






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


@skip_mps
class 					__SCREAMING_SNAKE_CASE(		A__      ,					A__      ,					A__      ,					unittest.TestCase		):
							_UpperCAmelCase	      =    StableDiffusionModelEditingPipeline
							_UpperCAmelCase	      =    TEXT_TO_IMAGE_PARAMS
							_UpperCAmelCase	      =    TEXT_TO_IMAGE_BATCH_PARAMS
							_UpperCAmelCase	      =    TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase	      =    TEXT_TO_IMAGE_IMAGE_PARAMS
							def lowerCAmelCase_	(       self: Union[str, Any]  )						->						List[str]:
											torch.manual_seed(0  )
											snake_case__													=       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    ,		)
											snake_case__													=       DDIMScheduler()
											torch.manual_seed(0  )
											snake_case__													=       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    ,		)
											torch.manual_seed(0  )
											snake_case__													=       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=10_00    ,		)
											snake_case__													=       CLIPTextModel(__snake_case  )
											snake_case__													=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

											snake_case__													=       {
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''safety_checker''': None,
											    '''feature_extractor''': None,
											}
											return components
							def lowerCAmelCase_	(       self: Optional[Any]    ,		UpperCamelCase: Any    ,		UpperCamelCase: Tuple=0  )						->						int:
											snake_case__													=       torch.manual_seed(__snake_case  )
											snake_case__													=       {
											    '''prompt''': '''A field of roses''',
											    '''generator''': generator,
											    # Setting height and width to None to prevent OOMs on CPU.
											    '''height''': None,
											    '''width''': None,
											    '''num_inference_steps''': 2,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs
							def lowerCAmelCase_	(       self: List[Any]  )						->						int:
											snake_case__													=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
											snake_case__													=       self.get_dummy_components()
											snake_case__													=       StableDiffusionModelEditingPipeline(**__snake_case  )
											snake_case__													=       sd_pipe.to(__snake_case  )
											sd_pipe.set_progress_bar_config(disable=__snake_case  )

											snake_case__													=       self.get_dummy_inputs(__snake_case  )
											snake_case__													=       sd_pipe(**__snake_case  ).images
											snake_case__													=       image[0, -3:, -3:, -1]
											assert image.shape == (1, 64, 64, 3)

											snake_case__													=       np.array([0.4_755, 0.5_132, 0.4_976, 0.3_904, 0.3_554, 0.4_765, 0.5_139, 0.5_158, 0.4_889]  )

											assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
							def lowerCAmelCase_	(       self: List[Any]  )						->						str:
											snake_case__													=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
											snake_case__													=       self.get_dummy_components()
											snake_case__													=       StableDiffusionModelEditingPipeline(**__snake_case  )
											snake_case__													=       sd_pipe.to(__snake_case  )
											sd_pipe.set_progress_bar_config(disable=__snake_case  )

											snake_case__													=       self.get_dummy_inputs(__snake_case  )
											snake_case__													=       '''french fries'''
											snake_case__													=       sd_pipe(**__snake_case    ,		negative_prompt=__snake_case  )
											snake_case__													=       output.images
											snake_case__													=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)

											snake_case__													=       np.array([0.4_992, 0.5_101, 0.5_004, 0.3_949, 0.3_604, 0.4_735, 0.5_216, 0.5_204, 0.4_913]  )

											assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
							def lowerCAmelCase_	(       self: List[Any]  )						->						List[Any]:
											snake_case__													=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
											snake_case__													=       self.get_dummy_components()
											snake_case__													=       EulerAncestralDiscreteScheduler(
											    beta_start=0.00_085    ,		beta_end=0.012    ,		beta_schedule='scaled_linear'  )
											snake_case__													=       StableDiffusionModelEditingPipeline(**__snake_case  )
											snake_case__													=       sd_pipe.to(__snake_case  )
											sd_pipe.set_progress_bar_config(disable=__snake_case  )

											snake_case__													=       self.get_dummy_inputs(__snake_case  )
											snake_case__													=       sd_pipe(**__snake_case  ).images
											snake_case__													=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)

											snake_case__													=       np.array([0.4_747, 0.5_372, 0.4_779, 0.4_982, 0.5_543, 0.4_816, 0.5_238, 0.4_904, 0.5_027]  )

											assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
							def lowerCAmelCase_	(       self: str  )						->						List[Any]:
											snake_case__													=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
											snake_case__													=       self.get_dummy_components()
											snake_case__													=       PNDMScheduler()
											snake_case__													=       StableDiffusionModelEditingPipeline(**__snake_case  )
											snake_case__													=       sd_pipe.to(__snake_case  )
											sd_pipe.set_progress_bar_config(disable=__snake_case  )

											snake_case__													=       self.get_dummy_inputs(__snake_case  )
											# the pipeline does not expect pndm so test if it raises error.
											with self.assertRaises(__snake_case  ):
															snake_case__													=       sd_pipe(**__snake_case  ).images
							def lowerCAmelCase_	(       self: int  )						->						Optional[int]:
											super().test_inference_batch_single_identical(expected_max_diff=5e-3  )


							def lowerCAmelCase_	(       self: List[Any]  )						->						Union[str, Any]:
											super().test_attention_slicing_forward_pass(expected_max_diff=5e-3  )


