

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   __UpperCamelCase		(   lowercase       ,       unittest.TestCase  ):
	SCREAMING_SNAKE_CASE__      			=       BartTokenizer
	SCREAMING_SNAKE_CASE__      			=       BartTokenizerFast
	SCREAMING_SNAKE_CASE__      			=       True
	SCREAMING_SNAKE_CASE__      			=       filter_roberta_detectors




	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







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

		UpperCAmelCase_    			=     os.path.join(self.tmpdirname						,		VOCAB_FILES_NAMES["vocab_file"]							)
		UpperCAmelCase_    			=     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(lowerCAmelCase							) + "\n"							)
		with open(self.merges_file						,		"w"						,		encoding="utf-8"							) as fp:
			fp.write("\n".join(lowerCAmelCase							)							)




	def  __A   (						self		:  Any						,		**lowerCAmelCase		:  Optional[Any]							):


		'''simple docstring'''







		kwargs.update(self.special_tokens_map							)
		return self.tokenizer_class.from_pretrained(self.tmpdirname						,		**lowerCAmelCase							)




	def  __A   (						self		:  List[str]						,		**lowerCAmelCase		:  Optional[int]							):


		'''simple docstring'''







		kwargs.update(self.special_tokens_map							)
		return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,		**lowerCAmelCase							)




	def  __A   (						self		:  Optional[int]						,		lowerCAmelCase		:  List[str]							):


		'''simple docstring'''







		return "lower newer", "lower newer"




	@cached_property
	def  __A   (						self		:  Optional[int]							):


		'''simple docstring'''







		return BartTokenizer.from_pretrained("facebook/bart-large"							)




	@cached_property
	def  __A   (						self		:  Union[str, Any]							):


		'''simple docstring'''







		return BartTokenizerFast.from_pretrained("facebook/bart-large"							)




	@require_torch
	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_    			=     ["A long paragraph for summarization.", "Another paragraph for summarization."]
		UpperCAmelCase_    			=     [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

			self.assertEqual((2, 9)						,		batch.input_ids.shape							)
			self.assertEqual((2, 9)						,		batch.attention_mask.shape							)
			UpperCAmelCase_    			=     batch.input_ids.tolist()[0]
			self.assertListEqual(lowerCAmelCase						,		lowerCAmelCase							)
			# Test that special tokens are reset




	@require_torch
	def  __A   (						self		:  str							):


		'''simple docstring'''







		UpperCAmelCase_    			=     ["A long paragraph for summarization.", "Another paragraph for summarization."]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			UpperCAmelCase_    			=     tokenizer(lowerCAmelCase						,		padding=lowerCAmelCase						,		return_tensors="pt"							)
			# check if input_ids are returned and no labels
			self.assertIn("input_ids"						,		lowerCAmelCase							)
			self.assertIn("attention_mask"						,		lowerCAmelCase							)
			self.assertNotIn("labels"						,		lowerCAmelCase							)
			self.assertNotIn("decoder_attention_mask"						,		lowerCAmelCase							)




	@require_torch
	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     [
		    "Summary of the text.",
		    "Another summary.",
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			UpperCAmelCase_    			=     tokenizer(text_target=lowerCAmelCase						,		max_length=32						,		padding="max_length"						,		return_tensors="pt"							)
			self.assertEqual(32						,		targets["input_ids"].shape[1]							)




	@require_torch
	def  __A   (						self		:  Any							):


		'''simple docstring'''







		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			UpperCAmelCase_    			=     tokenizer(
			    ["I am a small frog" * 1_024, "I am a small frog"]						,		padding=lowerCAmelCase						,		truncation=lowerCAmelCase						,		return_tensors="pt"							)
			self.assertIsInstance(lowerCAmelCase						,		lowerCAmelCase							)
			self.assertEqual(batch.input_ids.shape						,		(2, 1_024)							)




	@require_torch
	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_    			=     ["A long paragraph for summarization."]
		UpperCAmelCase_    			=     [
		    "Summary of the text.",
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			UpperCAmelCase_    			=     tokenizer(lowerCAmelCase						,		return_tensors="pt"							)
			UpperCAmelCase_    			=     tokenizer(text_target=lowerCAmelCase						,		return_tensors="pt"							)
			UpperCAmelCase_    			=     inputs["input_ids"]
			UpperCAmelCase_    			=     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  __A   (						self		:  int							):


		'''simple docstring'''







		pass




	def  __A   (						self		:  Dict							):


		'''simple docstring'''







		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
				UpperCAmelCase_    			=     self.rust_tokenizer_class.from_pretrained(lowerCAmelCase						,		**lowerCAmelCase							)
				UpperCAmelCase_    			=     self.tokenizer_class.from_pretrained(lowerCAmelCase						,		**lowerCAmelCase							)
				UpperCAmelCase_    			=     "A, <mask> AllenNLP sentence."
				UpperCAmelCase_    			=     tokenizer_r.encode_plus(lowerCAmelCase						,		add_special_tokens=lowerCAmelCase						,		return_token_type_ids=lowerCAmelCase							)
				UpperCAmelCase_    			=     tokenizer_p.encode_plus(lowerCAmelCase						,		add_special_tokens=lowerCAmelCase						,		return_token_type_ids=lowerCAmelCase							)

				# 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_    			=     tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]							)
				UpperCAmelCase_    			=     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(
				    lowerCAmelCase						,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]							)
				self.assertSequenceEqual(
				    lowerCAmelCase						,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]							)


from string import ascii_uppercase



def  __lowerCAmelCase (  A    ,       A    ,       A = False    ,       A = ascii_uppercase      ):
	UpperCAmelCase_    			=     keyword.upper()
	UpperCAmelCase_    			=     plaintext.upper()
	UpperCAmelCase_    			=     set(A      )

	# create a list of unique characters in the keyword - their order matters
	# it determines how we will map plaintext characters to the ciphertext
	UpperCAmelCase_    			=     []
	for char in keyword:
		if char in alphabet_set and char not in unique_chars:
			unique_chars.append(A      )
    # the number of those unique characters will determine the number of rows
	UpperCAmelCase_    			=     len(A      )

	# create a shifted version of the alphabet
	UpperCAmelCase_    			=     unique_chars + [
	    char for char in alphabet if char not in unique_chars
	]

	# create a modified alphabet by splitting the shifted alphabet into rows
	UpperCAmelCase_    			=     [
	    shifted_alphabet[k : k + num_unique_chars_in_keyword]
	    for k in range(0    ,       26    ,       A      )
	]

	# map the alphabet characters to the modified alphabet characters
	# going 'vertically' through the modified alphabet - consider columns first
	UpperCAmelCase_    			=     {}
	UpperCAmelCase_    			=     0
	for column in range(A      ):
		for row in modified_alphabet:
			# if current row (the last one) is too short, break out of loop
			if len(A      ) <= column:
				break

			# map current letter to letter in modified alphabet
			UpperCAmelCase_    			=     row[column]
			letter_index += 1

	if verbose:
		print(A      )
	# create the encrypted text by mapping the plaintext to the modified alphabet
	return "".join(mapping[char] if char in mapping else char for char in plaintext      )


if __name__ == "__main__":
				# example use
				print(mixed_keyword("""college""", """UNIVERSITY"""))