





'''simple docstring'''

import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class      SCREAMING_SNAKE_CASE__      (   snake_case__							,unittest.TestCase		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	BartTokenizer
					SCREAMING_SNAKE_CASE							=	BartTokenizerFast
					SCREAMING_SNAKE_CASE							=	True
					SCREAMING_SNAKE_CASE							=	filter_roberta_detectors
					def 		lowerCamelCase_   (   self       :      Optional[Any]		):







							"""simple docstring"""
							super().setUp()
							__UpperCAmelCase	:							List[Any]     =     [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							__UpperCAmelCase	:							Dict     =     dict(zip(UpperCAmelCase_		,	range(len(UpperCAmelCase_		)		)		)		)
							__UpperCAmelCase	:							str     =     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							__UpperCAmelCase	:							Optional[Any]     =     {"unk_token": "<unk>"}

							__UpperCAmelCase	:							Dict     =     os.path.join(self.tmpdirname		,	VOCAB_FILES_NAMES["vocab_file"]		)
							__UpperCAmelCase	:							Optional[int]     =     os.path.join(self.tmpdirname		,	VOCAB_FILES_NAMES["merges_file"]		)
							with open(self.vocab_file		,	"w"		,	encoding="utf-8"		) as fp:
									fp.write(json.dumps(UpperCAmelCase_		) + "\n"		)
							with open(self.merges_file		,	"w"		,	encoding="utf-8"		) as fp:
									fp.write("\n".join(UpperCAmelCase_		)		)
					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	**UpperCAmelCase_       :      int		):







							"""simple docstring"""
							kwargs.update(self.special_tokens_map		)
							return self.tokenizer_class.from_pretrained(self.tmpdirname		,	**UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      List[str]		,	**UpperCAmelCase_       :      Optional[Any]		):







							"""simple docstring"""
							kwargs.update(self.special_tokens_map		)
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,	**UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      Optional[int]		):







							"""simple docstring"""
							return "lower newer", "lower newer"
					@cached_property
					def 		lowerCamelCase_   (   self       :      List[Any]		):







							"""simple docstring"""
							return BartTokenizer.from_pretrained("facebook/bart-large"		)
					@cached_property
					def 		lowerCamelCase_   (   self       :      Any		):







							"""simple docstring"""
							return BartTokenizerFast.from_pretrained("facebook/bart-large"		)
					@require_torch
					def 		lowerCamelCase_   (   self       :      Union[str, Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     ["A long paragraph for summarization.", "Another paragraph for summarization."]
							__UpperCAmelCase	:							Optional[int]     =     [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									__UpperCAmelCase	:							Tuple     =     tokenizer(UpperCAmelCase_		,	max_length=len(UpperCAmelCase_		)		,	padding=UpperCAmelCase_		,	return_tensors="pt"		)
									self.assertIsInstance(UpperCAmelCase_		,	UpperCAmelCase_		)

									self.assertEqual((2, 9)		,	batch.input_ids.shape		)
									self.assertEqual((2, 9)		,	batch.attention_mask.shape		)
									__UpperCAmelCase	:							Optional[Any]     =     batch.input_ids.tolist()[0]
									self.assertListEqual(UpperCAmelCase_		,	UpperCAmelCase_		)
									# Test that special tokens are reset
					@require_torch
					def 		lowerCamelCase_   (   self       :      Optional[int]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[Any]     =     ["A long paragraph for summarization.", "Another paragraph for summarization."]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									__UpperCAmelCase	:							List[Any]     =     tokenizer(UpperCAmelCase_		,	padding=UpperCAmelCase_		,	return_tensors="pt"		)
									# check if input_ids are returned and no labels
									self.assertIn("input_ids"		,	UpperCAmelCase_		)
									self.assertIn("attention_mask"		,	UpperCAmelCase_		)
									self.assertNotIn("labels"		,	UpperCAmelCase_		)
									self.assertNotIn("decoder_attention_mask"		,	UpperCAmelCase_		)
					@require_torch
					def 		lowerCamelCase_   (   self       :      Optional[Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     [
							    "Summary of the text.",
							    "Another summary.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									__UpperCAmelCase	:							Any     =     tokenizer(text_target=UpperCAmelCase_		,	max_length=32		,	padding="max_length"		,	return_tensors="pt"		)
									self.assertEqual(32		,	targets["input_ids"].shape[1]		)
					@require_torch
					def 		lowerCamelCase_   (   self       :      Optional[int]		):







