



"""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


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


def      _lowercase							(    __snake_case   ,__snake_case      )		->  Tuple:
			__lowerCAmelCase :    Tuple				=			"\n".join(_UpperCAmelCase      )
			Path(_UpperCAmelCase      ).open("w"      ).writelines(_UpperCAmelCase      )


def      _lowercase							(    __snake_case      )		->  Dict:
			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      A__  (    a__				):




	'''simple docstring'''





	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]			,     )
	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Any)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			AutoTokenizer.from_pretrained(_snake_case)
				__lowerCAmelCase :    int				=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
				__lowerCAmelCase :    Tuple				=			max(len(tokenizer.encode(_snake_case)) for a in ARTICLES)
				__lowerCAmelCase :    Optional[Any]				=			max(len(tokenizer.encode(_snake_case)) for a in SUMMARIES)
				__lowerCAmelCase :    Any				=			4
				__lowerCAmelCase :    Tuple				=			8
				assert max_len_target > max_src_len  # Will be truncated
				assert max_len_source > max_src_len  # Will be truncated
				__lowerCAmelCase		,      __lowerCAmelCase :    Dict				=			"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
				__lowerCAmelCase :    str				=			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			,     )
				__lowerCAmelCase :    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
							__lowerCAmelCase :    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 				_SCREAMING_SNAKE_CASE  (	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						List[Any])							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			AutoTokenizer.from_pretrained(_snake_case)
				__lowerCAmelCase :    List[str]				=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
				__lowerCAmelCase :    Optional[int]				=			max(len(tokenizer.encode(_snake_case)) for a in ARTICLES)
				__lowerCAmelCase :    Union[str, Any]				=			max(len(tokenizer.encode(_snake_case)) for a in SUMMARIES)
				__lowerCAmelCase :    Union[str, Any]				=			4
				__lowerCAmelCase :    int				=			LegacySeqaSeqDataset(
				    _snake_case			,     data_dir=_snake_case			,     type_path="train"			,     max_source_length=20			,     max_target_length=_snake_case			,     )
				__lowerCAmelCase :    Dict				=			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 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 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")

				__lowerCAmelCase :    int				=			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
				__lowerCAmelCase :    Tuple				=			tmp_dir.joinpath("train.source").open().readlines()
				__lowerCAmelCase :    Optional[int]				=			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
				pack_data_dir(_snake_case			,     _snake_case			,     128			,     _snake_case)
				__lowerCAmelCase :    Any				=			{x.name for x in tmp_dir.iterdir()}
				__lowerCAmelCase :    Tuple				=			{x.name for x in save_dir.iterdir()}
				__lowerCAmelCase :    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(_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 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			List[str]:

				"""simple docstring"""

				if not FAIRSEQ_AVAILABLE:
							return
				__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			self._get_dataset(max_len=64)
				__lowerCAmelCase :    Union[str, Any]				=			64
				__lowerCAmelCase :    str				=			ds.make_dynamic_sampler(_snake_case			,     required_batch_size_multiple=_snake_case)
				__lowerCAmelCase :    Tuple				=			[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
				__lowerCAmelCase :    Any				=			DataLoader(_snake_case			,     batch_sampler=_snake_case			,     collate_fn=ds.collate_fn			,     num_workers=2)
				__lowerCAmelCase :    Optional[Any]				=			[]
				__lowerCAmelCase :    Dict				=			[]
				for batch in data_loader:
							__lowerCAmelCase :    str				=			batch["input_ids"].shape
							__lowerCAmelCase :    List[str]				=			src_shape[0]
							assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
							__lowerCAmelCase :    Tuple				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			Any:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    Optional[int]				=			self._get_dataset(max_len=512)
				__lowerCAmelCase :    List[str]				=			2
				__lowerCAmelCase :    str				=			ds.make_sortish_sampler(_snake_case			,     shuffle=_snake_case)

				__lowerCAmelCase :    Optional[Any]				=			DataLoader(_snake_case			,     batch_size=_snake_case			,     collate_fn=ds.collate_fn			,     num_workers=2)
				__lowerCAmelCase :    Tuple				=			DataLoader(_snake_case			,     batch_size=_snake_case			,     collate_fn=ds.collate_fn			,     num_workers=2			,     sampler=_snake_case)

				__lowerCAmelCase :    Tuple				=			tokenizer.pad_token_id

				def count_pad_tokens(_SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Optional[Any]="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 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     _SCREAMING_SNAKE_CASE:						int=1000			,     _SCREAMING_SNAKE_CASE:						Any=128)							->			str:

