

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase							:						str     			=		logging.get_logger(__name__)

lowerCAmelCase							:						Tuple     			=		{
    """google/pegasus-x-base""": """https://huggingface.co/google/pegasus-x-base/resolve/main/config.json""",
    """google/pegasus-x-large""": """https://huggingface.co/google/pegasus-x-large/resolve/main/config.json""",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}



class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							Optional[int]    							=  '''pegasus_x'''
			_UpperCAmelCase     :							Optional[int]    							=  ['''past_key_values''']
			_UpperCAmelCase     :							Optional[int]    							=  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



			def __init__(				self						:  List[str]     ,		lowerCAmelCase__						:  str=9_6103     ,		lowerCAmelCase__						:  str=1_6384     ,		lowerCAmelCase__						:  Tuple=16     ,		lowerCAmelCase__						:  Optional[Any]=4096     ,		lowerCAmelCase__						:  Dict=16     ,		lowerCAmelCase__						:  List[str]=16     ,		lowerCAmelCase__						:  List[str]=4096     ,		lowerCAmelCase__						:  Tuple=16     ,		lowerCAmelCase__						:  Any=0.0     ,		lowerCAmelCase__						:  Tuple=0.0     ,		lowerCAmelCase__						:  Optional[Any]=True     ,		lowerCAmelCase__						:  Tuple=True     ,		lowerCAmelCase__						:  Dict="gelu"     ,		lowerCAmelCase__						:  Union[str, Any]=1024     ,		lowerCAmelCase__						:  int=0.1     ,		lowerCAmelCase__						:  Any=0.0     ,		lowerCAmelCase__						:  str=0.0     ,		lowerCAmelCase__						:  Optional[int]=0.02     ,		lowerCAmelCase__						:  Union[str, Any]=0     ,		lowerCAmelCase__						:  Any=True     ,		lowerCAmelCase__						:  Dict=0     ,		lowerCAmelCase__						:  str=1     ,		lowerCAmelCase__						:  int=1     ,		lowerCAmelCase__						:  List[Any]=32     ,		lowerCAmelCase__						:  Dict=512     ,		lowerCAmelCase__						:  List[Any]=True     ,		**lowerCAmelCase__						:  Optional[Any]     ,		):
									SCREAMING_SNAKE_CASE_:				List[str]							= vocab_size
									SCREAMING_SNAKE_CASE_:				int							= max_position_embeddings
									SCREAMING_SNAKE_CASE_:				Dict							= d_model
									SCREAMING_SNAKE_CASE_:				Dict							= encoder_ffn_dim
									SCREAMING_SNAKE_CASE_:				str							= encoder_layers
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= encoder_attention_heads
									SCREAMING_SNAKE_CASE_:				Optional[int]							= decoder_ffn_dim
									SCREAMING_SNAKE_CASE_:				Any							= decoder_layers
									SCREAMING_SNAKE_CASE_:				Tuple							= decoder_attention_heads
									SCREAMING_SNAKE_CASE_:				int							= dropout
									SCREAMING_SNAKE_CASE_:				int							= attention_dropout
									SCREAMING_SNAKE_CASE_:				List[str]							= activation_dropout
									SCREAMING_SNAKE_CASE_:				str							= activation_function
									SCREAMING_SNAKE_CASE_:				int							= init_std
									SCREAMING_SNAKE_CASE_:				List[str]							= encoder_layerdrop
									SCREAMING_SNAKE_CASE_:				List[Any]							= decoder_layerdrop
									SCREAMING_SNAKE_CASE_:				List[str]							= use_cache
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= encoder_layers
									SCREAMING_SNAKE_CASE_:				str							= scale_embedding  # scale factor will be sqrt(d_model) if True

									SCREAMING_SNAKE_CASE_:				str							= num_global_tokens
									SCREAMING_SNAKE_CASE_:				int							= block_size
									SCREAMING_SNAKE_CASE_:				List[str]							= stagger_local_blocks

									super().__init__(
									    pad_token_id=lowerCAmelCase__     ,		eos_token_id=lowerCAmelCase__     ,		is_encoder_decoder=lowerCAmelCase__     ,		decoder_start_token_id=lowerCAmelCase__     ,		forced_eos_token_id=lowerCAmelCase__     ,		**lowerCAmelCase__     ,		)



			@property
			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict):
									return self.encoder_attention_heads




			@property
			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									return self.d_model









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							UNetaDModel
			_UpperCAmelCase     :							PNDMScheduler



			def __init__(				self						:  Optional[int]     ,		lowerCAmelCase__						:  UNetaDModel     ,		lowerCAmelCase__						:  PNDMScheduler):
									super().__init__()

									SCREAMING_SNAKE_CASE_:				Dict							= PNDMScheduler.from_config(scheduler.config)

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




			@torch.no_grad()
			def __call__(				self						:  int     ,		lowerCAmelCase__						:  int = 1     ,		lowerCAmelCase__						:  int = 50     ,		lowerCAmelCase__						:  Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		lowerCAmelCase__						:  Optional[str] = "pil"     ,		lowerCAmelCase__						:  bool = True     ,		**lowerCAmelCase__						:  str     ,		):
									SCREAMING_SNAKE_CASE_:				Dict							= randn_tensor(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,		generator=lowerCAmelCase__     ,		device=self.device     ,		)

									self.scheduler.set_timesteps(lowerCAmelCase__)
									for t in self.progress_bar(self.scheduler.timesteps):
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.unet(lowerCAmelCase__     ,		lowerCAmelCase__).sample

															SCREAMING_SNAKE_CASE_:				Any							= self.scheduler.step(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__).prev_sample

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= (image / 2 + 0.5).clamp(0     ,		1)
									SCREAMING_SNAKE_CASE_:				Dict							= image.cpu().permute(0     ,		2     ,		3     ,		1).numpy()
									if output_type == "pil":
															SCREAMING_SNAKE_CASE_:				Any							= self.numpy_to_pil(lowerCAmelCase__)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowerCAmelCase__)






