'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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:			Optional[Any]	       =       logging.get_logger(__name__)  # pylint: disable=invalid-name

__a:			Optional[Any]	       =       """
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=8					):
 lowercase__    :   Optional[Any]						= height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 lowercase__    :   List[str]						= width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=512		,					UpperCAmelCase=512					):
 lowercase__    :   str						= pil_image.resize((w, h)		,					resample=Image.BICUBIC		,					reducing_gap=1					)
 lowercase__    :   Optional[int]						= np.array(pil_image.convert('''RGB'''					)					)
 lowercase__    :   Any						= arr.astype(np.floataa					) / 1_2_7.5 - 1
 lowercase__    :   Optional[Any]						= np.transpose(UpperCAmelCase		,					[2, 0, 1]					)
 lowercase__    :   Any						= torch.from_numpy(UpperCAmelCase					).unsqueeze(0					)
 return image





class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  Tuple:
        super().__init__()

        self.register_modules(
            unet=__lowerCAmelCase     ,       scheduler=__lowerCAmelCase     ,       movq=__lowerCAmelCase     ,       )
        lowercase__    :   Dict						= 2 ** (len(self.movq.config.block_out_channels			) - 1)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Any:
        # get the original timestep using init_timestep
        lowercase__    :   Optional[Any]						= min(int(num_inference_steps * strength			)     ,       __lowerCAmelCase			)

        lowercase__    :   Union[str, Any]						= max(num_inference_steps - init_timestep     ,       0			)
        lowercase__    :   Any						= self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None			)  ->  Any:
        if not isinstance(__lowerCAmelCase     ,       (torch.Tensor, PIL.Image.Image, list)			):
         raise ValueError(
             F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__lowerCAmelCase			)}"""			)

        lowercase__    :   Dict						= image.to(device=__lowerCAmelCase     ,       dtype=__lowerCAmelCase			)

        lowercase__    :   Any						= batch_size * num_images_per_prompt

        if image.shape[1] == 4:
         lowercase__    :   List[Any]						= image

        else:
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			) and len(__lowerCAmelCase			) != batch_size:
          raise ValueError(
              F"""You have passed a list of generators of length {len(__lowerCAmelCase			)}, but requested an effective batch"""
              F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""			)

         elif isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   Optional[int]						= [
              self.movq.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(__lowerCAmelCase			)
          ]
          lowercase__    :   Any						= torch.cat(__lowerCAmelCase     ,       dim=0			)
         else:
          lowercase__    :   Dict						= self.movq.encode(__lowerCAmelCase			).latent_dist.sample(__lowerCAmelCase			)

         lowercase__    :   Union[str, Any]						= self.movq.config.scaling_factor * init_latents

        lowercase__    :   Optional[int]						= torch.cat([init_latents]     ,       dim=0			)

        lowercase__    :   Dict						= init_latents.shape
        lowercase__    :   List[str]						= randn_tensor(__lowerCAmelCase     ,       generator=__lowerCAmelCase     ,       device=__lowerCAmelCase     ,       dtype=__lowerCAmelCase			)

        # get latents
        lowercase__    :   Dict						= self.scheduler.add_noise(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)

        lowercase__    :   int						= init_latents

        return latents

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

        lowercase__    :   Tuple						= torch.device(F"""cuda:{gpu_id}"""			)

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase=0			)  ->  Dict:
        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.'''			)

        lowercase__    :   int						= torch.device(F"""cuda:{gpu_id}"""			)

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

        lowercase__    :   Optional[int]						= None
        for cpu_offloaded_model in [self.unet, self.movq]:
         lowercase__			,							lowercase__    :   Optional[Any]						= cpu_offload_with_hook(__lowerCAmelCase     ,       __lowerCAmelCase     ,       prev_module_hook=__lowerCAmelCase			)

        # We'll offload the last model manually.
        lowercase__    :   int						= hook

       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        if not hasattr(self.unet     ,       '''_hf_hook'''			):
         return self.device
        for module in self.unet.modules():
         if (
             hasattr(__lowerCAmelCase     ,       '''_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(__lowerCAmelCase			)
       def __call__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = 512     ,       __lowerCAmelCase = 512     ,       __lowerCAmelCase = 100     ,       __lowerCAmelCase = 4.0     ,       __lowerCAmelCase = 0.3     ,       __lowerCAmelCase = 1     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = "pil"     ,       __lowerCAmelCase = True     ,       )  ->  int:
        lowercase__    :   List[Any]						= self._execution_device

        lowercase__    :   Tuple						= guidance_scale > 1.0

        if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
         lowercase__    :   List[str]						= torch.cat(__lowerCAmelCase     ,       dim=0			)
        lowercase__    :   List[Any]						= image_embeds.shape[0]
        if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
         lowercase__    :   int						= torch.cat(__lowerCAmelCase     ,       dim=0			)

        if do_classifier_free_guidance:
         lowercase__    :   int						= image_embeds.repeat_interleave(__lowerCAmelCase     ,       dim=0			)
         lowercase__    :   int						= negative_image_embeds.repeat_interleave(__lowerCAmelCase     ,       dim=0			)

        lowercase__    :   Tuple						= torch.cat([negative_image_embeds, image_embeds]     ,       dim=0			).to(dtype=self.unet.dtype     ,       device=__lowerCAmelCase			)

