
'''simple docstring'''
import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device







def 						A_(			A		:						nn.Module):
							UpperCamelCase					       =			{}
							for name in unet.attn_processors.keys():
														UpperCamelCase					       =			None if name.endswith('attn1.processor') else unet.config.cross_attention_dim
														if name.startswith('mid_block'):
																					UpperCamelCase					       =			unet.config.block_out_channels[-1]
														elif name.startswith('up_blocks'):
																					UpperCamelCase					       =			int(name[len('up_blocks.')])
																					UpperCamelCase					       =			list(reversed(unet.config.block_out_channels))[block_id]
														elif name.startswith('down_blocks'):
																					UpperCamelCase					       =			int(name[len('down_blocks.')])
																					UpperCamelCase					       =			unet.config.block_out_channels[block_id]
														UpperCamelCase					       =			(
														    LoRAAttnProcessor2_0 if hasattr(A						,			'scaled_dot_product_attention') else LoRAAttnProcessor
														)
														UpperCamelCase					       =			lora_attn_processor_class(
														    hidden_size=A						,			cross_attention_dim=A)
							UpperCamelCase					       =			AttnProcsLayers(A)
							return lora_attn_procs, unet_lora_layers







def 						A_(			A		:						nn.Module):
							UpperCamelCase					       =			{}
							UpperCamelCase					       =			(
							    LoRAAttnProcessor2_0 if hasattr(A						,			'scaled_dot_product_attention') else LoRAAttnProcessor
							)
							for name, module in text_encoder.named_modules():
														if name.endswith(A):
																					UpperCamelCase					       =			lora_attn_processor_class(
																					    hidden_size=module.out_proj.out_features						,			cross_attention_dim=A)
							return text_lora_attn_procs







def 						A_(			A		:						nn.Module):
							UpperCamelCase					       =			create_text_encoder_lora_attn_procs(A)
							UpperCamelCase					       =			AttnProcsLayers(A)
							return text_encoder_lora_layers







def 						A_(			A		:						Optional[Any]						,			A		:						List[str]=False):
							for _, attn_proc in text_lora_attn_procs.items():
														# set up.weights
														for layer_name, layer_module in attn_proc.named_modules():
																					if layer_name.endswith('_lora'):
																												UpperCamelCase					       =			(
																												    torch.randn_like(layer_module.up.weight)
																												    if randn_weight
																												    else torch.zeros_like(layer_module.up.weight)
																												)
																												UpperCamelCase					       =			torch.nn.Parameter(A)





class        SCREAMING_SNAKE_CASE__	(			unittest.TestCase):







				def 		UpperCAmelCase_  (    self			)->						List[str]:







											'''simple docstring'''






											torch.manual_seed(0			)
											UpperCamelCase					       =			UNetaDConditionModel(
											    block_out_channels=(32, 64)		,							layers_per_block=2		,							sample_size=32		,							in_channels=4		,							out_channels=4		,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,							cross_attention_dim=32		,							)
											UpperCamelCase					       =			DDIMScheduler(
											    beta_start=0.00_085		,							beta_end=0.012		,							beta_schedule='scaled_linear'		,							clip_sample=A_		,							set_alpha_to_one=A_		,							steps_offset=1		,							)
											torch.manual_seed(0			)
											UpperCamelCase					       =			AutoencoderKL(
											    block_out_channels=[32, 64]		,							in_channels=3		,							out_channels=3		,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,							latent_channels=4		,							)
											UpperCamelCase					       =			CLIPTextConfig(
											    bos_token_id=0		,							eos_token_id=2		,							hidden_size=32		,							intermediate_size=37		,							layer_norm_eps=1e-05		,							num_attention_heads=4		,							num_hidden_layers=5		,							pad_token_id=1		,							vocab_size=1000		,							)
											UpperCamelCase					       =			CLIPTextModel(A_			)
											UpperCamelCase					       =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

											UpperCamelCase       ,     UpperCamelCase					       =			create_unet_lora_layers(A_			)
											UpperCamelCase					       =			create_text_encoder_lora_layers(A_			)

