


import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
			import torch
			from torch import nn

			from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
			from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import DPTImageProcessor

class        a	:




							"""simple docstring"""


							def __init__(							self :						Optional[int]					,			lowerCamelCase :						List[str]					,			lowerCamelCase :						List[str]=2					,			lowerCamelCase :						List[Any]=32					,			lowerCamelCase :						Dict=16					,			lowerCamelCase :						Union[str, Any]=3					,			lowerCamelCase :						Optional[int]=True					,			lowerCamelCase :						List[str]=True					,			lowerCamelCase :						Dict=32					,			lowerCamelCase :						Any=4					,			lowerCamelCase :						Tuple=[0, 1, 2, 3]					,			lowerCamelCase :						Optional[int]=4					,			lowerCamelCase :						Dict=37					,			lowerCamelCase :						Optional[Any]="gelu"					,			lowerCamelCase :						List[Any]=0.1					,			lowerCamelCase :						Tuple=0.1					,			lowerCamelCase :						Optional[int]=0.02					,			lowerCamelCase :						Optional[Any]=3					,			lowerCamelCase :						int=[1, 384, 24, 24]					,			lowerCamelCase :						List[Any]=True					,			lowerCamelCase :						Optional[int]=None					,			)			->					Tuple:
														__snake_case	:    Optional[Any]											=     parent
														__snake_case	:    List[Any]											=     batch_size
														__snake_case	:    Dict											=     image_size
														__snake_case	:    List[Any]											=     patch_size
														__snake_case	:    Any											=     num_channels
														__snake_case	:    List[str]											=     is_training
														__snake_case	:    Union[str, Any]											=     use_labels
														__snake_case	:    int											=     hidden_size
														__snake_case	:    Tuple											=     num_hidden_layers
														__snake_case	:    str											=     backbone_out_indices
														__snake_case	:    List[str]											=     num_attention_heads
														__snake_case	:    str											=     intermediate_size
														__snake_case	:    Dict											=     hidden_act
														__snake_case	:    List[Any]											=     hidden_dropout_prob
														__snake_case	:    Tuple											=     attention_probs_dropout_prob
														__snake_case	:    Union[str, Any]											=     initializer_range
														__snake_case	:    Any											=     num_labels
														__snake_case	:    Optional[int]											=     backbone_featmap_shape
														__snake_case	:    Dict											=     scope
														__snake_case	:    List[str]											=     is_hybrid
														# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
														__snake_case	:    Optional[int]											=     (image_size // patch_size) ** 2
														__snake_case	:    Union[str, Any]											=     num_patches + 1

							def 					__snake_case      (							self :						Any       )			->					Dict:
														__snake_case	:    Union[str, Any]											=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

														__snake_case	:    int											=     None
														if self.use_labels:
																					__snake_case	:    List[str]											=     ids_tensor([self.batch_size, self.image_size, self.image_size]					,			self.num_labels       )

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

														return config, pixel_values, labels

							def 					__snake_case      (							self :						Union[str, Any]       )			->					Dict:
														__snake_case	:    Union[str, Any]											=     {
														    "global_padding": "same",
														    "layer_type": "bottleneck",
														    "depths": [3, 4, 9],
														    "out_features": ["stage1", "stage2", "stage3"],
														    "embedding_dynamic_padding": True,
														    "hidden_sizes": [96, 192, 384, 768],
														    "num_groups": 2,
														}

														return DPTConfig(
														    image_size=self.image_size					,			patch_size=self.patch_size					,			num_channels=self.num_channels					,			hidden_size=self.hidden_size					,			num_hidden_layers=self.num_hidden_layers					,			backbone_out_indices=self.backbone_out_indices					,			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					,			is_decoder=lowerCamelCase					,			initializer_range=self.initializer_range					,			is_hybrid=self.is_hybrid					,			backbone_config=lowerCamelCase					,			backbone_featmap_shape=self.backbone_featmap_shape					,			)

