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





def  _A		(							lowercase__	):
						lowercase__							 =       {}
						lowercase__							 =       (
						    LoRAAttnProcessor2_0 if hasattr(lowercase__		,		"""scaled_dot_product_attention"""	) else LoRAAttnProcessor
						)
						for name, module in text_encoder.named_modules():
												if name.endswith(lowercase__	):
																		lowercase__							 =       lora_attn_processor_class(
																		    hidden_size=module.out_proj.out_features		,		cross_attention_dim=lowercase__	)
						return text_lora_attn_procs





def  _A		(							lowercase__	):
						lowercase__							 =       create_text_encoder_lora_attn_procs(lowercase__	)
						lowercase__							 =       AttnProcsLayers(lowercase__	)
						return text_encoder_lora_layers





def  _A		(							lowercase__		,		lowercase__=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"""	):
																								lowercase__							 =       (
																								    torch.randn_like(layer_module.up.weight	)
																								    if randn_weight
																								    else torch.zeros_like(layer_module.up.weight	)
																								)
																								lowercase__							 =       torch.nn.Parameter(lowercase__	)







class 			A							(       unittest.TestCase				):



			def A__ (					self       )			->		str:




									'''simple docstring'''



									torch.manual_seed(0       )
									lowercase__							 =       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       ,     )
									lowercase__							 =       DDIMScheduler(
									    beta_start=0.0_00_85       ,     beta_end=0.0_12       ,     beta_schedule="""scaled_linear"""       ,     clip_sample=lowerCamelCase__       ,     set_alpha_to_one=lowerCamelCase__       ,     steps_offset=1       ,     )
									torch.manual_seed(0       )
									lowercase__							 =       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       ,     )
									lowercase__							 =       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=1_000       ,     )
									lowercase__							 =       CLIPTextModel(lowerCamelCase__       )
									lowercase__							 =       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

									lowercase__ ,						lowercase__							 =       create_unet_lora_layers(lowerCamelCase__       )
									lowercase__							 =       create_text_encoder_lora_layers(lowerCamelCase__       )

									lowercase__							 =       {
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									lowercase__							 =       {
									    """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 A__ (					self       )			->		List[str]:




									'''simple docstring'''



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

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

									lowercase__							 =       {
									    """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 A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__							 =       77

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

									lowercase__							 =       {}
									lowercase__							 =       inputs
									return prepared_inputs



			def A__ (					self       ,     lowerCamelCase__       )			->		Optional[Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.get_dummy_components()
									LoraLoaderMixin.save_lora_weights(
									    save_directory=lowerCamelCase__       ,     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(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )



			def A__ (					self       )			->		str:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.get_dummy_components()
									lowercase__							 =       StableDiffusionPipeline(**lowerCamelCase__       )
									lowercase__							 =       sd_pipe.to(lowerCamelCase__       )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

									lowercase__ ,						lowercase__ ,						lowercase__							 =       self.get_dummy_inputs()

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

									with tempfile.TemporaryDirectory() as tmpdirname:
															LoraLoaderMixin.save_lora_weights(
															    save_directory=lowerCamelCase__       ,     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(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															sd_pipe.load_lora_weights(lowerCamelCase__       )

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

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



			def A__ (					self       )			->		str:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.get_dummy_components()
									lowercase__							 =       StableDiffusionPipeline(**lowerCamelCase__       )
									lowercase__							 =       sd_pipe.to(lowerCamelCase__       )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

									lowercase__ ,						lowercase__ ,						lowercase__							 =       self.get_dummy_inputs()

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

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

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

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



			def A__ (					self       )			->		int:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.get_dummy_components()
									lowercase__							 =       lora_components["""unet_lora_attn_procs"""]
									lowercase__							 =       StableDiffusionPipeline(**lowerCamelCase__       )
									lowercase__							 =       sd_pipe.to(lowerCamelCase__       )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

									lowercase__ ,						lowercase__ ,						lowercase__							 =       self.get_dummy_inputs()

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

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

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

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



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




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.get_dummy_components()
									lowercase__							 =       StableDiffusionPipeline(**lowerCamelCase__       )

									lowercase__							 =       self.get_dummy_tokens()

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

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

									# monkey patch
									pipe._modify_text_encoder(lowerCamelCase__       )

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

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

									assert torch.allclose(
									    lowerCamelCase__       ,     lowerCamelCase__       ), "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
									lowercase__							 =       create_text_encoder_lora_attn_procs(pipe.text_encoder       )
									set_lora_up_weights(lowerCamelCase__       ,     randn_weight=lowerCamelCase__       )

									# monkey patch
									pipe._modify_text_encoder(lowerCamelCase__       )

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

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

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



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




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.get_dummy_components()
									lowercase__							 =       StableDiffusionPipeline(**lowerCamelCase__       )

									lowercase__							 =       self.get_dummy_tokens()

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

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

									# monkey patch
									pipe._modify_text_encoder(lowerCamelCase__       )

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

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

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

									# remove monkey patch
									pipe._remove_text_encoder_monkey_patch()

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

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



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




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.get_dummy_components()
									lowercase__							 =       StableDiffusionPipeline(**lowerCamelCase__       )
									lowercase__							 =       sd_pipe.to(lowerCamelCase__       )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

									lowercase__ ,						lowercase__ ,						lowercase__							 =       self.get_dummy_inputs()

									with tempfile.TemporaryDirectory() as tmpdirname:
															LoraLoaderMixin.save_lora_weights(
															    save_directory=lowerCamelCase__       ,     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(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															sd_pipe.load_lora_weights(lowerCamelCase__       )

