





'''simple docstring'''

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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase_					= logging.get_logger(__name__)

lowercase_					= """▁"""

lowercase_					= {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

lowercase_					= {
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

lowercase_					= {"""vinai/bartpho-syllable""": 1_024}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = VOCAB_FILES_NAMES
    UpperCamelCase     = PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase     = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase     = ['''input_ids''', '''attention_mask''']


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

        _SCREAMING_SNAKE_CASE									=			{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=A ,		eos_token=A ,		unk_token=A ,		sep_token=A ,		cls_token=A ,		pad_token=A ,		mask_token=A ,		sp_model_kwargs=self.sp_model_kwargs ,		**A ,		)

        _SCREAMING_SNAKE_CASE									=			vocab_file
        _SCREAMING_SNAKE_CASE									=			monolingual_vocab_file
        _SCREAMING_SNAKE_CASE									=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
        self.sp_model.Load(str(A       )       )

        # Load the reduced vocab

        # Keep order of special tokens for backward compatibility
        _SCREAMING_SNAKE_CASE									=			{}
        _SCREAMING_SNAKE_CASE									=			0
        for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
            if str(A       ) not in self.fairseq_tokens_to_ids:
                _SCREAMING_SNAKE_CASE									=			cnt
                cnt += 1
        with open(A ,		"""r""" ,		encoding="""utf-8"""       ) as f:
            for line in f.readlines():
                _SCREAMING_SNAKE_CASE									=			line.strip().split()[0]
                _SCREAMING_SNAKE_CASE									=			len(self.fairseq_tokens_to_ids       )
        if str(A       ) not in self.fairseq_tokens_to_ids:
            _SCREAMING_SNAKE_CASE									=			len(self.fairseq_tokens_to_ids       )

        _SCREAMING_SNAKE_CASE									=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}


    def __getstate__(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			self.__dict__.copy()
        _SCREAMING_SNAKE_CASE									=			None
        _SCREAMING_SNAKE_CASE									=			self.sp_model.serialized_model_proto()
        return state


    def __setstate__(		self ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			d

        # for backward compatibility
        if not hasattr(self ,		"""sp_model_kwargs"""       ):
            _SCREAMING_SNAKE_CASE									=			{}

        _SCREAMING_SNAKE_CASE									=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )


    def 			snake_case_(		self ,		A ,		A = None       )		->   List[int]:

        if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _SCREAMING_SNAKE_CASE									=			[self.cls_token_id]
        _SCREAMING_SNAKE_CASE									=			[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


    def 			snake_case_(		self ,		A ,		A = None ,		A = False       )		->   List[int]:

        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=A ,		token_ids_a=A ,		already_has_special_tokens=A       )

        if token_ids_a is None:
            return [1] + ([0] * len(A       )) + [1]
        return [1] + ([0] * len(A       )) + [1, 1] + ([0] * len(A       )) + [1]


    def 			snake_case_(		self ,		A ,		A = None       )		->   List[int]:
        _SCREAMING_SNAKE_CASE									=			[self.sep_token_id]
        _SCREAMING_SNAKE_CASE									=			[self.cls_token_id]

        if token_ids_a is None:
            return len(cls + token_ids_a + sep       ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]


    @property
    def 			snake_case_(		self       )		->   int:
        return len(self.fairseq_ids_to_tokens       )


    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			{self.convert_ids_to_tokens(A       ): i for i in range(self.vocab_size       )}
        vocab.update(self.added_tokens_encoder       )
        return vocab


    def 			snake_case_(		self ,		A       )		->   List[str]:
        return self.sp_model.encode(A ,		out_type=A       )


    def 			snake_case_(		self ,		A       )		->   Dict:
        if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
        else:
            return self.unk_token_id


    def 			snake_case_(		self ,		A       )		->   Optional[int]:
        return self.fairseq_ids_to_tokens[index]


    def 			snake_case_(		self ,		A       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			"""""".join(A       ).replace(A ,		""" """       ).strip()
        return out_string





    def 			snake_case_(		self ,		A ,		A = None       )		->   Tuple[str]:
        if not os.path.isdir(A       ):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
            return
        _SCREAMING_SNAKE_CASE									=			os.path.join(
            A ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
        _SCREAMING_SNAKE_CASE									=			os.path.join(
            A ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""] ,		)

        if os.path.abspath(self.vocab_file       ) != os.path.abspath(A       ) and os.path.isfile(self.vocab_file       ):
            copyfile(self.vocab_file ,		A       )
        elif not os.path.isfile(self.vocab_file       ):
            with open(A ,		"""wb"""       ) as fi:
                _SCREAMING_SNAKE_CASE									=			self.sp_model.serialized_model_proto()
                fi.write(A       )

        if os.path.abspath(self.monolingual_vocab_file       ) != os.path.abspath(
            A       ) and os.path.isfile(self.monolingual_vocab_file       ):
            copyfile(self.monolingual_vocab_file ,		A       )
        elif not os.path.isfile(self.monolingual_vocab_file       ):
            with open(A ,		"""w""" ,		encoding="""utf-8"""       ) as fp:
                for token in self.fairseq_tokens_to_ids:
                    if token not in self.all_special_tokens:
                        fp.write(f'{str(A       )} \n'       )

        return out_vocab_file, out_monolingual_vocab_file










'''simple docstring'''

from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowercase_					= logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase_					= """
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    List[Any]=8    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    _SCREAMING_SNAKE_CASE									=			width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor






class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		)		->   Union[str, Any]:
        super().__init__()

        self.register_modules(
            unet=A ,		scheduler=A ,		movq=A ,		)
        _SCREAMING_SNAKE_CASE									=			2 ** (len(self.movq.config.block_out_channels       ) - 1)


