

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


UpperCAmelCase_							:					str		 =   '''bert-base-cased'''
UpperCAmelCase_							:					str		 =   '''google/pegasus-xsum'''
UpperCAmelCase_							:					str		 =   [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
UpperCAmelCase_							:					Optional[Any]		 =   ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
UpperCAmelCase_							:					List[Any]		 =   '''patrickvonplaten/t5-tiny-random'''
UpperCAmelCase_							:					str		 =   '''sshleifer/bart-tiny-random'''
UpperCAmelCase_							:					Optional[int]		 =   '''sshleifer/tiny-mbart'''
UpperCAmelCase_							:					Optional[int]		 =   '''sshleifer/tiny-marian-en-de'''
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Path			,a__    :   list       )					->				List[Any]:
	__A :			Optional[int]		    =			"""\n""".join(a__       )
	Path(a__       ).open("""w"""       ).writelines(a__       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Union[str, Any]       )					->				List[Any]:
	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(a__			,f"""{split}.source"""       )			,a__       )
		_dump_articles(os.path.join(a__			,f"""{split}.target"""       )			,a__       )
	return tmp_dir







class      lowerCamelCase_      (	_lowercase	):




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

		__A :			Tuple		    =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
		__A :			Tuple		    =			tmp_dir.joinpath("""train.source"""							).open().readlines()
		__A :			Dict		    =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
		pack_data_dir(__A					,      __A					,      128					,      __A							)
		__A :			Dict		    =			{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(__A							) < len(__A							)
		assert len(__A							) == 1
		assert len(packed_examples[0]							) == sum(len(__A							) for x in orig_examples							)
		assert orig_paths == new_paths




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




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A    ,				__A    ,				__A :			List[Any]		    =			self._get_dataset(max_len=512							)
		__A :			Union[str, Any]		    =			2
		__A :			Any		    =			ds.make_sortish_sampler(__A					,      shuffle=__A							)

		__A :			List[Any]		    =			DataLoader(__A					,      batch_size=__A					,      collate_fn=ds.collate_fn					,      num_workers=2							)
		__A :			str		    =			DataLoader(__A					,      batch_size=__A					,      collate_fn=ds.collate_fn					,      num_workers=2					,      sampler=__A							)

		__A :			int		    =			tokenizer.pad_token_id

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

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




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     Union[str, Any]=1000					,      __A						:     List[Any]=128							):
		if os.getenv("""USE_REAL_DATA"""					,      __A							):
			__A :			List[Any]		    =			"""examples/seq2seq/wmt_en_ro"""
			__A :			Union[str, Any]		    =			max_len * 2 * 64
			if not Path(__A							).joinpath("""train.len"""							).exists():
				save_len_file(__A					,      __A							)
		else:
			__A :			Union[str, Any]		    =			"""examples/seq2seq/test_data/wmt_en_ro"""
			__A :			str		    =			max_len * 4
			save_len_file(__A					,      __A							)

		__A :			Tuple		    =			AutoTokenizer.from_pretrained(__A							)
		__A :			int		    =			SeqaSeqDataset(
		    __A					,      data_dir=__A					,      type_path="""train"""					,      max_source_length=__A					,      max_target_length=__A					,      n_obs=__A					,      )
		return ds, max_tokens, tokenizer




	def        lowerCAmelCase_       (     self						:     List[str]							):
		__A    ,				__A    ,				__A :			int		    =			self._get_dataset()
		__A :			List[Any]		    =			set(DistributedSortishSampler(__A					,      256					,      num_replicas=2					,      rank=0					,      add_extra_examples=__A							)							)
		__A :			Dict		    =			set(DistributedSortishSampler(__A					,      256					,      num_replicas=2					,      rank=1					,      add_extra_examples=__A							)							)
		assert idsa.intersection(__A							) == set()




