
'''simple docstring'''

import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
UpperCAmelCase__				:	List[str]					=						logging.getLogger(__name__)







def 					A       ( )   ->    Dict:







			'''simple docstring'''



			lowerCAmelCase__									=							argparse.ArgumentParser(description="PyTorch Transformer Language Model" )
			parser.add_argument("--model_name"  ,		type=UpperCamelCase_  ,		default="transfo-xl-wt103"  ,		help="pretrained model name" )
			parser.add_argument(
			    "--split"  ,		type=UpperCamelCase_  ,		default="test"  ,		choices=["all", "valid", "test"]  ,		help="which split to evaluate" )
			parser.add_argument("--batch_size"  ,		type=UpperCamelCase_  ,		default=10  ,		help="batch size" )
			parser.add_argument("--tgt_len"  ,		type=UpperCamelCase_  ,		default=1_28  ,		help="number of tokens to predict" )
			parser.add_argument("--ext_len"  ,		type=UpperCamelCase_  ,		default=0  ,		help="length of the extended context" )
			parser.add_argument("--mem_len"  ,		type=UpperCamelCase_  ,		default=16_00  ,		help="length of the retained previous heads" )
			parser.add_argument("--clamp_len"  ,		type=UpperCamelCase_  ,		default=10_00  ,		help="max positional embedding index" )
			parser.add_argument("--no_cuda"  ,		action="store_true"  ,		help="Do not use CUDA even though CUA is available" )
			parser.add_argument("--work_dir"  ,		type=UpperCamelCase_  ,		required=UpperCamelCase_  ,		help="path to the work_dir" )
			parser.add_argument("--no_log"  ,		action="store_true"  ,		help="do not log the eval result" )
			parser.add_argument("--same_length"  ,		action="store_true"  ,		help="set same length attention with masking" )
			parser.add_argument("--server_ip"  ,		type=UpperCamelCase_  ,		default=""  ,		help="Can be used for distant debugging." )
			parser.add_argument("--server_port"  ,		type=UpperCamelCase_  ,		default=""  ,		help="Can be used for distant debugging." )
			lowerCAmelCase__									=							parser.parse_args()
			assert args.ext_len >= 0, "extended context length must be non-negative"

			if args.server_ip and args.server_port:
						# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
						import ptvsd

						print("Waiting for debugger attach" )
						ptvsd.enable_attach(address=(args.server_ip, args.server_port)  ,		redirect_output=UpperCamelCase_ )
						ptvsd.wait_for_attach()

			lowerCAmelCase__									=							torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
			logger.info("device: {}".format(UpperCamelCase_ ) )

			# Load a pre-processed dataset
			# You can also build the corpus yourself using TransfoXLCorpus methods
			# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
			# and tokenizing the dataset
			# The pre-processed corpus is a convertion (using the conversion script )
			lowerCAmelCase__									=							TransfoXLCorpus.from_pretrained(args.model_name )

			lowerCAmelCase__									=							corpus.get_iterator("valid"  ,		args.batch_size  ,		args.tgt_len  ,		device=UpperCamelCase_  ,		ext_len=args.ext_len )
			lowerCAmelCase__									=							corpus.get_iterator("test"  ,		args.batch_size  ,		args.tgt_len  ,		device=UpperCamelCase_  ,		ext_len=args.ext_len )

			# Load a pre-trained model
			lowerCAmelCase__									=							TransfoXLLMHeadModel.from_pretrained(args.model_name )
			model.to(UpperCamelCase_ )

			logger.info(
			    "Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}".format(
			        args.batch_size  ,		args.tgt_len  ,		args.ext_len  ,		args.mem_len  ,		args.clamp_len ) )

			model.reset_memory_length(args.mem_len )
			if args.clamp_len > 0:
						lowerCAmelCase__									=							args.clamp_len
			if args.same_length:
						lowerCAmelCase__									=							True

			###############################################################################
			# Evaluation code
			###############################################################################
			def evaluate(UpperCamelCase_   :       List[Any] ):
						# Turn on evaluation mode which disables dropout.
						model.eval()
						lowerCAmelCase__									=							0, 0.0
						lowerCAmelCase__									=							time.time()
						with torch.no_grad():
									lowerCAmelCase__									=							None
									for idx, (data, target, seq_len) in enumerate(UpperCamelCase_ ):
												lowerCAmelCase__									=							model(UpperCamelCase_  ,		lm_labels=UpperCamelCase_  ,		mems=UpperCamelCase_ )
												lowerCAmelCase__									=							ret
												lowerCAmelCase__									=							loss.mean()
												total_loss += seq_len * loss.item()
												total_len += seq_len
									lowerCAmelCase__									=							time.time() - start_time
						logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(UpperCamelCase_  ,		10_00 * total_time / (idx + 1) ) )
						return total_loss / total_len