							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						int					,			lowerCamelCase :						int					,			lowerCamelCase :						Optional[Any]       )			->					Tuple:
														__snake_case	:    Optional[Any]											=     DPTModel(config=lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														__snake_case	:    str											=     model(lowerCamelCase       )
														self.parent.assertEqual(result.last_hidden_state.shape					,			(self.batch_size, self.seq_length, self.hidden_size)       )

							def 					__snake_case      (							self :						str					,			lowerCamelCase :						int					,			lowerCamelCase :						Any					,			lowerCamelCase :						Any       )			->					Union[str, Any]:
														__snake_case	:    Tuple											=     self.num_labels
														__snake_case	:    str											=     DPTForDepthEstimation(lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														__snake_case	:    Union[str, Any]											=     model(lowerCamelCase       )
														self.parent.assertEqual(result.predicted_depth.shape					,			(self.batch_size, self.image_size, self.image_size)       )

							def 					__snake_case      (							self :						List[Any]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						Union[str, Any]					,			lowerCamelCase :						Optional[Any]       )			->					int:
														__snake_case	:    Optional[Any]											=     self.num_labels
														__snake_case	:    str											=     DPTForSemanticSegmentation(lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														__snake_case	:    int											=     model(lowerCamelCase					,			labels=lowerCamelCase       )
														self.parent.assertEqual(
														    result.logits.shape					,			(self.batch_size, self.num_labels, self.image_size, self.image_size)       )






							def 					__snake_case      (							self :						List[str]       )			->					str:
														__snake_case	:    List[str]											=     self.prepare_config_and_inputs()
														__snake_case	:    Union[str, Any]											=     config_and_inputs
														__snake_case	:    List[Any]											=     {"pixel_values": pixel_values}
														return config, inputs_dict

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




							"""simple docstring"""


							__UpperCAmelCase					:							List[Any]  						=						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							__UpperCAmelCase					:							int  						=						(
							    {
							        "depth-estimation": DPTForDepthEstimation,
							        "feature-extraction": DPTModel,
							        "image-segmentation": DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							__UpperCAmelCase					:							int  						=						False
							__UpperCAmelCase					:							Tuple  						=						False
							__UpperCAmelCase					:							Tuple  						=						False

							def 					__snake_case      (							self :						Dict       )			->					Dict:
														__snake_case	:    int											=     DPTModelTester(self       )
														__snake_case	:    Dict											=     ConfigTester(self					,			config_class=lowerCamelCase					,			has_text_modality=lowerCamelCase					,			hidden_size=37       )

							def 					__snake_case      (							self :						Optional[Any]       )			->					Union[str, Any]:
														self.config_tester.run_common_tests()

							@unittest.skip(reason="DPT does not use inputs_embeds"       )
							def 					__snake_case      (							self :						str       )			->					int:
														pass

							def 					__snake_case      (							self :						Any       )			->					str:
														__snake_case	:    Tuple											=     self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					__snake_case	:    Union[str, Any]											=     model_class(lowerCamelCase       )
																					self.assertIsInstance(model.get_input_embeddings()					,			(nn.Module)       )
																					__snake_case	:    Dict											=     model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(lowerCamelCase					,			nn.Linear       )       )

							def 					__snake_case      (							self :						Tuple       )			->					List[str]:
														__snake_case	:    Optional[int]											=     self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					__snake_case	:    Any											=     model_class(lowerCamelCase       )
																					__snake_case	:    str											=     inspect.signature(model.forward       )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					__snake_case	:    List[str]											=     [*signature.parameters.keys()]

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

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

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

							def 					__snake_case      (							self :						str       )			->					Optional[Any]:
														__snake_case	:    str											=     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase       )

							def 					__snake_case      (							self :						Optional[Any]       )			->					Optional[Any]:
														for model_class in self.all_model_classes:
																					if model_class.__name__ == "DPTForDepthEstimation":
																												continue

																					__snake_case	:    List[Any]											=     self.model_tester.prepare_config_and_inputs_for_common()
																					__snake_case	:    int											=     True

																					if model_class in get_values(lowerCamelCase       ):
																												continue

