




"""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


a         =      logging.get_logger(__name__)

a         =      {'''vocab_file''': '''sentencepiece.bpe.model'''}

a         =      {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

a         =      {
    '''camembert-base''': 512,
}

a         =      '''▁'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					int				  =     VOCAB_FILES_NAMES
 UpperCAmelCase   :					List[str]				  =     PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase   :					Any				  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase   :					Dict				  =     ['''input_ids''', '''attention_mask''']

 def __init__(			self	:	Any   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Dict="<s>"   ,							_UpperCAmelCase	:	List[Any]="</s>"   ,							_UpperCAmelCase	:	Tuple="</s>"   ,							_UpperCAmelCase	:	int="<s>"   ,							_UpperCAmelCase	:	Optional[Any]="<unk>"   ,							_UpperCAmelCase	:	List[str]="<pad>"   ,							_UpperCAmelCase	:	Dict="<mask>"   ,							_UpperCAmelCase	:	str=["<s>NOTUSED", "</s>NOTUSED"]   ,							_UpperCAmelCase	:	Optional[Dict[str, Any]] = None   ,							**_UpperCAmelCase	:	List[str]   ,							):
       # Mask token behave like a normal word, i.e. include the space before it
       _A        =       AddedToken(_UpperCAmelCase   ,							lstrip=_UpperCAmelCase   ,							rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		) else mask_token

       _A        =       {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=_UpperCAmelCase   ,							eos_token=_UpperCAmelCase   ,							unk_token=_UpperCAmelCase   ,							sep_token=_UpperCAmelCase   ,							cls_token=_UpperCAmelCase   ,							pad_token=_UpperCAmelCase   ,							mask_token=_UpperCAmelCase   ,							additional_special_tokens=_UpperCAmelCase   ,							sp_model_kwargs=self.sp_model_kwargs   ,							**_UpperCAmelCase   ,							)
       _A        =       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
       self.sp_model.Load(str(_UpperCAmelCase		)		)
       _A        =       vocab_file
       # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
       # sentencepiece vocabulary (this is the case for <s> and </s>
       _A        =       {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
       _A        =       len(self.fairseq_tokens_to_ids		)
       _A        =       len(self.sp_model		) + len(self.fairseq_tokens_to_ids		)
       _A        =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	List[int]   ,							_UpperCAmelCase	:	Optional[List[int]] = None		):

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

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	List[int]   ,							_UpperCAmelCase	:	Optional[List[int]] = None   ,							_UpperCAmelCase	:	bool = False		):
       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_UpperCAmelCase   ,							token_ids_a=_UpperCAmelCase   ,							already_has_special_tokens=_UpperCAmelCase		)

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

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	List[int]   ,							_UpperCAmelCase	:	Optional[List[int]] = None		):
       _A        =       [self.sep_token_id]
       _A        =       [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       lowerCAmelCase_							(			self	:	str		):
       return len(self.fairseq_tokens_to_ids		) + len(self.sp_model		)

 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       {self.convert_ids_to_tokens(_UpperCAmelCase		): i for i in range(self.vocab_size		)}
       vocab.update(self.added_tokens_encoder		)
       return vocab

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	str		):
       return self.sp_model.encode(_UpperCAmelCase   ,							out_type=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	Optional[int]		):
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       elif self.sp_model.PieceToId(_UpperCAmelCase		) == 0:
             # Convert sentence piece unk token to fairseq unk token index
             return self.unk_token_id
       return self.fairseq_offset + self.sp_model.PieceToId(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	List[str]		):
       if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset		)

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	List[str]		):
       _A        =       []
       _A        =       ''
       _A        =       False
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   if not prev_is_special:
                         out_string += " "
                   out_string += self.sp_model.decode(_UpperCAmelCase		) + token
                   _A        =       True
                   _A        =       []
             else:
                   current_sub_tokens.append(_UpperCAmelCase		)
                   _A        =       False
       out_string += self.sp_model.decode(_UpperCAmelCase		)
       return out_string.strip()

 def __getstate__(			self	:	str		):
       _A        =       self.__dict__.copy()
       _A        =       None
       return state

 def __setstate__(			self	:	str   ,							_UpperCAmelCase	:	Optional[int]		):
       _A        =       d

