


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__						(   a						: Path						,	a						: list					)     ->						Union[str, Any]:





			"""simple docstring"""
			a__			:List[Any]    	=     "\n".join(__lowercase					)
			Path(__lowercase					).open("w"					).writelines(__lowercase					)


def 					lowerCamelCase__						(   a						: Any					)     ->						Optional[int]:





			"""simple docstring"""

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







class        lowerCAmelCase_			(							lowercase_):




						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ] ,  )
						@slow
						def 			_snake_case       (						self    : Dict ,  __A    : Tuple    )   ->Optional[Any]:






									"""simple docstring"""






									a__			:Dict    	=     AutoTokenizer.from_pretrained(UpperCamelCase_    )
									a__			:Optional[Any]    	=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
									a__			:Optional[Any]    	=     max(len(tokenizer.encode(UpperCamelCase_    )    ) for a in ARTICLES    )
									a__			:Optional[Any]    	=     max(len(tokenizer.encode(UpperCamelCase_    )    ) for a in SUMMARIES    )
									a__			:Union[str, Any]    	=     4
									a__			:Optional[Any]    	=     8
									assert max_len_target > max_src_len  # Will be truncated
									assert max_len_source > max_src_len  # Will be truncated
									a__	,	a__			:Optional[int]    	=     "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
									a__			:Optional[int]    	=     SeqaSeqDataset(
									    UpperCamelCase_ ,  data_dir=UpperCamelCase_ ,  type_path="train" ,  max_source_length=UpperCamelCase_ ,  max_target_length=UpperCamelCase_ ,  src_lang=UpperCamelCase_ ,  tgt_lang=UpperCamelCase_ ,  )
									a__			:Union[str, Any]    	=     DataLoader(UpperCamelCase_ ,  batch_size=2 ,  collate_fn=train_dataset.collate_fn    )
									for batch in dataloader:
												assert isinstance(UpperCamelCase_ ,  UpperCamelCase_    )
												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__			:List[Any]    	=     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    : List[str] ,  __A    : Any    )   ->str:






									"""simple docstring"""






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






									"""simple docstring"""






									a__			:Tuple    	=     AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"    )

									a__			:Any    	=     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )    )
									a__			:Tuple    	=     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(UpperCamelCase_ ,  UpperCamelCase_ ,  128 ,  UpperCamelCase_    )
									a__			:int    	=     {x.name for x in tmp_dir.iterdir()}
									a__			:int    	=     {x.name for x in save_dir.iterdir()}
									a__			:Any    	=     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(UpperCamelCase_    ) < len(UpperCamelCase_    )
									assert len(UpperCamelCase_    ) == 1
									assert len(packed_examples[0]    ) == sum(len(UpperCamelCase_    ) 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    : Dict    )   ->int:






									"""simple docstring"""






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




						def 			_snake_case       (						self    : Tuple    )   ->Tuple:






									"""simple docstring"""






									a__	,	a__	,	a__			:Dict    	=     self._get_dataset(max_len=512    )
									a__			:Optional[int]    	=     2
									a__			:Optional[int]    	=     ds.make_sortish_sampler(UpperCamelCase_ ,  shuffle=UpperCamelCase_    )

									a__			:List[Any]    	=     DataLoader(UpperCamelCase_ ,  batch_size=UpperCamelCase_ ,  collate_fn=ds.collate_fn ,  num_workers=2    )
									a__			:Optional[Any]    	=     DataLoader(UpperCamelCase_ ,  batch_size=UpperCamelCase_ ,  collate_fn=ds.collate_fn ,  num_workers=2 ,  sampler=UpperCamelCase_    )

									a__			:Optional[Any]    	=     tokenizer.pad_token_id

									def count_pad_tokens(__A    : str ,  __A    : Dict="input_ids"    ):
												return [batch[k].eq(UpperCamelCase_    ).sum().item() for batch in data_loader]

									assert sum(count_pad_tokens(UpperCamelCase_ ,  k="labels"    )    ) < sum(count_pad_tokens(UpperCamelCase_ ,  k="labels"    )    )
									assert sum(count_pad_tokens(UpperCamelCase_    )    ) < sum(count_pad_tokens(UpperCamelCase_    )    )
									assert len(UpperCamelCase_    ) == len(UpperCamelCase_    )




						def 			_snake_case       (						self    : str ,  __A    : Tuple=1000 ,  __A    : Dict=128    )   ->str:






									"""simple docstring"""






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

									a__			:Any    	=     AutoTokenizer.from_pretrained(UpperCamelCase_    )
									a__			:Any    	=     SeqaSeqDataset(
									    UpperCamelCase_ ,  data_dir=UpperCamelCase_ ,  type_path="train" ,  max_source_length=UpperCamelCase_ ,  max_target_length=UpperCamelCase_ ,  n_obs=UpperCamelCase_ ,  )
									return ds, max_tokens, tokenizer




						def 			_snake_case       (						self    : List[str]    )   ->List[str]:






									"""simple docstring"""






									a__	,	a__	,	a__			:Optional[int]    	=     self._get_dataset()
									a__			:int    	=     set(DistributedSortishSampler(UpperCamelCase_ ,  256 ,  num_replicas=2 ,  rank=0 ,  add_extra_examples=UpperCamelCase_    )    )
									a__			:Tuple    	=     set(DistributedSortishSampler(UpperCamelCase_ ,  256 ,  num_replicas=2 ,  rank=1 ,  add_extra_examples=UpperCamelCase_    )    )
									assert idsa.intersection(UpperCamelCase_    ) == set()






						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ] ,  )
						def 			_snake_case       (						self    : int ,  __A    : Any    )   ->str:






									"""simple docstring"""






									a__			:Tuple    	=     AutoTokenizer.from_pretrained(UpperCamelCase_ ,  use_fast=UpperCamelCase_    )
									if tok_name == MBART_TINY:
												a__			:Any    	=     SeqaSeqDataset(
												    UpperCamelCase_ ,  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__			:Optional[int]    	=     train_dataset.dataset_kwargs
												assert "src_lang" in kwargs and "tgt_lang" in kwargs
									else:
												a__			:Optional[int]    	=     SeqaSeqDataset(
												    UpperCamelCase_ ,  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__			: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(UpperCamelCase_    ) == 1 if tok_name == BART_TINY else len(UpperCamelCase_    ) == 0










from __future__ import annotations



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[int]      ,					__lowercase :      int     )    ->   bool:





    """simple docstring"""

    if len(__lowercase     ) == 0:
        return False
    __A       =						len(__lowercase     ) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]      ,					__lowercase     )
    else:
        return binary_search(a_list[midpoint + 1 :]      ,					__lowercase     )


if __name__ == "__main__":
  __a   :							Tuple						= input("Enter numbers separated by comma:\n").strip()
  __a   :							Any						= [int(item.strip()) for item in user_input.split(",")]
  __a   :							List[Any]						= int(input("Enter the number to be found in the list:\n").strip())
  __a   :							Optional[int]						= "" if binary_search(sequence, target) else "not "
  print(f"""{target} was {not_str}found in {sequence}""")




