



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


__SCREAMING_SNAKE_CASE           =  get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")

@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_    ( _A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  GPTSwaTokenizer
						a__   =  False
						a__   =  True
						a__   =  False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Tuple:
									super().setUp()

									# We have a SentencePiece fixture for testing
									A							:					Optional[Any]              =						GPTSwaTokenizer(__lowerCamelCase	,	eos_token="<unk>"	,	bos_token="<unk>"	,	pad_token="<unk>" )

									tokenizer.save_pretrained(self.tmpdirname )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			Optional[Any] )      ->				Optional[Any]:
									A							:					Any              =						"This is a test"
									A							:					Optional[int]              =						"This is a test"
									return input_text, output_text







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Union[str, Any]:
									A							:					Optional[Any]              =						"<s>"
									A							:					Tuple              =						1

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Optional[int]:
									A							:					str              =						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(__lowerCamelCase )	,	20_00 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				str:
									self.assertEqual(self.get_tokenizer().vocab_size	,	20_00 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Any:
									A							:					Optional[int]              =						GPTSwaTokenizer(__lowerCamelCase )

									A							:					Optional[int]              =						tokenizer.tokenize("This is a test" )
									self.assertListEqual(__lowerCamelCase	,	["▁This", "▁is", "▁a", "▁t", "est"] )

									self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase )	,	[4_65, 2_87, 2_65, 6_31, 8_42] )

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

									A							:					List[str]              =						tokenizer.convert_tokens_to_ids(__lowerCamelCase )
									self.assertListEqual(
									    __lowerCamelCase	,	[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]	,	)

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Tuple:
									A							:					Optional[Any]              =						GPTSwaTokenizer(__lowerCamelCase )
									A							:					Dict              =						["This is a test", "I was born in 92000, and this is falsé."]
									A							:					Dict              =						[
									    [4_65, 2_87, 2_65, 6_31, 8_42],
									    [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
									]

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

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







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Any:
									A							:					List[str]              =						[
									    "<|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
									A							:					Dict              =						{"input_ids": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 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=__lowerCamelCase	,	model_name="AI-Sweden/gpt-sw3-126m"	,	sequences=__lowerCamelCase	,	)




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch

@require_torch
class 	lowerCamelCase_    :






						'''simple docstring'''







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				str:
									return self.get_dummy_input()







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									if self.block_type == "down":
												return (4, 32, 16, 16)
									elif self.block_type == "mid":
												return (4, 32, 32, 32)
									elif self.block_type == "up":
												return (4, 32, 64, 64)

									raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			Union[str, Any]=True	,	__lowerCamelCase      :			int=False	,	__lowerCamelCase      :			int=False	,	__lowerCamelCase      :			Optional[int]=False	,	)      ->				Dict:
									A							:					Optional[Any]              =						4
									A							:					List[str]              =						32
									A							:					Any              =						(32, 32)

									A							:					str              =						torch.manual_seed(0 )
									A							:					int              =						torch.device(__lowerCamelCase )
									A							:					List[str]              =						(batch_size, num_channels) + sizes
									A							:					Dict              =						randn_tensor(__lowerCamelCase	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )
									A							:					int              =						{"hidden_states": hidden_states}

									if include_temb:
												A							:					Any              =						1_28
												A							:					List[str]              =						randn_tensor((batch_size, temb_channels)	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )

									if include_res_hidden_states_tuple:
												A							:					str              =						torch.manual_seed(1 )
												A							:					Tuple              =						(randn_tensor(__lowerCamelCase	,	generator=__lowerCamelCase	,	device=__lowerCamelCase ),)

									if include_encoder_hidden_states:
												A							:					Dict              =						floats_tensor((batch_size, 32, 32) ).to(__lowerCamelCase )

									if include_skip_sample:
												A							:					Optional[int]              =						randn_tensor(((batch_size, 3) + sizes)	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )

									return dummy_input







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Union[str, Any]:
									A							:					Dict              =						{
									    "in_channels": 32,
									    "out_channels": 32,
									    "temb_channels": 1_28,
									}
									if self.block_type == "up":
												A							:					Dict              =						32

									if self.block_type == "mid":
												init_dict.pop("out_channels" )

									A							:					str              =						self.dummy_input
									return init_dict, inputs_dict







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Optional[int] )      ->				Union[str, Any]:
									A       , A							:					str              =						self.prepare_init_args_and_inputs_for_common()
									A							:					List[Any]              =						self.block_class(**__lowerCamelCase )
									unet_block.to(__lowerCamelCase )
									unet_block.eval()

									with torch.no_grad():
												A							:					int              =						unet_block(**__lowerCamelCase )

									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												A							:					Union[str, Any]              =						output[0]

									self.assertEqual(output.shape	,	self.output_shape )

									A							:					Any              =						output[0, -1, -3:, -3:]
									A							:					Union[str, Any]              =						torch.tensor(__lowerCamelCase ).to(__lowerCamelCase )
									assert torch_all_close(output_slice.flatten()	,	__lowerCamelCase	,	atol=5e-3 )







						@unittest.skipIf(torch_device == "mps"	,	"Training is not supported in mps" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Dict:
									A       , A							:					Tuple              =						self.prepare_init_args_and_inputs_for_common()
									A							:					str              =						self.block_class(**__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.train()
									A							:					Optional[int]              =						model(**__lowerCamelCase )

									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												A							:					Optional[Any]              =						output[0]

									A							:					List[str]              =						torch.device(__lowerCamelCase )
									A							:					List[str]              =						randn_tensor(output.shape	,	device=__lowerCamelCase )
									A							:					Dict              =						torch.nn.functional.mse_loss(__lowerCamelCase	,	__lowerCamelCase )
									loss.backward()