


"""simple docstring"""




import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
     from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor
class 						__A							:






     def __init__(		self    ,					a__    ,					a__=13    ,					a__=32    ,					a__=3    ,					a__=4    ,					a__=[10, 20, 30, 40]    ,					a__=[2, 2, 3, 2]    ,					a__=True    ,					a__=True    ,					a__=37    ,					a__="gelu"    ,					a__=10    ,					a__=0.0_2    ,					a__=["stage2", "stage3", "stage4"]    ,					a__=[2, 3, 4]    ,					a__=None    ,					):
       _lowerCAmelCase						:	List[Any]        =					parent
       _lowerCAmelCase						:	str        =					batch_size
       _lowerCAmelCase						:	Tuple        =					image_size
       _lowerCAmelCase						:	Dict        =					num_channels
       _lowerCAmelCase						:	Optional[Any]        =					num_stages
       _lowerCAmelCase						:	int        =					hidden_sizes
       _lowerCAmelCase						:	Optional[Any]        =					depths
       _lowerCAmelCase						:	Any        =					is_training
       _lowerCAmelCase						:	List[Any]        =					use_labels
       _lowerCAmelCase						:	Tuple        =					intermediate_size
       _lowerCAmelCase						:	Optional[Any]        =					hidden_act
       _lowerCAmelCase						:	Optional[Any]        =					num_labels
       _lowerCAmelCase						:	Optional[int]        =					initializer_range
       _lowerCAmelCase						:	Optional[int]        =					out_features
       _lowerCAmelCase						:	Tuple        =					out_indices
       _lowerCAmelCase						:	str        =					scope






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

       _lowerCAmelCase						:	Optional[int]        =					None
       if self.use_labels:
         _lowerCAmelCase						:	str        =					ids_tensor([self.batch_size]    ,					self.num_labels  )

       _lowerCAmelCase						:	Tuple        =					self.get_config()

       return config, pixel_values, labels






     def    __A  (		self  ):
       return ConvNextVaConfig(
           num_channels=self.num_channels    ,					hidden_sizes=self.hidden_sizes    ,					depths=self.depths    ,					num_stages=self.num_stages    ,					hidden_act=self.hidden_act    ,					is_decoder=a__    ,					initializer_range=self.initializer_range    ,					out_features=self.out_features    ,					out_indices=self.out_indices    ,					num_labels=self.num_labels    ,					)






     def    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	int        =					ConvNextVaModel(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	str        =					model(a__  )
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,					)






     def    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Optional[int]        =					ConvNextVaForImageClassification(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	Optional[int]        =					model(a__    ,					labels=a__  )
       self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_labels)  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Any        =					ConvNextVaBackbone(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	Tuple        =					model(a__  )

       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps  )    ,					len(config.out_features  )  )
       self.parent.assertListEqual(list(result.feature_maps[0].shape  )    ,					[self.batch_size, self.hidden_sizes[1], 4, 4]  )

       # verify channels
       self.parent.assertEqual(len(model.channels  )    ,					len(config.out_features  )  )
       self.parent.assertListEqual(model.channels    ,					config.hidden_sizes[1:]  )

       # verify backbone works with out_features=None
       _lowerCAmelCase						:	List[str]        =					None
       _lowerCAmelCase						:	int        =					ConvNextVaBackbone(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	Optional[int]        =					model(a__  )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps  )    ,					1  )
       self.parent.assertListEqual(list(result.feature_maps[0].shape  )    ,					[self.batch_size, self.hidden_sizes[-1], 1, 1]  )

       # verify channels
       self.parent.assertEqual(len(model.channels  )    ,					1  )
       self.parent.assertListEqual(model.channels    ,					[config.hidden_sizes[-1]]  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.prepare_config_and_inputs()
       _lowerCAmelCase						:	Union[str, Any]        =					config_and_inputs
       _lowerCAmelCase						:	Any        =					{"""pixel_values""": pixel_values}
       return config, inputs_dict






     def    __A  (		self  ):
       _lowerCAmelCase						:	int        =					self.prepare_config_and_inputs()
       _lowerCAmelCase						:	Optional[Any]        =					config_and_inputs
       _lowerCAmelCase						:	int        =					{"""pixel_values""": pixel_values, """labels""": labels}
       return config, inputs_dict







