					,			add_extra_examples=lowerCamelCase       )       )
														__snake_case	:    int											=     set(DistributedSortishSampler(lowerCamelCase					,			256					,			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 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Union[str, Any]       )			->					str:
														__snake_case	:    Tuple											=     AutoTokenizer.from_pretrained(lowerCamelCase					,			use_fast=lowerCamelCase       )
														if tok_name == MBART_TINY:
																					__snake_case	:    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					,			src_lang="EN"					,			tgt_lang="FR"					,			)
																					__snake_case	:    Any											=     train_dataset.dataset_kwargs
																					assert "src_lang" in kwargs and "tgt_lang" in kwargs
														else:
																					__snake_case	:    Union[str, Any]											=     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					,			)
																					__snake_case	:    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(lowerCamelCase       ) == 1 if tok_name == BART_TINY else len(lowerCamelCase       ) == 0

