

"""simple docstring"""





import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

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

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


_a							=							1e-4

if is_torch_available():
   import torch

   from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
   from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder




@require_torch
class  _lowerCAmelCase  :







 """simple docstring"""







 def __init__(      self							:      int, UpperCAmelCase__							:      Tuple, UpperCAmelCase__							:      Tuple=1_6, UpperCAmelCase__							:      Optional[Any]=1_3, UpperCAmelCase__							:      List[Any]=7, UpperCAmelCase__							:      List[Any]=1_4, UpperCAmelCase__							:      Dict=1_0, UpperCAmelCase__							:      Dict=1_9, UpperCAmelCase__							:      List[Any]=5, UpperCAmelCase__							:      Optional[int]=4, UpperCAmelCase__							:      Union[str, Any]=True, UpperCAmelCase__							:      int=1_6, UpperCAmelCase__							:      Dict=2, UpperCAmelCase__							:      Union[str, Any]=4, UpperCAmelCase__							:      List[Any]=4, UpperCAmelCase__							:      int="gelu", UpperCAmelCase__							:      Tuple=0.1, UpperCAmelCase__							:      int=0.1, UpperCAmelCase__							:      Union[str, Any]=[1, 2, 3, 4, 5], UpperCAmelCase__							:      Optional[int]=2_5, UpperCAmelCase__							:      Tuple=5, ):
    __lowercase								=						d_model
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						prediction_length
    __lowercase								=						context_length
    __lowercase								=						cardinality
    __lowercase								=						num_time_features
    __lowercase								=						lags_sequence
    __lowercase								=						embedding_dimension
    __lowercase								=						is_training
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_act
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob

    __lowercase								=						context_length
    __lowercase								=						prediction_length + label_length
    __lowercase								=						label_length

    __lowercase								=						moving_average
    __lowercase								=						autocorrelation_factor


 def  _lowercase			(      self							:      List[Any]     ):
    return AutoformerConfig(
        d_model=self.d_model, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, prediction_length=self.prediction_length, context_length=self.context_length, label_length=self.label_length, lags_sequence=self.lags_sequence, num_time_features=self.num_time_features, num_static_categorical_features=1, cardinality=[self.cardinality], embedding_dimension=[self.embedding_dimension], moving_average=self.moving_average, )


 def  _lowercase			(      self							:      str, UpperCAmelCase__							:      int     ):
    __lowercase								=						config.context_length + max(config.lags_sequence     )

    __lowercase								=						ids_tensor([self.batch_size, 1], config.cardinality[0]     )
    __lowercase								=						floats_tensor([self.batch_size, _past_length, config.num_time_features]     )
    __lowercase								=						floats_tensor([self.batch_size, _past_length]     )
    __lowercase								=						floats_tensor([self.batch_size, _past_length]     ) > 0.5

    # decoder inputs
    __lowercase								=						floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]     )
    __lowercase								=						floats_tensor([self.batch_size, config.prediction_length]     )

    __lowercase								=						{
        "past_values": past_values,
        "static_categorical_features": static_categorical_features,
        "past_time_features": past_time_features,
        "past_observed_mask": past_observed_mask,
        "future_time_features": future_time_features,
        "future_values": future_values,
    }
    return inputs_dict


 def  _lowercase			(      self							:      List[Any]     ):
    __lowercase								=						self.get_config()
    __lowercase								=						self.prepare_autoformer_inputs_dict(UpperCAmelCase__     )
    return config, inputs_dict


 def  _lowercase			(      self							:      Tuple     ):
    __lowercase		,__lowercase								=						self.prepare_config_and_inputs()
    return config, inputs_dict



 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      int, UpperCAmelCase__							:      Union[str, Any]     ):
    __lowercase								=						AutoformerModel(config=UpperCAmelCase__     ).to(UpperCAmelCase__     ).eval()
    __lowercase								=						model(**UpperCAmelCase__     )

