'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_mbartaa import MBartaaTokenizer
else:
							lowercase_  		=			None


lowercase_  		=			logging.get_logger(__name__)

lowercase_  		=			{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase_  		=			{
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

lowercase_  		=			{
    '''facebook/mbart-large-50-one-to-many-mmt''': 1024,
}

# fmt: off
lowercase_  		=			['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']
class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   Tuple							=				VOCAB_FILES_NAMES
		lowercase :   int							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase :   Optional[int]							=				PRETRAINED_VOCAB_FILES_MAP
		lowercase :   Tuple							=				['input_ids', 'attention_mask']
		lowercase :   Union[str, Any]							=				MBartaaTokenizer

		lowercase :   List[int]							=				[]
		lowercase :   List[int]							=				[]






		def __init__( self		,    __A=None		,    __A=None		,    __A=None		,    __A=None		,    __A="</s>"		,    __A="</s>"		,    __A="<s>"		,    __A="<unk>"		,    __A="<pad>"		,    __A="<mask>"		,    **__A		,    )			->  Tuple:
								# Mask token behave like a normal word, i.e. include the space before it
								_lowerCAmelCase					    =AddedToken(__A		,    lstrip=__A		,    rstrip=__A					) if isinstance(__A		,    __A					) else mask_token

								_lowerCAmelCase					    =kwargs.get('additional_special_tokens'		,    []					)
								kwargs["additional_special_tokens"] += [
								    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    __A		,    src_lang=__A		,    tgt_lang=__A		,    tokenizer_file=__A		,    eos_token=__A		,    sep_token=__A		,    cls_token=__A		,    unk_token=__A		,    pad_token=__A		,    mask_token=__A		,    **__A		,    )

								_lowerCAmelCase					    =vocab_file
								_lowerCAmelCase					    =False if not self.vocab_file else True

								_lowerCAmelCase					    ={
								    lang_code: self.convert_tokens_to_ids(__A					) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

								_lowerCAmelCase					    =src_lang if src_lang is not None else 'en_XX'
								_lowerCAmelCase					    =tgt_lang
								_lowerCAmelCase					    =self.lang_code_to_id[self._src_lang]
								self.set_src_lang_special_tokens(self._src_lang					)






		@property
		def 		UpperCamelCase__							( self					)			->  str:
								return self._src_lang






		@src_lang.setter
		def 		UpperCamelCase__							( self		,    __A					)			->  None:
								_lowerCAmelCase					    =new_src_lang
								self.set_src_lang_special_tokens(self._src_lang					)






		def 		UpperCamelCase__							( self		,    __A		,    __A = None					)			->  List[int]:
								if token_ids_a is None:
														return self.prefix_tokens + token_ids_a + self.suffix_tokens
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






		def 		UpperCamelCase__							( self		,    __A		,    __A = "en_XX"		,    __A = None		,    __A = "ro_RO"		,    **__A		,    )			->  BatchEncoding:
								_lowerCAmelCase					    =src_lang
								_lowerCAmelCase					    =tgt_lang
								return super().prepare_seqaseq_batch(__A		,    __A		,    **__A					)






		def 		UpperCamelCase__							( self					)			->  Optional[Any]:
								return self.set_src_lang_special_tokens(self.src_lang					)






		def 		UpperCamelCase__							( self					)			->  Union[str, Any]:
								return self.set_tgt_lang_special_tokens(self.tgt_lang					)






		def 		UpperCamelCase__							( self		,    __A					)			->  None:
								_lowerCAmelCase					    =self.convert_tokens_to_ids(__A					)
								_lowerCAmelCase					    =[self.cur_lang_code_id]
								_lowerCAmelCase					    =[self.eos_token_id]

								_lowerCAmelCase					    =self.convert_ids_to_tokens(self.prefix_tokens					)
								_lowerCAmelCase					    =self.convert_ids_to_tokens(self.suffix_tokens					)

								_lowerCAmelCase					    =processors.TemplateProcessing(
								    single=prefix_tokens_str + ['$A'] + suffix_tokens_str		,    pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str		,    special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,    self.prefix_tokens + self.suffix_tokens					)					)		,    )






		def 		UpperCamelCase__							( self		,    __A					)			->  None:
								_lowerCAmelCase					    =self.convert_tokens_to_ids(__A					)
								_lowerCAmelCase					    =[self.cur_lang_code_id]
								_lowerCAmelCase					    =[self.eos_token_id]

