





'''simple docstring'''




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase							,  _UpperCAmelCase     							=   image.size
     _UpperCAmelCase							,  _UpperCAmelCase     							=   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     _UpperCAmelCase     							=   image.resize((w, h)			,     resample=PIL_INTERPOLATION["lanczos"]       )
     _UpperCAmelCase     							=   np.array(__lowercase       ).astype(np.floataa       ) / 255.0
     _UpperCAmelCase     							=   image[None].transpose(0			,     3			,     1			,     2       )
     _UpperCAmelCase     							=   torch.from_numpy(__lowercase       )
     return 2.0 * image - 1.0







class   A_	(  lowerCAmelCase_			):




 def __init__(					self       :    Optional[Any]	,			snake_case_       :    VQModel	,			snake_case_       :    UNetaDModel	,			snake_case_       :    Union[
         DDIMScheduler,
         PNDMScheduler,
         LMSDiscreteScheduler,
         EulerDiscreteScheduler,
         EulerAncestralDiscreteScheduler,
         DPMSolverMultistepScheduler,
     ]	,			):
      super().__init__()
      self.register_modules(vqvae=snake_case_	,			unet=snake_case_	,			scheduler=snake_case_    )




 @torch.no_grad()
 def __call__(					self       :    Any	,			snake_case_       :    Union[torch.Tensor, PIL.Image.Image] = None	,			snake_case_       :    Optional[int] = 1	,			snake_case_       :    Optional[int] = 1_0_0	,			snake_case_       :    Optional[float] = 0.0	,			snake_case_       :    Optional[Union[torch.Generator, List[torch.Generator]]] = None	,			snake_case_       :    Optional[str] = "pil"	,			snake_case_       :    bool = True	,			):
      if isinstance(snake_case_	,			PIL.Image.Image    ):
           _UpperCAmelCase     							=   1
      elif isinstance(snake_case_	,			torch.Tensor    ):
           _UpperCAmelCase     							=   image.shape[0]
      else:
           raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case_    )}'    )

      if isinstance(snake_case_	,			PIL.Image.Image    ):
           _UpperCAmelCase     							=   preprocess(snake_case_    )

      _UpperCAmelCase							,  _UpperCAmelCase     							=   image.shape[-2:]

      # in_channels should be 6: 3 for latents, 3 for low resolution image
      _UpperCAmelCase     							=   (batch_size, self.unet.config.in_channels // 2, height, width)
      _UpperCAmelCase     							=   next(self.unet.parameters()    ).dtype

      _UpperCAmelCase     							=   randn_tensor(snake_case_	,			generator=snake_case_	,			device=self.device	,			dtype=snake_case_    )

      _UpperCAmelCase     							=   image.to(device=self.device	,			dtype=snake_case_    )

      # set timesteps and move to the correct device
      self.scheduler.set_timesteps(snake_case_	,			device=self.device    )
      _UpperCAmelCase     							=   self.scheduler.timesteps

      # scale the initial noise by the standard deviation required by the scheduler
      _UpperCAmelCase     							=   latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      _UpperCAmelCase     							=   "eta" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
      _UpperCAmelCase     							=   {}
      if accepts_eta:
           _UpperCAmelCase     							=   eta

      for t in self.progress_bar(snake_case_    ):
           # concat latents and low resolution image in the channel dimension.
           _UpperCAmelCase     							=   torch.cat([latents, image]	,			dim=1    )
           _UpperCAmelCase     							=   self.scheduler.scale_model_input(snake_case_	,			snake_case_    )
           # predict the noise residual
           _UpperCAmelCase     							=   self.unet(snake_case_	,			snake_case_    ).sample
           # compute the previous noisy sample x_t -> x_t-1
           _UpperCAmelCase     							=   self.scheduler.step(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    ).prev_sample

      # decode the image latents with the VQVAE
      _UpperCAmelCase     							=   self.vqvae.decode(snake_case_    ).sample
      _UpperCAmelCase     							=   torch.clamp(snake_case_	,			-1.0	,			1.0    )
      _UpperCAmelCase     							=   image / 2 + 0.5
      _UpperCAmelCase     							=   image.cpu().permute(0	,			2	,			3	,			1    ).numpy()

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

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=snake_case_    )






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  Tuple      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]   )				->		Optional[Any]:







	'''simple docstring'''


	if isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Union[str, Any]	   =     np.full((len(_snake_case   ), sequence_length, 2)      ,   _snake_case   )
	else:
		__magic_name__		:		List[Any]	   =     np.full((len(_snake_case   ), sequence_length)      ,   _snake_case   )

	for i, tensor in enumerate(_snake_case   ):
		if padding_side == "right":
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Union[str, Any]	   =     tensor[:sequence_length]
		else:
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		List[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]

	return out_tensor.tolist()






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     ord(_snake_case   )
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
		return True
	__magic_name__		:		Any	   =     unicodedata.category(_snake_case   )
	if cat.startswith("P"   ):
		return True
	return False





@dataclass
class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    42
			UpperCamelCase__          =    True
			UpperCamelCase__          =    None
			UpperCamelCase__          =    None
			UpperCamelCase__          =    -100
			UpperCamelCase__          =    "pt"







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				import torch

				__magic_name__		:		List[str]	   =     "label" if "label" in features[0].keys() else "labels"
				__magic_name__		:		Union[str, Any]	   =     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
				__magic_name__		:		Optional[int]	   =     self.tokenizer.pad(
				    _a						,   padding=self.padding						,   max_length=self.max_length						,   pad_to_multiple_of=self.pad_to_multiple_of						,   return_tensors="pt" if labels is None else None						,   )

				if labels is None:
					return batch

				__magic_name__		:		Dict	   =     torch.tensor(batch["entity_ids"]		).shape[1]
				__magic_name__		:		List[Any]	   =     self.tokenizer.padding_side
				if padding_side == "right":
					__magic_name__		:		str	   =     [
					    list(_a		) + [self.label_pad_token_id] * (sequence_length - len(_a		)) for label in labels
					]
				else:
					__magic_name__		:		int	   =     [
					    [self.label_pad_token_id] * (sequence_length - len(_a		)) + list(_a		) for label in labels
					]

				__magic_name__		:		Dict	   =     [feature["ner_tags"] for feature in features]
				__magic_name__		:		List[Any]	   =     padding_tensor(_a						,   -1						,   _a						,   _a		)
				__magic_name__		:		Any	   =     [feature["original_entity_spans"] for feature in features]
				__magic_name__		:		Any	   =     padding_tensor(_a						,   (-1, -1)						,   _a						,   _a		)
				__magic_name__		:		List[Any]	   =     {k: torch.tensor(_a						,   dtype=torch.intaa		) for k, v in batch.items()}

				return batch
