





'''simple docstring'''



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       ,_lowerCamelCase       ,_lowerCamelCase	)							->							Any:







				'''simple docstring'''





				super().__init__()

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

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


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







				'''simple docstring'''





				if isinstance(self.unet.config.sample_size       ,_lowerCamelCase	):
							__lowercase			  =							(
							    batch_size,
							    self.unet.config.in_channels,
							    self.unet.config.sample_size,
							    self.unet.config.sample_size,
							)
				else:
							__lowercase			  =							(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

				__lowercase			  =							randn_tensor(_lowerCamelCase       ,generator=_lowerCamelCase       ,device=self.device       ,dtype=self.unet.dtype	)

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

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

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

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_lowerCamelCase	)












'''simple docstring'''



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


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Dict     =    "open-llama"


	def __init__(self       ,_lowerCamelCase=100000       ,_lowerCamelCase=4096       ,_lowerCamelCase=11008       ,_lowerCamelCase=32       ,_lowerCamelCase=32       ,_lowerCamelCase="silu"       ,_lowerCamelCase=2048       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-6       ,_lowerCamelCase=True       ,_lowerCamelCase=0       ,_lowerCamelCase=1       ,_lowerCamelCase=2       ,_lowerCamelCase=False       ,_lowerCamelCase=True       ,_lowerCamelCase=0.1       ,_lowerCamelCase=0.1       ,_lowerCamelCase=True       ,_lowerCamelCase=True       ,_lowerCamelCase=None       ,**_lowerCamelCase       ,)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							vocab_size
				__lowercase			  =							max_position_embeddings
				__lowercase			  =							hidden_size
				__lowercase			  =							intermediate_size
				__lowercase			  =							num_hidden_layers
				__lowercase			  =							num_attention_heads
				__lowercase			  =							hidden_act
				__lowercase			  =							initializer_range
				__lowercase			  =							rms_norm_eps
				__lowercase			  =							use_cache
				__lowercase			  =							kwargs.pop(
				    '''use_memorry_efficient_attention'''       ,_lowerCamelCase	)
				__lowercase			  =							hidden_dropout_prob
				__lowercase			  =							attention_dropout_prob
				__lowercase			  =							use_stable_embedding
				__lowercase			  =							shared_input_output_embedding
				__lowercase			  =							rope_scaling
				self._rope_scaling_validation()

				super().__init__(
				    pad_token_id=_lowerCamelCase       ,bos_token_id=_lowerCamelCase       ,eos_token_id=_lowerCamelCase       ,tie_word_embeddings=_lowerCamelCase       ,**_lowerCamelCase       ,)


	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				if self.rope_scaling is None:
							return

				if not isinstance(self.rope_scaling       ,_lowerCamelCase	) or len(self.rope_scaling	) != 2:
							raise ValueError(
							    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
							    f"got {self.rope_scaling}"	)
				__lowercase			  =							self.rope_scaling.get('''type'''       ,_lowerCamelCase	)
				__lowercase			  =							self.rope_scaling.get('''factor'''       ,_lowerCamelCase	)
				if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
							raise ValueError(
							    f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"	)
				if rope_scaling_factor is None or not isinstance(_lowerCamelCase       ,_lowerCamelCase	) or rope_scaling_factor <= 1.0:
							raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"	)