    __lowercase								=						outputs.encoder_last_hidden_state
    __lowercase								=						outputs.last_hidden_state

    with tempfile.TemporaryDirectory() as tmpdirname:
       __lowercase								=						model.get_encoder()
       encoder.save_pretrained(UpperCAmelCase__     )
       __lowercase								=						AutoformerEncoder.from_pretrained(UpperCAmelCase__     ).to(UpperCAmelCase__     )

    __lowercase		,__lowercase		,__lowercase		,__lowercase		,__lowercase								=						model.create_network_inputs(**UpperCAmelCase__     )
    __lowercase		,__lowercase								=						model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]     )

    __lowercase								=						torch.cat(
        (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]), dim=-1, )
    __lowercase								=						encoder(inputs_embeds=UpperCAmelCase__     )[0]
    self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3     )

    __lowercase								=						(
        torch.mean(transformer_inputs[:, : config.context_length, ...], dim=1     )
        .unsqueeze(1     )
        .repeat(1, config.prediction_length, 1     )
    )
    __lowercase								=						torch.zeros(
        [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]], device=enc_input.device, )

    __lowercase								=						torch.cat(
        (
            torch.cat((seasonal_input[:, -config.label_length :, ...], zeros), dim=1     ),
            feature[:, config.context_length - config.label_length :, ...],
        ), dim=-1, )
    __lowercase								=						torch.cat(
        (
            torch.cat((trend_input[:, -config.label_length :, ...], mean), dim=1     ),
            feature[:, config.context_length - config.label_length :, ...],
        ), dim=-1, )

    with tempfile.TemporaryDirectory() as tmpdirname:
       __lowercase								=						model.get_decoder()
       decoder.save_pretrained(UpperCAmelCase__     )
       __lowercase								=						AutoformerDecoder.from_pretrained(UpperCAmelCase__     ).to(UpperCAmelCase__     )

    __lowercase								=						decoder(
        trend=UpperCAmelCase__, inputs_embeds=UpperCAmelCase__, encoder_hidden_states=UpperCAmelCase__, )[0]

    self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3     )







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







 """simple docstring"""







 __UpperCAmelCase     : str       		=     (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
 __UpperCAmelCase     : List[str]       		=     (AutoformerForPrediction,) if is_torch_available() else ()
 __UpperCAmelCase     : Dict       		=     {"feature-extraction": AutoformerModel} if is_torch_available() else {}
 __UpperCAmelCase     : Tuple       		=     False
 __UpperCAmelCase     : Optional[int]       		=     False
 __UpperCAmelCase     : int       		=     False
 __UpperCAmelCase     : Dict       		=     False
 __UpperCAmelCase     : Optional[int]       		=     False
 __UpperCAmelCase     : Any       		=     False


 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						AutoformerModelTester(self     )
    __lowercase								=						ConfigTester(self, config_class=UpperCAmelCase__, has_text_modality=UpperCAmelCase__     )


 def  _lowercase			(      self							:      List[Any]     ):
    self.config_tester.run_common_tests()


 def  _lowercase			(      self							:      List[Any]     ):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
       __lowercase								=						model_class(UpperCAmelCase__     )

       with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(UpperCAmelCase__     )
          __lowercase		,__lowercase								=						model_class.from_pretrained(UpperCAmelCase__, output_loading_info=UpperCAmelCase__     )
       self.assertEqual(info["missing_keys"], []     )


 def  _lowercase			(      self							:      str     ):
    __lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_encoder_decoder_model_standalone(*UpperCAmelCase__     )


