




from __future__ import annotations

from PIL import Image

# Define glider example
__lowerCAmelCase	:       Dict     =	[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
__lowerCAmelCase	:       Tuple     =	[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def     __magic_name__       (				A	:      list[list[int]]     ):






			'''simple docstring'''







			a					=    []
			for i in range(len(A     )     ):
						a					=    []
						for j in range(len(cells[i]     )     ):
									# Get the number of live neighbours
									a					=    0
									if i > 0 and j > 0:
												neighbour_count += cells[i - 1][j - 1]
									if i > 0:
												neighbour_count += cells[i - 1][j]
									if i > 0 and j < len(cells[i]     ) - 1:
												neighbour_count += cells[i - 1][j + 1]
									if j > 0:
												neighbour_count += cells[i][j - 1]
									if j < len(cells[i]     ) - 1:
												neighbour_count += cells[i][j + 1]
									if i < len(A     ) - 1 and j > 0:
												neighbour_count += cells[i + 1][j - 1]
									if i < len(A     ) - 1:
												neighbour_count += cells[i + 1][j]
									if i < len(A     ) - 1 and j < len(cells[i]     ) - 1:
												neighbour_count += cells[i + 1][j + 1]

									# Rules of the game of life (excerpt from Wikipedia):
									# 1. Any live cell with two or three live neighbours survives.
									# 2. Any dead cell with three live neighbours becomes a live cell.
									# 3. All other live cells die in the next generation.
									#    Similarly, all other dead cells stay dead.
									a					=    cells[i][j] == 1
									if (
									    (alive and 2 <= neighbour_count <= 3)
									    or not alive
									    and neighbour_count == 3
									):
												next_generation_row.append(1     )
									else:
												next_generation_row.append(0     )

						next_generation.append(A     )
			return next_generation




def     __magic_name__       (				A	:      list[list[int]],						A	:      int     ):






			'''simple docstring'''







			a					=    []
			for _ in range(A     ):
						# Create output image
						a					=    Image.new("RGB",						(len(cells[0]     ), len(A     ))     )
						a					=    img.load()

						# Save cells to image
						for x in range(len(A     )     ):
									for y in range(len(cells[0]     )     ):
												a					=    255 - cells[y][x] * 255
												a					=    (colour, colour, colour)

        # Save image
						images.append(A     )
						a					=    new_generation(A     )
			return images


if __name__ == "__main__":
		__lowerCAmelCase	:       Union[str, Any]     =	generate_images(GLIDER, 16)
		images[0].save('out.gif', save_all=True, append_images=images[1:])












import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 					snake_case__							(_UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							List[str]											=      LongformerTokenizer
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      True
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      LongformerTokenizerFast
				SCREAMING_SNAKE_CASE_ :							str											=      True





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  str:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							a					=    [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							a					=    dict(zip(__lowerCamelCase      ,   range(len(__lowerCamelCase							)							)							)							)
							a					=    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							a					=    {"unk_token": "<unk>"}

							a					=    os.path.join(self.tmpdirname      ,   VOCAB_FILES_NAMES["vocab_file"]							)
							a					=    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 							__UpperCAmelCase							(   self       :  Dict      ,   **__lowerCamelCase       :  Dict							)      ->  Any:
							kwargs.update(self.special_tokens_map							)
							return self.tokenizer_class.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   **__lowerCamelCase       :  Any							)      ->  List[Any]:
							kwargs.update(self.special_tokens_map							)
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  List[Any]							)      ->  Union[str, Any]:
							a					=    "lower newer"
							a					=    "lower newer"
							return input_text, output_text





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Optional[Any]:
							a					=    self.tokenizer_class(self.vocab_file      ,   self.merges_file      ,   **self.special_tokens_map							)
							a					=    "lower newer"
							a					=    ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
							a					=    tokenizer.tokenize(__lowerCamelCase							)  # , add_prefix_space=True)
							self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase							)

							a					=    tokens + [tokenizer.unk_token]
							a					=    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase							)      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  Tuple:
							a					=    self.get_tokenizer()

							self.assertListEqual(tokenizer.encode("Hello world!"      ,   add_special_tokens=__lowerCamelCase							)      ,   [0, 3_14_14, 2_32, 3_28, 2]							)
							self.assertListEqual(
							    tokenizer.encode("Hello world! cécé herlolip 418"      ,   add_special_tokens=__lowerCamelCase							)      ,   [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]      ,   )





				@slow
				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  List[Any]:
							a					=    self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"							)

							a					=    tokenizer.encode("sequence builders"      ,   add_special_tokens=__lowerCamelCase							)
							a					=    tokenizer.encode("multi-sequence build"      ,   add_special_tokens=__lowerCamelCase							)

							a					=    tokenizer.encode(
							    "sequence builders"      ,   add_special_tokens=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase							)
							a					=    tokenizer.encode(
							    "sequence builders"      ,   "multi-sequence build"      ,   add_special_tokens=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase							)

							a					=    tokenizer.build_inputs_with_special_tokens(__lowerCamelCase							)
							a					=    tokenizer.build_inputs_with_special_tokens(__lowerCamelCase      ,   __lowerCamelCase							)

