



"""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  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       @property
       def 	lowercase__							(				self     :    Any       )					->		Optional[int]:


             '''simple docstring'''




             torch.manual_seed(0       )
             UpperCAmelCase_     					=				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 	lowercase__							(				self     :    Optional[Any]       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.dummy_uncond_unet
             UpperCAmelCase_     					=				KarrasVeScheduler()

             UpperCAmelCase_     					=				KarrasVePipeline(unet=_UpperCAmelCase						,							scheduler=_UpperCAmelCase       )
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(num_inference_steps=2						,							generator=_UpperCAmelCase						,							output_type="numpy"       ).images

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(num_inference_steps=2						,							generator=_UpperCAmelCase						,							output_type="numpy"						,							return_dict=_UpperCAmelCase       )[0]

             UpperCAmelCase_     					=				image[0, -3:, -3:, -1]
             UpperCAmelCase_     					=				image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             UpperCAmelCase_     					=				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  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       def 	lowercase__							(				self     :    List[Any]       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				"google/ncsnpp-celebahq-256"
             UpperCAmelCase_     					=				UNetaDModel.from_pretrained(_UpperCAmelCase       )
             UpperCAmelCase_     					=				KarrasVeScheduler()

             UpperCAmelCase_     					=				KarrasVePipeline(unet=_UpperCAmelCase						,							scheduler=_UpperCAmelCase       )
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(num_inference_steps=20						,							generator=_UpperCAmelCase						,							output_type="numpy"       ).images

             UpperCAmelCase_     					=				image[0, -3:, -3:, -1]
             assert image.shape == (1, 256, 256, 3)
             UpperCAmelCase_     					=				np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]       )

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







"""simple docstring"""




import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		List[str]       		=				PhobertTokenizer
			lowerCamelCase__ :		Union[str, Any]       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Any:
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						SCREAMING_SNAKE_CASE__										=   ["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""]
						SCREAMING_SNAKE_CASE__										=   dict(zip(__UpperCAmelCase     ,					range(len(__UpperCAmelCase							)							)							)							)
						SCREAMING_SNAKE_CASE__										=   ["""#version: 0.2""", """l à</w>"""]
						SCREAMING_SNAKE_CASE__										=   {"""unk_token""": """<unk>"""}

						SCREAMING_SNAKE_CASE__										=   os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["""vocab_file"""]							)
						SCREAMING_SNAKE_CASE__										=   os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["""merges_file"""]							)

						with open(self.vocab_file     ,					"""w"""     ,					encoding="""utf-8"""							) as fp:
									for token in vocab_tokens:
												fp.write(F"""{token} {vocab_tokens[token]}\n"""							)
						with open(self.merges_file     ,					"""w"""     ,					encoding="""utf-8"""							) as fp:
									fp.write("""\n""".join(__UpperCAmelCase							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					**__UpperCAmelCase					:    int							) ->     Optional[int]:
						kwargs.update(self.special_tokens_map							)
						return PhobertTokenizer.from_pretrained(self.tmpdirname     ,					**__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    int							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   """Tôi là VinAI Research"""
						SCREAMING_SNAKE_CASE__										=   """T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
						return input_text, output_text





			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   PhobertTokenizer(self.vocab_file     ,					self.merges_file     ,					**self.special_tokens_map							)
						SCREAMING_SNAKE_CASE__										=   """Tôi là VinAI Research"""
						SCREAMING_SNAKE_CASE__										=   """T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
						SCREAMING_SNAKE_CASE__										=   tokenizer.tokenize(__UpperCAmelCase							)
						print(__UpperCAmelCase							)
						self.assertListEqual(__UpperCAmelCase     ,					__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   tokens + [tokenizer.unk_token]

						SCREAMING_SNAKE_CASE__										=   [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase							)     ,					__UpperCAmelCase							)