											UpperCamelCase					       =			{
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'safety_checker': None,
											    'feature_extractor': None,
											}
											UpperCamelCase					       =			{
											    'unet_lora_layers': unet_lora_layers,
											    'text_encoder_lora_layers': text_encoder_lora_layers,
											    'unet_lora_attn_procs': unet_lora_attn_procs,
											}
											return pipeline_components, lora_components







				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											UpperCamelCase					       =			1
											UpperCamelCase					       =			10
											UpperCamelCase					       =			4
											UpperCamelCase					       =			(32, 32)

											UpperCamelCase					       =			torch.manual_seed(0			)
											UpperCamelCase					       =			floats_tensor((batch_size, num_channels) + sizes			)
											UpperCamelCase					       =			torch.randint(1		,							A_		,							size=(batch_size, sequence_length)		,							generator=A_			)

											UpperCamelCase					       =			{
											    'prompt': 'A painting of a squirrel eating a burger',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}

											return noise, input_ids, pipeline_inputs

											# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb







				def 		UpperCAmelCase_  (    self			)->						List[str]:







											'''simple docstring'''






											UpperCamelCase					       =			77

											UpperCamelCase					       =			torch.randint(2		,							56		,							size=(1, max_seq_length)		,							generator=torch.manual_seed(0			)			)

											UpperCamelCase					       =			{}
											UpperCamelCase					       =			inputs
											return prepared_inputs







				def 		UpperCAmelCase_  (    self		,							A_			)->						List[str]:







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_components()
											LoraLoaderMixin.save_lora_weights(
											    save_directory=A_		,							unet_lora_layers=lora_components['unet_lora_layers']		,							text_encoder_lora_layers=lora_components['text_encoder_lora_layers']		,							)
											self.assertTrue(os.path.isfile(os.path.join(A_		,							'pytorch_lora_weights.bin'			)			)			)







				def 		UpperCAmelCase_  (    self			)->						int:







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_components()
											UpperCamelCase					       =			StableDiffusionPipeline(**A_			)
											UpperCamelCase					       =			sd_pipe.to(A_			)
											sd_pipe.set_progress_bar_config(disable=A_			)

											UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_inputs()

											UpperCamelCase					       =			sd_pipe(**A_			).images
											UpperCamelCase					       =			original_images[0, -3:, -3:, -1]

											with tempfile.TemporaryDirectory() as tmpdirname:
																		LoraLoaderMixin.save_lora_weights(
																		    save_directory=A_		,							unet_lora_layers=lora_components['unet_lora_layers']		,							text_encoder_lora_layers=lora_components['text_encoder_lora_layers']		,							)
																		self.assertTrue(os.path.isfile(os.path.join(A_		,							'pytorch_lora_weights.bin'			)			)			)
																		sd_pipe.load_lora_weights(A_			)

											UpperCamelCase					       =			sd_pipe(**A_			).images
											UpperCamelCase					       =			lora_images[0, -3:, -3:, -1]

											# Outputs shouldn't match.
											self.assertFalse(torch.allclose(torch.from_numpy(A_			)		,							torch.from_numpy(A_			)			)			)







				def 		UpperCAmelCase_  (    self			)->						Tuple:







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_components()
											UpperCamelCase					       =			StableDiffusionPipeline(**A_			)
											UpperCamelCase					       =			sd_pipe.to(A_			)
											sd_pipe.set_progress_bar_config(disable=A_			)

											UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_inputs()

											UpperCamelCase					       =			sd_pipe(**A_			).images
											UpperCamelCase					       =			original_images[0, -3:, -3:, -1]

											with tempfile.TemporaryDirectory() as tmpdirname:
																		LoraLoaderMixin.save_lora_weights(
																		    save_directory=A_		,							unet_lora_layers=lora_components['unet_lora_layers']		,							text_encoder_lora_layers=lora_components['text_encoder_lora_layers']		,							safe_serialization=A_		,							)
																		self.assertTrue(os.path.isfile(os.path.join(A_		,							'pytorch_lora_weights.safetensors'			)			)			)
																		sd_pipe.load_lora_weights(A_			)