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  str:
							a					=    self.get_tokenizer()

							a					=    "Encode this sequence."
							a					=    tokenizer.byte_encoder[" ".encode("utf-8"							)[0]]

							# Testing encoder arguments
							a					=    tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase							)
							a					=    tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
							self.assertNotEqual(__lowerCamelCase      ,   __lowerCamelCase							)

							a					=    tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase							)
							a					=    tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
							self.assertEqual(__lowerCamelCase      ,   __lowerCamelCase							)

							tokenizer.add_special_tokens({"bos_token": "<s>"}							)
							a					=    tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
							a					=    tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
							self.assertNotEqual(__lowerCamelCase      ,   __lowerCamelCase							)

							# Testing spaces after special tokens
							a					=    "<mask>"
							tokenizer.add_special_tokens(
							    {"mask_token": AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase							)}							)  # mask token has a left space
							a					=    tokenizer.convert_tokens_to_ids(__lowerCamelCase							)

							a					=    "Encode <mask> sequence"
							a					=    "Encode <mask>sequence"

							a					=    tokenizer.encode(__lowerCamelCase							)
							a					=    encoded.index(__lowerCamelCase							)
							a					=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
							self.assertEqual(__lowerCamelCase      ,   __lowerCamelCase							)

							a					=    tokenizer.encode(__lowerCamelCase							)
							a					=    encoded.index(__lowerCamelCase							)
							a					=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
							self.assertNotEqual(__lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  str							)      ->  List[str]:
							pass





				def 							__UpperCAmelCase							(   self       :  int							)      ->  int:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
													a					=    self.rust_tokenizer_class.from_pretrained(__lowerCamelCase      ,   **__lowerCamelCase							)
													a					=    self.tokenizer_class.from_pretrained(__lowerCamelCase      ,   **__lowerCamelCase							)
													a					=    "A, <mask> AllenNLP sentence."
													a					=    tokenizer_r.encode_plus(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase      ,   return_token_type_ids=__lowerCamelCase							)
													a					=    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"]							)      ,   )

													a					=    tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]							)
													a					=    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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)
													self.assertSequenceEqual(tokens_r["input_ids"]      ,   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)

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





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Union[str, Any]:
							for trim_offsets, add_prefix_space in itertools.product([True, False]      ,   repeat=2							):
										a					=    self.rust_tokenizer_class.from_pretrained(
										    self.tmpdirname      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)

										a					=    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
										a					=    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

										self.assertEqual(pre_tokenizer_state["add_prefix_space"]      ,   __lowerCamelCase							)

										self.assertEqual(post_processor_state["add_prefix_space"]      ,   __lowerCamelCase							)
										self.assertEqual(post_processor_state["trim_offsets"]      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Dict:
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
							# `trim_offsets`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
													a					=    "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
													a					=    f"""{text_of_1_token} {text_of_1_token}"""

													a					=    self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)
													a					=    tokenizer_r(__lowerCamelCase      ,   return_offsets_mapping=__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]      ,   (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,   (len(__lowerCamelCase							) + 1, len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))      ,   )

													a					=    self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)
													a					=    tokenizer_r(__lowerCamelCase      ,   return_offsets_mapping=__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]      ,   (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,   (len(__lowerCamelCase							) + 1, len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))      ,   )

													a					=    self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)
													a					=    tokenizer_r(__lowerCamelCase      ,   return_offsets_mapping=__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]      ,   (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,   (len(__lowerCamelCase							), len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))      ,   )

													a					=    self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)
													a					=    tokenizer_r(__lowerCamelCase      ,   return_offsets_mapping=__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]      ,   (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,   (len(__lowerCamelCase							), len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))      ,   )

													a					=    f""" {text}"""

													# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
													#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
													# )
													# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
													# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
													# self.assertEqual(
													#     encoding.offset_mapping[1],
													#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
													# )

													a					=    self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)
													a					=    tokenizer_r(__lowerCamelCase      ,   return_offsets_mapping=__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]      ,   (1, 1 + len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,   (1 + len(__lowerCamelCase							) + 1, 1 + len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))      ,   )

													a					=    self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)
													a					=    tokenizer_r(__lowerCamelCase      ,   return_offsets_mapping=__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]      ,   (0, 1 + len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,   (1 + len(__lowerCamelCase							), 1 + len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))      ,   )

													a					=    self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase      ,   use_fast=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase							)
													a					=    tokenizer_r(__lowerCamelCase      ,   return_offsets_mapping=__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]      ,   (0, 1 + len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]      ,   (1 + len(__lowerCamelCase							), 1 + len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))      ,   )