								_lowerCAmelCase					    =self.convert_ids_to_tokens(self.prefix_tokens					)
								_lowerCAmelCase					    =self.convert_ids_to_tokens(self.suffix_tokens					)

								_lowerCAmelCase					    =processors.TemplateProcessing(
								    single=prefix_tokens_str + ['$A'] + suffix_tokens_str		,    pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str		,    special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,    self.prefix_tokens + self.suffix_tokens					)					)		,    )






		def 		UpperCamelCase__							( self		,    __A		,    __A		,    __A		,    __A		,    **__A					)			->  Dict:
								if src_lang is None or tgt_lang is None:
														raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'					)
								_lowerCAmelCase					    =src_lang
								_lowerCAmelCase					    =self(__A		,    add_special_tokens=__A		,    return_tensors=__A		,    **__A					)
								_lowerCAmelCase					    =self.convert_tokens_to_ids(__A					)
								_lowerCAmelCase					    =tgt_lang_id
								return inputs






		def 		UpperCamelCase__							( self		,    __A		,    __A = None					)			->  Tuple[str]:
								if not self.can_save_slow_tokenizer:
														raise ValueError(
														    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
														    'tokenizer.'					)

								if not os.path.isdir(__A					):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
														return
								_lowerCAmelCase					    =os.path.join(
								    __A		,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(__A					):
														copyfile(self.vocab_file		,    __A					)

								return (out_vocab_file,)






'''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 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""






		def __init__( self		,    __A		,    __A		,    __A		,    __A		,    __A		,    )			->  Any:
								super().__init__()

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






		def 		UpperCamelCase__							( self		,    __A					)			->  List[Any]:
								with torch.no_grad():
														_lowerCAmelCase					    =self.vae.encode(tfms.ToTensor()(__A					).unsqueeze(0					).to(self.device					) * 2 - 1					)
														_lowerCAmelCase					    =0.18_215 * latent.latent_dist.sample()
								return latent






		def 		UpperCamelCase__							( self		,    __A					)			->  Dict:
								_lowerCAmelCase					    =(1 / 0.18_215) * latent
								with torch.no_grad():
														_lowerCAmelCase					    =self.vae.decode(__A					).sample
								_lowerCAmelCase					    =(img / 2 + 0.5).clamp(0		,    1					)
								_lowerCAmelCase					    =img.detach().cpu().permute(0		,    2		,    3		,    1					).numpy()
								_lowerCAmelCase					    =(img * 255).round().astype('uint8'					)
								return Image.fromarray(img[0]					)






		def 		UpperCamelCase__							( self		,    __A					)			->  str:
								_lowerCAmelCase					    =self.tokenizer(
								    __A		,    padding='max_length'		,    max_length=self.tokenizer.model_max_length		,    truncation=__A		,    return_tensors='pt'		,    )

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

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

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

								return torch.cat([uncond_embedding, text_embedding]					)






		def __call__( self		,    __A		,    __A		,    __A = 0.3		,    __A = 0.6		,    __A = 0.5		,    __A = 42		,    __A = 50		,    __A = 7.5		,    )			->  Image.Image:
								_lowerCAmelCase					    =steps - int(kmin * steps					)
								_lowerCAmelCase					    =steps - int(kmax * steps					)

								_lowerCAmelCase					    =self.prep_text(__A					)

								self.scheduler.set_timesteps(__A					)

								_lowerCAmelCase    ,					_lowerCAmelCase					    =img.size
								_lowerCAmelCase					    =self.encode(__A					)

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

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

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

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

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

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

								_lowerCAmelCase					    =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
																										_lowerCAmelCase					    =self.scheduler.add_noise(
																										    __A		,    __A		,    timesteps=__A		,    )

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

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

																				_lowerCAmelCase					    =self.scheduler.scale_model_input(__A		,    __A					)

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

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

																				_lowerCAmelCase					    =self.scheduler.step(__A		,    __A		,    __A					).prev_sample

								return self.decode(__A					)





