






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


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

def        UpperCamelCase		(			_A,       _A					):






						"""simple docstring"""







						__magic_name__     :  Dict  		=   """\n""".join(snake_case__					)
						Path(snake_case__					).open("""w"""					).writelines(snake_case__					)

def        UpperCamelCase		(			_A					):






						"""simple docstring"""







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





class 			snake_case__   (				_SCREAMING_SNAKE_CASE ):
	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]     ,  )
	@slow
	def      __magic_name__							(							self     ,  lowerCAmelCase__					)				-> str:
							__magic_name__     :  Optional[int]  		=   AutoTokenizer.from_pretrained(lowerCAmelCase_					)
							__magic_name__     :  Optional[int]  		=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
							__magic_name__     :  Union[str, Any]  		=   max(len(tokenizer.encode(lowerCAmelCase_					)					) for a in ARTICLES					)
							__magic_name__     :  Tuple  		=   max(len(tokenizer.encode(lowerCAmelCase_					)					) for a in SUMMARIES					)
							__magic_name__     :  Any  		=   4
							__magic_name__     :  Tuple  		=   8
							assert max_len_target > max_src_len  # Will be truncated
							assert max_len_source > max_src_len  # Will be truncated
							__magic_name__     :  List[str]  		=   """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
							__magic_name__     :  str  		=   SeqaSeqDataset(
							    lowerCAmelCase_     ,  data_dir=lowerCAmelCase_     ,  type_path="""train"""     ,  max_source_length=lowerCAmelCase_     ,  max_target_length=lowerCAmelCase_     ,  src_lang=lowerCAmelCase_     ,  tgt_lang=lowerCAmelCase_     ,  )
							__magic_name__     :  List[str]  		=   DataLoader(lowerCAmelCase_     ,  batch_size=2     ,  collate_fn=train_dataset.collate_fn					)
							for batch in dataloader:
													assert isinstance(lowerCAmelCase_     ,  lowerCAmelCase_					)
													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
													__magic_name__     :  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      __magic_name__							(							self     ,  lowerCAmelCase__					)				-> Tuple:
							__magic_name__     :  Any  		=   AutoTokenizer.from_pretrained(lowerCAmelCase_					)
							__magic_name__     :  int  		=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
							__magic_name__     :  int  		=   max(len(tokenizer.encode(lowerCAmelCase_					)					) for a in ARTICLES					)
							__magic_name__     :  Optional[int]  		=   max(len(tokenizer.encode(lowerCAmelCase_					)					) for a in SUMMARIES					)
							__magic_name__     :  str  		=   4
							__magic_name__     :  List[Any]  		=   LegacySeqaSeqDataset(
							    lowerCAmelCase_     ,  data_dir=lowerCAmelCase_     ,  type_path="""train"""     ,  max_source_length=20     ,  max_target_length=lowerCAmelCase_     ,  )
							__magic_name__     :  int  		=   DataLoader(lowerCAmelCase_     ,  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      __magic_name__							(							self					)				-> Any:
							__magic_name__     :  Optional[int]  		=   AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""					)

							__magic_name__     :  Union[str, Any]  		=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
							__magic_name__     :  Optional[Any]  		=   tmp_dir.joinpath("""train.source"""					).open().readlines()
							__magic_name__     :  Dict  		=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
							pack_data_dir(lowerCAmelCase_     ,  lowerCAmelCase_     ,  1_28     ,  lowerCAmelCase_					)
							__magic_name__     :  List[Any]  		=   {x.name for x in tmp_dir.iterdir()}
							__magic_name__     :  str  		=   {x.name for x in save_dir.iterdir()}
							__magic_name__     :  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(lowerCAmelCase_					) < len(lowerCAmelCase_					)
							assert len(lowerCAmelCase_					) == 1
							assert len(packed_examples[0]					) == sum(len(lowerCAmelCase_					) for x in orig_examples					)
							assert orig_paths == new_paths
	@pytest.mark.skipif(not FAIRSEQ_AVAILABLE     ,  reason="""This test requires fairseq"""					)
	def      __magic_name__							(							self					)				-> Optional[Any]:
							if not FAIRSEQ_AVAILABLE:
													return
							__magic_name__     :  str  		=   self._get_dataset(max_len=64					)
							__magic_name__     :  List[Any]  		=   64
							__magic_name__     :  int  		=   ds.make_dynamic_sampler(lowerCAmelCase_     ,  required_batch_size_multiple=lowerCAmelCase_					)
							__magic_name__     :  Union[str, Any]  		=   [len(lowerCAmelCase_					) for x in batch_sampler]
							assert len(set(lowerCAmelCase_					)					) > 1  # it's not dynamic batch size if every batch is the same length
							assert sum(lowerCAmelCase_					) == len(lowerCAmelCase_					)  # no dropped or added examples
							__magic_name__     :  Optional[int]  		=   DataLoader(lowerCAmelCase_     ,  batch_sampler=lowerCAmelCase_     ,  collate_fn=ds.collate_fn     ,  num_workers=2					)
							__magic_name__     :  List[Any]  		=   []
							__magic_name__     :  Tuple  		=   []
							for batch in data_loader:
													__magic_name__     :  Tuple  		=   batch["""input_ids"""].shape
													__magic_name__     :  Any  		=   src_shape[0]
													assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
													__magic_name__     :  int  		=   np.product(batch["""input_ids"""].shape					)
													num_src_per_batch.append(lowerCAmelCase_					)
													if num_src_tokens > (max_tokens * 1.1):
																			failures.append(lowerCAmelCase_					)
							assert num_src_per_batch[0] == max(lowerCAmelCase_					)
							if failures:
													raise AssertionError(F'too many tokens in {len(lowerCAmelCase_					)} batches'					)
	def      __magic_name__							(							self					)				-> Optional[Any]:
							__magic_name__     :  Tuple  		=   self._get_dataset(max_len=5_12					)
							__magic_name__     :  List[str]  		=   2
							__magic_name__     :  str  		=   ds.make_sortish_sampler(lowerCAmelCase_     ,  shuffle=lowerCAmelCase_					)

