


import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_rjieba
@require_tokenizers
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						RoFormerTokenizer
    __magic_name__         =						RoFormerTokenizerFast
    __magic_name__         =						True
    __magic_name__         =						True
    def 						a_     (			self       ):
         super().setUp()
    def 						a_     (			self     ,   **__snake_case       ):
         return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''     ,   **lowerCAmelCase__       )
    def 						a_     (			self     ,   **__snake_case       ):
         return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''     ,   **lowerCAmelCase__       )
    def 						a_     (			self       ):
         snake_case											=							'''永和服装饰品有限公司,今天天气非常好'''
         snake_case											=							'''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
         return input_text, output_text
    def 						a_     (			self       ):
         snake_case											=							self.get_tokenizer()
         snake_case					,    snake_case											=							self.get_chinese_input_output_texts()
         snake_case											=							tokenizer.tokenize(lowerCAmelCase__       )

         self.assertListEqual(lowerCAmelCase__     ,   output_text.split()       )

         snake_case											=							tokens + [tokenizer.unk_token]
         snake_case											=							[2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )     ,   lowerCAmelCase__       )
    def 						a_     (			self       ):
         snake_case											=							self.get_rust_tokenizer()
         snake_case					,    snake_case											=							self.get_chinese_input_output_texts()
         snake_case											=							tokenizer.tokenize(lowerCAmelCase__       )
         self.assertListEqual(lowerCAmelCase__     ,   output_text.split()       )
         snake_case											=							tokens + [tokenizer.unk_token]
         snake_case											=							[2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )     ,   lowerCAmelCase__       )
    def 						a_     (			self       ):
         pass
    def 						a_     (			self       ):
         pass




    def 						a_     (			self       ):
         pass











'''simple docstring'''

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__		:     int			=							argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
lowercase__		:     Union[str, Any]			=							parser.parse_args()


lowercase__		:     Union[str, Any]			=							'cpu'
lowercase__		:     Tuple			=							'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

lowercase__		:     List[Any]			=							'path-to-your-trained-model'
lowercase__		:     Optional[Any]			=							StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
   lowercase__		:     Union[str, Any]			=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase__		:     Dict			=							pipe.to(device)

# to channels last
lowercase__		:     Any			=							pipe.unet.to(memory_format=torch.channels_last)
lowercase__		:     Dict			=							pipe.vae.to(memory_format=torch.channels_last)
lowercase__		:     Any			=							pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
   lowercase__		:     Union[str, Any]			=							pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowercase__		:     List[str]			=							torch.randn(2, 4, 64, 64)
lowercase__		:     int			=							torch.rand(1) * 9_99
lowercase__		:     Optional[int]			=							torch.randn(2, 77, 7_68)
lowercase__		:     List[str]			=							(sample, timestep, encoder_hidden_status)
try:
   lowercase__		:     Optional[Any]			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
   lowercase__		:     Any			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Tuple			=							ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Any			=							ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   lowercase__		:     Union[str, Any]			=							ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase__		:     Optional[Any]			=							6_66
lowercase__		:     Tuple			=							torch.Generator(device).manual_seed(seed)
lowercase__		:     Dict			=							{'generator': generator}
if args.steps is not None:
   lowercase__		:     int			=							args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
   lowercase__		:     Dict			=							pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('generated.png')