 @unittest.skip(reason="Model has no tokens embeddings"     )
 def  _lowercase			(      self							:      int     ):
    pass


 def  _lowercase			(      self							:      str     ):
    __lowercase								=						inspect.signature(getattr(UpperCAmelCase__, "forward"     )     )
    # The main input is the name of the argument after `self`
    __lowercase								=						list(model_signature.parameters.keys()     )[1]
    self.assertEqual(AutoformerModel.main_input_name, UpperCAmelCase__     )


 def  _lowercase			(      self							:      Tuple     ):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowercase								=						[
           "past_values",
           "past_time_features",
           "past_observed_mask",
           "static_categorical_features",
           "static_real_features",
           "future_values",
           "future_time_features",
       ]

       if model.__class__.__name__ in ["AutoformerForPrediction"]:
          expected_arg_names.append("future_observed_mask"     )

       expected_arg_names.extend(
           [
               "decoder_attention_mask",
               "head_mask",
               "decoder_head_mask",
               "cross_attn_head_mask",
               "encoder_outputs",
               "past_key_values",
               "output_hidden_states",
               "output_attentions",
               "use_cache",
               "return_dict",
           ]     )

       self.assertListEqual(arg_names[: len(UpperCAmelCase__     )], UpperCAmelCase__     )


 def  _lowercase			(      self							:      Any     ):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()
    __lowercase								=						True

    __lowercase								=						getattr(self.model_tester, "seq_length", UpperCAmelCase__     )
    __lowercase								=						getattr(self.model_tester, "decoder_seq_length", UpperCAmelCase__     )
    __lowercase								=						getattr(self.model_tester, "encoder_seq_length", UpperCAmelCase__     )
    __lowercase								=						getattr(self.model_tester, "d_model", UpperCAmelCase__     )
    __lowercase								=						getattr(self.model_tester, "num_attention_heads", UpperCAmelCase__     )
    __lowercase								=						d_model // num_attention_heads

    for model_class in self.all_model_classes:
       __lowercase								=						True
       __lowercase								=						False
       __lowercase								=						True
       __lowercase								=						model_class(UpperCAmelCase__     )
       model.to(UpperCAmelCase__     )
       model.eval()
       with torch.no_grad():
          __lowercase								=						model(**self._prepare_for_class(UpperCAmelCase__, UpperCAmelCase__     )     )
       __lowercase								=						outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
       self.assertEqual(len(UpperCAmelCase__     ), self.model_tester.num_hidden_layers     )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       __lowercase								=						True
       __lowercase								=						model_class(UpperCAmelCase__     )
       model.to(UpperCAmelCase__     )
       model.eval()
       with torch.no_grad():
          __lowercase								=						model(**self._prepare_for_class(UpperCAmelCase__, UpperCAmelCase__     )     )
       __lowercase								=						outputs.encoder_attentions
       self.assertEqual(len(UpperCAmelCase__     ), self.model_tester.num_hidden_layers     )

       self.assertListEqual(
           list(attentions[0].shape[-3:]     ), [self.model_tester.num_attention_heads, encoder_seq_length, dim], )
       __lowercase								=						len(UpperCAmelCase__     )

       __lowercase								=						7

       if "last_hidden_state" in outputs:
          correct_outlen += 1

       if "trend" in outputs:
          correct_outlen += 1

       if "past_key_values" in outputs:
          correct_outlen += 1  # past_key_values have been returned

       if "loss" in outputs:
          correct_outlen += 1

       if "params" in outputs:
          correct_outlen += 1

       self.assertEqual(UpperCAmelCase__, UpperCAmelCase__     )

       # decoder attentions
       __lowercase								=						outputs.decoder_attentions
       self.assertIsInstance(UpperCAmelCase__, (list, tuple)     )
       self.assertEqual(len(UpperCAmelCase__     ), self.model_tester.num_hidden_layers     )
       self.assertListEqual(
           list(decoder_attentions[0].shape[-3:]     ), [self.model_tester.num_attention_heads, decoder_seq_length, dim], )

       # cross attentions
       __lowercase								=						outputs.cross_attentions
       self.assertIsInstance(UpperCAmelCase__, (list, tuple)     )
       self.assertEqual(len(UpperCAmelCase__     ), self.model_tester.num_hidden_layers     )
       self.assertListEqual(
           list(cross_attentions[0].shape[-3:]     ), [self.model_tester.num_attention_heads, decoder_seq_length, dim], )