							"""simple docstring"""
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									__UpperCAmelCase	:							Dict     =     tokenizer(
									    ["I am a small frog" * 1_024, "I am a small frog"]		,	padding=UpperCAmelCase_		,	truncation=UpperCAmelCase_		,	return_tensors="pt"		)
									self.assertIsInstance(UpperCAmelCase_		,	UpperCAmelCase_		)
									self.assertEqual(batch.input_ids.shape		,	(2, 1_024)		)
					@require_torch
					def 		lowerCamelCase_   (   self       :      Optional[Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[Any]     =     ["A long paragraph for summarization."]
							__UpperCAmelCase	:							Optional[int]     =     [
							    "Summary of the text.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									__UpperCAmelCase	:							str     =     tokenizer(UpperCAmelCase_		,	return_tensors="pt"		)
									__UpperCAmelCase	:							int     =     tokenizer(text_target=UpperCAmelCase_		,	return_tensors="pt"		)
									__UpperCAmelCase	:							Any     =     inputs["input_ids"]
									__UpperCAmelCase	:							Tuple     =     targets["input_ids"]
									self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
									self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
									self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
									self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)
					def 		lowerCamelCase_   (   self       :      Optional[int]		):







							"""simple docstring"""
							pass






					def 		lowerCamelCase_   (   self       :      Tuple		):







							"""simple docstring"""
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
											__UpperCAmelCase	:							Tuple     =     self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_		,	**UpperCAmelCase_		)
											__UpperCAmelCase	:							Tuple     =     self.tokenizer_class.from_pretrained(UpperCAmelCase_		,	**UpperCAmelCase_		)
											__UpperCAmelCase	:							Any     =     "A, <mask> AllenNLP sentence."
											__UpperCAmelCase	:							int     =     tokenizer_r.encode_plus(UpperCAmelCase_		,	add_special_tokens=UpperCAmelCase_		,	return_token_type_ids=UpperCAmelCase_		)
											__UpperCAmelCase	:							str     =     tokenizer_p.encode_plus(UpperCAmelCase_		,	add_special_tokens=UpperCAmelCase_		,	return_token_type_ids=UpperCAmelCase_		)

											# token_type_ids should put 0 everywhere
											self.assertEqual(sum(tokens_r["token_type_ids"]		)		,	sum(tokens_p["token_type_ids"]		)		)

											# attention_mask should put 1 everywhere, so sum over length should be 1
											self.assertEqual(
											    sum(tokens_r["attention_mask"]		) / len(tokens_r["attention_mask"]		)		,	sum(tokens_p["attention_mask"]		) / len(tokens_p["attention_mask"]		)		,	)

											__UpperCAmelCase	:							List[Any]     =     tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]		)
											__UpperCAmelCase	:							List[Any]     =     tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]		)

											# Rust correctly handles the space before the mask while python doesnt
											self.assertSequenceEqual(tokens_p["input_ids"]		,	[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
											self.assertSequenceEqual(tokens_r["input_ids"]		,	[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)

											self.assertSequenceEqual(
											    UpperCAmelCase_		,	["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]		)
											self.assertSequenceEqual(
											    UpperCAmelCase_		,	["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]		)











'''simple docstring'''

def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       ):
		__UpperCAmelCase	:							Optional[int]     =     list(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     list(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     len(_UpperCAmelCase  )
		__UpperCAmelCase	:							Dict     =     len(_UpperCAmelCase  )