@require_torch
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      Optional[int]		 =		(
         (
             ConvNextVaModel,
             ConvNextVaForImageClassification,
             ConvNextVaBackbone,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:      Optional[Any]		 =		(
         {"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
         if is_torch_available()
         else {}
     )

     _UpperCamelCase							:      Optional[int]		 =		False
     _UpperCamelCase							:      Optional[Any]		 =		False
     _UpperCamelCase							:      Optional[int]		 =		False
     _UpperCamelCase							:      Union[str, Any]		 =		False
     _UpperCamelCase							:      List[Any]		 =		False






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					ConvNextVaModelTester(self  )
       _lowerCAmelCase						:	List[Any]        =					ConfigTester(self    ,					config_class=a__    ,					has_text_modality=a__    ,					hidden_size=37  )






     def    __A  (		self  ):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()






     def    __A  (		self  ):
       return






     @unittest.skip(reason="""ConvNextV2 does not use inputs_embeds"""  )
     def    __A  (		self  ):
       pass






     @unittest.skip(reason="""ConvNextV2 does not support input and output embeddings"""  )
     def    __A  (		self  ):
       pass






     @unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""  )
     def    __A  (		self  ):
       pass






     def    __A  (		self  ):
       if not self.model_tester.is_training:
         return

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:	int        =					self.model_tester.prepare_config_and_inputs_with_labels()
         _lowerCAmelCase						:	Tuple        =					True

         if model_class.__name__ in [
             *get_values(a__  ),
             *get_values(a__  ),
         ]:
           continue

         _lowerCAmelCase						:	Optional[Any]        =					model_class(a__  )
         model.to(a__  )
         model.train()
         _lowerCAmelCase						:	str        =					self._prepare_for_class(a__    ,					a__    ,					return_labels=a__  )
         _lowerCAmelCase						:	Optional[int]        =					model(**a__  ).loss
         loss.backward()






     def    __A  (		self  ):
       if not self.model_tester.is_training:
         return

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:	Optional[Any]        =					self.model_tester.prepare_config_and_inputs_with_labels()
         _lowerCAmelCase						:	int        =					False
         _lowerCAmelCase						:	List[Any]        =					True

         if (
             model_class.__name__
             in [*get_values(a__  ), *get_values(a__  )]
             or not model_class.supports_gradient_checkpointing
         ):
           continue

         _lowerCAmelCase						:	Tuple        =					model_class(a__  )
         model.to(a__  )
         model.gradient_checkpointing_enable()
         model.train()
         _lowerCAmelCase						:	Dict        =					self._prepare_for_class(a__    ,					a__    ,					return_labels=a__  )
         _lowerCAmelCase						:	Optional[int]        =					model(**a__  ).loss
         loss.backward()






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:	Tuple        =					model_class(a__  )
         _lowerCAmelCase						:	Optional[int]        =					inspect.signature(model.forward  )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCAmelCase						:	Union[str, Any]        =					[*signature.parameters.keys()]

         _lowerCAmelCase						:	Dict        =					["""pixel_values"""]
         self.assertListEqual(arg_names[:1]    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	int        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__  )






     def    __A  (		self  ):
       def check_hidden_states_output(a__    ,					a__    ,					a__  ):
         _lowerCAmelCase						:	Tuple        =					model_class(a__  )
         model.to(a__  )
         model.eval()

         with torch.no_grad():
           _lowerCAmelCase						:	Tuple        =					model(**self._prepare_for_class(a__    ,					a__  )  )

         _lowerCAmelCase						:	List[Any]        =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         _lowerCAmelCase						:	Tuple        =					self.model_tester.num_stages
         self.assertEqual(len(a__  )    ,					expected_num_stages + 1  )

         # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]  )    ,					[self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,					)

       _lowerCAmelCase						:	List[Any]        =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:	int        =					True
         check_hidden_states_output(a__    ,					a__    ,					a__  )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowerCAmelCase						:	List[str]        =					True

         check_hidden_states_output(a__    ,					a__    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Tuple        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a__  )






     @slow
     def    __A  (		self  ):
       for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:	Dict        =					ConvNextVaModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )







def 							SCREAMING_SNAKE_CASE					(      )	->				Any:
  _lowerCAmelCase						:	List[Any]        =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
  return image






@require_torch
@require_vision
class 						__A							(			unittest.TestCase		):






     @cached_property
     def    __A  (		self  ):
       return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224"""  ) if is_vision_available() else None






     @slow
     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224"""  ).to(a__  )

       _lowerCAmelCase						:	int        =					self.default_image_processor
       _lowerCAmelCase						:	Dict        =					prepare_img()
       _lowerCAmelCase						:	Dict        =					preprocessor(images=a__    ,					return_tensors="""pt"""  ).to(a__  )

       # forward pass
       with torch.no_grad():
         _lowerCAmelCase						:	int        =					model(**a__  )

       # verify the logits
       _lowerCAmelCase						:	Union[str, Any]        =					torch.Size((1, 1000)  )
       self.assertEqual(outputs.logits.shape    ,					a__  )

       _lowerCAmelCase						:	int        =					torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6]  ).to(a__  )
       self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,					a__    ,					atol=1e-4  )  )





"""simple docstring"""




import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


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





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Path      ,_lowerCamelCase						:	list     )	->				str:
  _lowerCAmelCase						:	List[Any]        =					"""\n""".join(_lowerCamelCase     )
  Path(_lowerCamelCase     ).open("""w"""     ).writelines(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]     )	->				Union[str, Any]:
  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							(			SCREAMING_SNAKE_CASE_		):






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

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

       _lowerCAmelCase						:	Dict        =					DataLoader(a__    ,					batch_size=a__    ,					collate_fn=ds.collate_fn    ,					num_workers=2  )
       _lowerCAmelCase						:	int        =					DataLoader(a__    ,					batch_size=a__    ,					collate_fn=ds.collate_fn    ,					num_workers=2    ,					sampler=a__  )

       _lowerCAmelCase						:	int        =					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    __A  (		self    ,					a__=1000    ,					a__=128  ):
       if os.getenv("""USE_REAL_DATA"""    ,					a__  ):
         _lowerCAmelCase						:	List[str]        =					"""examples/seq2seq/wmt_en_ro"""
         _lowerCAmelCase						:	str        =					max_len * 2 * 64
         if not Path(a__  ).joinpath("""train.len"""  ).exists():
           save_len_file(a__    ,					a__  )
       else:
         _lowerCAmelCase						:	List[str]        =					"""examples/seq2seq/test_data/wmt_en_ro"""
         _lowerCAmelCase						:	Dict        =					max_len * 4
         save_len_file(a__    ,					a__  )

       _lowerCAmelCase						:	Optional[Any]        =					AutoTokenizer.from_pretrained(a__  )
       _lowerCAmelCase						:	Dict        =					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    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Union[str, Any]        =					self._get_dataset()
       _lowerCAmelCase						:	Any        =					set(DistributedSortishSampler(a__    ,					256    ,					num_replicas=2    ,					rank=0    ,					add_extra_examples=a__  )  )
       _lowerCAmelCase						:	Optional[int]        =					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    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	int        =					AutoTokenizer.from_pretrained(a__    ,					use_fast=a__  )
       if tok_name == MBART_TINY:
         _lowerCAmelCase						:	Dict        =					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"""    ,					)
         _lowerCAmelCase						:	Optional[Any]        =					train_dataset.dataset_kwargs
         assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
         _lowerCAmelCase						:	List[Any]        =					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    ,					)
         _lowerCAmelCase						:	Tuple        =					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