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Union[str, Any]:
        if latents is None:
            _SCREAMING_SNAKE_CASE									=			randn_tensor(A ,		generator=A ,		device=A ,		dtype=A       )
        else:
            if latents.shape != shape:
                raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'       )
            _SCREAMING_SNAKE_CASE									=			latents.to(A       )

        _SCREAMING_SNAKE_CASE									=			latents * scheduler.init_noise_sigma
        return latents


    def 			snake_case_(		self ,		A=0       )		->   Dict:
        if is_accelerate_available():
            from accelerate import cpu_offload
        else:
            raise ImportError("""Please install accelerate via `pip install accelerate`"""       )

        _SCREAMING_SNAKE_CASE									=			torch.device(f'cuda:{gpu_id}'       )

        _SCREAMING_SNAKE_CASE									=			[
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
                cpu_offload(A ,		A       )


    def 			snake_case_(		self ,		A=0       )		->   str:
        if is_accelerate_available() and is_accelerate_version(""">=""" ,		"""0.17.0.dev0"""       ):
            from accelerate import cpu_offload_with_hook
        else:
            raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""       )

        _SCREAMING_SNAKE_CASE									=			torch.device(f'cuda:{gpu_id}'       )

        if self.device.type != "cpu":
            self.to("""cpu""" ,		silence_dtype_warnings=A       )
            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        _SCREAMING_SNAKE_CASE									=			None
        for cpu_offloaded_model in [self.unet, self.movq]:
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			cpu_offload_with_hook(A ,		A ,		prev_module_hook=A       )

        # We'll offload the last model manually.
        _SCREAMING_SNAKE_CASE									=			hook


    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def 			snake_case_(		self       )		->   Tuple:
        if not hasattr(self.unet ,		"""_hf_hook"""       ):
            return self.device
        for module in self.unet.modules():
            if (
                hasattr(A ,		"""_hf_hook"""       )
                and hasattr(module._hf_hook ,		"""execution_device"""       )
                and module._hf_hook.execution_device is not None
            ):
                return torch.device(module._hf_hook.execution_device       )
        return self.device





    @torch.no_grad()
    @replace_example_docstring(A       )
    def __call__(		self ,		A ,		A ,		A = 512 ,		A = 512 ,		A = 100 ,		A = 4.0 ,		A = 1 ,		A = None ,		A = None ,		A = "pil" ,		A = True ,		)		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self._execution_device

        _SCREAMING_SNAKE_CASE									=			guidance_scale > 1.0

        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			torch.cat(A ,		dim=0       )
        _SCREAMING_SNAKE_CASE									=			image_embeds.shape[0] * num_images_per_prompt
        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			torch.cat(A ,		dim=0       )

        if do_classifier_free_guidance:
            _SCREAMING_SNAKE_CASE									=			image_embeds.repeat_interleave(A ,		dim=0       )
            _SCREAMING_SNAKE_CASE									=			negative_image_embeds.repeat_interleave(A ,		dim=0       )

        _SCREAMING_SNAKE_CASE									=			torch.cat([negative_image_embeds, image_embeds] ,		dim=0       ).to(dtype=self.unet.dtype ,		device=A       )

        self.scheduler.set_timesteps(A ,		device=A       )
        _SCREAMING_SNAKE_CASE									=			self.scheduler.timesteps

        _SCREAMING_SNAKE_CASE									=			self.unet.config.in_channels

        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			downscale_height_and_width(A ,		A ,		self.movq_scale_factor       )

        # create initial latent
        _SCREAMING_SNAKE_CASE									=			self.prepare_latents(
            (batch_size, num_channels_latents, height, width) ,		image_embeds.dtype ,		A ,		A ,		A ,		self.scheduler ,		)

        for i, t in enumerate(self.progress_bar(A       )       ):
            # expand the latents if we are doing classifier free guidance
            _SCREAMING_SNAKE_CASE									=			torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

            _SCREAMING_SNAKE_CASE									=			{"""image_embeds""": image_embeds}
            _SCREAMING_SNAKE_CASE									=			self.unet(
                sample=A ,		timestep=A ,		encoder_hidden_states=A ,		added_cond_kwargs=A ,		return_dict=A ,		)[0]

            if do_classifier_free_guidance:
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			noise_pred.split(latents.shape[1] ,		dim=1       )
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			noise_pred.chunk(2       )
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			variance_pred.chunk(2       )
                _SCREAMING_SNAKE_CASE									=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                _SCREAMING_SNAKE_CASE									=			torch.cat([noise_pred, variance_pred_text] ,		dim=1       )

            if not (
                hasattr(self.scheduler.config ,		"""variance_type"""       )
                and self.scheduler.config.variance_type in ["learned", "learned_range"]
            ):
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			noise_pred.split(latents.shape[1] ,		dim=1       )

            # compute the previous noisy sample x_t -> x_t-1
            _SCREAMING_SNAKE_CASE									=			self.scheduler.step(
                A ,		A ,		A ,		generator=A ,		)[0]
        # post-processing
        _SCREAMING_SNAKE_CASE									=			self.movq.decode(A ,		force_not_quantize=A       )["""sample"""]

        if output_type not in ["pt", "np", "pil"]:
            raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'       )

        if output_type in ["np", "pil"]:
            _SCREAMING_SNAKE_CASE									=			image * 0.5 + 0.5
            _SCREAMING_SNAKE_CASE									=			image.clamp(0 ,		1       )
            _SCREAMING_SNAKE_CASE									=			image.cpu().permute(0 ,		2 ,		3 ,		1       ).float().numpy()

        if output_type == "pil":
            _SCREAMING_SNAKE_CASE									=			self.numpy_to_pil(A       )

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=A       )



