



import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer






class 		lowercase						(			tf.keras.layers.Layer):






	"""simple docstring"""


	def __init__(     self     :						Dict						,  __UpperCAmelCase     :						Dict[str, int]						,  __UpperCAmelCase     :						List[str]						,  __UpperCAmelCase     :						int = None						,  __UpperCAmelCase     :						int = None					)      ->			Tuple:
							super().__init__()
							UpperCAmelCase_=       pad_token_id
							UpperCAmelCase_=       max_length
							UpperCAmelCase_=       vocab
							UpperCAmelCase_=       merges
							UpperCAmelCase_=       BytePairTokenizer(__UpperCAmelCase						,  __UpperCAmelCase						,  sequence_length=__UpperCAmelCase					)




	@classmethod
	def   _SCREAMING_SNAKE_CASE		(     cls     :						int						,  __UpperCAmelCase     :						GPTaTokenizer						,  *__UpperCAmelCase     :						Any						,  **__UpperCAmelCase     :						Union[str, Any]					)      ->			Tuple:
							UpperCAmelCase_=       [""" """.join(__UpperCAmelCase					) for m in tokenizer.bpe_ranks.keys()]
							UpperCAmelCase_=       tokenizer.get_vocab()
							return cls(__UpperCAmelCase						,  __UpperCAmelCase						,  *__UpperCAmelCase						,  **__UpperCAmelCase					)




	@classmethod
	def   _SCREAMING_SNAKE_CASE		(     cls     :						List[str]						,  __UpperCAmelCase     :						Union[str, os.PathLike]						,  *__UpperCAmelCase     :						Optional[int]						,  **__UpperCAmelCase     :						List[Any]					)      ->			Union[str, Any]:
							UpperCAmelCase_=       GPTaTokenizer.from_pretrained(__UpperCAmelCase						,  *__UpperCAmelCase						,  **__UpperCAmelCase					)
							return cls.from_tokenizer(__UpperCAmelCase						,  *__UpperCAmelCase						,  **__UpperCAmelCase					)




	@classmethod
	def   _SCREAMING_SNAKE_CASE		(     cls     :						Optional[int]						,  __UpperCAmelCase     :						Union[str, Any]					)      ->			int:
							return cls(**__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[str]:
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						int = None					)      ->			Optional[Any]:
							UpperCAmelCase_=       self.tf_tokenizer(__UpperCAmelCase					)
							UpperCAmelCase_=       tf.ones_like(__UpperCAmelCase					)

							if self.pad_token_id is not None:
													# pad the tokens up to max length
													UpperCAmelCase_=       max_length if max_length is not None else self.max_length

													if max_length is not None:
																			UpperCAmelCase_,       UpperCAmelCase_=       pad_model_inputs(
																			    __UpperCAmelCase						,  max_seq_length=__UpperCAmelCase						,  pad_value=self.pad_token_id					)

							return {"attention_mask": attention_mask, "input_ids": input_ids}









from typing import List, Optional, Tuple, Union

import torch

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






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Dict						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						List[Any]					)      ->			List[str]:
							super().__init__()

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

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




	@torch.no_grad()
	def __call__(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None						,  __UpperCAmelCase     :						float = 0.0						,  __UpperCAmelCase     :						int = 50						,  __UpperCAmelCase     :						Optional[bool] = None						,  __UpperCAmelCase     :						Optional[str] = "pil"						,  __UpperCAmelCase     :						bool = True						,  )      ->			Union[ImagePipelineOutput, Tuple]:

							# Sample gaussian noise to begin loop
							if isinstance(self.unet.config.sample_size						,  __UpperCAmelCase					):
													UpperCAmelCase_=       (
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
							else:
													UpperCAmelCase_=       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

							UpperCAmelCase_=       randn_tensor(__UpperCAmelCase						,  generator=__UpperCAmelCase						,  device=self.device						,  dtype=self.unet.dtype					)

							# set step values
							self.scheduler.set_timesteps(__UpperCAmelCase					)

							for t in self.progress_bar(self.scheduler.timesteps					):
													# 1. predict noise model_output
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					).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
													UpperCAmelCase_=       self.scheduler.step(
													    __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  eta=__UpperCAmelCase						,  use_clipped_model_output=__UpperCAmelCase						,  generator=__UpperCAmelCase					).prev_sample

							UpperCAmelCase_=       (image / 2 + 0.5).clamp(0						,  1					)
							UpperCAmelCase_=       image.cpu().permute(0						,  2						,  3						,  1					).numpy()
							if output_type == "pil":
													UpperCAmelCase_=       self.numpy_to_pil(__UpperCAmelCase					)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=__UpperCAmelCase					)