											UpperCamelCase					       =			sd_pipe(**A_			).images
											UpperCamelCase					       =			lora_images[0, -3:, -3:, -1]

											# Outputs shouldn't match.
											self.assertFalse(torch.allclose(torch.from_numpy(A_			)		,							torch.from_numpy(A_			)			)			)







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







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_components()
											UpperCamelCase					       =			lora_components['unet_lora_attn_procs']
											UpperCamelCase					       =			StableDiffusionPipeline(**A_			)
											UpperCamelCase					       =			sd_pipe.to(A_			)
											sd_pipe.set_progress_bar_config(disable=A_			)

											UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_inputs()

											UpperCamelCase					       =			sd_pipe(**A_			).images
											UpperCamelCase					       =			original_images[0, -3:, -3:, -1]

											with tempfile.TemporaryDirectory() as tmpdirname:
																		UpperCamelCase					       =			sd_pipe.unet
																		unet.set_attn_processor(A_			)
																		unet.save_attn_procs(A_			)
																		self.assertTrue(os.path.isfile(os.path.join(A_		,							'pytorch_lora_weights.bin'			)			)			)
																		sd_pipe.load_lora_weights(A_			)

											UpperCamelCase					       =			sd_pipe(**A_			).images
											UpperCamelCase					       =			lora_images[0, -3:, -3:, -1]

											# Outputs shouldn't match.
											self.assertFalse(torch.allclose(torch.from_numpy(A_			)		,							torch.from_numpy(A_			)			)			)







				def 		UpperCAmelCase_  (    self			)->						Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_components()
											UpperCamelCase					       =			StableDiffusionPipeline(**A_			)

											UpperCamelCase					       =			self.get_dummy_tokens()

											# inference without lora
											UpperCamelCase					       =			pipe.text_encoder(**A_			)[0]
											assert outputs_without_lora.shape == (1, 77, 32)

											# create lora_attn_procs with zeroed out up.weights
											UpperCamelCase					       =			create_text_encoder_lora_attn_procs(pipe.text_encoder			)
											set_lora_up_weights(A_		,							randn_weight=A_			)

											# monkey patch
											pipe._modify_text_encoder(A_			)

											# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
											del text_attn_procs
											gc.collect()

											# inference with lora
											UpperCamelCase					       =			pipe.text_encoder(**A_			)[0]
											assert outputs_with_lora.shape == (1, 77, 32)

											assert torch.allclose(
											    A_		,							A_			), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

											# create lora_attn_procs with randn up.weights
											UpperCamelCase					       =			create_text_encoder_lora_attn_procs(pipe.text_encoder			)
											set_lora_up_weights(A_		,							randn_weight=A_			)

											# monkey patch
											pipe._modify_text_encoder(A_			)

											# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
											del text_attn_procs
											gc.collect()

											# inference with lora
											UpperCamelCase					       =			pipe.text_encoder(**A_			)[0]
											assert outputs_with_lora.shape == (1, 77, 32)

											assert not torch.allclose(
											    A_		,							A_			), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"







				def 		UpperCAmelCase_  (    self			)->						Optional[int]:







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_components()
											UpperCamelCase					       =			StableDiffusionPipeline(**A_			)

											UpperCamelCase					       =			self.get_dummy_tokens()

											# inference without lora
											UpperCamelCase					       =			pipe.text_encoder(**A_			)[0]
											assert outputs_without_lora.shape == (1, 77, 32)

											# create lora_attn_procs with randn up.weights
											UpperCamelCase					       =			create_text_encoder_lora_attn_procs(pipe.text_encoder			)
											set_lora_up_weights(A_		,							randn_weight=A_			)

											# monkey patch
											pipe._modify_text_encoder(A_			)

											# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
											del text_attn_procs
											gc.collect()

											# inference with lora
											UpperCamelCase					       =			pipe.text_encoder(**A_			)[0]
											assert outputs_with_lora.shape == (1, 77, 32)

											assert not torch.allclose(
											    A_		,							A_			), "lora outputs should be different to without lora outputs"

