






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


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


     """simple docstring"""
     __lowerCamelCase						:       int				   =				"""\n""".join(_A     )
     Path(_A     ).open("""w"""     ).writelines(_A     )
def     __lowercase  (_lowercase     )   ->     str:


     """simple docstring"""

     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(_A,						f"{split}.source"     ),						_A     )
          _dump_articles(os.path.join(_A,						f"{split}.target"     ),						_A     )
     return tmp_dir







class 	SCREAMING_SNAKE_CASE	(      _UpperCAmelCase  ):




       @parameterized.expand(
           [
               MBART_TINY,
               MARIAN_TINY,
               T5_TINY,
               BART_TINY,
               PEGASUS_XSUM,
           ]	,    )
       @slow
       def 					a_					(					self      : List[Any]	,    A__      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				AutoTokenizer.from_pretrained(__UpperCamelCase					)
            __lowerCamelCase						:       Optional[int]				   =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
            __lowerCamelCase						:       int				   =				max(len(tokenizer.encode(__UpperCamelCase					)					) for a in ARTICLES					)
            __lowerCamelCase						:       Optional[int]				   =				max(len(tokenizer.encode(__UpperCamelCase					)					) for a in SUMMARIES					)
            __lowerCamelCase						:       List[str]				   =				4
            __lowerCamelCase						:       Dict				   =				8
            assert max_len_target > max_src_len  # Will be truncated
            assert max_len_source > max_src_len  # Will be truncated
            __lowerCamelCase   ,      __lowerCamelCase						:       Optional[Any]				   =				"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
            __lowerCamelCase						:       Optional[int]				   =				SeqaSeqDataset(
                __UpperCamelCase	,    data_dir=__UpperCamelCase	,    type_path="""train"""	,    max_source_length=__UpperCamelCase	,    max_target_length=__UpperCamelCase	,    src_lang=__UpperCamelCase	,    tgt_lang=__UpperCamelCase	,    )
            __lowerCamelCase						:       Dict				   =				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						:       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 					a_					(					self      : List[Any]	,    A__      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				AutoTokenizer.from_pretrained(__UpperCamelCase					)
            __lowerCamelCase						:       List[Any]				   =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
            __lowerCamelCase						:       List[Any]				   =				max(len(tokenizer.encode(__UpperCamelCase					)					) for a in ARTICLES					)
            __lowerCamelCase						:       List[str]				   =				max(len(tokenizer.encode(__UpperCamelCase					)					) for a in SUMMARIES					)
            __lowerCamelCase						:       Any				   =				4
            __lowerCamelCase						:       Dict				   =				LegacySeqaSeqDataset(
                __UpperCamelCase	,    data_dir=__UpperCamelCase	,    type_path="""train"""	,    max_source_length=20	,    max_target_length=__UpperCamelCase	,    )
            __lowerCamelCase						:       List[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 					a_					(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""					)

            __lowerCamelCase						:       Optional[int]				   =				Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
            __lowerCamelCase						:       Optional[int]				   =				tmp_dir.joinpath("""train.source"""					).open().readlines()
            __lowerCamelCase						:       List[Any]				   =				Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
            pack_data_dir(__UpperCamelCase	,    __UpperCamelCase	,    128	,    __UpperCamelCase					)
            __lowerCamelCase						:       Dict				   =				{x.name for x in tmp_dir.iterdir()}
            __lowerCamelCase						:       Union[str, Any]				   =				{x.name for x in save_dir.iterdir()}
            __lowerCamelCase						:       str				   =				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 					a_					(					self      : Dict					):


            """simple docstring"""







            if not FAIRSEQ_AVAILABLE:
                 return
            __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       Any				   =				self._get_dataset(max_len=64					)
            __lowerCamelCase						:       int				   =				64
            __lowerCamelCase						:       Optional[int]				   =				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[str]				   =				DataLoader(__UpperCamelCase	,    batch_sampler=__UpperCamelCase	,    collate_fn=ds.collate_fn	,    num_workers=2					)
            __lowerCamelCase						:       str				   =				[]
            __lowerCamelCase						:       Optional[Any]				   =				[]
            for batch in data_loader:
                 __lowerCamelCase						:       Optional[Any]				   =				batch["""input_ids"""].shape
                 __lowerCamelCase						:       Dict				   =				src_shape[0]
                 assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
                 __lowerCamelCase						:       Union[str, Any]				   =				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 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       Tuple				   =				self._get_dataset(max_len=512					)
            __lowerCamelCase						:       List[str]				   =				2
            __lowerCamelCase						:       List[str]				   =				ds.make_sortish_sampler(__UpperCamelCase	,    shuffle=__UpperCamelCase					)