							__magic_name__     :  Dict  		=   DataLoader(lowerCAmelCase_     ,  batch_size=lowerCAmelCase_     ,  collate_fn=ds.collate_fn     ,  num_workers=2					)
							__magic_name__     :  Union[str, Any]  		=   DataLoader(lowerCAmelCase_     ,  batch_size=lowerCAmelCase_     ,  collate_fn=ds.collate_fn     ,  num_workers=2     ,  sampler=lowerCAmelCase_					)

							__magic_name__     :  Optional[int]  		=   tokenizer.pad_token_id

							def count_pad_tokens(lowerCAmelCase__     ,  lowerCAmelCase__="input_ids"					):
													return [batch[k].eq(lowerCAmelCase_					).sum().item() for batch in data_loader]

							assert sum(count_pad_tokens(lowerCAmelCase_     ,  k="""labels"""					)					) < sum(count_pad_tokens(lowerCAmelCase_     ,  k="""labels"""					)					)
							assert sum(count_pad_tokens(lowerCAmelCase_					)					) < sum(count_pad_tokens(lowerCAmelCase_					)					)
							assert len(lowerCAmelCase_					) == len(lowerCAmelCase_					)
	def      __magic_name__							(							self     ,  lowerCAmelCase__=10_00     ,  lowerCAmelCase__=1_28					)				-> Optional[int]:
							if os.getenv("""USE_REAL_DATA"""     ,  lowerCAmelCase_					):
													__magic_name__     :  str  		=   """examples/seq2seq/wmt_en_ro"""
													__magic_name__     :  Tuple  		=   max_len * 2 * 64
													if not Path(lowerCAmelCase_					).joinpath("""train.len"""					).exists():
																			save_len_file(lowerCAmelCase_     ,  lowerCAmelCase_					)
							else:
													__magic_name__     :  Optional[int]  		=   """examples/seq2seq/test_data/wmt_en_ro"""
													__magic_name__     :  Optional[Any]  		=   max_len * 4
													save_len_file(lowerCAmelCase_     ,  lowerCAmelCase_					)

							__magic_name__     :  List[Any]  		=   AutoTokenizer.from_pretrained(lowerCAmelCase_					)
							__magic_name__     :  Optional[Any]  		=   SeqaSeqDataset(
							    lowerCAmelCase_     ,  data_dir=lowerCAmelCase_     ,  type_path="""train"""     ,  max_source_length=lowerCAmelCase_     ,  max_target_length=lowerCAmelCase_     ,  n_obs=lowerCAmelCase_     ,  )
							return ds, max_tokens, tokenizer
	def      __magic_name__							(							self					)				-> Optional[Any]:
							__magic_name__     :  Optional[Any]  		=   self._get_dataset()
							__magic_name__     :  Tuple  		=   set(DistributedSortishSampler(lowerCAmelCase_     ,  2_56     ,  num_replicas=2     ,  rank=0     ,  add_extra_examples=lowerCAmelCase_					)					)
							__magic_name__     :  Dict  		=   set(DistributedSortishSampler(lowerCAmelCase_     ,  2_56     ,  num_replicas=2     ,  rank=1     ,  add_extra_examples=lowerCAmelCase_					)					)
							assert idsa.intersection(lowerCAmelCase_					) == set()







