






from typing import List, Optional, Tuple, Union

import torch

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




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					42
	lowerCAmelCase						=					42




	def __init__(			self			,     a			,     a   )			->       Tuple:
								super().__init__()
								self.register_modules(unet=a			,     scheduler=a   )






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     **a			,     )			->       Union[Tuple, ImagePipelineOutput]:
								snake_case_           =				self.unet.config.sample_size
								snake_case_           =				(batch_size, 3, img_size, img_size)

								snake_case_           =				self.unet

								# sample x_0 ~ N(0, sigma_0^2 * I)
								snake_case_           =				randn_tensor(a			,     generator=a			,     device=self.device   ) * self.scheduler.init_noise_sigma

								self.scheduler.set_timesteps(a   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
															# here sigma_t == t_i from the paper
															snake_case_           =				self.scheduler.schedule[t]
															snake_case_           =				self.scheduler.schedule[t - 1] if t > 0 else 0

															# 1. Select temporarily increased noise level sigma_hat
															# 2. Add new noise to move from sample_i to sample_hat
															snake_case_     ,						snake_case_           =				self.scheduler.add_noise_to_input(a			,     a			,     generator=a   )

															# 3. Predict the noise residual given the noise magnitude `sigma_hat`
															# The model inputs and output are adjusted by following eq. (213) in [1].
															snake_case_           =				(sigma_hat / 2) * model((sample_hat + 1) / 2			,     sigma_hat / 2   ).sample

															# 4. Evaluate dx/dt at sigma_hat
															# 5. Take Euler step from sigma to sigma_prev
															snake_case_           =				self.scheduler.step(a			,     a			,     a			,     a   )

															if sigma_prev != 0:
																						# 6. Apply 2nd order correction
																						# The model inputs and output are adjusted by following eq. (213) in [1].
																						snake_case_           =				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2			,     sigma_prev / 2   ).sample
																						snake_case_           =				self.scheduler.step_correct(
																						    a			,     a			,     a			,     a			,     step_output.prev_sample			,     step_output['derivative']			,     )
															snake_case_           =				step_output.prev_sample

								snake_case_           =				(sample / 2 + 0.5).clamp(0			,     1   )
								snake_case_           =				sample.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   )













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.'''   )





