
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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Tuple       ) ->       List[str]:

				"""simple docstring"""







				A__       ,							A__  :	Optional[int]         =							image.size
				A__       ,							A__  :	str         =							(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				A__  :	Optional[int]         =							image.resize((w, h)							,						resample=PIL_INTERPOLATION['''lanczos''']       )
				A__  :	Tuple         =							np.array(__UpperCamelCase       ).astype(np.floataa       ) / 2_5_5.0
				A__  :	List[str]         =							image[None].transpose(0							,						3							,						1							,						2       )
				A__  :	List[str]         =							torch.from_numpy(__UpperCamelCase       )
				return 2.0 * image - 1.0







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       ):
					super().__init__()
					self.register_modules(vqvae=UpperCamelCase__				,       unet=UpperCamelCase__				,       scheduler=UpperCamelCase__		)





	@torch.no_grad()
	def __call__(			self				,       UpperCamelCase__ = None				,       UpperCamelCase__ = 1				,       UpperCamelCase__ = 100				,       UpperCamelCase__ = 0.0				,       UpperCamelCase__ = None				,       UpperCamelCase__ = "pil"				,       UpperCamelCase__ = True				,       ):
					if isinstance(UpperCamelCase__				,       PIL.Image.Image		):
									A__  :	str         =							1
					elif isinstance(UpperCamelCase__				,       torch.Tensor		):
									A__  :	Dict         =							image.shape[0]
					else:
									raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__		)}"		)

					if isinstance(UpperCamelCase__				,       PIL.Image.Image		):
									A__  :	Any         =							preprocess(UpperCamelCase__		)

					A__       ,							A__  :	List[Any]         =							image.shape[-2:]

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

					A__  :	str         =							randn_tensor(UpperCamelCase__				,       generator=UpperCamelCase__				,       device=self.device				,       dtype=UpperCamelCase__		)

					A__  :	Dict         =							image.to(device=self.device				,       dtype=UpperCamelCase__		)

					# set timesteps and move to the correct device
					self.scheduler.set_timesteps(UpperCamelCase__				,       device=self.device		)
					A__  :	Union[str, Any]         =							self.scheduler.timesteps

					# scale the initial noise by the standard deviation required by the scheduler
					A__  :	Optional[Any]         =							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]
					A__  :	Optional[Any]         =							'''eta''' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
					A__  :	Dict         =							{}
					if accepts_eta:
									A__  :	Tuple         =							eta

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

					# decode the image latents with the VQVAE
					A__  :	int         =							self.vqvae.decode(UpperCamelCase__		).sample
					A__  :	Union[str, Any]         =							torch.clamp(UpperCamelCase__				,       -1.0				,       1.0		)
					A__  :	Optional[int]         =							image / 2 + 0.5
					A__  :	Tuple         =							image.cpu().permute(0				,       2				,       3				,       1		).numpy()

					if output_type == "pil":
									A__  :	List[str]         =							self.numpy_to_pil(UpperCamelCase__		)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=UpperCamelCase__		)

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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE    :      Optional[int]          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE    :      List[str]          =			{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

_SCREAMING_SNAKE_CASE    :      Dict          =			{
    'gpt-neox-20b': 2_0_4_8,
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







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





	def __init__(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__=False				,       **UpperCamelCase__				,       ):
					super().__init__(
					    UpperCamelCase__				,       UpperCamelCase__				,       tokenizer_file=UpperCamelCase__				,       unk_token=UpperCamelCase__				,       bos_token=UpperCamelCase__				,       eos_token=UpperCamelCase__				,       add_prefix_space=UpperCamelCase__				,       **UpperCamelCase__				,       )

					A__  :	Optional[Any]         =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
					if pre_tok_state.get('''add_prefix_space'''				,       UpperCamelCase__		) != add_prefix_space:
									A__  :	Union[str, Any]         =							getattr(UpperCamelCase__				,       pre_tok_state.pop('''type'''		)		)
									A__  :	List[Any]         =							add_prefix_space
									A__  :	Any         =							pre_tok_class(**UpperCamelCase__		)

					A__  :	List[Any]         =							add_prefix_space





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					A__  :	Any         =							self._tokenizer.model.save(UpperCamelCase__				,       name=UpperCamelCase__		)
					return tuple(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							[]
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		) + [self.eos_token_id]		)

					if len(UpperCamelCase__		) > self.model_max_length:
									A__  :	Tuple         =							input_ids[-self.model_max_length :]
					return input_ids