    # Check attention is always last and order is fine
    __lowercase								=						True
    __lowercase								=						True
    __lowercase								=						model_class(UpperCAmelCase__     )
    model.to(UpperCAmelCase__     )
    model.eval()
    with torch.no_grad():
       __lowercase								=						model(**self._prepare_for_class(UpperCAmelCase__, UpperCAmelCase__     )     )

    self.assertEqual(out_len + 2, len(UpperCAmelCase__     )     )

    __lowercase								=						outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

    self.assertEqual(len(UpperCAmelCase__     ), self.model_tester.num_hidden_layers     )
    self.assertListEqual(
        list(self_attentions[0].shape[-3:]     ), [self.model_tester.num_attention_heads, encoder_seq_length, dim], )



 @is_flaky()
 def  _lowercase			(      self							:      Optional[int]     ):
    super().test_retain_grad_hidden_states_attentions()







def   _A  (  UpperCamelCase_					:	Dict="train-batch.pt")     ->						List[str]:
   '''simple docstring'''


   __lowercase								=						hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch",      filename=UpperCamelCase_,      repo_type="dataset")
   __lowercase								=						torch.load(UpperCamelCase_,      map_location=UpperCamelCase_)
   return batch




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







 """simple docstring"""







 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase								=						AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly"     ).to(UpperCAmelCase__     )
    __lowercase								=						prepare_batch()

    with torch.no_grad():
       __lowercase								=						model(
           past_values=batch["past_values"], past_time_features=batch["past_time_features"], past_observed_mask=batch["past_observed_mask"], static_categorical_features=batch["static_categorical_features"], future_values=batch["future_values"], future_time_features=batch["future_time_features"], )[0]

    __lowercase								=						torch.Size(
        (6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size)     )
    self.assertEqual(output.shape, UpperCAmelCase__     )

    __lowercase								=						torch.tensor(
        [[0.3_593, -1.3_398, 0.6_330], [0.2_279, 1.5_396, -0.1_792], [0.0_450, 1.3_225, -0.2_335]], device=UpperCAmelCase__     )
    self.assertTrue(torch.allclose(output[0, :3, :3], UpperCAmelCase__, atol=UpperCAmelCase__     )     )


 def  _lowercase			(      self							:      str     ):
    __lowercase								=						AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly"     ).to(UpperCAmelCase__     )
    __lowercase								=						prepare_batch("val-batch.pt"     )
    with torch.no_grad():
       __lowercase								=						model(
           past_values=batch["past_values"], past_time_features=batch["past_time_features"], past_observed_mask=batch["past_observed_mask"], static_categorical_features=batch["static_categorical_features"], ).encoder_last_hidden_state
    __lowercase								=						torch.Size((6_4, model.config.context_length, model.config.d_model)     )
    self.assertEqual(output.shape, UpperCAmelCase__     )

    __lowercase								=						torch.tensor(
        [[-0.0_734, -0.9_036, 0.8_358], [4.7_186, 2.4_113, 1.9_581], [1.7_953, 2.3_558, 1.2_970]], device=UpperCAmelCase__     )
    self.assertTrue(torch.allclose(output[0, :3, :3], UpperCAmelCase__, atol=UpperCAmelCase__     )     )



 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly"     ).to(UpperCAmelCase__     )
    __lowercase								=						prepare_batch("val-batch.pt"     )
    with torch.no_grad():
       __lowercase								=						model.generate(
           static_categorical_features=batch["static_categorical_features"], past_time_features=batch["past_time_features"], past_values=batch["past_values"], future_time_features=batch["future_time_features"], past_observed_mask=batch["past_observed_mask"], )
    __lowercase								=						torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length)     )
    self.assertEqual(outputs.sequences.shape, UpperCAmelCase__     )

    __lowercase								=						torch.tensor([3_130.6_763, 4_056.5_293, 7_053.0_786], device=UpperCAmelCase__     )
    __lowercase								=						outputs.sequences.mean(dim=1     )
    self.assertTrue(torch.allclose(mean_prediction[0, -3:], UpperCAmelCase__, rtol=1E-1     )     )







