
from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


snake_case :		List[str]							      =  transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def   lowerCAmelCase_						(					_snake_case      :  Any   )				->		Dict:







	'''simple docstring'''


	if isinstance(_snake_case      ,   torch.Tensor   ):
		return image
	elif isinstance(_snake_case      ,   PIL.Image.Image   ):
		__magic_name__		:		List[Any]	   =     [image]

	__magic_name__		:		Dict	   =     [trans(img.convert("RGB"   )   ) for img in image]
	__magic_name__		:		Dict	   =     torch.stack(_snake_case   )
	return image





class       _snake_case					(     snake_case				):







			def __init__(				self						,   _a						,   _a		):
				super().__init__()

				# make sure scheduler can always be converted to DDIM
				__magic_name__		:		int	   =     DDIMScheduler.from_config(scheduler.config		)

				self.register_modules(unet=_a						,   scheduler=_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if strength < 0 or strength > 1:
					raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}'''		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a		):
				# get the original timestep using init_timestep
				__magic_name__		:		int	   =     min(int(num_inference_steps * strength		)						,   _a		)

				__magic_name__		:		str	   =     max(num_inference_steps - init_timestep						,   0		)
				__magic_name__		:		Optional[int]	   =     self.scheduler.timesteps[t_start:]

				return timesteps, num_inference_steps - t_start







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a=None		):
				if not isinstance(_a						,   (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(_a		)}'''		)

				__magic_name__		:		int	   =     image.to(device=_a						,   dtype=_a		)

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

				__magic_name__		:		Tuple	   =     init_latents.shape
				__magic_name__		:		Tuple	   =     randn_tensor(_a						,   generator=_a						,   device=_a						,   dtype=_a		)

				# get latents
				print("add noise to latents at timestep"						,   _a		)
				__magic_name__		:		int	   =     self.scheduler.add_noise(_a						,   _a						,   _a		)
				__magic_name__		:		Dict	   =     init_latents

				return latents







			@torch.no_grad()
			def __call__(				self						,   _a = None						,   _a = 0.8						,   _a = 1						,   _a = None						,   _a = 0.0						,   _a = 50						,   _a = None						,   _a = "pil"						,   _a = True						,   ):
				self.check_inputs(_a		)

				# 2. Preprocess image
				__magic_name__		:		List[Any]	   =     preprocess(_a		)

				# 3. set timesteps
				self.scheduler.set_timesteps(_a						,   device=self.device		)
				__magic_name__   ,    __magic_name__		:		List[str]	   =     self.get_timesteps(_a						,   _a						,   self.device		)
				__magic_name__		:		Optional[Any]	   =     timesteps[:1].repeat(_a		)

				# 4. Prepare latent variables
				__magic_name__		:		Tuple	   =     self.prepare_latents(_a						,   _a						,   _a						,   self.unet.dtype						,   self.device						,   _a		)
				__magic_name__		:		Dict	   =     latents

				# 5. Denoising loop
				for t in self.progress_bar(_a		):
					# 1. predict noise model_output
					__magic_name__		:		Tuple	   =     self.unet(_a						,   _a		).sample

					# 2. predict previous mean of image x_t-1 and add variance depending on eta
					# eta corresponds to η in paper and should be between [0, 1]
					# do x_t -> x_t-1
					__magic_name__		:		List[str]	   =     self.scheduler.step(
					    _a						,   _a						,   _a						,   eta=_a						,   use_clipped_model_output=_a						,   generator=_a						,   ).prev_sample

				__magic_name__		:		Any	   =     (image / 2 + 0.5).clamp(0						,   1		)
				__magic_name__		:		Tuple	   =     image.cpu().permute(0						,   2						,   3						,   1		).numpy()
				if output_type == "pil":
					__magic_name__		:		str	   =     self.numpy_to_pil(_a		)

				if not return_dict:
					return (image, latent_timestep.item())

				return ImagePipelineOutput(images=_a		)


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
