

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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Path     ,					SCREAMING_SNAKE_CASE_		:   list      ):



     '''simple docstring'''





     lowercase__   :       Union[str, Any]				=	'\n'.join(SCREAMING_SNAKE_CASE_      )
     Path(SCREAMING_SNAKE_CASE_      ).open('w'      ).writelines(SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[Any]      ):



     '''simple docstring'''





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


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]						,       )
    @slow
    def 				snake_case_    (				self						,       a):
         lowercase__   :       Optional[Any]				=	AutoTokenizer.from_pretrained(a)
         lowercase__   :       Any				=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
         lowercase__   :       int				=	max(len(tokenizer.encode(a)) for a in ARTICLES)
         lowercase__   :       Any				=	max(len(tokenizer.encode(a)) for a in SUMMARIES)
         lowercase__   :       int				=	4
         lowercase__   :       Optional[int]				=	8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         lowercase__   :       List[str]				=	'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
         lowercase__   :       List[str]				=	SeqaSeqDataset(
             a						,       data_dir=a						,       type_path='train'						,       max_source_length=a						,       max_target_length=a						,       src_lang=a						,       tgt_lang=a						,       )
         lowercase__   :       int				=	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
              lowercase__   :       Tuple				=	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						,       a):
         lowercase__   :       Optional[Any]				=	AutoTokenizer.from_pretrained(a)
         lowercase__   :       Optional[int]				=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
         lowercase__   :       List[Any]				=	max(len(tokenizer.encode(a)) for a in ARTICLES)
         lowercase__   :       str				=	max(len(tokenizer.encode(a)) for a in SUMMARIES)
         lowercase__   :       Optional[Any]				=	4
         lowercase__   :       Tuple				=	LegacySeqaSeqDataset(
             a						,       data_dir=a						,       type_path='train'						,       max_source_length=20						,       max_target_length=a						,       )
         lowercase__   :       Optional[int]				=	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 				snake_case_    (				self):
         lowercase__   :       Any				=	AutoTokenizer.from_pretrained('facebook/mbart-large-cc25')

         lowercase__   :       Any				=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
         lowercase__   :       List[Any]				=	tmp_dir.joinpath('train.source').open().readlines()
         lowercase__   :       Optional[Any]				=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
         pack_data_dir(a						,       a						,       128						,       a)
         lowercase__   :       Optional[int]				=	{x.name for x in tmp_dir.iterdir()}
         lowercase__   :       Optional[int]				=	{x.name for x in save_dir.iterdir()}
         lowercase__   :       Tuple				=	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 				snake_case_    (				self):
         if not FAIRSEQ_AVAILABLE:
              return
         lowercase__   :       Dict				=	self._get_dataset(max_len=64)
         lowercase__   :       List[str]				=	64
         lowercase__   :       Tuple				=	ds.make_dynamic_sampler(a						,       required_batch_size_multiple=a)
         lowercase__   :       Optional[int]				=	[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
         lowercase__   :       Dict				=	DataLoader(a						,       batch_sampler=a						,       collate_fn=ds.collate_fn						,       num_workers=2)
         lowercase__   :       Optional[int]				=	[]
         lowercase__   :       Tuple				=	[]
         for batch in data_loader:
              lowercase__   :       int				=	batch['input_ids'].shape
              lowercase__   :       int				=	src_shape[0]
              assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
              lowercase__   :       List[str]				=	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 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	self._get_dataset(max_len=512)
         lowercase__   :       List[str]				=	2
         lowercase__   :       Union[str, Any]				=	ds.make_sortish_sampler(a						,       shuffle=a)

         lowercase__   :       Tuple				=	DataLoader(a						,       batch_size=a						,       collate_fn=ds.collate_fn						,       num_workers=2)
         lowercase__   :       str				=	DataLoader(a						,       batch_size=a						,       collate_fn=ds.collate_fn						,       num_workers=2						,       sampler=a)

         lowercase__   :       Union[str, Any]				=	tokenizer.pad_token_id

         def count_pad_tokens(a						,       a="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 				snake_case_    (				self						,       a=1000						,       a=128):
         if os.getenv('USE_REAL_DATA'						,       a):
              lowercase__   :       Optional[Any]				=	'examples/seq2seq/wmt_en_ro'
              lowercase__   :       Tuple				=	max_len * 2 * 64
              if not Path(a).joinpath('train.len').exists():
                   save_len_file(a						,       a)
         else:
              lowercase__   :       Any				=	'examples/seq2seq/test_data/wmt_en_ro'
              lowercase__   :       str				=	max_len * 4
              save_len_file(a						,       a)

         lowercase__   :       str				=	AutoTokenizer.from_pretrained(a)
         lowercase__   :       Any				=	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 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	self._get_dataset()
         lowercase__   :       Dict				=	set(DistributedSortishSampler(a						,       256						,       num_replicas=2						,       rank=0						,       add_extra_examples=a))
         lowercase__   :       Any				=	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 				snake_case_    (				self						,       a):
         lowercase__   :       Optional[int]				=	AutoTokenizer.from_pretrained(a						,       use_fast=a)
         if tok_name == MBART_TINY:
              lowercase__   :       Tuple				=	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'						,       )
              lowercase__   :       Optional[int]				=	train_dataset.dataset_kwargs
              assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
              lowercase__   :       Optional[int]				=	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						,       )
              lowercase__   :       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(a) == 1 if tok_name == BART_TINY else len(a) == 0




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


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[Any]														=					"""speech_to_text_2"""
    __lowerCamelCase						:	str														=					["""past_key_values"""]
    __lowerCamelCase						:	List[Any]														=					{"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}





    def __init__(				self						,       a=1_0000						,       a=6						,       a=2048						,       a=4						,       a=0.0						,       a=True						,       a="relu"						,       a=256						,       a=0.1						,       a=0.0						,       a=0.0						,       a=0.02						,       a=2						,       a=True						,       a=1						,       a=0						,       a=2						,       a=1024						,       **a						,       ):
         lowercase__   :       Optional[int]				=	vocab_size
         lowercase__   :       List[str]				=	d_model
         lowercase__   :       int				=	decoder_ffn_dim
         lowercase__   :       Optional[Any]				=	decoder_layers
         lowercase__   :       int				=	decoder_attention_heads
         lowercase__   :       Dict				=	dropout
         lowercase__   :       Optional[int]				=	attention_dropout
         lowercase__   :       Optional[int]				=	activation_dropout
         lowercase__   :       Optional[int]				=	activation_function
         lowercase__   :       Dict				=	init_std
         lowercase__   :       List[Any]				=	decoder_layerdrop
         lowercase__   :       int				=	use_cache
         lowercase__   :       Any				=	decoder_layers
         lowercase__   :       Optional[int]				=	scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase__   :       Tuple				=	max_target_positions

         super().__init__(
             pad_token_id=a						,       bos_token_id=a						,       eos_token_id=a						,       decoder_start_token_id=a						,       **a						,       )