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

       _A        =       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
       self.sp_model.Load(self.vocab_file		)



 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[str] = None		):
       if not os.path.isdir(_UpperCAmelCase		):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
             return
       _A        =       os.path.join(
           _UpperCAmelCase   ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

       return (out_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,
)


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

a         =      '''
    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 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: List[Any]    ,  _snake_case							: List[Any]=8							)   -> Optional[int]:






      '''simple docstring'''


      _A        =       height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      _A        =       width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor







class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''

 def __init__(			self	:	Dict   ,							_UpperCAmelCase	:	UNetaDConditionModel   ,							_UpperCAmelCase	:	DDPMScheduler   ,							_UpperCAmelCase	:	VQModel   ,							):
       super().__init__()

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

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	int		):
       if latents is None:
             _A        =       randn_tensor(_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							device=_UpperCAmelCase   ,							dtype=_UpperCAmelCase		)
       else:
             if latents.shape != shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''		)
             _A        =       latents.to(_UpperCAmelCase		)

       _A        =       latents * scheduler.init_noise_sigma
       return latents

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	Tuple=0		):
       if is_accelerate_available():
             from accelerate import cpu_offload
       else:
             raise ImportError('Please install accelerate via `pip install accelerate`'		)

       _A        =       torch.device(F'''cuda:{gpu_id}'''		)

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

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Union[str, Any]=0		):
       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.'		)

       _A        =       torch.device(F'''cuda:{gpu_id}'''		)

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

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

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

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def       lowerCAmelCase_							(			self	:	int		):
       if not hasattr(self.unet   ,							'_hf_hook'		):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(_UpperCAmelCase   ,							'_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(_UpperCAmelCase		)
 def __call__(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Union[torch.FloatTensor, List[torch.FloatTensor]]   ,							_UpperCAmelCase	:	Union[torch.FloatTensor, List[torch.FloatTensor]]   ,							_UpperCAmelCase	:	int = 512   ,							_UpperCAmelCase	:	int = 512   ,							_UpperCAmelCase	:	int = 100   ,							_UpperCAmelCase	:	float = 4.0   ,							_UpperCAmelCase	:	int = 1   ,							_UpperCAmelCase	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,							_UpperCAmelCase	:	Optional[torch.FloatTensor] = None   ,							_UpperCAmelCase	:	Optional[str] = "pil"   ,							_UpperCAmelCase	:	bool = True   ,							):
       _A        =       self._execution_device

       _A        =       guidance_scale > 1.0

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

       if do_classifier_free_guidance:
             _A        =       image_embeds.repeat_interleave(_UpperCAmelCase   ,							dim=0		)
             _A        =       negative_image_embeds.repeat_interleave(_UpperCAmelCase   ,							dim=0		)

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

       self.scheduler.set_timesteps(_UpperCAmelCase   ,							device=_UpperCAmelCase		)
       _A        =       self.scheduler.timesteps

       _A        =       self.unet.config.in_channels

       _A       ,   _A        =       downscale_height_and_width(_UpperCAmelCase   ,							_UpperCAmelCase   ,							self.movq_scale_factor		)

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

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

             _A        =       {'image_embeds': image_embeds}
             _A        =       self.unet(
                 sample=_UpperCAmelCase   ,							timestep=_UpperCAmelCase   ,							encoder_hidden_states=_UpperCAmelCase   ,							added_cond_kwargs=_UpperCAmelCase   ,							return_dict=_UpperCAmelCase   ,							)[0]

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

             # compute the previous noisy sample x_t -> x_t-1
             _A        =       self.scheduler.step(
                 _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							)[0]
       # post-processing
       _A        =       self.movq.decode(_UpperCAmelCase   ,							force_not_quantize=_UpperCAmelCase		)['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"]:
             _A        =       image * 0.5 + 0.5
             _A        =       image.clamp(0   ,							1		)
             _A        =       image.cpu().permute(0   ,							2   ,							3   ,							1		).float().numpy()

       if output_type == "pil":
             _A        =       self.numpy_to_pil(_UpperCAmelCase		)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase		)

