






import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase :      str										=				get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")



@require_sentencepiece
@require_tokenizers
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		GPTSwaTokenizer
					a_     =		False
					a_     =		True
					a_     =		False



					def 	_lowercase					( self					:  Optional[Any]	):
						super().setUp()

						# We have a SentencePiece fixture for testing
						snake_case__			:				Tuple       =  GPTSwaTokenizer(__A							,		eos_token="<unk>"							,		bos_token="<unk>"							,		pad_token="<unk>"	)

						tokenizer.save_pretrained(self.tmpdirname	)



					def 	_lowercase					( self					:  Optional[Any]							,		__A					:  Optional[Any]	):
						snake_case__			:				List[Any]       =  "This is a test"
						snake_case__			:				Any       =  "This is a test"
						return input_text, output_text



					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Optional[int]       =  "<s>"
						snake_case__			:				Optional[int]       =  1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A	)							,		__A	)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A	)							,		__A	)



					def 	_lowercase					( self					:  Any	):
						snake_case__			:				int       =  list(self.get_tokenizer().get_vocab().keys()	)

						self.assertEqual(vocab_keys[0]							,		"<unk>"	)
						self.assertEqual(vocab_keys[1]							,		"<s>"	)
						self.assertEqual(vocab_keys[-1]							,		"j"	)
						self.assertEqual(len(__A	)							,		2_0_0_0	)



					def 	_lowercase					( self					:  Optional[int]	):
						self.assertEqual(self.get_tokenizer().vocab_size							,		2_0_0_0	)



					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				int       =  GPTSwaTokenizer(__A	)

						snake_case__			:				List[Any]       =  tokenizer.tokenize("This is a test"	)
						self.assertListEqual(__A							,		["▁This", "▁is", "▁a", "▁t", "est"]	)

						self.assertListEqual(tokenizer.convert_tokens_to_ids(__A	)							,		[4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2]	)

						snake_case__			:				Optional[int]       =  tokenizer.tokenize("I was born in 92000, and this is falsé."	)
						# fmt: off
						self.assertListEqual(
						    __A							,		["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]							,		)
						# fmt: on

						snake_case__			:				Union[str, Any]       =  tokenizer.convert_tokens_to_ids(__A	)
						self.assertListEqual(
						    __A							,		[2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]							,		)

						snake_case__			:				List[Any]       =  tokenizer.convert_ids_to_tokens(__A	)
						# fmt: off
						self.assertListEqual(
						    __A							,		["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]	)
						# fmt: on



					def 	_lowercase					( self					:  int	):
						snake_case__			:				Union[str, Any]       =  GPTSwaTokenizer(__A	)
						snake_case__			:				List[Any]       =  ["This is a test", "I was born in 92000, and this is falsé."]
						snake_case__			:				Optional[Any]       =  [
						    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
						    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
						]

						# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
						for text, expected_ids in zip(__A							,		__A	):
							self.assertListEqual(tokenizer.encode_fast(__A	)							,		__A	)

						# Test that decode_fast returns the input text
						for text, token_ids in zip(__A							,		__A	):
							self.assertEqual(tokenizer.decode_fast(__A	)							,		__A	)






					@slow
					def 	_lowercase					( self					:  Any	):
						snake_case__			:				Any       =  [
						    "<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')",
						    "Hey there, how are you doing this fine day?",
						    "This is a text with a trailing spaces followed by a dot     .",
						    "Häj sväjs lillebrör! =)",
						    "Det är inget fel på Mr. Cool",
						]

						# fmt: off
						snake_case__			:				Optional[int]       =  {"input_ids": [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[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, 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, 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]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
						# fmt: on
						self.tokenizer_integration_test_util(
						    expected_encoding=__A							,		model_name="AI-Sweden/gpt-sw3-126m"							,		sequences=__A							,		)











from datetime import datetime

import matplotlib.pyplot as plt
import torch







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	for param in module.parameters():
		snake_case__			:				Tuple       =  False







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				Any       =  "cuda" if torch.cuda.is_available() else "cpu"
	if torch.backends.mps.is_available() and torch.backends.mps.is_built():
		snake_case__			:				List[Any]       =  "mps"
	if device == "mps":
		print(
		    "WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
		    " errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
		    " with generations."       )
	return device







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				List[str]       =  plt.imshow(snake_case_       )
	fig.axes.get_xaxis().set_visible(snake_case_       )
	fig.axes.get_yaxis().set_visible(snake_case_       )
	plt.show()







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				str       =  datetime.now()
	snake_case__			:				Optional[Any]       =  current_time.strftime("%H:%M:%S"       )
	return timestamp



