






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


lowercase    				=   logging.get_logger(__name__)

lowercase    				=   {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					'''transfo-xl'''
	lowerCAmelCase						=					['''mems''']
	lowerCAmelCase						=					{
	    '''n_token''': '''vocab_size''',
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}




	def __init__(			self			,     a=26_77_35			,     a=[2_00_00, 4_00_00, 20_00_00]			,     a=10_24			,     a=10_24			,     a=16			,     a=64			,     a=40_96			,     a=4			,     a=False			,     a=18			,     a=16_00			,     a=10_00			,     a=True			,     a=True			,     a=0			,     a=-1			,     a=True			,     a=0.1			,     a=0.0			,     a=True			,     a="normal"			,     a=0.01			,     a=0.01			,     a=0.02			,     a=1E-5			,     a=0			,     **a			,     )			->       Union[str, Any]:
								snake_case_           =				vocab_size
								snake_case_           =				[]
								self.cutoffs.extend(a   )
								if proj_share_all_but_first:
															snake_case_           =				[False] + [True] * len(self.cutoffs   )
								else:
															snake_case_           =				[False] + [False] * len(self.cutoffs   )
								snake_case_           =				d_model
								snake_case_           =				d_embed
								snake_case_           =				d_head
								snake_case_           =				d_inner
								snake_case_           =				div_val
								snake_case_           =				pre_lnorm
								snake_case_           =				n_layer
								snake_case_           =				n_head
								snake_case_           =				mem_len
								snake_case_           =				same_length
								snake_case_           =				attn_type
								snake_case_           =				clamp_len
								snake_case_           =				sample_softmax
								snake_case_           =				adaptive
								snake_case_           =				dropout
								snake_case_           =				dropatt
								snake_case_           =				untie_r
								snake_case_           =				init
								snake_case_           =				init_range
								snake_case_           =				proj_init_std
								snake_case_           =				init_std
								snake_case_           =				layer_norm_epsilon
								super().__init__(eos_token_id=a			,     **a   )




	@property
	def 				_UpperCamelCase     (			self   )			->       int:
								# Message copied from Transformer-XL documentation
								logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )
								return -1






	@max_position_embeddings.setter
	def 				_UpperCamelCase     (			self			,     a   )			->       List[str]:
								# Message copied from Transformer-XL documentation
								raise NotImplementedError(
								    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )













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 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Optional[Any]:
								super().__init__()

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

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






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = None			,     a = 0.0			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     )			->       Union[ImagePipelineOutput, Tuple]:

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

								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.'''   )

								snake_case_           =				randn_tensor(a			,     generator=a			,     device=self.device			,     dtype=self.unet.dtype   )

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

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

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

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a   )





