



"""simple docstring"""
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__SCREAMING_SNAKE_CASE     :  Tuple										=					False


@nightly
@require_torch_gpu
class 							lowerCamelCase_(      unittest.TestCase			):





					'''simple docstring'''



					def 	snake_case__					(			self      ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(lowerCamelCase__      )
									pipe.set_progress_bar_config(disable=lowerCamelCase__      )

									_lowerCamelCase					     = load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''      )

									_lowerCamelCase					     = torch.manual_seed(0      )
									_lowerCamelCase					     = pipe(
									    prompt='''first prompt''' ,				image=lowerCamelCase__ ,				text_to_image_strength=0.7_5 ,				generator=lowerCamelCase__ ,				guidance_scale=7.5 ,				num_inference_steps=2 ,				output_type='''numpy''' ,				).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(lowerCamelCase__      )
													_lowerCamelCase					     = VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCamelCase__      )

									pipe.to(lowerCamelCase__      )
									pipe.set_progress_bar_config(disable=lowerCamelCase__      )

									_lowerCamelCase					     = generator.manual_seed(0      )
									_lowerCamelCase					     = pipe(
									    prompt='''first prompt''' ,				image=lowerCamelCase__ ,				text_to_image_strength=0.7_5 ,				generator=lowerCamelCase__ ,				guidance_scale=7.5 ,				num_inference_steps=2 ,				output_type='''numpy''' ,				).images

									assert np.abs(image - new_image      ).sum() < 1e-5, "Models don't have the same forward pass"





					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
									pipe.remove_unused_weights()
									pipe.to(lowerCamelCase__      )
									pipe.set_progress_bar_config(disable=lowerCamelCase__      )

									_lowerCamelCase					     = '''cyberpunk 2077'''
									_lowerCamelCase					     = load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''      )
									_lowerCamelCase					     = torch.manual_seed(0      )
									_lowerCamelCase					     = pipe(
									    prompt=lowerCamelCase__ ,				image=lowerCamelCase__ ,				text_to_image_strength=0.7_5 ,				generator=lowerCamelCase__ ,				guidance_scale=7.5 ,				num_inference_steps=5_0 ,				output_type='''numpy''' ,				).images

									_lowerCamelCase					     = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									_lowerCamelCase					     = np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2





"""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
)
__SCREAMING_SNAKE_CASE     :  Dict										=					logging.getLogger(__name__)



def 				lowerCAmelCase_(      )		->				Optional[int]:
				_lowerCamelCase					     = argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''							)
				parser.add_argument('''--model_name'''  ,      type=lowercase_  ,      default='''transfo-xl-wt103'''  ,      help='''pretrained model name'''							)
				parser.add_argument(
				    '''--split'''  ,      type=lowercase_  ,      default='''test'''  ,      choices=['''all''', '''valid''', '''test''']  ,      help='''which split to evaluate'''							)
				parser.add_argument('''--batch_size'''  ,      type=lowercase_  ,      default=10  ,      help='''batch size'''							)
				parser.add_argument('''--tgt_len'''  ,      type=lowercase_  ,      default=1_28  ,      help='''number of tokens to predict'''							)
				parser.add_argument('''--ext_len'''  ,      type=lowercase_  ,      default=0  ,      help='''length of the extended context'''							)
				parser.add_argument('''--mem_len'''  ,      type=lowercase_  ,      default=16_00  ,      help='''length of the retained previous heads'''							)
				parser.add_argument('''--clamp_len'''  ,      type=lowercase_  ,      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=lowercase_  ,      required=lowercase_  ,      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=lowercase_  ,      default=''''''  ,      help='''Can be used for distant debugging.'''							)
				parser.add_argument('''--server_port'''  ,      type=lowercase_  ,      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=lowercase_							)
								ptvsd.wait_for_attach()

				_lowerCamelCase					     = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
				logger.info('''device: {}'''.format(lowercase_							)							)

				# 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=lowercase_  ,      ext_len=args.ext_len							)
				_lowerCamelCase					     = corpus.get_iterator('''test'''  ,      args.batch_size  ,      args.tgt_len  ,      device=lowercase_  ,      ext_len=args.ext_len							)

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

				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(lowercase_   :							Any							):
								# Turn on evaluation mode which disables dropout.
								model.eval()
								_lowerCamelCase     ,   _lowerCamelCase					     = 0, 0.0
								_lowerCamelCase					     = time.time()
								with torch.no_grad():
												_lowerCamelCase					     = None
												for idx, (data, target, seq_len) in enumerate(lowercase_							):
																_lowerCamelCase					     = model(lowercase_  ,      lm_labels=lowercase_  ,      mems=lowercase_							)
																_lowerCamelCase     ,   _lowerCamelCase     ,   _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(lowercase_  ,      10_00 * total_time / (idx + 1)							)							)
								return total_loss / total_len

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

				def format_log(lowercase_   :							Optional[Any]  ,      lowercase_   :							Optional[Any]							):
								_lowerCamelCase					     = '''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(lowercase_  ,      lowercase_  ,      math.exp(lowercase_							)							)
								return log_str

				_lowerCamelCase					     = ''''''
				if valid_loss is not None:
								log_str += format_log(lowercase_  ,      '''valid'''							)
				if test_loss is not None:
								log_str += format_log(lowercase_  ,      '''test'''							)

				logger.info('''=''' * 1_00							)
				logger.info(lowercase_							)
				logger.info('''=''' * 1_00							)


if __name__ == "__main__":
			main()