	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]     ,  )
	def      __magic_name__							(							self     ,  lowerCAmelCase__					)				-> Any:
							__magic_name__     :  int  		=   AutoTokenizer.from_pretrained(lowerCAmelCase_     ,  use_fast=lowerCAmelCase_					)
							if tok_name == MBART_TINY:
													__magic_name__     :  int  		=   SeqaSeqDataset(
													    lowerCAmelCase_     ,  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"""     ,  )
													__magic_name__     :  List[Any]  		=   train_dataset.dataset_kwargs
													assert "src_lang" in kwargs and "tgt_lang" in kwargs
							else:
													__magic_name__     :  int  		=   SeqaSeqDataset(
													    lowerCAmelCase_     ,  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     ,  )
													__magic_name__     :  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(lowerCAmelCase_					) == 1 if tok_name == BART_TINY else len(lowerCAmelCase_					) == 0








"""simple docstring"""
from __future__ import annotations

from collections.abc import Sequence
from typing import Literal






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Optional[int]						     = list(snake_case__       )
	A_		:    List[Any]						     = list(snake_case__       )
	A_		:    List[str]						     = 0
	for i in range(len(snake_case__       )       ):
		if lista[i] != lista[i]:
			count += 1
			A_		:    Optional[int]						     = """_"""
	if count > 1:
		return False
	else:
		return "".join(snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    List[str]						     = []
	while True:
		A_		:    Any						     = ["""$"""] * len(snake_case__       )
		A_		:    int						     = []
		for i in range(len(snake_case__       )       ):
			for j in range(i + 1 , len(snake_case__       )       ):
				A_		:    int						     = compare_string(binary[i] , binary[j]       )
				if k is False:
					A_		:    Optional[int]						     = """*"""
					A_		:    str						     = """*"""
					temp.append("""X"""       )
		for i in range(len(snake_case__       )       ):
			if checka[i] == "$":
				pi.append(binary[i]       )
		if len(snake_case__       ) == 0:
			return pi
		A_		:    Tuple						     = list(set(snake_case__       )       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    str						     = []
	for minterm in minterms:
		A_		:    Any						     = """"""
		for _ in range(snake_case__       ):
			A_		:    int						     = str(minterm % 2       ) + string
			minterm //= 2
		temp.append(snake_case__       )
	return temp






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__       ):
	A_		:    str						     = list(snake_case__       )
	A_		:    List[Any]						     = list(snake_case__       )
	A_		:    Optional[Any]						     = 0
	for i in range(len(snake_case__       )       ):
		if lista[i] != lista[i]:
			count_n += 1
	return count_n == count






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    str						     = []
	A_		:    Tuple						     = [0] * len(snake_case__       )
	for i in range(len(chart[0]       )       ):
		A_		:    Optional[int]						     = 0
		A_		:    List[str]						     = -1
		for j in range(len(snake_case__       )       ):
			if chart[j][i] == 1:
				count += 1
				A_		:    Union[str, Any]						     = j
		if count == 1:
			A_		:    str						     = 1
	for i in range(len(snake_case__       )       ):
		if select[i] == 1:
			for j in range(len(chart[0]       )       ):
				if chart[i][j] == 1:
					for k in range(len(snake_case__       )       ):
						A_		:    Optional[int]						     = 0
			temp.append(prime_implicants[i]       )
	while True:
		A_		:    List[str]						     = 0
		A_		:    List[str]						     = -1
		A_		:    Union[str, Any]						     = 0
		for i in range(len(snake_case__       )       ):
			A_		:    List[str]						     = chart[i].count(1       )
			if count_n > max_n:
				A_		:    Union[str, Any]						     = count_n
				A_		:    Optional[int]						     = i

		if max_n == 0:
			return temp

		temp.append(prime_implicants[rem]       )

		for i in range(len(chart[0]       )       ):
			if chart[rem][i] == 1:
				for j in range(len(snake_case__       )       ):
					A_		:    Optional[Any]						     = 0






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Optional[int]						     = [[0 for x in range(len(snake_case__       )       )] for x in range(len(snake_case__       )       )]
	for i in range(len(snake_case__       )       ):
		A_		:    List[str]						     = prime_implicants[i].count("""_"""       )
		for j in range(len(snake_case__       )       ):
			if is_for_table(prime_implicants[i] , binary[j] , snake_case__       ):
				A_		:    Union[str, Any]						     = 1

	return chart






def       __UpperCamelCase						(		):
	A_		:    Union[str, Any]						     = int(input("""Enter the no. of variables\n"""       )       )
	A_		:    List[Any]						     = [
	    float(snake_case__       )
	    for x in input(
	        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""       ).split()
	]
	A_		:    List[str]						     = decimal_to_binary(snake_case__ , snake_case__       )

	A_		:    str						     = check(snake_case__       )
	print("""Prime Implicants are:"""       )
	print(snake_case__       )
	A_		:    List[str]						     = prime_implicant_chart(snake_case__ , snake_case__       )

	A_		:    int						     = selection(snake_case__ , snake_case__       )
	print("""Essential Prime Implicants are:"""       )
	print(snake_case__       )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

