





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






def lowerCamelCase__   (							snake_case_     :  Path	,				snake_case_     :  list	)				->    Dict:
   __snake_case								=       '''\n'''.join(snake_case_	)
   Path(snake_case_	).open('''w'''	).writelines(snake_case_	)






def lowerCamelCase__   (							snake_case_     :  List[Any]	)				->    int:
   for split in ["train", "val", "test"]:
      _dump_articles(os.path.join(snake_case_	,				f"""{split}.source"""	)	,				snake_case_	)
      _dump_articles(os.path.join(snake_case_	,				f"""{split}.target"""	)	,				snake_case_	)
   return tmp_dir







class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]    ,      )
      @slow
      def        a			(self	:				Union[str, Any]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       AutoTokenizer.from_pretrained(a__				)
         __snake_case								=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
         __snake_case								=       max(len(tokenizer.encode(a__				)				) for a in ARTICLES				)
         __snake_case								=       max(len(tokenizer.encode(a__				)				) for a in SUMMARIES				)
         __snake_case								=       4
         __snake_case								=       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								=       '''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
         __snake_case								=       SeqaSeqDataset(
             a__    ,      data_dir=a__    ,      type_path='''train'''    ,      max_source_length=a__    ,      max_target_length=a__    ,      src_lang=a__    ,      tgt_lang=a__    ,      )
         __snake_case								=       DataLoader(a__    ,      batch_size=2    ,      collate_fn=train_dataset.collate_fn				)
         for batch in dataloader:
            assert isinstance(a__    ,      a__				)
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_src_len
            # show that targets are the same len
            assert batch["labels"].shape[1] == max_tgt_len
            if tok_name != MBART_TINY:
               continue
            # check language codes in correct place
            __snake_case								=       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	:				Tuple    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       AutoTokenizer.from_pretrained(a__				)
         __snake_case								=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
         __snake_case								=       max(len(tokenizer.encode(a__				)				) for a in ARTICLES				)
         __snake_case								=       max(len(tokenizer.encode(a__				)				) for a in SUMMARIES				)
         __snake_case								=       4
         __snake_case								=       LegacySeqaSeqDataset(
             a__    ,      data_dir=a__    ,      type_path='''train'''    ,      max_source_length=20    ,      max_target_length=a__    ,      )
         __snake_case								=       DataLoader(a__    ,      batch_size=2    ,      collate_fn=train_dataset.collate_fn				)
         for batch in dataloader:
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_len_source
            assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
            # show that targets were truncated
            assert batch["labels"].shape[1] == trunc_target  # Truncated
            assert max_len_target > trunc_target  # Truncated
            break  # No need to test every batch


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''				)

         __snake_case								=       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
         __snake_case								=       tmp_dir.joinpath('''train.source'''				).open().readlines()
         __snake_case								=       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
         pack_data_dir(a__    ,      a__    ,      128    ,      a__				)
         __snake_case								=       {x.name for x in tmp_dir.iterdir()}
         __snake_case								=       {x.name for x in save_dir.iterdir()}
         __snake_case								=       save_dir.joinpath('''train.source'''				).open().readlines()
         # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
         # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
         assert len(a__				) < len(a__				)
         assert len(a__				) == 1
         assert len(packed_examples[0]				) == sum(len(a__				) for x in orig_examples				)
         assert orig_paths == new_paths


      @pytest.mark.skipif(not FAIRSEQ_AVAILABLE    ,      reason='''This test requires fairseq'''				)
      def        a			(self	:				Any				):





         """simple docstring"""



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


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case	,     __snake_case	,     __snake_case								=       self._get_dataset(max_len=512				)
         __snake_case								=       2
         __snake_case								=       ds.make_sortish_sampler(a__    ,      shuffle=a__				)

         __snake_case								=       DataLoader(a__    ,      batch_size=a__    ,      collate_fn=ds.collate_fn    ,      num_workers=2				)
         __snake_case								=       DataLoader(a__    ,      batch_size=a__    ,      collate_fn=ds.collate_fn    ,      num_workers=2    ,      sampler=a__				)

         __snake_case								=       tokenizer.pad_token_id

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

         assert sum(count_pad_tokens(a__    ,      k='''labels'''				)				) < sum(count_pad_tokens(a__    ,      k='''labels'''				)				)
         assert sum(count_pad_tokens(a__				)				) < sum(count_pad_tokens(a__				)				)
         assert len(a__				) == len(a__				)


      def        a			(self	:				int    ,      a__	:				Optional[Any]=1000    ,      a__	:				Optional[Any]=128				):





         """simple docstring"""



         if os.getenv('''USE_REAL_DATA'''    ,      a__				):
            __snake_case								=       '''examples/seq2seq/wmt_en_ro'''
            __snake_case								=       max_len * 2 * 64
            if not Path(a__				).joinpath('''train.len'''				).exists():
               save_len_file(a__    ,      a__				)
         else:
            __snake_case								=       '''examples/seq2seq/test_data/wmt_en_ro'''
            __snake_case								=       max_len * 4
            save_len_file(a__    ,      a__				)

         __snake_case								=       AutoTokenizer.from_pretrained(a__				)
         __snake_case								=       SeqaSeqDataset(
             a__    ,      data_dir=a__    ,      type_path='''train'''    ,      max_source_length=a__    ,      max_target_length=a__    ,      n_obs=a__    ,      )
         return ds, max_tokens, tokenizer