            __lowerCamelCase						:       str				   =				DataLoader(__UpperCamelCase	,    batch_size=__UpperCamelCase	,    collate_fn=ds.collate_fn	,    num_workers=2					)
            __lowerCamelCase						:       Optional[int]				   =				DataLoader(__UpperCamelCase	,    batch_size=__UpperCamelCase	,    collate_fn=ds.collate_fn	,    num_workers=2	,    sampler=__UpperCamelCase					)

            __lowerCamelCase						:       Any				   =				tokenizer.pad_token_id

            def count_pad_tokens(A__      : Union[str, Any]	,    A__      : 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 					a_					(					self      : int	,    A__      : Tuple=1000	,    A__      : List[Any]=128					):


            """simple docstring"""







            if os.getenv("""USE_REAL_DATA"""	,    __UpperCamelCase					):
                 __lowerCamelCase						:       Any				   =				"""examples/seq2seq/wmt_en_ro"""
                 __lowerCamelCase						:       Any				   =				max_len * 2 * 64
                 if not Path(__UpperCamelCase					).joinpath("""train.len"""					).exists():
                      save_len_file(__UpperCamelCase	,    __UpperCamelCase					)
            else:
                 __lowerCamelCase						:       Dict				   =				"""examples/seq2seq/test_data/wmt_en_ro"""
                 __lowerCamelCase						:       List[Any]				   =				max_len * 4
                 save_len_file(__UpperCamelCase	,    __UpperCamelCase					)

            __lowerCamelCase						:       List[str]				   =				AutoTokenizer.from_pretrained(__UpperCamelCase					)
            __lowerCamelCase						:       List[Any]				   =				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 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       Dict				   =				self._get_dataset()
            __lowerCamelCase						:       Dict				   =				set(DistributedSortishSampler(__UpperCamelCase	,    256	,    num_replicas=2	,    rank=0	,    add_extra_examples=__UpperCamelCase					)					)
            __lowerCamelCase						:       Optional[int]				   =				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 					a_					(					self      : Dict	,    A__      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				AutoTokenizer.from_pretrained(__UpperCamelCase	,    use_fast=__UpperCamelCase					)
            if tok_name == MBART_TINY:
                 __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	,    src_lang="""EN"""	,    tgt_lang="""FR"""	,    )
                 __lowerCamelCase						:       int				   =				train_dataset.dataset_kwargs
                 assert "src_lang" in kwargs and "tgt_lang" in kwargs
            else:
                 __lowerCamelCase						:       Union[str, 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	,    )
                 __lowerCamelCase						:       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











'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__						:Optional[int]          =			logging.get_logger(__name__)

UpperCAmelCase__						:List[Any]          =			{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     Dict   		=			'sew-d'




       def __init__(					self      : Tuple	,    A__      : Optional[int]=32	,    A__      : Optional[int]=768	,    A__      : Any=12	,    A__      : List[str]=12	,    A__      : List[Any]=3072	,    A__      : str=2	,    A__      : Dict=512	,    A__      : Optional[Any]=256	,    A__      : Optional[Any]=True	,    A__      : Any=True	,    A__      : List[str]=("p2c", "c2p")	,    A__      : List[Any]="layer_norm"	,    A__      : Union[str, Any]="gelu_python"	,    A__      : int=0.1	,    A__      : Dict=0.1	,    A__      : int=0.1	,    A__      : Dict=0.0	,    A__      : Optional[Any]=0.1	,    A__      : Dict=0.02	,    A__      : Dict=1e-7	,    A__      : List[Any]=1e-5	,    A__      : Any="group"	,    A__      : Any="gelu"	,    A__      : Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)	,    A__      : Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)	,    A__      : str=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)	,    A__      : Optional[Any]=False	,    A__      : Any=128	,    A__      : Optional[Any]=16	,    A__      : Union[str, Any]=True	,    A__      : Any=0.05	,    A__      : List[str]=10	,    A__      : Union[str, Any]=2	,    A__      : Dict=0.0	,    A__      : str=10	,    A__      : Tuple=0	,    A__      : Any="mean"	,    A__      : Optional[int]=False	,    A__      : int=False	,    A__      : List[str]=256	,    A__      : Union[str, Any]=0	,    A__      : int=1	,    A__      : Optional[Any]=2	,    **A__      : List[Any]	,    ):