											# remove monkey patch
											pipe._remove_text_encoder_monkey_patch()

											# inference with removed lora
											UpperCamelCase					       =			pipe.text_encoder(**A_			)[0]
											assert outputs_without_lora_removed.shape == (1, 77, 32)

											assert torch.allclose(
											    A_		,							A_			), "remove lora monkey patch should restore the original outputs"







				def 		UpperCAmelCase_  (    self			)->						Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_components()
											UpperCamelCase					       =			StableDiffusionPipeline(**A_			)
											UpperCamelCase					       =			sd_pipe.to(A_			)
											sd_pipe.set_progress_bar_config(disable=A_			)

											UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_inputs()

											with tempfile.TemporaryDirectory() as tmpdirname:
																		LoraLoaderMixin.save_lora_weights(
																		    save_directory=A_		,							unet_lora_layers=lora_components['unet_lora_layers']		,							text_encoder_lora_layers=lora_components['text_encoder_lora_layers']		,							)
																		self.assertTrue(os.path.isfile(os.path.join(A_		,							'pytorch_lora_weights.bin'			)			)			)
																		sd_pipe.load_lora_weights(A_			)

											UpperCamelCase					       =			sd_pipe(**A_			).images
											UpperCamelCase					       =			lora_images[0, -3:, -3:, -1]

											UpperCamelCase					       =			sd_pipe(**A_		,							cross_attention_kwargs={'scale': 0.5}			).images
											UpperCamelCase					       =			lora_images_with_scale[0, -3:, -3:, -1]

											# Outputs shouldn't match.
											self.assertFalse(
											    torch.allclose(torch.from_numpy(A_			)		,							torch.from_numpy(A_			)			)			)







				def 		UpperCAmelCase_  (    self			)->						str:







											'''simple docstring'''






											with tempfile.TemporaryDirectory() as tmpdirname:
																		self.create_lora_weight_file(A_			)

																		UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_components()
																		UpperCamelCase					       =			StableDiffusionPipeline(**A_			)
																		UpperCamelCase					       =			sd_pipe.to(A_			)
																		sd_pipe.set_progress_bar_config(disable=A_			)

																		# check if vanilla attention processors are used
																		for _, module in sd_pipe.unet.named_modules():
																									if isinstance(A_		,							A_			):
																																self.assertIsInstance(module.processor		,							(AttnProcessor, AttnProcessor2_0)			)

            # load LoRA weight file
																		sd_pipe.load_lora_weights(A_			)

																		# check if lora attention processors are used
																		for _, module in sd_pipe.unet.named_modules():
																									if isinstance(A_		,							A_			):
																																UpperCamelCase					       =			(
																																    LoRAAttnProcessor2_0 if hasattr(A_		,							'scaled_dot_product_attention'			) else LoRAAttnProcessor
																																)
																																self.assertIsInstance(module.processor		,							A_			)







				@unittest.skipIf(torch_device != 'cuda'		,							'This test is supposed to run on GPU'			)
				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											with tempfile.TemporaryDirectory() as tmpdirname:
																		self.create_lora_weight_file(A_			)

																		UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_components()
																		UpperCamelCase					       =			StableDiffusionPipeline(**A_			)
																		UpperCamelCase					       =			sd_pipe.to(A_			)
																		sd_pipe.set_progress_bar_config(disable=A_			)

																		# enable XFormers
																		sd_pipe.enable_xformers_memory_efficient_attention()

																		# check if xFormers attention processors are used
																		for _, module in sd_pipe.unet.named_modules():
																									if isinstance(A_		,							A_			):
																																self.assertIsInstance(module.processor		,							A_			)

            # load LoRA weight file
																		sd_pipe.load_lora_weights(A_			)

																		# check if lora attention processors are used
																		for _, module in sd_pipe.unet.named_modules():
																									if isinstance(A_		,							A_			):
																																self.assertIsInstance(module.processor		,							A_			)







				@unittest.skipIf(torch_device != 'cuda'		,							'This test is supposed to run on GPU'			)
				def 		UpperCAmelCase_  (    self			)->						Tuple:







