
from __future__ import annotations




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    list[str] | None = None  ,       lowerCAmelCase__							:    dict[str, float] | None = None  ,       lowerCAmelCase__							:    bool = False  ,       ):
 __a			:  Tuple		    =  cipher_alphabet or [chr(lowerCAmelCase__  ) for i in range(9_7  ,       1_2_3  )]

 # If the argument is None or the user provided an empty dictionary
 if not frequencies_dict:
  # Frequencies of letters in the english language (how much they show up)
  __a			:  int		    =  {
      '''a''': 0.0_84_97,
      '''b''': 0.0_14_92,
      '''c''': 0.0_22_02,
      '''d''': 0.0_42_53,
      '''e''': 0.1_11_62,
      '''f''': 0.0_22_28,
      '''g''': 0.0_20_15,
      '''h''': 0.0_60_94,
      '''i''': 0.0_75_46,
      '''j''': 0.0_01_53,
      '''k''': 0.0_12_92,
      '''l''': 0.0_40_25,
      '''m''': 0.0_24_06,
      '''n''': 0.0_67_49,
      '''o''': 0.0_75_07,
      '''p''': 0.0_19_29,
      '''q''': 0.0_00_95,
      '''r''': 0.0_75_87,
      '''s''': 0.0_63_27,
      '''t''': 0.0_93_56,
      '''u''': 0.0_27_58,
      '''v''': 0.0_09_78,
      '''w''': 0.0_25_60,
      '''x''': 0.0_01_50,
      '''y''': 0.0_19_94,
      '''z''': 0.0_00_77,
  }
 else:
  # Custom frequencies dictionary
  __a			:  Union[str, Any]		    =  frequencies_dict

 if not case_sensitive:
  __a			:  Any		    =  ciphertext.lower()

 # Chi squared statistic values
 __a			:  dict[int, tuple[float, str]]		    =  {}

 # cycle through all of the shifts
 for shift in range(len(lowerCAmelCase__  )  ):
  __a			:  Tuple		    =  ''''''

  # decrypt the message with the shift
  for letter in ciphertext:
   try:
    # Try to index the letter in the alphabet
    __a			:  Optional[int]		    =  (alphabet_letters.index(letter.lower()  ) - shift) % len(
        lowerCAmelCase__  )
    decrypted_with_shift += (
        alphabet_letters[new_key].upper()
        if case_sensitive and letter.isupper()
        else alphabet_letters[new_key]
    )
   except ValueError:
    # Append the character if it isn't in the alphabet
    decrypted_with_shift += letter

  __a			:  str		    =  0.0

  # Loop through each letter in the decoded message with the shift
  for letter in decrypted_with_shift:
   if case_sensitive:
    __a			:  Optional[int]		    =  letter.lower()
    if letter in frequencies:
     # Get the amount of times the letter occurs in the message
     __a			:  Tuple		    =  decrypted_with_shift.lower().count(lowerCAmelCase__  )

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     __a			:  Any		    =  frequencies[letter] * occurrences

     # Complete the chi squared statistic formula
     __a			:  Optional[int]		    =  ((occurrences - expected) ** 2) / expected

     # Add the margin of error to the total chi squared statistic
     chi_squared_statistic += chi_letter_value
   else:
    if letter.lower() in frequencies:
     # Get the amount of times the letter occurs in the message
     __a			:  Optional[Any]		    =  decrypted_with_shift.count(lowerCAmelCase__  )

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     __a			:  Dict		    =  frequencies[letter] * occurrences

     # Complete the chi squared statistic formula
     __a			:  int		    =  ((occurrences - expected) ** 2) / expected

     # Add the margin of error to the total chi squared statistic
     chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
  __a			:  Optional[int]		    =  (
      chi_squared_statistic,
      decrypted_with_shift,
  )

 # Get the most likely cipher by finding the cipher with the smallest chi squared
 # statistic
 def chi_squared_statistic_values_sorting_key(lowerCAmelCase__							:    int  ) -> tuple[float, str]:
  return chi_squared_statistic_values[key]

 __a			:  int		    =  min(
     lowerCAmelCase__  ,       key=lowerCAmelCase__  ,       )

 # Get all the data from the most likely cipher (key, decoded message)
 (
     (
     __a
 )    ,	(
     __a
 )    ,	
 )			:  int		    =  chi_squared_statistic_values[most_likely_cipher]

 # Return the data on the most likely shift
 return (
     most_likely_cipher,
     most_likely_cipher_chi_squared_value,
     decoded_most_likely_cipher,
 )


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    ,	__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    ,	__a    ,	__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    ,	__a    ,	__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    ,	__a    ,	__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
