
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   __UpperCamelCase				(    snake_case__				,		unittest.TestCase   ):






     """simple docstring"""







     lowerCAmelCase_     =	HerbertTokenizer
     lowerCAmelCase_     =	HerbertTokenizerFast
     lowerCAmelCase_     =	True



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """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:
         __SCREAMING_SNAKE_CASE     :					str			=						f_data.read().replace('''\n\n'''      ,		'''\n'''				).strip()

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						[
           '<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>',
       ]
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						dict(zip(_A      ,		range(len(_A				)				)				)				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						['l o 123', 'lo w 1456', 'e r</w> 1789', '']

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES['''vocab_file''']				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES['''merges_file''']				)
       with open(self.vocab_file      ,		'''w'''				) as fp:
         fp.write(json.dumps(_A				)				)
       with open(self.merges_file      ,		'''w'''				) as fp:
         fp.write('''\n'''.join(_A				)				)



     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						'lower newer'
       __SCREAMING_SNAKE_CASE     :					int			=						'lower newer'
       return input_text, output_text



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						self.tokenizer_class(vocab_file=self.vocab_file      ,		merges_file=self.merges_file				)

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						'lower'
       __SCREAMING_SNAKE_CASE     :					Any			=						['low', 'er</w>']
       __SCREAMING_SNAKE_CASE     :					Any			=						tokenizer.tokenize(_A				)
       self.assertListEqual(_A      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						tokens + ['<unk>']
       __SCREAMING_SNAKE_CASE     :					int			=						[16, 17, 23]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_A				)      ,		_A				)



     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       if not self.test_rust_tokenizer:
         return

       __SCREAMING_SNAKE_CASE     :					Tuple			=						self.get_tokenizer()
       __SCREAMING_SNAKE_CASE     :					Tuple			=						self.get_rust_tokenizer()

       __SCREAMING_SNAKE_CASE     :					Dict			=						'lower,newer'

       __SCREAMING_SNAKE_CASE     :					Tuple			=						tokenizer.tokenize(_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						rust_tokenizer.tokenize(_A				)
       self.assertListEqual(_A      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					str			=						tokenizer.encode(_A      ,		add_special_tokens=_A				)
       __SCREAMING_SNAKE_CASE     :					Any			=						rust_tokenizer.encode(_A      ,		add_special_tokens=_A				)
       self.assertListEqual(_A      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					str			=						self.get_rust_tokenizer()
       __SCREAMING_SNAKE_CASE     :					Tuple			=						tokenizer.encode(_A				)
       __SCREAMING_SNAKE_CASE     :					Dict			=						rust_tokenizer.encode(_A				)
       self.assertListEqual(_A      ,		_A				)




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







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''				)

       __SCREAMING_SNAKE_CASE     :					Any			=						tokenizer.encode('''konstruowanie sekwencji'''      ,		add_special_tokens=_A				)
       __SCREAMING_SNAKE_CASE     :					Any			=						tokenizer.encode('''konstruowanie wielu sekwencji'''      ,		add_special_tokens=_A				)

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						tokenizer.build_inputs_with_special_tokens(_A				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						tokenizer.build_inputs_with_special_tokens(_A      ,		_A				)

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


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   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     def __init__(							self    :    Optional[int]      ,		_A    :    AutoencoderKL      ,		_A    :    CLIPTextModel      ,		_A    :    CLIPTokenizer      ,		_A    :    UNetaDConditionModel      ,		_A    :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]      ,		):







       """simple docstring"""




       super().__init__()

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



     def 				UpperCAmelCase__    (							self    :    Optional[int]      ,		_A    :    Dict				):







       """simple docstring"""




       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Dict			=						self.vae.encode(tfms.ToTensor()(_A				).unsqueeze(0				).to(self.device				) * 2 - 1				)
         __SCREAMING_SNAKE_CASE     :					str			=						0.1_82_15 * latent.latent_dist.sample()
       return latent



     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						(1 / 0.1_82_15) * latent
       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Any			=						self.vae.decode(_A				).sample
       __SCREAMING_SNAKE_CASE     :					Any			=						(img / 2 + 0.5).clamp(0      ,		1				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						img.detach().cpu().permute(0      ,		2      ,		3      ,		1				).numpy()
       __SCREAMING_SNAKE_CASE     :					List[str]			=						(img * 255).round().astype('''uint8'''				)
       return Image.fromarray(img[0]				)



     def 				UpperCAmelCase__    (							self    :    Dict      ,		_A    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						self.tokenizer(
           _A      ,		padding='''max_length'''      ,		max_length=self.tokenizer.model_max_length      ,		truncation=_A      ,		return_tensors='''pt'''      ,		)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.text_encoder(text_input.input_ids.to(self.device				)				)[0]

       __SCREAMING_SNAKE_CASE     :					Tuple			=						self.tokenizer(
           ''''''      ,		padding='''max_length'''      ,		max_length=self.tokenizer.model_max_length      ,		truncation=_A      ,		return_tensors='''pt'''      ,		)

       __SCREAMING_SNAKE_CASE     :					str			=						self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

       return torch.cat([uncond_embedding, text_embedding]				)




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







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						steps - int(kmin * steps				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						steps - int(kmax * steps				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.prep_text(_A				)

       self.scheduler.set_timesteps(_A				)

       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Dict			=						img.size
       __SCREAMING_SNAKE_CASE     :					Dict			=						self.encode(_A				)

       torch.manual_seed(_A				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)      ,		).to(self.device				)

       __SCREAMING_SNAKE_CASE     :					Dict			=						self.scheduler.add_noise(
           _A      ,		_A      ,		timesteps=self.scheduler.timesteps[tmax]      ,		)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						torch.cat([latents] * 2				)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.scheduler.scale_model_input(_A      ,		self.scheduler.timesteps[tmax]				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.unet(
             _A      ,		self.scheduler.timesteps[tmax]      ,		encoder_hidden_states=_A      ,		).sample

       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[str]			=						pred.chunk(2				)
       __SCREAMING_SNAKE_CASE     :					str			=						pred_uncond + guidance_scale * (pred_text - pred_uncond)

       __SCREAMING_SNAKE_CASE     :					Any			=						self.scheduler.step(_A      ,		self.scheduler.timesteps[tmax]      ,		_A				).prev_sample

       for i, t in enumerate(tqdm(self.scheduler.timesteps				)				):
         if i > tmax:
           if i < tmin:  # layout generation phase
             __SCREAMING_SNAKE_CASE     :					List[str]			=						self.scheduler.add_noise(
                 _A      ,		_A      ,		timesteps=_A      ,		)

             __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						(mix_factor * latents) + (
                 1 - mix_factor
             ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
             __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						torch.cat([input] * 2				)

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

           __SCREAMING_SNAKE_CASE     :					str			=						self.scheduler.scale_model_input(_A      ,		_A				)

           with torch.no_grad():
             __SCREAMING_SNAKE_CASE     :					int			=						self.unet(
                 _A      ,		_A      ,		encoder_hidden_states=_A      ,		).sample

           __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Optional[int]			=						pred.chunk(2				)
           __SCREAMING_SNAKE_CASE     :					Optional[int]			=						pred_uncond + guidance_scale * (pred_text - pred_uncond)

           __SCREAMING_SNAKE_CASE     :					Any			=						self.scheduler.step(_A      ,		_A      ,		_A				).prev_sample

       return self.decode(_A				)
