import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class     a_			(						SCREAMING_SNAKE_CASE	,					unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_     			= BioGptTokenizer
 UpperCAmelCase_     			= False




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowerCAmelCase__					     = [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            'w</w>',
            'r</w>',
            't</w>',
            'lo',
            'low',
            'er</w>',
            'low</w>',
            'lowest</w>',
            'newer</w>',
            'wider</w>',
            '<unk>',
        ]
        lowerCAmelCase__					     = dict(zip(lowercase__      ,   range(len(lowercase__))))
        lowerCAmelCase__					     = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

        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') as fp:
               fp.write(json.dumps(lowercase__))
        with open(self.merges_file      ,   'w') as fp:
               fp.write('\n'.join(lowercase__))




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = 'lower newer'
        lowerCAmelCase__					     = 'lower newer'
        return input_text, output_text




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = BioGptTokenizer(self.vocab_file      ,   self.merges_file)

        lowerCAmelCase__					     = 'lower'
        lowerCAmelCase__					     = ['low', 'er</w>']
        lowerCAmelCase__					     = tokenizer.tokenize(lowercase__)
        self.assertListEqual(lowercase__      ,   lowercase__)

        lowerCAmelCase__					     = tokens + ['<unk>']
        lowerCAmelCase__					     = [14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__)      ,   lowercase__)




 @slow
 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = BioGptTokenizer.from_pretrained('microsoft/biogpt')

        lowerCAmelCase__					     = tokenizer.encode('sequence builders'      ,   add_special_tokens=lowercase__)
        lowerCAmelCase__					     = tokenizer.encode('multi-sequence build'      ,   add_special_tokens=lowercase__)

        lowerCAmelCase__					     = tokenizer.build_inputs_with_special_tokens(lowercase__)
        lowerCAmelCase__					     = tokenizer.build_inputs_with_special_tokens(lowercase__      ,   lowercase__)

        self.assertTrue(encoded_sentence == [2] + text)
        self.assertTrue(encoded_pair == [2] + text + [2] + text_a)

import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = argparse.ArgumentParser()
       parser.add_argument(
           '-m'     ,  '--pretrained_model_name_or_path'     ,  type=lowerCAmelCase__     ,  default=lowerCAmelCase__     ,  required=lowerCAmelCase__     ,  help='Path to pretrained model or model identifier from huggingface.co/models.'     ,  )
       parser.add_argument(
           '-c'     ,  '--caption'     ,  type=lowerCAmelCase__     ,  default='robotic cat with wings'     ,  help='Text used to generate images.'     ,  )
       parser.add_argument(
           '-n'     ,  '--images_num'     ,  type=lowerCAmelCase__     ,  default=4     ,  help='How much images to generate.'     ,  )
       parser.add_argument(
           '-s'     ,  '--seed'     ,  type=lowerCAmelCase__     ,  default=4_2     ,  help='Seed for random process.'     ,  )
       parser.add_argument(
           '-ci'     ,  '--cuda_id'     ,  type=lowerCAmelCase__     ,  default=0     ,  help='cuda_id.'     ,  )
       lowerCAmelCase__					     = parser.parse_args()
       return args






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       if not len(lowerCAmelCase__     ) == rows * cols:
              raise ValueError('The specified number of rows and columns are not correct.'     )

       lowerCAmelCase__     , lowerCAmelCase__					     = imgs[0].size
       lowerCAmelCase__					     = Image.new('RGB'     ,  size=(cols * w, rows * h)     )
       lowerCAmelCase__     , lowerCAmelCase__					     = grid.size

       for i, img in enumerate(lowerCAmelCase__     ):
              grid.paste(lowerCAmelCase__     ,  box=(i % cols * w, i // cols * h)     )
       return grid






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__="robotic cat with wings"     ,  lowerCAmelCase__=7.5     ,  lowerCAmelCase__=5_0     ,  lowerCAmelCase__=1     ,  lowerCAmelCase__=4_2     ,  ):
       lowerCAmelCase__					     = torch.Generator(pipeline.device     ).manual_seed(lowerCAmelCase__     )
       lowerCAmelCase__					     = pipeline(
           lowerCAmelCase__     ,  guidance_scale=lowerCAmelCase__     ,  num_inference_steps=lowerCAmelCase__     ,  generator=lowerCAmelCase__     ,  num_images_per_prompt=lowerCAmelCase__     ,  ).images
       lowerCAmelCase__					     = int(math.sqrt(lowerCAmelCase__     )     )
       lowerCAmelCase__					     = image_grid(lowerCAmelCase__     ,  rows=_rows     ,  cols=num_images_per_prompt // _rows     )
       return grid, images


lowerCAmelCase__     						=			parse_args()
# Load models and create wrapper for stable diffusion
lowerCAmelCase__     						=			CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
lowerCAmelCase__     						=			CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
lowerCAmelCase__     						=			AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
lowerCAmelCase__     						=			UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')

lowerCAmelCase__     						=			StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase__     						=			lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
    lowerCAmelCase__     						=			load(args.pretrained_model_name_or_path, model=unet)
    unet.eval()
    setattr(pipeline, 'unet', unet)
else:
    lowerCAmelCase__     						=			unet.to(torch.device('cuda', args.cuda_id))
lowerCAmelCase__     						=			pipeline.to(unet.device)
lowerCAmelCase__						,		lowerCAmelCase__     						=			generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
lowerCAmelCase__     						=			os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
    image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))
