




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFResNetForImageClassification, TFResNetModel
	from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor
class        __snake_case  :



				def __init__(      self      :		str		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		List[str]=3		,	_UpperCAmelCase      :		List[str]=32		,	_UpperCAmelCase      :		str=3		,	_UpperCAmelCase      :		Tuple=10		,	_UpperCAmelCase      :		Optional[Any]=[10, 20, 30, 40]		,	_UpperCAmelCase      :		int=[1, 1, 2, 1]		,	_UpperCAmelCase      :		str=True		,	_UpperCAmelCase      :		List[str]=True		,	_UpperCAmelCase      :		str="relu"		,	_UpperCAmelCase      :		int=3		,	_UpperCAmelCase      :		int=None		,	)  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      parent
								_lowerCAmelCase    :    int										=      batch_size
								_lowerCAmelCase    :    List[str]										=      image_size
								_lowerCAmelCase    :    Optional[int]										=      num_channels
								_lowerCAmelCase    :    List[str]										=      embeddings_size
								_lowerCAmelCase    :    int										=      hidden_sizes
								_lowerCAmelCase    :    str										=      depths
								_lowerCAmelCase    :    int										=      is_training
								_lowerCAmelCase    :    Dict										=      use_labels
								_lowerCAmelCase    :    Dict										=      hidden_act
								_lowerCAmelCase    :    Dict										=      num_labels
								_lowerCAmelCase    :    List[Any]										=      scope
								_lowerCAmelCase    :    Optional[int]										=      len(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    int:





								'''simple docstring'''




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

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

								_lowerCAmelCase    :    Dict										=      self.get_config()

								return config, pixel_values, labels



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    int:





								'''simple docstring'''




								return ResNetConfig(
								    num_channels=self.num_channels		,	embeddings_size=self.embeddings_size		,	hidden_sizes=self.hidden_sizes		,	depths=self.depths		,	hidden_act=self.hidden_act		,	num_labels=self.num_labels		,	image_size=self.image_size		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      TFResNetModel(config=_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      model(_UpperCAmelCase      )
								# 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       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Optional[Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.num_labels
								_lowerCAmelCase    :    Any										=      TFResNetForImageClassification(_UpperCAmelCase      )
								_lowerCAmelCase    :    str										=      model(_UpperCAmelCase		,	labels=_UpperCAmelCase      )
								self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      self.prepare_config_and_inputs()
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      config_and_inputs
								_lowerCAmelCase    :    Tuple										=      {"""pixel_values""": pixel_values}
								return config, inputs_dict






@require_tf
class        __snake_case  (_a						,	_a						,	unittest.TestCase		):
				lowerCAmelCase__				=     (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
				lowerCAmelCase__				=     (
				    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
				    if is_tf_available()
				    else {}
				)

				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      TFResNetModelTester(self      )
								_lowerCAmelCase    :    Union[str, Any]										=      ConfigTester(self		,	config_class=_UpperCAmelCase		,	has_text_modality=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''simple docstring'''




								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       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Union[str, Any]:





								'''simple docstring'''




								return



				@unittest.skip(reason="""ResNet does not use inputs_embeds"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								pass



				@unittest.skip(reason="""ResNet does not support input and output embeddings"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Union[str, Any]:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    Dict										=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												_lowerCAmelCase    :    Any										=      model_class(_UpperCAmelCase      )
												_lowerCAmelCase    :    Optional[Any]										=      inspect.signature(model.call      )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												_lowerCAmelCase    :    Tuple										=      [*signature.parameters.keys()]

												_lowerCAmelCase    :    Optional[Any]										=      ["""pixel_values"""]
												self.assertListEqual(arg_names[:1]		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    str:





								'''simple docstring'''




								def check_hidden_states_output(_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Tuple      ):
												_lowerCAmelCase    :    Dict										=      model_class(_UpperCAmelCase      )
												_lowerCAmelCase    :    Optional[int]										=      model(**self._prepare_for_class(_UpperCAmelCase		,	_UpperCAmelCase      )      )