				"""simple docstring"""

				if os.getenv("USE_REAL_DATA"			,     _snake_case):
							__lowerCAmelCase :    List[Any]				=			"examples/seq2seq/wmt_en_ro"
							__lowerCAmelCase :    Optional[int]				=			max_len * 2 * 64
							if not Path(_snake_case).joinpath("train.len").exists():
										save_len_file(_snake_case			,     _snake_case)
				else:
							__lowerCAmelCase :    Union[str, Any]				=			"examples/seq2seq/test_data/wmt_en_ro"
							__lowerCAmelCase :    List[str]				=			max_len * 4
							save_len_file(_snake_case			,     _snake_case)

				__lowerCAmelCase :    Optional[Any]				=			AutoTokenizer.from_pretrained(_snake_case)
				__lowerCAmelCase :    str				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			str:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    str				=			self._get_dataset()
				__lowerCAmelCase :    List[Any]				=			set(DistributedSortishSampler(_snake_case			,     256			,     num_replicas=2			,     rank=0			,     add_extra_examples=_snake_case))
				__lowerCAmelCase :    Optional[int]				=			set(DistributedSortishSampler(_snake_case			,     256			,     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 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     _SCREAMING_SNAKE_CASE:						List[str])							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			AutoTokenizer.from_pretrained(_snake_case			,     use_fast=_snake_case)
				if tok_name == MBART_TINY:
							__lowerCAmelCase :    Dict				=			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"			,     )
							__lowerCAmelCase :    List[str]				=			train_dataset.dataset_kwargs
							assert "src_lang" in kwargs and "tgt_lang" in kwargs
				else:
							__lowerCAmelCase :    Dict				=			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			,     )
							__lowerCAmelCase :    Optional[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






"""simple docstring"""
import argparse
import os
import re

import packaging.version


__UpperCamelCase     :						Union[str, Any]						       =  '''examples/'''
__UpperCamelCase     :						str						       =  {
    '''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'''),
}
__UpperCamelCase     :						List[str]						       =  {
    '''init''': '''src/transformers/__init__.py''',
    '''setup''': '''setup.py''',
}
__UpperCamelCase     :						Optional[int]						       =  '''README.md'''

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    Dict  ,     _UpperCAmelCase					:    Any			):
 with open(_UpperCAmelCase  ,     'r'  ,     encoding='utf-8'  ,     newline='\n'			) as f:
  lowerCAmelCase    				= f.read()
 lowerCAmelCase				,lowerCAmelCase    				= REPLACE_PATTERNS[pattern]
 lowerCAmelCase    				= replace.replace('VERSION'  ,     _UpperCAmelCase			)
 lowerCAmelCase    				= re_pattern.sub(_UpperCAmelCase  ,     _UpperCAmelCase			)
 with open(_UpperCAmelCase  ,     'w'  ,     encoding='utf-8'  ,     newline='\n'			) as f:
  f.write(_UpperCAmelCase			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Union[str, Any]			):
 for folder, directories, fnames in os.walk(_UpperCAmelCase			):
  # 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(_UpperCAmelCase  ,     _UpperCAmelCase			)  ,     _UpperCAmelCase  ,     pattern='examples'			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    Dict=False			):
 for pattern, fname in REPLACE_FILES.items():
  update_version_in_file(_UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase			)
 if not patch:
  update_version_in_examples(_UpperCAmelCase			)

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= '🤗 Transformers currently provides the following architectures'
 lowerCAmelCase    				= '1. Want to contribute a new model?'
 with open(_UpperCAmelCase  ,     'r'  ,     encoding='utf-8'  ,     newline='\n'			) as f:
  lowerCAmelCase    				= f.readlines()

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

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

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

def 		_SCREAMING_SNAKE_CASE			():
 with open(REPLACE_FILES['init']  ,     'r'			) as f:
  lowerCAmelCase    				= f.read()
 lowerCAmelCase    				= REPLACE_PATTERNS['init'][0].search(_UpperCAmelCase			).groups()[0]
 return packaging.version.parse(_UpperCAmelCase			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Tuple=False			):
 lowerCAmelCase    				= 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:
  lowerCAmelCase    				= default_version.base_version
 elif patch:
  lowerCAmelCase    				= F'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
 else:
  lowerCAmelCase    				= F'{default_version.major}.{default_version.minor + 1}.0'

 # Now let's ask nicely if that's the right one.
 lowerCAmelCase    				= input(F'Which version are you releasing? [{default_version}]'			)
 if len(_UpperCAmelCase			) == 0:
  lowerCAmelCase    				= default_version

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

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= get_version()
 lowerCAmelCase    				= F'{current_version.major}.{current_version.minor + 1}.0.dev0'
 lowerCAmelCase    				= current_version.base_version

 # Check with the user we got that right.
 lowerCAmelCase    				= input(F'Which version are we developing now? [{dev_version}]'			)
 if len(_UpperCAmelCase			) == 0:
  lowerCAmelCase    				= dev_version

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


if __name__ == "__main__":
       __UpperCamelCase     :						Optional[int]						       =  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.''')
       __UpperCamelCase     :						Optional[int]						       =  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()





