

'''simple docstring'''

import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 					lowerCAmelCase       ( A				,     unittest.TestCase				):
       lowerCAmelCase_				=	HerbertTokenizer
       lowerCAmelCase_				=	HerbertTokenizerFast
       lowerCAmelCase_				=	True







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            super().setUp()

            # Use a simpler test file without japanese/chinese characters
            with open(f'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''  , encoding='utf-8' ) as f_data:
                 __lowercase     						=f_data.read().replace('\n\n'  , '\n' ).strip()

            __lowercase     						=[
                '<s>',
                '</s>',
                '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>',
                ',</w>',
                '<unk>',
            ]
            __lowercase     						=dict(zip(__lowercase  , range(len(__lowercase ) ) ) )
            __lowercase     						=['l o 123', 'lo w 1456', 'e r</w> 1789', '']

            __lowercase     						=os.path.join(self.tmpdirname  , VOCAB_FILES_NAMES['vocab_file'] )
            __lowercase     						=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     :	Optional[Any]  , __lowercase     :	str ):







            """simple docstring"""







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







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            __lowercase     						=self.tokenizer_class(vocab_file=self.vocab_file  , merges_file=self.merges_file )

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

            __lowercase     						=tokens + ['<unk>']
            __lowercase     						=[16, 17, 23]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase )  , __lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            if not self.test_rust_tokenizer:
                 return

            __lowercase     						=self.get_tokenizer()
            __lowercase     						=self.get_rust_tokenizer()

            __lowercase     						='lower,newer'

            __lowercase     						=tokenizer.tokenize(__lowercase )
            __lowercase     						=rust_tokenizer.tokenize(__lowercase )
            self.assertListEqual(__lowercase  , __lowercase )

            __lowercase     						=tokenizer.encode(__lowercase  , add_special_tokens=__lowercase )
            __lowercase     						=rust_tokenizer.encode(__lowercase  , add_special_tokens=__lowercase )
            self.assertListEqual(__lowercase  , __lowercase )

            __lowercase     						=self.get_rust_tokenizer()
            __lowercase     						=tokenizer.encode(__lowercase )
            __lowercase     						=rust_tokenizer.encode(__lowercase )
            self.assertListEqual(__lowercase  , __lowercase )







       @slow
       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase     						=self.tokenizer_class.from_pretrained('allegro/herbert-base-cased' )

            __lowercase     						=tokenizer.encode('konstruowanie sekwencji'  , add_special_tokens=__lowercase )
            __lowercase     						=tokenizer.encode('konstruowanie wielu sekwencji'  , add_special_tokens=__lowercase )

            __lowercase     						=tokenizer.build_inputs_with_special_tokens(__lowercase )
            __lowercase     						=tokenizer.build_inputs_with_special_tokens(__lowercase  , __lowercase )

            assert encoded_sentence == [0] + text + [2]
            assert encoded_pair == [0] + text + [2] + text_a + [2]





'''simple docstring'''

from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)


class 					lowerCAmelCase       ( A				):







       def __init__(							self     :	Union[str, Any]  , __lowercase     :	AutoencoderKL  , __lowercase     :	CLIPTextModel  , __lowercase     :	CLIPTokenizer  , __lowercase     :	UNetaDConditionModel  , __lowercase     :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]  , ):







            """simple docstring"""







            super().__init__()

            self.register_modules(vae=__lowercase  , text_encoder=__lowercase  , tokenizer=__lowercase  , unet=__lowercase  , scheduler=__lowercase )







       def      snake_case				(							self     :	str  , __lowercase     :	Optional[int] ):







            """simple docstring"""







            with torch.no_grad():
                 __lowercase     						=self.vae.encode(tfms.ToTensor()(__lowercase ).unsqueeze(0 ).to(self.device ) * 2 - 1 )
                 __lowercase     						=0.1_8_2_1_5 * latent.latent_dist.sample()
            return latent







       def      snake_case				(							self     :	List[str]  , __lowercase     :	Tuple ):