            """simple docstring"""







            super().__init__(**A__	,    pad_token_id=A__	,    bos_token_id=A__	,    eos_token_id=A__					)
            __lowerCamelCase						:       List[str]				   =				hidden_size
            __lowerCamelCase						:       Tuple				   =				feat_extract_norm
            __lowerCamelCase						:       Tuple				   =				feat_extract_activation
            __lowerCamelCase						:       List[Any]				   =				list(A__					)
            __lowerCamelCase						:       int				   =				list(A__					)
            __lowerCamelCase						:       Optional[Any]				   =				list(A__					)
            __lowerCamelCase						:       Tuple				   =				conv_bias
            __lowerCamelCase						:       List[str]				   =				num_conv_pos_embeddings
            __lowerCamelCase						:       Tuple				   =				num_conv_pos_embedding_groups
            __lowerCamelCase						:       int				   =				len(self.conv_dim					)
            __lowerCamelCase						:       Optional[int]				   =				num_hidden_layers
            __lowerCamelCase						:       Dict				   =				intermediate_size
            __lowerCamelCase						:       Optional[int]				   =				squeeze_factor
            __lowerCamelCase						:       Union[str, Any]				   =				max_position_embeddings
            __lowerCamelCase						:       int				   =				position_buckets
            __lowerCamelCase						:       Tuple				   =				share_att_key
            __lowerCamelCase						:       Any				   =				relative_attention
            __lowerCamelCase						:       Any				   =				norm_rel_ebd
            __lowerCamelCase						:       Dict				   =				list(A__					)
            __lowerCamelCase						:       Optional[Any]				   =				hidden_act
            __lowerCamelCase						:       List[str]				   =				num_attention_heads
            __lowerCamelCase						:       List[str]				   =				hidden_dropout
            __lowerCamelCase						:       Union[str, Any]				   =				attention_dropout
            __lowerCamelCase						:       Tuple				   =				activation_dropout
            __lowerCamelCase						:       Union[str, Any]				   =				feat_proj_dropout
            __lowerCamelCase						:       Union[str, Any]				   =				final_dropout
            __lowerCamelCase						:       List[str]				   =				layer_norm_eps
            __lowerCamelCase						:       Tuple				   =				feature_layer_norm_eps
            __lowerCamelCase						:       Union[str, Any]				   =				initializer_range
            __lowerCamelCase						:       List[str]				   =				vocab_size

            if (
                (len(self.conv_stride					) != self.num_feat_extract_layers)
                or (len(self.conv_kernel					) != self.num_feat_extract_layers)
                or (len(self.conv_dim					) != self.num_feat_extract_layers)
            ):
                 raise ValueError(
                     """Configuration for convolutional layers is incorrect."""
                     """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
                     f"but is `len(config.conv_dim) = {len(self.conv_dim					)}`, `len(config.conv_stride)"
                     f"= {len(self.conv_stride					)}`, `len(config.conv_kernel) = {len(self.conv_kernel					)}`."					)

            # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
            __lowerCamelCase						:       Any				   =				apply_spec_augment
            __lowerCamelCase						:       str				   =				mask_time_prob
            __lowerCamelCase						:       Tuple				   =				mask_time_length
            __lowerCamelCase						:       Optional[int]				   =				mask_time_min_masks
            __lowerCamelCase						:       Dict				   =				mask_feature_prob
            __lowerCamelCase						:       Optional[Any]				   =				mask_feature_length
            __lowerCamelCase						:       str				   =				mask_feature_min_masks

            # ctc loss
            __lowerCamelCase						:       Any				   =				ctc_loss_reduction
            __lowerCamelCase						:       str				   =				ctc_zero_infinity

            # sequence classification
            __lowerCamelCase						:       Dict				   =				use_weighted_layer_sum
            __lowerCamelCase						:       List[Any]				   =				classifier_proj_size




       @property
       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            return functools.reduce(operator.mul	,    self.conv_stride	,    1					)