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




def   _A  (  UpperCamelCase_					:	Path,      UpperCamelCase_					:	list)     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						"\n".join(UpperCamelCase_)
   Path(UpperCamelCase_).open("w").writelines(UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	Union[str, Any])     ->						int:
   '''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  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ], )
 @slow
 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in ARTICLES     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in SUMMARIES     )
    __lowercase								=						4
    __lowercase								=						8
    assert max_len_target > max_src_len  # Will be truncated
    assert max_len_source > max_src_len  # Will be truncated
    __lowercase		,__lowercase								=						"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
    __lowercase								=						SeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=UpperCAmelCase__, max_target_length=UpperCAmelCase__, src_lang=UpperCAmelCase__, tgt_lang=UpperCAmelCase__, )
    __lowercase								=						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
       __lowercase								=						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  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      int     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in ARTICLES     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in SUMMARIES     )
    __lowercase								=						4
    __lowercase								=						LegacySeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=2_0, max_target_length=UpperCAmelCase__, )
    __lowercase								=						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 2_0 >= 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  _lowercase			(      self							:      str     ):
    __lowercase								=						AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"     )

    __lowercase								=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
    __lowercase								=						tmp_dir.joinpath("train.source"     ).open().readlines()
    __lowercase								=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
    pack_data_dir(UpperCAmelCase__, UpperCAmelCase__, 1_2_8, UpperCAmelCase__     )
    __lowercase								=						{x.name for x in tmp_dir.iterdir()}
    __lowercase								=						{x.name for x in save_dir.iterdir()}
    __lowercase								=						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  _lowercase			(      self							:      List[str]     ):
    if not FAIRSEQ_AVAILABLE:
       return
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset(max_len=6_4     )
    __lowercase								=						6_4
    __lowercase								=						ds.make_dynamic_sampler(UpperCAmelCase__, required_batch_size_multiple=UpperCAmelCase__     )
    __lowercase								=						[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
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_sampler=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2     )
    __lowercase								=						[]
    __lowercase								=						[]
    for batch in data_loader:
       __lowercase								=						batch["input_ids"].shape
       __lowercase								=						src_shape[0]
       assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
       __lowercase								=						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  _lowercase			(      self							:      Any     ):
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset(max_len=5_1_2     )
    __lowercase								=						2
    __lowercase								=						ds.make_sortish_sampler(UpperCAmelCase__, shuffle=UpperCAmelCase__     )

    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2     )
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2, sampler=UpperCAmelCase__     )

    __lowercase								=						tokenizer.pad_token_id

    def count_pad_tokens(UpperCAmelCase__							:      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  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      int=1_0_0_0, UpperCAmelCase__							:      str=1_2_8     ):
    if os.getenv("USE_REAL_DATA", UpperCAmelCase__     ):
       __lowercase								=						"examples/seq2seq/wmt_en_ro"
       __lowercase								=						max_len * 2 * 6_4
       if not Path(UpperCAmelCase__     ).joinpath("train.len"     ).exists():
          save_len_file(UpperCAmelCase__, UpperCAmelCase__     )
    else:
       __lowercase								=						"examples/seq2seq/test_data/wmt_en_ro"
       __lowercase								=						max_len * 4
       save_len_file(UpperCAmelCase__, UpperCAmelCase__     )

    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						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  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset()
    __lowercase								=						set(DistributedSortishSampler(UpperCAmelCase__, 2_5_6, num_replicas=2, rank=0, add_extra_examples=UpperCAmelCase__     )     )
    __lowercase								=						set(DistributedSortishSampler(UpperCAmelCase__, 2_5_6, 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  _lowercase			(      self							:      int, UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__, use_fast=UpperCAmelCase__     )
    if tok_name == MBART_TINY:
       __lowercase								=						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", )
       __lowercase								=						train_dataset.dataset_kwargs
       assert "src_lang" in kwargs and "tgt_lang" in kwargs
    else:
       __lowercase								=						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, )
       __lowercase								=						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




