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


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




def        __UpperCamelCase					(    lowerCAmelCase__							:    Path  ,       lowerCAmelCase__							:    list  ):
 __a			:  List[Any]		    =  '''\n'''.join(lowerCAmelCase__  )
 Path(lowerCAmelCase__  ).open('''w'''  ).writelines(lowerCAmelCase__  )




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

    __a			:  Any		    =  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
    __a			:  Optional[int]		    =  tmp_dir.joinpath('''train.source'''       ).open().readlines()
    __a			:  List[Any]		    =  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
    pack_data_dir(snake_case_ ,    snake_case_ ,    1_2_8 ,    snake_case_       )
    __a			:  Optional[Any]		    =  {x.name for x in tmp_dir.iterdir()}
    __a			:  Union[str, Any]		    =  {x.name for x in save_dir.iterdir()}
    __a			:  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(snake_case_       ) < len(snake_case_       )
    assert len(snake_case_       ) == 1
    assert len(packed_examples[0]       ) == sum(len(snake_case_       ) for x in orig_examples       )
    assert orig_paths == new_paths
   @pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,    reason='''This test requires fairseq'''       )
   def 				lowerCAmelCase      (self			: Any       ):
    if not FAIRSEQ_AVAILABLE:
     return
    __a			:  Any		    =  self._get_dataset(max_len=6_4       )
    __a			:  int		    =  6_4
    __a			:  List[str]		    =  ds.make_dynamic_sampler(snake_case_ ,    required_batch_size_multiple=snake_case_       )
    __a			:  List[str]		    =  [len(snake_case_       ) for x in batch_sampler]
    assert len(set(snake_case_       )       ) > 1  # it's not dynamic batch size if every batch is the same length
    assert sum(snake_case_       ) == len(snake_case_       )  # no dropped or added examples
    __a			:  Union[str, Any]		    =  DataLoader(snake_case_ ,    batch_sampler=snake_case_ ,    collate_fn=ds.collate_fn ,    num_workers=2       )
    __a			:  Tuple		    =  []
    __a			:  Union[str, Any]		    =  []
    for batch in data_loader:
     __a			:  Any		    =  batch['''input_ids'''].shape
     __a			:  str		    =  src_shape[0]
     assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
     __a			:  Optional[Any]		    =  np.product(batch['''input_ids'''].shape       )
     num_src_per_batch.append(snake_case_       )
     if num_src_tokens > (max_tokens * 1.1):
      failures.append(snake_case_       )
    assert num_src_per_batch[0] == max(snake_case_       )
    if failures:
     raise AssertionError(f"too many tokens in {len(snake_case_       )} batches"       )
   def 				lowerCAmelCase      (self			: int       ):
    __a			:  Optional[int]		    =  self._get_dataset(max_len=5_1_2       )
    __a			:  Union[str, Any]		    =  2
    __a			:  str		    =  ds.make_sortish_sampler(snake_case_ ,    shuffle=snake_case_       )

    __a			:  Tuple		    =  DataLoader(snake_case_ ,    batch_size=snake_case_ ,    collate_fn=ds.collate_fn ,    num_workers=2       )
    __a			:  Tuple		    =  DataLoader(snake_case_ ,    batch_size=snake_case_ ,    collate_fn=ds.collate_fn ,    num_workers=2 ,    sampler=snake_case_       )

    __a			:  Optional[int]		    =  tokenizer.pad_token_id

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

    assert sum(count_pad_tokens(snake_case_ ,    k='''labels'''       )       ) < sum(count_pad_tokens(snake_case_ ,    k='''labels'''       )       )
    assert sum(count_pad_tokens(snake_case_       )       ) < sum(count_pad_tokens(snake_case_       )       )
    assert len(snake_case_       ) == len(snake_case_       )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: int=1_0_0_0 ,    snake_case_			: Optional[Any]=1_2_8       ):
    if os.getenv('''USE_REAL_DATA''' ,    snake_case_       ):
     __a			:  Optional[int]		    =  '''examples/seq2seq/wmt_en_ro'''
     __a			:  List[Any]		    =  max_len * 2 * 6_4
     if not Path(snake_case_       ).joinpath('''train.len'''       ).exists():
      save_len_file(snake_case_ ,    snake_case_       )
    else:
     __a			:  int		    =  '''examples/seq2seq/test_data/wmt_en_ro'''
     __a			:  List[str]		    =  max_len * 4
     save_len_file(snake_case_ ,    snake_case_       )

    __a			:  str		    =  AutoTokenizer.from_pretrained(snake_case_       )
    __a			:  Optional[int]		    =  SeqaSeqDataset(
        snake_case_ ,    data_dir=snake_case_ ,    type_path='''train''' ,    max_source_length=snake_case_ ,    max_target_length=snake_case_ ,    n_obs=snake_case_ ,    )
    return ds, max_tokens, tokenizer
   def 				lowerCAmelCase      (self			: List[str]       ):
    __a			:  str		    =  self._get_dataset()
    __a			:  Optional[Any]		    =  set(DistributedSortishSampler(snake_case_ ,    2_5_6 ,    num_replicas=2 ,    rank=0 ,    add_extra_examples=snake_case_       )       )
    __a			:  Tuple		    =  set(DistributedSortishSampler(snake_case_ ,    2_5_6 ,    num_replicas=2 ,    rank=1 ,    add_extra_examples=snake_case_       )       )
    assert idsa.intersection(snake_case_       ) == set()