			# Run on test data.
			if args.split == "all":
						lowerCAmelCase__									=							evaluate(UpperCamelCase_ )
						lowerCAmelCase__									=							evaluate(UpperCamelCase_ )
			elif args.split == "valid":
						lowerCAmelCase__									=							evaluate(UpperCamelCase_ )
						lowerCAmelCase__									=							None
			elif args.split == "test":
						lowerCAmelCase__									=							evaluate(UpperCamelCase_ )
						lowerCAmelCase__									=							None

			def format_log(UpperCamelCase_   :       Dict  ,		UpperCamelCase_   :       List[str] ):
						lowerCAmelCase__									=							'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(UpperCamelCase_  ,		UpperCamelCase_  ,		math.exp(UpperCamelCase_ ) )
						return log_str

			lowerCAmelCase__									=							''
			if valid_loss is not None:
						log_str += format_log(UpperCamelCase_  ,		"valid" )
			if test_loss is not None:
						log_str += format_log(UpperCamelCase_  ,		"test" )

			logger.info("=" * 1_00 )
			logger.info(UpperCamelCase_ )
			logger.info("=" * 1_00 )


if __name__ == "__main__":
							main()







'''simple docstring'''

from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


UpperCAmelCase__				:	Tuple					=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Dict				       =       ['safety_checker', 'feature_extractor']





	def __init__(   self     :    Optional[Any] ,							__magic_name__     :    AutoencoderKL ,							__magic_name__     :    CLIPTextModel ,							__magic_name__     :    CLIPTokenizer ,							__magic_name__     :    UNetaDConditionModel ,							__magic_name__     :    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,							__magic_name__     :    StableDiffusionSafetyChecker ,							__magic_name__     :    CLIPImageProcessor ,							__magic_name__     :    bool = True ,							):


				"""simple docstring"""

				super().__init__()
				if hasattr(scheduler.config ,							"steps_offset"							) and scheduler.config.steps_offset != 1:
							lowerCAmelCase__									=							(
							    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
							    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
							    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
							    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
							    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
							    " file"
							)
							deprecate("steps_offset!=1" ,							"1.0.0" ,							__magic_name__ ,							standard_warn=__magic_name__							)
							lowerCAmelCase__									=							dict(scheduler.config							)
							lowerCAmelCase__									=							1
							lowerCAmelCase__									=							FrozenDict(__magic_name__							)

				self.register_modules(
				    vae=__magic_name__ ,							text_encoder=__magic_name__ ,							tokenizer=__magic_name__ ,							unet=__magic_name__ ,							scheduler=__magic_name__ ,							safety_checker=__magic_name__ ,							feature_extractor=__magic_name__ ,							)
				self.register_to_config(requires_safety_checker=__magic_name__							)





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				return {k: getattr(self ,							__magic_name__							) for k in self.config.keys() if not k.startswith("_"							)}





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							__magic_name__     :    Optional[Union[str, int]] = "auto"							):


				"""simple docstring"""

				if slice_size == "auto":
							# half the attention head size is usually a good trade-off between
							# speed and memory
							lowerCAmelCase__									=							self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				self.enable_attention_slicing(__magic_name__							)





	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    float = 0.8 ,							__magic_name__     :    Optional[int] = 50 ,							__magic_name__     :    Optional[float] = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    Optional[float] = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							):


				"""simple docstring"""

				return StableDiffusionInpaintPipelineLegacy(**self.components							)(
				    prompt=__magic_name__ ,							image=__magic_name__ ,							mask_image=__magic_name__ ,							strength=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							)





	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    float = 0.8 ,							__magic_name__     :    Optional[int] = 50 ,							__magic_name__     :    Optional[float] = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    Optional[float] = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							**__magic_name__     :    List[str] ,							):


				"""simple docstring"""

				return StableDiffusionImgaImgPipeline(**self.components							)(
				    prompt=__magic_name__ ,							image=__magic_name__ ,							strength=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							callback_steps=__magic_name__ ,							)







	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    int = 512 ,							__magic_name__     :    int = 512 ,							__magic_name__     :    int = 50 ,							__magic_name__     :    float = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    float = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[torch.FloatTensor] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							):


				"""simple docstring"""

				return StableDiffusionPipeline(**self.components							)(
				    prompt=__magic_name__ ,							height=__magic_name__ ,							width=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							latents=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							callback_steps=__magic_name__ ,							)