            """simple docstring"""







            __lowercase     						=(1 / 0.1_8_2_1_5) * latent
            with torch.no_grad():
                 __lowercase     						=self.vae.decode(__lowercase ).sample
            __lowercase     						=(img / 2 + 0.5).clamp(0  , 1 )
            __lowercase     						=img.detach().cpu().permute(0  , 2  , 3  , 1 ).numpy()
            __lowercase     						=(img * 255).round().astype('uint8' )
            return Image.fromarray(img[0] )







       def      snake_case				(							self     :	int  , __lowercase     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=self.tokenizer(
                __lowercase  , padding='max_length'  , max_length=self.tokenizer.model_max_length  , truncation=__lowercase  , return_tensors='pt'  , )

            __lowercase     						=self.text_encoder(text_input.input_ids.to(self.device ) )[0]

            __lowercase     						=self.tokenizer(
                ''  , padding='max_length'  , max_length=self.tokenizer.model_max_length  , truncation=__lowercase  , return_tensors='pt'  , )

            __lowercase     						=self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

            return torch.cat([uncond_embedding, text_embedding] )







       def __call__(							self     :	Any  , __lowercase     :	Image.Image  , __lowercase     :	str  , __lowercase     :	float = 0.3  , __lowercase     :	float = 0.6  , __lowercase     :	float = 0.5  , __lowercase     :	int = 42  , __lowercase     :	int = 50  , __lowercase     :	float = 7.5  , ):







            """simple docstring"""







            __lowercase     						=steps - int(kmin * steps )
            __lowercase     						=steps - int(kmax * steps )

            __lowercase     						=self.prep_text(__lowercase )

            self.scheduler.set_timesteps(__lowercase )

            __lowercase						,       __lowercase     						=img.size
            __lowercase     						=self.encode(__lowercase )

            torch.manual_seed(__lowercase )
            __lowercase     						=torch.randn(
                (1, self.unet.config.in_channels, height // 8, width // 8)  , ).to(self.device )

            __lowercase     						=self.scheduler.add_noise(
                __lowercase  , __lowercase  , timesteps=self.scheduler.timesteps[tmax]  , )

            __lowercase     						=torch.cat([latents] * 2 )

            __lowercase     						=self.scheduler.scale_model_input(__lowercase  , self.scheduler.timesteps[tmax] )

            with torch.no_grad():
                 __lowercase     						=self.unet(
                     __lowercase  , self.scheduler.timesteps[tmax]  , encoder_hidden_states=__lowercase  , ).sample

            __lowercase						,       __lowercase     						=pred.chunk(2 )
            __lowercase     						=pred_uncond + guidance_scale * (pred_text - pred_uncond)

            __lowercase     						=self.scheduler.step(__lowercase  , self.scheduler.timesteps[tmax]  , __lowercase ).prev_sample

            for i, t in enumerate(tqdm(self.scheduler.timesteps ) ):
                 if i > tmax:
                      if i < tmin:  # layout generation phase
                           __lowercase     						=self.scheduler.add_noise(
                               __lowercase  , __lowercase  , timesteps=__lowercase  , )

                           __lowercase     						=(mix_factor * latents) + (
                               1 - mix_factor
                           ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                           __lowercase     						=torch.cat([input] * 2 )

                      else:  # content generation phase
                           __lowercase     						=torch.cat([latents] * 2 )

                      __lowercase     						=self.scheduler.scale_model_input(__lowercase  , __lowercase )

                      with torch.no_grad():
                           __lowercase     						=self.unet(
                               __lowercase  , __lowercase  , encoder_hidden_states=__lowercase  , ).sample

                      __lowercase						,       __lowercase     						=pred.chunk(2 )
                      __lowercase     						=pred_uncond + guidance_scale * (pred_text - pred_uncond)

                      __lowercase     						=self.scheduler.step(__lowercase  , __lowercase  , __lowercase ).prev_sample

            return self.decode(__lowercase )