   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ] ,    )
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Union[str, Any]       ):
    __a			:  Union[str, Any]		    =  AutoTokenizer.from_pretrained(snake_case_ ,    use_fast=snake_case_       )
    if tok_name == MBART_TINY:
     __a			:  Any		    =  SeqaSeqDataset(
         snake_case_ ,    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''' ,    )
     __a			:  Tuple		    =  train_dataset.dataset_kwargs
     assert "src_lang" in kwargs and "tgt_lang" in kwargs
    else:
     __a			:  Optional[Any]		    =  SeqaSeqDataset(
         snake_case_ ,    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 ,    )
     __a			:  List[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(snake_case_       ) == 1 if tok_name == BART_TINY else len(snake_case_       ) == 0


import argparse
import os
import re

import packaging.version


lowercase__          ='examples/'
lowercase__          ={
    'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(R'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(R'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), R'\1version="VERSION",'),
    'doc': (re.compile(R'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
lowercase__          ={
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
lowercase__          ='README.md'




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    Tuple  ):
 with open(lowerCAmelCase__  ,       '''r'''  ,       encoding='''utf-8'''  ,       newline='''\n'''  ) as f:
  __a			:  Tuple		    =  f.read()
 __a    ,	__a			:  Optional[int]		    =  REPLACE_PATTERNS[pattern]
 __a			:  List[Any]		    =  replace.replace('''VERSION'''  ,       lowerCAmelCase__  )
 __a			:  Any		    =  re_pattern.sub(lowerCAmelCase__  ,       lowerCAmelCase__  )
 with open(lowerCAmelCase__  ,       '''w'''  ,       encoding='''utf-8'''  ,       newline='''\n'''  ) as f:
  f.write(lowerCAmelCase__  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ):
 for folder, directories, fnames in os.walk(lowerCAmelCase__  ):
  # Removing some of the folders with non-actively maintained examples from the walk
  if "research_projects" in directories:
   directories.remove('''research_projects'''  )
  if "legacy" in directories:
   directories.remove('''legacy'''  )
  for fname in fnames:
   if fname.endswith('''.py'''  ):
    update_version_in_file(os.path.join(lowerCAmelCase__  ,       lowerCAmelCase__  )  ,       lowerCAmelCase__  ,       pattern='''examples'''  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Dict=False  ):
 for pattern, fname in REPLACE_FILES.items():
  update_version_in_file(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
 if not patch:
  update_version_in_examples(lowerCAmelCase__  )




def        __UpperCamelCase					(    ):
 __a			:  Optional[int]		    =  '''🤗 Transformers currently provides the following architectures'''
 __a			:  int		    =  '''1. Want to contribute a new model?'''
 with open(lowerCAmelCase__  ,       '''r'''  ,       encoding='''utf-8'''  ,       newline='''\n'''  ) as f:
  __a			:  Tuple		    =  f.readlines()

 # Find the start of the list.
 __a			:  Optional[int]		    =  0
 while not lines[start_index].startswith(_start_prompt  ):
  start_index += 1
 start_index += 1

 __a			:  Any		    =  start_index
 # Update the lines in the model list.
 while not lines[index].startswith(_end_prompt  ):
  if lines[index].startswith('''1.'''  ):
   __a			:  str		    =  lines[index].replace(
       '''https://huggingface.co/docs/transformers/main/model_doc'''  ,       '''https://huggingface.co/docs/transformers/model_doc'''  ,       )
  index += 1

 with open(lowerCAmelCase__  ,       '''w'''  ,       encoding='''utf-8'''  ,       newline='''\n'''  ) as f:
  f.writelines(lowerCAmelCase__  )




def        __UpperCamelCase					(    ):
 with open(REPLACE_FILES['''init''']  ,       '''r'''  ) as f:
  __a			:  Optional[int]		    =  f.read()
 __a			:  str		    =  REPLACE_PATTERNS['''init'''][0].search(lowerCAmelCase__  ).groups()[0]
 return packaging.version.parse(lowerCAmelCase__  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]=False  ):
 __a			:  str		    =  get_version()
 if patch and default_version.is_devrelease:
  raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''  )
 if default_version.is_devrelease:
  __a			:  Union[str, Any]		    =  default_version.base_version
 elif patch:
  __a			:  Tuple		    =  f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
 else:
  __a			:  List[str]		    =  f"{default_version.major}.{default_version.minor + 1}.0"

 # Now let's ask nicely if that's the right one.
 __a			:  List[str]		    =  input(f"Which version are you releasing? [{default_version}]"  )
 if len(lowerCAmelCase__  ) == 0:
  __a			:  Tuple		    =  default_version

 print(f"Updating version to {version}."  )
 global_version_update(lowerCAmelCase__  ,       patch=lowerCAmelCase__  )
 if not patch:
  print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''  )
  clean_main_ref_in_model_list()




def        __UpperCamelCase					(    ):
 __a			:  Dict		    =  get_version()
 __a			:  str		    =  f"{current_version.major}.{current_version.minor + 1}.0.dev0"
 __a			:  Any		    =  current_version.base_version

 # Check with the user we got that right.
 __a			:  Any		    =  input(f"Which version are we developing now? [{dev_version}]"  )
 if len(lowerCAmelCase__  ) == 0:
  __a			:  Any		    =  dev_version

 print(f"Updating version to {version}."  )
 global_version_update(lowerCAmelCase__  )
 print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''  )
 clean_main_ref_in_model_list()


if __name__ == "__main__":
      lowercase__          =argparse.ArgumentParser()
      parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
      parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
      lowercase__          =parser.parse_args()
      if not args.post_release:
            pre_release_work(patch=args.patch)
      elif args.patch:
            print('Nothing to do after a patch :-)')
      else:
            post_release_work()