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case	,     __snake_case	,     __snake_case								=       self._get_dataset()
         __snake_case								=       set(DistributedSortishSampler(a__    ,      256    ,      num_replicas=2    ,      rank=0    ,      add_extra_examples=a__				)				)
         __snake_case								=       set(DistributedSortishSampler(a__    ,      256    ,      num_replicas=2    ,      rank=1    ,      add_extra_examples=a__				)				)
         assert idsa.intersection(a__				) == set()


      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]    ,      )
      def        a			(self	:				Optional[int]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       AutoTokenizer.from_pretrained(a__    ,      use_fast=a__				)
         if tok_name == MBART_TINY:
            __snake_case								=       SeqaSeqDataset(
                a__    ,      data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)    ,      type_path='''train'''    ,      max_source_length=4    ,      max_target_length=8    ,      src_lang='''EN'''    ,      tgt_lang='''FR'''    ,      )
            __snake_case								=       train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
            __snake_case								=       SeqaSeqDataset(
                a__    ,      data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)    ,      type_path='''train'''    ,      max_source_length=4    ,      max_target_length=8    ,      )
            __snake_case								=       train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(a__				) == 1 if tok_name == BART_TINY else len(a__				) == 0








import functools
import operator

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


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {
    '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__    (	_UpperCAmelCase  ):
      A_						:    str							=   'sew-d'


      def __init__(self	:				str    ,      a__	:				List[Any]=32    ,      a__	:				Optional[int]=768    ,      a__	:				Union[str, Any]=12    ,      a__	:				Tuple=12    ,      a__	:				int=3072    ,      a__	:				List[Any]=2    ,      a__	:				Dict=512    ,      a__	:				Dict=256    ,      a__	:				List[str]=True    ,      a__	:				List[str]=True    ,      a__	:				str=("p2c", "c2p")    ,      a__	:				str="layer_norm"    ,      a__	:				str="gelu_python"    ,      a__	:				List[Any]=0.1    ,      a__	:				Dict=0.1    ,      a__	:				Optional[Any]=0.1    ,      a__	:				Union[str, Any]=0.0    ,      a__	:				Dict=0.1    ,      a__	:				int=0.0_2    ,      a__	:				str=1E-7    ,      a__	:				Union[str, Any]=1E-5    ,      a__	:				Union[str, Any]="group"    ,      a__	:				Optional[Any]="gelu"    ,      a__	:				List[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,      a__	:				Union[str, Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,      a__	:				Optional[int]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,      a__	:				List[str]=False    ,      a__	:				Optional[Any]=128    ,      a__	:				Union[str, Any]=16    ,      a__	:				Optional[Any]=True    ,      a__	:				List[Any]=0.0_5    ,      a__	:				Tuple=10    ,      a__	:				str=2    ,      a__	:				int=0.0    ,      a__	:				Optional[Any]=10    ,      a__	:				List[Any]=0    ,      a__	:				Optional[int]="mean"    ,      a__	:				List[str]=False    ,      a__	:				Tuple=False    ,      a__	:				Optional[int]=256    ,      a__	:				str=0    ,      a__	:				List[Any]=1    ,      a__	:				int=2    ,      **a__	:				List[str]    ,      ):





         """simple docstring"""



         super().__init__(**a__    ,      pad_token_id=a__    ,      bos_token_id=a__    ,      eos_token_id=a__				)
         __snake_case								=       hidden_size
         __snake_case								=       feat_extract_norm
         __snake_case								=       feat_extract_activation
         __snake_case								=       list(a__				)
         __snake_case								=       list(a__				)
         __snake_case								=       list(a__				)
         __snake_case								=       conv_bias
         __snake_case								=       num_conv_pos_embeddings
         __snake_case								=       num_conv_pos_embedding_groups
         __snake_case								=       len(self.conv_dim				)
         __snake_case								=       num_hidden_layers
         __snake_case								=       intermediate_size
         __snake_case								=       squeeze_factor
         __snake_case								=       max_position_embeddings
         __snake_case								=       position_buckets
         __snake_case								=       share_att_key
         __snake_case								=       relative_attention
         __snake_case								=       norm_rel_ebd
         __snake_case								=       list(a__				)
         __snake_case								=       hidden_act
         __snake_case								=       num_attention_heads
         __snake_case								=       hidden_dropout
         __snake_case								=       attention_dropout
         __snake_case								=       activation_dropout
         __snake_case								=       feat_proj_dropout
         __snake_case								=       final_dropout
         __snake_case								=       layer_norm_eps
         __snake_case								=       feature_layer_norm_eps
         __snake_case								=       initializer_range
         __snake_case								=       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
         __snake_case								=       apply_spec_augment
         __snake_case								=       mask_time_prob
         __snake_case								=       mask_time_length
         __snake_case								=       mask_time_min_masks
         __snake_case								=       mask_feature_prob
         __snake_case								=       mask_feature_length
         __snake_case								=       mask_feature_min_masks

         # ctc loss
         __snake_case								=       ctc_loss_reduction
         __snake_case								=       ctc_zero_infinity

         # sequence classification
         __snake_case								=       use_weighted_layer_sum
         __snake_case								=       classifier_proj_size


      @property
      def        a			(self	:				List[Any]				):





         """simple docstring"""



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

