"""simple docstring"""






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


_A   					=    get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')

@require_sentencepiece
@require_tokenizers
class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		GPTSwaTokenizer
					a   	=		False
					a   	=		True
					a   	=		False



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

									# We have a SentencePiece fixture for testing
									SCREAMING_SNAKE_CASE__		  =				GPTSwaTokenizer(_snake_case   , eos_token="<unk>"   , bos_token="<unk>"   , pad_token="<unk>"		)

									tokenizer.save_pretrained(self.tmpdirname		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				List[str]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				"This is a test"
									SCREAMING_SNAKE_CASE__		  =				"This is a test"
									return input_text, output_text



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

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



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				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(_snake_case		)   , 2000		)



					def 			lowerCAmelCase_					(						self			:				int		)							->       List[str]:
									self.assertEqual(self.get_tokenizer().vocab_size   , 2000		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				GPTSwaTokenizer(_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				tokenizer.tokenize("This is a test"		)
									self.assertListEqual(_snake_case   , ["▁This", "▁is", "▁a", "▁t", "est"]		)

									self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case		)   , [465, 287, 265, 631, 842]		)

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

									SCREAMING_SNAKE_CASE__		  =				tokenizer.convert_tokens_to_ids(_snake_case		)
									self.assertListEqual(
									    _snake_case   , [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]   , )

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



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				GPTSwaTokenizer(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				["This is a test", "I was born in 92000, and this is falsé."]
									SCREAMING_SNAKE_CASE__		  =				[
									    [465, 287, 265, 631, 842],
									    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
									]

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

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



					@slow
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				[
									    "<|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
									SCREAMING_SNAKE_CASE__		  =				{"input_ids": [[63423, 5, 6811, 14954, 282, 816, 3821, 63466, 63425, 63462, 18, 63978, 678, 301, 1320, 63423, 63455, 63458, 18, 63982, 4246, 3940, 1901, 47789, 5547, 18994], [19630, 1100, 63446, 1342, 633, 544, 4488, 593, 5102, 2416, 63495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 58593, 22413, 9106, 546, 268, 33213, 63979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55130, 63450, 924, 63449, 2249, 4062, 1558, 318, 63504, 21498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 63443, 26801, 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=_snake_case   , model_name="AI-Sweden/gpt-sw3-126m"   , sequences=_snake_case   , )





"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				int   , _snake_case			:				Optional[int]   , _snake_case			:				Dict		)							->       Dict:
									super().__init__()

									# make sure scheduler can always be converted to DDIM
									SCREAMING_SNAKE_CASE__		  =				DDIMScheduler.from_config(scheduler.config		)

									self.register_modules(unet=_snake_case   , scheduler=_snake_case		)



					@torch.no_grad()
					def __call__(						self			:				int   , _snake_case			:				int = 1   , _snake_case			:				Optional[Union[torch.Generator, List[torch.Generator]]] = None   , _snake_case			:				float = 0.0   , _snake_case			:				int = 50   , _snake_case			:				Optional[bool] = None   , _snake_case			:				Optional[str] = "pil"   , _snake_case			:				bool = True   , )							->       Union[ImagePipelineOutput, Tuple]:

									# Sample gaussian noise to begin loop
									if isinstance(self.unet.config.sample_size   , _snake_case		):
													SCREAMING_SNAKE_CASE__		  =				(
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
									else:
													SCREAMING_SNAKE_CASE__		  =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

									if isinstance(_snake_case   , _snake_case		) and len(_snake_case		) != batch_size:
													raise ValueError(
													    F"""You have passed a list of generators of length {len(_snake_case		)}, but requested an effective batch"""
													    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""		)

									SCREAMING_SNAKE_CASE__		  =				randn_tensor(_snake_case   , generator=_snake_case   , device=self.device   , dtype=self.unet.dtype		)

									# set step values
									self.scheduler.set_timesteps(_snake_case		)

									for t in self.progress_bar(self.scheduler.timesteps		):
													# 1. predict noise model_output
													SCREAMING_SNAKE_CASE__		  =				self.unet(_snake_case   , _snake_case		).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													SCREAMING_SNAKE_CASE__		  =				self.scheduler.step(
													    _snake_case   , _snake_case   , _snake_case   , eta=_snake_case   , use_clipped_model_output=_snake_case   , generator=_snake_case		).prev_sample

									SCREAMING_SNAKE_CASE__		  =				(image / 2 + 0.5).clamp(0   , 1		)
									SCREAMING_SNAKE_CASE__		  =				image.cpu().permute(0   , 2   , 3   , 1		).numpy()
									if output_type == "pil":
													SCREAMING_SNAKE_CASE__		  =				self.numpy_to_pil(_snake_case		)

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=_snake_case		)