        if not isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
         lowercase__    :   int						= [image]
        if not all(isinstance(__lowerCAmelCase     ,       (PIL.Image.Image, torch.Tensor)			) for i in image			):
         raise ValueError(
             F"""Input is in incorrect format: {[type(__lowerCAmelCase			) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""			)

        lowercase__    :   int						= torch.cat([prepare_image(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			) for i in image]     ,       dim=0			)
        lowercase__    :   Optional[int]						= image.to(dtype=image_embeds.dtype     ,       device=__lowerCAmelCase			)

        lowercase__    :   str						= self.movq.encode(__lowerCAmelCase			)['''latents''']
        lowercase__    :   str						= latents.repeat_interleave(__lowerCAmelCase     ,       dim=0			)
        self.scheduler.set_timesteps(__lowerCAmelCase     ,       device=__lowerCAmelCase			)
        lowercase__			,							lowercase__    :   int						= self.get_timesteps(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        lowercase__    :   Optional[Any]						= timesteps[:1].repeat(batch_size * num_images_per_prompt			)
        lowercase__			,							lowercase__    :   Tuple						= downscale_height_and_width(__lowerCAmelCase     ,       __lowerCAmelCase     ,       self.movq_scale_factor			)
        lowercase__    :   int						= self.prepare_latents(
            __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       image_embeds.dtype     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        for i, t in enumerate(self.progress_bar(__lowerCAmelCase			)			):
         # expand the latents if we are doing classifier free guidance
         lowercase__    :   str						= torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

         lowercase__    :   Optional[Any]						= {'''image_embeds''': image_embeds}
         lowercase__    :   Union[str, Any]						= self.unet(
             sample=__lowerCAmelCase     ,       timestep=__lowerCAmelCase     ,       encoder_hidden_states=__lowerCAmelCase     ,       added_cond_kwargs=__lowerCAmelCase     ,       return_dict=__lowerCAmelCase     ,       )[0]

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

         # compute the previous noisy sample x_t -> x_t-1
         lowercase__    :   int						= self.scheduler.step(
             __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       generator=__lowerCAmelCase     ,       )[0]

        # post-processing
        lowercase__    :   Union[str, Any]						= self.movq.decode(__lowerCAmelCase     ,       force_not_quantize=__lowerCAmelCase			)['''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"]:
         lowercase__    :   List[Any]						= image * 0.5 + 0.5
         lowercase__    :   List[str]						= image.clamp(0     ,       1			)
         lowercase__    :   List[str]						= image.cpu().permute(0     ,       2     ,       3     ,       1			).float().numpy()

        if output_type == "pil":
         lowercase__    :   Tuple						= self.numpy_to_pil(__lowerCAmelCase			)

        if not return_dict:
         return (image,)

        return ImagePipelineOutput(images=__lowerCAmelCase			)


'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


__a:			Union[str, Any]	       =       logging.get_logger(__name__)

__a:			Tuple	       =       {"""tokenizer_file""": """tokenizer.json"""}

__a:			Union[str, Any]	       =       {
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE 		=   PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE 		=   ["input_ids", "attention_mask"]
       SCREAMING_SNAKE_CASE 		=   None

       def __init__(						self     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase="<unk>"     ,       __lowerCAmelCase="<s>"     ,       __lowerCAmelCase="</s>"     ,       __lowerCAmelCase="<pad>"     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase=False     ,       **__lowerCAmelCase     ,       )  ->  Union[str, Any]:
        super().__init__(
            __lowerCAmelCase     ,       __lowerCAmelCase     ,       tokenizer_file=__lowerCAmelCase     ,       unk_token=__lowerCAmelCase     ,       bos_token=__lowerCAmelCase     ,       eos_token=__lowerCAmelCase     ,       pad_token=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase     ,       clean_up_tokenization_spaces=__lowerCAmelCase     ,       **__lowerCAmelCase     ,       )
        lowercase__    :   int						= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
        if pre_tok_state.get('''add_prefix_space'''     ,       __lowerCAmelCase			) != add_prefix_space:
         lowercase__    :   int						= getattr(__lowerCAmelCase     ,       pre_tok_state.pop('''type'''			)			)
         lowercase__    :   Tuple						= add_prefix_space
         lowercase__    :   List[str]						= pre_tok_class(**__lowerCAmelCase			)

        lowercase__    :   Union[str, Any]						= add_prefix_space

       def _lowerCAmelCase(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  BatchEncoding:
        lowercase__    :   Dict						= kwargs.get('''is_split_into_words'''     ,       __lowerCAmelCase			)
        if not (self.add_prefix_space or not is_split_into_words):
         raise Exception(
             F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
             ''' pretokenized inputs.'''			)

        return super()._batch_encode_plus(*__lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  BatchEncoding:
        lowercase__    :   str						= kwargs.get('''is_split_into_words'''     ,       __lowerCAmelCase			)

        if not (self.add_prefix_space or not is_split_into_words):
         raise Exception(
             F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
             ''' pretokenized inputs.'''			)

        return super()._encode_plus(*__lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Tuple[str]:
        lowercase__    :   List[Any]						= self._tokenizer.model.save(__lowerCAmelCase     ,       name=__lowerCAmelCase			)
        return tuple(__lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[int]:
        lowercase__    :   Dict						= []
        for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase			) + [self.eos_token_id]			)

        if len(__lowerCAmelCase			) > self.model_max_length:
         lowercase__    :   Optional[Any]						= input_ids[-self.model_max_length :]
        return input_ids