																					__snake_case	:    int											=     model_class(lowerCamelCase       )
																					model.to(lowerCamelCase       )
																					model.train()
																					__snake_case	:    Dict											=     self._prepare_for_class(lowerCamelCase					,			lowerCamelCase					,			return_labels=lowerCamelCase       )
																					__snake_case	:    Dict											=     model(**lowerCamelCase       ).loss
																					loss.backward()

							def 					__snake_case      (							self :						str       )			->					Optional[int]:
														for model_class in self.all_model_classes:
																					if model_class.__name__ == "DPTForDepthEstimation":
																												continue

																					__snake_case	:    List[str]											=     self.model_tester.prepare_config_and_inputs_for_common()
																					__snake_case	:    int											=     False
																					__snake_case	:    Optional[int]											=     True

																					if model_class in get_values(lowerCamelCase       ) or not model_class.supports_gradient_checkpointing:
																												continue
																					__snake_case	:    str											=     model_class(lowerCamelCase       )
																					model.to(lowerCamelCase       )
																					model.gradient_checkpointing_enable()
																					model.train()
																					__snake_case	:    Optional[Any]											=     self._prepare_for_class(lowerCamelCase					,			lowerCamelCase					,			return_labels=lowerCamelCase       )
																					__snake_case	:    str											=     model(**lowerCamelCase       ).loss
																					loss.backward()

							def 					__snake_case      (							self :						Dict       )			->					Dict:
														__snake_case	:    List[Any]											=     self.model_tester.prepare_config_and_inputs_for_common()

														__snake_case	:    Optional[int]											=     _config_zero_init(lowerCamelCase       )
														for model_class in self.all_model_classes:
																					__snake_case	:    int											=     model_class(config=lowerCamelCase       )
																					# Skip the check for the backbone
																					__snake_case	:    Optional[Any]											=     []
																					for name, module in model.named_modules():
																												if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																																			__snake_case	:    Optional[Any]											=     [F'{name}.{key}' for key in module.state_dict().keys()]
																																			break

																					for name, param in model.named_parameters():
																												if param.requires_grad:
																																			if name in backbone_params:
																																										continue
																																			self.assertIn(
																																			    ((param.data.mean() * 1E9).round() / 1E9).item()					,			[0.0, 1.0]					,			msg=F'Parameter {name} of model {model_class} seems not properly initialized'					,			)

							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
							def 					__snake_case      (							self :						Any       )			->					Dict:
														pass

							@slow
							def 					__snake_case      (							self :						List[str]       )			->					Any:
														for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
																					__snake_case	:    Any											=     DPTModel.from_pretrained(lowerCamelCase       )
																					self.assertIsNotNone(lowerCamelCase       )






							def 					__snake_case      (							self :						Optional[Any]       )			->					str:
														# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
														__snake_case	:    List[str]											=     self.model_tester.prepare_config_and_inputs_for_common()
														__snake_case	:    Optional[int]											=     "add"
														with self.assertRaises(lowerCamelCase       ):
																					__snake_case	:    Optional[Any]											=     DPTForDepthEstimation(lowerCamelCase       )







def        lowerCAmelCase_  (      ):
							__snake_case	:    Any											=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
							return image

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




							"""simple docstring"""


							def 					__snake_case      (							self :						Optional[int]       )			->					Tuple:
														__snake_case	:    Tuple											=     DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"       )
														__snake_case	:    List[str]											=     DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"       ).to(lowerCamelCase       )

														__snake_case	:    List[str]											=     prepare_img()
														__snake_case	:    List[str]											=     image_processor(images=lowerCamelCase					,			return_tensors="pt"       ).to(lowerCamelCase       )

														# forward pass
														with torch.no_grad():
																					__snake_case	:    List[str]											=     model(**lowerCamelCase       )
																					__snake_case	:    Union[str, Any]											=     outputs.predicted_depth

														# verify the predicted depth
														__snake_case	:    List[str]											=     torch.Size((1, 384, 384)       )
														self.assertEqual(predicted_depth.shape					,			lowerCamelCase       )

														__snake_case	:    List[str]											=     torch.tensor(
														    [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]       ).to(lowerCamelCase       )

														self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100					,			lowerCamelCase					,			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


_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

