

"""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 					_lowerCamelCase  (  self		)   ->					str:
									torch.manual_seed(0		)
									lowerCamelCase__						=			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 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
									lowerCamelCase__						=			self.dummy_uncond_unet
									lowerCamelCase__						=			KarrasVeScheduler()

									lowerCamelCase__						=			KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__    ,						scheduler=SCREAMING_SNAKE_CASE__		)
									pipe.to(SCREAMING_SNAKE_CASE__		)
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__		)

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

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

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

									assert image.shape == (1, 32, 32, 3)
									lowerCamelCase__						=			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 					_lowerCamelCase  (  self		)   ->					Tuple:
									lowerCamelCase__						=			"google/ncsnpp-celebahq-256"
									lowerCamelCase__						=			UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__		)
									lowerCamelCase__						=			KarrasVeScheduler()

									lowerCamelCase__						=			KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__    ,						scheduler=SCREAMING_SNAKE_CASE__		)
									pipe.to(SCREAMING_SNAKE_CASE__		)
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__		)

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

									lowerCamelCase__						=			image[0, -3:, -3:, -1]
									assert image.shape == (1, 256, 256, 3)
									lowerCamelCase__						=			np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86]		)

									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 		_A       (			__a				,       unittest.TestCase					):
      __a								=	PhobertTokenizer
      __a								=	False





      def 					_lowerCamelCase  (  self		)   ->					Any:
         super().setUp()

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

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





      def 					_lowerCamelCase  (  self    ,						**SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         kwargs.update(self.special_tokens_map		)
         return PhobertTokenizer.from_pretrained(self.tmpdirname    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			"Tôi là VinAI Research"
         lowerCamelCase__						=			"T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
         return input_text, output_text





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

         lowerCamelCase__						=			tokens + [tokenizer.unk_token]

         lowerCamelCase__						=			[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(SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__		)