	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]					,      )
	def        lowerCAmelCase_       (     self						:     List[Any]					,      __A						:     Union[str, Any]							):
		__A :			Tuple		    =			AutoTokenizer.from_pretrained(__A					,      use_fast=__A							)
		if tok_name == MBART_TINY:
			__A :			Union[str, Any]		    =			SeqaSeqDataset(
			    __A					,      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 :			int		    =			train_dataset.dataset_kwargs
			assert "src_lang" in kwargs and "tgt_lang" in kwargs
		else:
			__A :			Tuple		    =			SeqaSeqDataset(
			    __A					,      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 :			Tuple		    =			train_dataset.dataset_kwargs
			assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
			assert len(__A							) == 1 if tok_name == BART_TINY else len(__A							) == 0








from __future__ import annotations

from collections.abc import Sequence
from typing import Literal
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str       )					->				str | Literal[False]:
	__A :			Tuple		    =			list(a__       )
	__A :			Optional[int]		    =			list(a__       )
	__A :			int		    =			0
	for i in range(len(a__       )       ):
		if lista[i] != lista[i]:
			count += 1
			__A :			int		    =			"""_"""
	if count > 1:
		return False
	else:
		return "".join(a__       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[str]       )					->				list[str]:
	__A :			Optional[Any]		    =			[]
	while True:
		__A :			Tuple		    =			["""$"""] * len(a__       )
		__A :			Union[str, Any]		    =			[]
		for i in range(len(a__       )       ):
			for j in range(i + 1			,len(a__       )       ):
				__A :			int		    =			compare_string(binary[i]			,binary[j]       )
				if k is False:
					__A :			List[str]		    =			"""*"""
					__A :			Any		    =			"""*"""
					temp.append("""X"""       )
		for i in range(len(a__       )       ):
			if checka[i] == "$":
				pi.append(binary[i]       )
		if len(a__       ) == 0:
			return pi
		__A :			Optional[Any]		    =			list(set(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   Sequence[float]       )					->				list[str]:
	__A :			List[str]		    =			[]
	for minterm in minterms:
		__A :			List[Any]		    =			""""""
		for _ in range(a__       ):
			__A :			Union[str, Any]		    =			str(minterm % 2       ) + string
			minterm //= 2
		temp.append(a__       )
	return temp
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str			,a__    :   int       )					->				bool:
	__A :			Optional[Any]		    =			list(a__       )
	__A :			Tuple		    =			list(a__       )
	__A :			Any		    =			0
	for i in range(len(a__       )       ):
		if lista[i] != lista[i]:
			count_n += 1
	return count_n == count
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[list[int]]			,a__    :   list[str]       )					->				list[str]:
	__A :			Optional[int]		    =			[]
	__A :			Tuple		    =			[0] * len(a__       )
	for i in range(len(chart[0]       )       ):
		__A :			str		    =			0
		__A :			Any		    =			-1
		for j in range(len(a__       )       ):
			if chart[j][i] == 1:
				count += 1
				__A :			Optional[Any]		    =			j
		if count == 1:
			__A :			int		    =			1
	for i in range(len(a__       )       ):
		if select[i] == 1:
			for j in range(len(chart[0]       )       ):
				if chart[i][j] == 1:
					for k in range(len(a__       )       ):
						__A :			List[str]		    =			0
			temp.append(prime_implicants[i]       )
	while True:
		__A :			Optional[Any]		    =			0
		__A :			Any		    =			-1
		__A :			int		    =			0
		for i in range(len(a__       )       ):
			__A :			List[Any]		    =			chart[i].count(1       )
			if count_n > max_n:
				__A :			Dict		    =			count_n
				__A :			Tuple		    =			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(a__       )       ):
					__A :			Union[str, Any]		    =			0
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[str]			,a__    :   list[str]       )					->				list[list[int]]:
	__A :			Any		    =			[[0 for x in range(len(a__       )       )] for x in range(len(a__       )       )]
	for i in range(len(a__       )       ):
		__A :			List[Any]		    =			prime_implicants[i].count("""_"""       )
		for j in range(len(a__       )       ):
			if is_for_table(prime_implicants[i]			,binary[j]			,a__       ):
				__A :			Union[str, Any]		    =			1

	return chart
def 							__SCREAMING_SNAKE_CASE     (   )					->				None:
	__A :			Any		    =			int(input("""Enter the no. of variables\n"""       )       )
	__A :			List[str]		    =			[
	    float(a__       )
	    for x in input(
	        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""       ).split()
	]
	__A :			Dict		    =			decimal_to_binary(a__			,a__       )

	__A :			Union[str, Any]		    =			check(a__       )
	print("""Prime Implicants are:"""       )
	print(a__       )
	__A :			Optional[Any]		    =			prime_implicant_chart(a__			,a__       )

	__A :			Any		    =			selection(a__			,a__       )
	print("""Essential Prime Implicants are:"""       )
	print(a__       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()