		__UpperCAmelCase	:							int     =     [
		    [0 for _ in range(len_destination_seq + 1  )] for _ in range(len_source_seq + 1  )
		]
		__UpperCAmelCase	:							Any     =     [
		    ["0" for _ in range(len_destination_seq + 1  )] for _ in range(len_source_seq + 1  )
		]

		for i in range(1,       len_source_seq + 1  ):
				__UpperCAmelCase	:							Optional[Any]     =     i * delete_cost
				__UpperCAmelCase	:							Optional[int]     =     F"D{source_seq[i - 1]:c}"

		for i in range(1,       len_destination_seq + 1  ):
				__UpperCAmelCase	:							Optional[int]     =     i * insert_cost
				__UpperCAmelCase	:							List[Any]     =     F"I{destination_seq[i - 1]:c}"

		for i in range(1,       len_source_seq + 1  ):
				for j in range(1,       len_destination_seq + 1  ):
						if source_seq[i - 1] == destination_seq[j - 1]:
								__UpperCAmelCase	:							List[str]     =     costs[i - 1][j - 1] + copy_cost
								__UpperCAmelCase	:							Tuple     =     F"C{source_seq[i - 1]:c}"
						else:
								__UpperCAmelCase	:							Optional[int]     =     costs[i - 1][j - 1] + replace_cost
								__UpperCAmelCase	:							Dict     =     F"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]  )

						if costs[i - 1][j] + delete_cost < costs[i][j]:
								__UpperCAmelCase	:							Union[str, Any]     =     costs[i - 1][j] + delete_cost
								__UpperCAmelCase	:							int     =     F"D{source_seq[i - 1]:c}"

						if costs[i][j - 1] + insert_cost < costs[i][j]:
								__UpperCAmelCase	:							List[str]     =     costs[i][j - 1] + insert_cost
								__UpperCAmelCase	:							Dict     =     F"I{destination_seq[j - 1]:c}"

		return costs, ops


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		if i == 0 and j == 0:
				return []
		else:
				if ops[i][j][0] == "C" or ops[i][j][0] == "R":
						__UpperCAmelCase	:							int     =     assemble_transformation(_UpperCAmelCase,       i - 1,       j - 1  )
						seq.append(ops[i][j]  )
						return seq
				elif ops[i][j][0] == "D":
						__UpperCAmelCase	:							List[Any]     =     assemble_transformation(_UpperCAmelCase,       i - 1,       _UpperCAmelCase  )
						seq.append(ops[i][j]  )
						return seq
				else:
						__UpperCAmelCase	:							Optional[Any]     =     assemble_transformation(_UpperCAmelCase,       _UpperCAmelCase,       j - 1  )
						seq.append(ops[i][j]  )
						return seq


if __name__ == "__main__":
			lowerCAmelCase__	,			lowerCAmelCase__      :   Optional[int]      	=			compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

			lowerCAmelCase__      :   Optional[Any]      	=			len(operations)
			lowerCAmelCase__      :   int      	=			len(operations[0])
			lowerCAmelCase__      :   Dict      	=			assemble_transformation(operations, m - 1, n - 1)

			lowerCAmelCase__      :   str      	=			list("Python")
			lowerCAmelCase__      :   Any      	=			0
			lowerCAmelCase__      :   Dict      	=			0

			with open("min_cost.txt", "w") as file:
						for op in sequence:
									print("".join(string))

									if op[0] == "C":
												file.write("%-16s" % "Copy %c" % op[1])
												file.write("\t\t\t" + "".join(string))
												file.write("\r\n")

												cost -= 1
									elif op[0] == "R":
												lowerCAmelCase__      :   Optional[int]      	=			op[2]

												file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))
												file.write("\t\t" + "".join(string))
												file.write("\r\n")

												cost += 1
									elif op[0] == "D":
												string.pop(i)

												file.write("%-16s" % "Delete %c" % op[1])
												file.write("\t\t\t" + "".join(string))
												file.write("\r\n")

												cost += 2
									else:
												string.insert(i, op[1])

												file.write("%-16s" % "Insert %c" % op[1])
												file.write("\t\t\t" + "".join(string))
												file.write("\r\n")

												cost += 2

									i += 1

						print("".join(string))
						print("Cost: ", cost)

						file.write("\r\nMinimum cost: " + str(cost))