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_components()
											UpperCamelCase					       =			StableDiffusionPipeline(**A_			)
											UpperCamelCase					       =			sd_pipe.to(A_			)
											sd_pipe.set_progress_bar_config(disable=A_			)

											UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			self.get_dummy_inputs()

											# enable XFormers
											sd_pipe.enable_xformers_memory_efficient_attention()

											UpperCamelCase					       =			sd_pipe(**A_			).images
											UpperCamelCase					       =			original_images[0, -3:, -3:, -1]

											with tempfile.TemporaryDirectory() as tmpdirname:
																		LoraLoaderMixin.save_lora_weights(
																		    save_directory=A_		,							unet_lora_layers=lora_components['unet_lora_layers']		,							text_encoder_lora_layers=lora_components['text_encoder_lora_layers']		,							)
																		self.assertTrue(os.path.isfile(os.path.join(A_		,							'pytorch_lora_weights.bin'			)			)			)
																		sd_pipe.load_lora_weights(A_			)

											UpperCamelCase					       =			sd_pipe(**A_			).images
											UpperCamelCase					       =			lora_images[0, -3:, -3:, -1]

											# Outputs shouldn't match.
											self.assertFalse(torch.allclose(torch.from_numpy(A_			)		,							torch.from_numpy(A_			)			)			)






'''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
)
lowerCAmelCase :  Optional[Any]      =      logging.getLogger(__name__)







def 						A_(			):
							UpperCamelCase					       =			argparse.ArgumentParser(description='PyTorch Transformer Language Model')
							parser.add_argument('--model_name'						,			type=A						,			default='transfo-xl-wt103'						,			help='pretrained model name')
							parser.add_argument(
							    '--split'						,			type=A						,			default='test'						,			choices=['all', 'valid', 'test']						,			help='which split to evaluate')
							parser.add_argument('--batch_size'						,			type=A						,			default=10						,			help='batch size')
							parser.add_argument('--tgt_len'						,			type=A						,			default=128						,			help='number of tokens to predict')
							parser.add_argument('--ext_len'						,			type=A						,			default=0						,			help='length of the extended context')
							parser.add_argument('--mem_len'						,			type=A						,			default=1600						,			help='length of the retained previous heads')
							parser.add_argument('--clamp_len'						,			type=A						,			default=1000						,			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=A						,			required=A						,			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=A						,			default=''						,			help='Can be used for distant debugging.')
							parser.add_argument('--server_port'						,			type=A						,			default=''						,			help='Can be used for distant debugging.')
							UpperCamelCase					       =			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=A)
														ptvsd.wait_for_attach()

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

							# 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 )
							UpperCamelCase					       =			TransfoXLCorpus.from_pretrained(args.model_name)

							UpperCamelCase					       =			corpus.get_iterator('valid'						,			args.batch_size						,			args.tgt_len						,			device=A						,			ext_len=args.ext_len)
							UpperCamelCase					       =			corpus.get_iterator('test'						,			args.batch_size						,			args.tgt_len						,			device=A						,			ext_len=args.ext_len)

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

							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:
														UpperCamelCase					       =			args.clamp_len
							if args.same_length:
														UpperCamelCase					       =			True

							###############################################################################
							# Evaluation code
							###############################################################################
							def evaluate(A		:						Dict):
														# Turn on evaluation mode which disables dropout.
														model.eval()
														UpperCamelCase       ,     UpperCamelCase					       =			0, 0.0
														UpperCamelCase					       =			time.time()
														with torch.no_grad():
																					UpperCamelCase					       =			None
																					for idx, (data, target, seq_len) in enumerate(A):
																												UpperCamelCase					       =			model(A						,			lm_labels=A						,			mems=A)
																												UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			ret
																												UpperCamelCase					       =			loss.mean()
																												total_loss += seq_len * loss.item()
																												total_len += seq_len
																					UpperCamelCase					       =			time.time() - start_time
														logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(A						,			1000 * total_time / (idx + 1)))
														return total_loss / total_len

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

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

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

							logger.info('=' * 100)
							logger.info(A)
							logger.info('=' * 100)


if __name__ == "__main__":
						main()