@slow
@require_torch_gpu
class 					__SCREAMING_SNAKE_CASE(		unittest.TestCase		):
							def lowerCAmelCase_	(       self: Optional[int]  )						->						int:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
							def lowerCAmelCase_	(       self: int    ,		UpperCamelCase: Optional[int]=0  )						->						List[Any]:
											snake_case__													=       torch.manual_seed(__snake_case  )
											snake_case__													=       {
											    '''prompt''': '''A field of roses''',
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''guidance_scale''': 7.5,
											    '''output_type''': '''numpy''',
											}
											return inputs
							def lowerCAmelCase_	(       self: List[str]  )						->						List[str]:
											snake_case__													=       '''CompVis/stable-diffusion-v1-4'''
											snake_case__													=       StableDiffusionModelEditingPipeline.from_pretrained(__snake_case    ,		safety_checker=__snake_case  )
											pipe.to(__snake_case  )
											pipe.set_progress_bar_config(disable=__snake_case  )
											pipe.enable_attention_slicing()

											snake_case__													=       self.get_inputs()
											snake_case__													=       pipe(**__snake_case  ).images
											snake_case__													=       image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_12, 5_12, 3)

											snake_case__													=       np.array(
											    [0.6_749_496, 0.6_386_453, 0.51_443_267, 0.66_094_905, 0.61_921_215, 0.5_491_332, 0.5_744_417, 0.58_075_106, 0.5_174_658]  )

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

											# make sure image changes after editing
											pipe.edit_model('A pack of roses'    ,		'A pack of blue roses'  )

											snake_case__													=       pipe(**__snake_case  ).images
											snake_case__													=       image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_12, 5_12, 3)

											assert np.abs(expected_slice - image_slice  ).max() > 1e-1


							def lowerCAmelCase_	(       self: str  )						->						Optional[int]:
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											snake_case__													=       '''CompVis/stable-diffusion-v1-4'''
											snake_case__													=       DDIMScheduler.from_pretrained(__snake_case    ,		subfolder='scheduler'  )
											snake_case__													=       StableDiffusionModelEditingPipeline.from_pretrained(
											    __snake_case    ,		scheduler=__snake_case    ,		safety_checker=__snake_case  )
											snake_case__													=       pipe.to(__snake_case  )
											pipe.set_progress_bar_config(disable=__snake_case  )
											pipe.enable_attention_slicing(1  )
											pipe.enable_sequential_cpu_offload()

											snake_case__													=       self.get_inputs()
											snake_case__													=       pipe(**__snake_case  )

											snake_case__													=       torch.cuda.max_memory_allocated()
											# make sure that less than 4.4 GB is allocated
											assert mem_bytes < 4.4 * 10**9












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[Any]							=  logging.getLogger(__name__)
def        a_	( )     ->     int:







				"""simple docstring"""
				snake_case__													=       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.'					)
				snake_case__													=       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()

				snake_case__													=       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 )
				snake_case__													=       TransfoXLCorpus.from_pretrained(args.model_name					)

				snake_case__													=       corpus.get_iterator('valid' ,				args.batch_size ,				args.tgt_len ,				device=_A ,				ext_len=args.ext_len					)
				snake_case__													=       corpus.get_iterator('test' ,				args.batch_size ,				args.tgt_len ,				device=_A ,				ext_len=args.ext_len					)

				# Load a pre-trained model
				snake_case__													=       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:
								snake_case__													=       args.clamp_len
				if args.same_length:
								snake_case__													=       True

				###############################################################################
				# Evaluation code
				###############################################################################
				def evaluate(_A					):
								# Turn on evaluation mode which disables dropout.
								model.eval()
								snake_case__							,  snake_case__													=       0, 0.0
								snake_case__													=       time.time()
								with torch.no_grad():
												snake_case__													=       None
												for idx, (data, target, seq_len) in enumerate(_A					):
																snake_case__													=       model(_A ,				lm_labels=_A ,				mems=_A					)
																snake_case__							,  snake_case__							,  snake_case__													=       ret
																snake_case__													=       loss.mean()
																total_loss += seq_len * loss.item()
																total_len += seq_len
												snake_case__													=       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":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       evaluate(_A					)
				elif args.split == "valid":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       None
				elif args.split == "test":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       None

				def format_log(_A ,				_A					):
								snake_case__													=       '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(_A ,				_A ,				math.exp(_A					)					)
								return log_str

				snake_case__													=       ''
				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()