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

												_lowerCAmelCase    :    Dict										=      self.model_tester.num_stages
												self.assertEqual(len(_UpperCAmelCase      )		,	expected_num_stages + 1      )

												# ResNet'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					,    _lowerCAmelCase    :    Any										=      self.model_tester.prepare_config_and_inputs_for_common()
								_lowerCAmelCase    :    str										=      ["""basic""", """bottleneck"""]
								for model_class in self.all_model_classes:
												for layer_type in layers_type:
																_lowerCAmelCase    :    Tuple										=      layer_type
																_lowerCAmelCase    :    Any										=      True
																check_hidden_states_output(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )

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

																check_hidden_states_output(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase      )



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    str:





								'''simple docstring'''




								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_lowerCAmelCase    :    Dict										=      TFResNetModel.from_pretrained(_UpperCAmelCase      )
												self.assertIsNotNone(_UpperCAmelCase      )






def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[int]										=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
				return image





@require_tf
@require_vision
class        __snake_case  (unittest.TestCase		):



				@cached_property
				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''simple docstring'''




								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
								    if is_vision_available()
								    else None
								)



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )

								_lowerCAmelCase    :    Optional[int]										=      self.default_image_processor
								_lowerCAmelCase    :    int										=      prepare_img()
								_lowerCAmelCase    :    int										=      image_processor(images=_UpperCAmelCase		,	return_tensors="""tf"""      )

								# forward pass
								_lowerCAmelCase    :    int										=      model(**_UpperCAmelCase      )

								# verify the logits
								_lowerCAmelCase    :    Union[str, Any]										=      tf.TensorShape((1, 1000)      )
								self.assertEqual(outputs.logits.shape		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    Any										=      tf.constant([-11.1069, -9.7877, -8.3777]      )

								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()		,	_UpperCAmelCase		,	atol=1E-4      )      )







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


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




def 							_UpperCAmelCase  (UpperCamelCase_ : Path      ,    UpperCamelCase_ : list       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Union[str, Any]										=      """\n""".join(UpperCamelCase_       )
				Path(UpperCamelCase_       ).open("""w"""       ).writelines(UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(UpperCamelCase_      ,    F"{split}.source"       )      ,    UpperCamelCase_       )
								_dump_articles(os.path.join(UpperCamelCase_      ,    F"{split}.target"       )      ,    UpperCamelCase_       )
				return tmp_dir





class        __snake_case  (_a		):



				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]		,	)
				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
								_lowerCAmelCase    :    Any										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in ARTICLES      )
								_lowerCAmelCase    :    Dict										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in SUMMARIES      )
								_lowerCAmelCase    :    List[str]										=      4
								_lowerCAmelCase    :    List[Any]										=      8
								assert max_len_target > max_src_len  # Will be truncated
								assert max_len_source > max_src_len  # Will be truncated
								_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
								_lowerCAmelCase    :    Tuple										=      SeqaSeqDataset(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=_UpperCAmelCase		,	max_target_length=_UpperCAmelCase		,	src_lang=_UpperCAmelCase		,	tgt_lang=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    int										=      DataLoader(_UpperCAmelCase		,	batch_size=2		,	collate_fn=train_dataset.collate_fn      )
								for batch in dataloader:
												assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )
												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    :    Dict										=      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      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    str										=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
								_lowerCAmelCase    :    Optional[int]										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in ARTICLES      )
								_lowerCAmelCase    :    str										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in SUMMARIES      )
								_lowerCAmelCase    :    Union[str, Any]										=      4
								_lowerCAmelCase    :    Dict										=      LegacySeqaSeqDataset(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=20		,	max_target_length=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    str										=      DataLoader(_UpperCAmelCase		,	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      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""      )

