





'''simple docstring'''







import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 					__a      (lowerCamelCase       ,		unittest.TestCase     ):
							__a					:   Optional[Any]									=       MgpstrTokenizer
							__a					:   str									=       False
							__a					:   Union[str, Any]									=       {}
							__a					:   Any									=       False

							def     UpperCAmelCase__	(							self							:     str   )					->       str:







													"""simple docstring"""
													super().setUp()

													# fmt: off
													UpperCAmelCase_			:      Union[str, Any]     						=  ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
													# fmt: on
													UpperCAmelCase_			:      Dict     						=  dict(zip(__magic_name__       ,     range(len(__magic_name__   )   )   )   )

													UpperCAmelCase_			:      Optional[int]     						=  os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['''vocab_file''']   )
													with open(self.vocab_file       ,     '''w'''       ,     encoding='''utf-8'''   ) as fp:
																			fp.write(json.dumps(__magic_name__   ) + '''\n'''   )

							def     UpperCAmelCase__	(							self							:     List[Any]       ,     **__magic_name__							:     str   )					->       int:







													"""simple docstring"""
													return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,     **__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     Optional[int]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  '''tester'''
													UpperCAmelCase_			:      Any     						=  '''tester'''
													return input_text, output_text

							@unittest.skip('''MGP-STR always lower cases letters.'''   )
							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Union[str, Any]:







													"""simple docstring"""
													pass

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  self.get_tokenizers(do_lower_case=__magic_name__   )
													for tokenizer in tokenizers:
																			with self.subTest(F"""{tokenizer.__class__.__name__}"""   ):
																									UpperCAmelCase_			:      int     						=  '''[SPECIAL_TOKEN]'''

																									tokenizer.add_special_tokens({'''cls_token''': special_token}   )
																									UpperCAmelCase_			:      List[Any]     						=  tokenizer.encode([special_token]       ,     add_special_tokens=__magic_name__   )
																									self.assertEqual(len(__magic_name__   )       ,     1   )

																									UpperCAmelCase_			:      int     						=  tokenizer.decode(__magic_name__       ,     skip_special_tokens=__magic_name__   )
																									self.assertTrue(special_token not in decoded   )

							def     UpperCAmelCase__	(							self							:     Dict   )					->       Any:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  self.get_tokenizers()
													for tokenizer in tokenizers:
																			with self.subTest(F"""{tokenizer.__class__.__name__}"""   ):
																									UpperCAmelCase_						,       UpperCAmelCase_			:      Any     						=  self.get_input_output_texts(__magic_name__   )

																									UpperCAmelCase_			:      int     						=  tokenizer.tokenize(__magic_name__   )
																									UpperCAmelCase_			:      Optional[Any]     						=  tokenizer.convert_tokens_to_ids(__magic_name__   )
																									UpperCAmelCase_			:      Any     						=  tokenizer.encode(__magic_name__       ,     add_special_tokens=__magic_name__   )
																									self.assertListEqual(__magic_name__       ,     __magic_name__   )

																									UpperCAmelCase_			:      int     						=  tokenizer.convert_ids_to_tokens(__magic_name__   )
																									self.assertNotEqual(len(__magic_name__   )       ,     0   )
																									UpperCAmelCase_			:      Dict     						=  tokenizer.decode(__magic_name__   )
																									self.assertIsInstance(__magic_name__       ,     __magic_name__   )

																									self.assertEqual(text_a.replace(''' '''       ,     ''''''   )       ,     __magic_name__   )

							@unittest.skip('''MGP-STR tokenizer only handles one sequence.'''   )
							def     UpperCAmelCase__	(							self							:     str   )					->       Optional[int]:







													"""simple docstring"""
													pass



							@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''   )
							def     UpperCAmelCase__	(							self							:     int   )					->       List[Any]:







													"""simple docstring"""
													pass












'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 					__a      (unittest.TestCase     ):

							@property
							def     UpperCAmelCase__	(							self							:     Dict   )					->       str:







													"""simple docstring"""
													torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  UNetaDModel(
													    block_out_channels=(32, 64)       ,     layers_per_block=2       ,     sample_size=32       ,     in_channels=3       ,     out_channels=3       ,     down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       ,     up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       ,     )
													return model



							def     UpperCAmelCase__	(							self							:     Dict   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  self.dummy_uncond_unet
													UpperCAmelCase_			:      Dict     						=  KarrasVeScheduler()

													UpperCAmelCase_			:      Union[str, Any]     						=  KarrasVePipeline(unet=__magic_name__       ,     scheduler=__magic_name__   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[int]     						=  pipe(num_inference_steps=2       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Tuple     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      str     						=  pipe(num_inference_steps=2       ,     generator=__magic_name__       ,     output_type='''numpy'''       ,     return_dict=__magic_name__   )[0]

													UpperCAmelCase_			:      Union[str, Any]     						=  image[0, -3:, -3:, -1]
													UpperCAmelCase_			:      Optional[Any]     						=  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													UpperCAmelCase_			:      Dict     						=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

													assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2





@slow
@require_torch
class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     int   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  '''google/ncsnpp-celebahq-256'''
													UpperCAmelCase_			:      List[str]     						=  UNetaDModel.from_pretrained(__magic_name__   )
													UpperCAmelCase_			:      List[Any]     						=  KarrasVeScheduler()

													UpperCAmelCase_			:      Any     						=  KarrasVePipeline(unet=__magic_name__       ,     scheduler=__magic_name__   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  pipe(num_inference_steps=20       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Any     						=  image[0, -3:, -3:, -1]
													assert image.shape == (1, 2_56, 2_56, 3)
													UpperCAmelCase_			:      Optional[Any]     						=  np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

													assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2





