"""simple docstring"""
from typing import List, Optional, Tuple, Union

import torch

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



class 	lowerCAmelCase_     (      lowerCAmelCase       ):



				"""simple docstring"""

				def __init__(					self		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										super().__init__()
										self.register_modules(unet=lowerCAmelCase		,     scheduler=lowerCAmelCase     )

				@torch.no_grad()
				def __call__(					self		,     lowerCAmelCase = 1		,     lowerCAmelCase = None		,     lowerCAmelCase = 10_00		,     lowerCAmelCase = "pil"		,     lowerCAmelCase = True		,     ):






										"""simple docstring"""

										if isinstance(self.unet.config.sample_size		,     lowerCAmelCase     ):
																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 self.device.type == "mps":
																# randn does not work reproducibly on mps
																snake_case             =      randn_tensor(lowerCAmelCase		,     generator=lowerCAmelCase     )
																snake_case             =      image.to(self.device     )
										else:
																snake_case             =      randn_tensor(lowerCAmelCase		,     generator=lowerCAmelCase		,     device=self.device     )

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

										for t in self.progress_bar(self.scheduler.timesteps     ):
																# 1. predict noise model_output
																snake_case             =      self.unet(lowerCAmelCase		,     lowerCAmelCase     ).sample

																# 2. compute previous image: x_t -> x_t-1
																snake_case             =      self.scheduler.step(lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase		,     generator=lowerCAmelCase     ).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(lowerCAmelCase     )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase     )



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__       =	{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	[
					    "OpenLlamaForCausalLM",
					    "OpenLlamaModel",
					    "OpenLlamaPreTrainedModel",
					    "OpenLlamaForSequenceClassification",
					]


if TYPE_CHECKING:
					from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_open_llama import (
										    OpenLlamaForCausalLM,
										    OpenLlamaForSequenceClassification,
										    OpenLlamaModel,
										    OpenLlamaPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