								_lowerCAmelCase    :    List[str]										=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
								_lowerCAmelCase    :    List[Any]										=      tmp_dir.joinpath("""train.source"""      ).open().readlines()
								_lowerCAmelCase    :    Tuple										=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
								pack_data_dir(_UpperCAmelCase		,	_UpperCAmelCase		,	128		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      {x.name for x in tmp_dir.iterdir()}
								_lowerCAmelCase    :    List[Any]										=      {x.name for x in save_dir.iterdir()}
								_lowerCAmelCase    :    Optional[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(_UpperCAmelCase      ) < len(_UpperCAmelCase      )
								assert len(_UpperCAmelCase      ) == 1
								assert len(packed_examples[0]      ) == sum(len(_UpperCAmelCase      ) 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      :		Any      )  ->    str:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    Union[str, Any]										=      self._get_dataset(max_len=512      )
								_lowerCAmelCase    :    List[Any]										=      2
								_lowerCAmelCase    :    Tuple										=      ds.make_sortish_sampler(_UpperCAmelCase		,	shuffle=_UpperCAmelCase      )

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

								_lowerCAmelCase    :    Dict										=      tokenizer.pad_token_id

								def count_pad_tokens(_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Union[str, Any]="input_ids"      ):
												return [batch[k].eq(_UpperCAmelCase      ).sum().item() for batch in data_loader]

								assert sum(count_pad_tokens(_UpperCAmelCase		,	k="""labels"""      )      ) < sum(count_pad_tokens(_UpperCAmelCase		,	k="""labels"""      )      )
								assert sum(count_pad_tokens(_UpperCAmelCase      )      ) < sum(count_pad_tokens(_UpperCAmelCase      )      )
								assert len(_UpperCAmelCase      ) == len(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		str=1000		,	_UpperCAmelCase      :		Dict=128      )  ->    Dict:





								'''simple docstring'''




								if os.getenv("""USE_REAL_DATA"""		,	_UpperCAmelCase      ):
												_lowerCAmelCase    :    Optional[int]										=      """examples/seq2seq/wmt_en_ro"""
												_lowerCAmelCase    :    Optional[int]										=      max_len * 2 * 64
												if not Path(_UpperCAmelCase      ).joinpath("""train.len"""      ).exists():
																save_len_file(_UpperCAmelCase		,	_UpperCAmelCase      )
								else:
												_lowerCAmelCase    :    int										=      """examples/seq2seq/test_data/wmt_en_ro"""
												_lowerCAmelCase    :    int										=      max_len * 4
												save_len_file(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      SeqaSeqDataset(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=_UpperCAmelCase		,	max_target_length=_UpperCAmelCase		,	n_obs=_UpperCAmelCase		,	)
								return ds, max_tokens, tokenizer



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      self._get_dataset()
								_lowerCAmelCase    :    Optional[Any]										=      set(DistributedSortishSampler(_UpperCAmelCase		,	256		,	num_replicas=2		,	rank=0		,	add_extra_examples=_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Any										=      set(DistributedSortishSampler(_UpperCAmelCase		,	256		,	num_replicas=2		,	rank=1		,	add_extra_examples=_UpperCAmelCase      )      )
								assert idsa.intersection(_UpperCAmelCase      ) == set()



				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]		,	)
				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      AutoTokenizer.from_pretrained(_UpperCAmelCase		,	use_fast=_UpperCAmelCase      )
								if tok_name == MBART_TINY:
												_lowerCAmelCase    :    Optional[Any]										=      SeqaSeqDataset(
												    _UpperCAmelCase		,	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    :    Tuple										=      train_dataset.dataset_kwargs
												assert "src_lang" in kwargs and "tgt_lang" in kwargs
								else:
												_lowerCAmelCase    :    Dict										=      SeqaSeqDataset(
												    _UpperCAmelCase		,	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    :    Optional[Any]										=      train_dataset.dataset_kwargs
												assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
												assert len(_UpperCAmelCase      ) == 1 if tok_name == BART_TINY else len(_UpperCAmelCase      ) == 0