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

									lowercase__							 =       sd_pipe(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).images
									lowercase__							 =       lora_images_with_scale[0, -3:, -3:, -1]

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



			def A__ (					self       )			->		Any:




									'''simple docstring'''



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

															lowercase__ ,						lowercase__							 =       self.get_dummy_components()
															lowercase__							 =       StableDiffusionPipeline(**lowerCamelCase__       )
															lowercase__							 =       sd_pipe.to(lowerCamelCase__       )
															sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

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

            # load LoRA weight file
															sd_pipe.load_lora_weights(lowerCamelCase__       )

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



			@unittest.skipIf(torch_device != """cuda"""       ,     """This test is supposed to run on GPU"""       )
			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



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

															lowercase__ ,						lowercase__							 =       self.get_dummy_components()
															lowercase__							 =       StableDiffusionPipeline(**lowerCamelCase__       )
															lowercase__							 =       sd_pipe.to(lowerCamelCase__       )
															sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

															# 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(lowerCamelCase__       ,     lowerCamelCase__       ):
																											self.assertIsInstance(module.processor       ,     lowerCamelCase__       )

            # load LoRA weight file
															sd_pipe.load_lora_weights(lowerCamelCase__       )

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



			@unittest.skipIf(torch_device != """cuda"""       ,     """This test is supposed to run on GPU"""       )
			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.get_dummy_components()
									lowercase__							 =       StableDiffusionPipeline(**lowerCamelCase__       )
									lowercase__							 =       sd_pipe.to(lowerCamelCase__       )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

									lowercase__ ,						lowercase__ ,						lowercase__							 =       self.get_dummy_inputs()

									# enable XFormers
									sd_pipe.enable_xformers_memory_efficient_attention()

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

									with tempfile.TemporaryDirectory() as tmpdirname:
															LoraLoaderMixin.save_lora_weights(
															    save_directory=lowerCamelCase__       ,     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(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															sd_pipe.load_lora_weights(lowerCamelCase__       )

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

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








'''simple docstring'''



import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser





def  _A		(							lowercase__=None		,		lowercase__=None	):
						return field(default_factory=lambda: default		,		metadata=lowercase__	)





@dataclass
class 			A							:
			lowerCamelCase							:							str       		=  field(
			    metadata={"""help""": """The csv file to plot."""}		,						)
			lowerCamelCase							:							bool       		=  field(
			    default=__UpperCAmelCase		,						metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}		,						)
			lowerCamelCase							:							bool       		=  field(
			    default=__UpperCAmelCase		,						metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}		,						)
			lowerCamelCase							:							bool       		=  field(
			    default=__UpperCAmelCase		,						metadata={"""help""": """Disable logarithmic scale when plotting"""}		,						)
			lowerCamelCase							:							bool       		=  field(
			    default=__UpperCAmelCase		,						metadata={
			        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
			    }		,						)
			lowerCamelCase							:							Optional[str]       		=  field(
			    default=__UpperCAmelCase		,						metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}		,						)
			lowerCamelCase							:							Optional[List[str]]       		=  list_field(
			    default=__UpperCAmelCase		,						metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}				)





def  _A		(							lowercase__	):
						try:
												int(lowercase__	)
												return True
						except ValueError:
												return False





def  _A		(							lowercase__	):
						try:
												float(lowercase__	)
												return True
						except ValueError:
												return False





class 			A							:



			def __init__(					self       ,     lowerCamelCase__       )			->		int:




									'''simple docstring'''



									lowercase__							 =       args
									lowercase__							 =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

									with open(self.args.csv_file       ,     newline=""""""       ) as csv_file:
															lowercase__							 =       csv.DictReader(lowerCamelCase__       )
															for row in reader:
																					lowercase__							 =       row["""model"""]
																					self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]       )       )
																					self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]       )       )
																					if can_convert_to_int(row["""result"""]       ):
																											# value is not None
																											lowercase__							 =       int(row["""result"""]       )
																					elif can_convert_to_float(row["""result"""]       ):
																											# value is not None
																											lowercase__							 =       float(row["""result"""]       )



			def A__ (					self       )			->		Tuple:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       plt.subplots()
									lowercase__							 =       """Time usage""" if self.args.is_time else """Memory usage"""
									lowercase__							 =       title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

									if not self.args.no_log_scale:
															# set logarithm scales
															ax.set_xscale("""log"""       )
															ax.set_yscale("""log"""       )

									for axis in [ax.xaxis, ax.yaxis]:
															axis.set_major_formatter(ScalarFormatter()       )

									for model_name_idx, model_name in enumerate(self.result_dict.keys()       ):
															lowercase__							 =       sorted(set(self.result_dict[model_name]["""bsz"""]       )       )
															lowercase__							 =       sorted(set(self.result_dict[model_name]["""seq_len"""]       )       )
															lowercase__							 =       self.result_dict[model_name]["""result"""]

															((lowercase__) ,						(lowercase__))							 =       (
															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
															)

															lowercase__							 =       (
															    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
															)

															for inner_loop_value in inner_loop_array:
																					if self.args.plot_along_batch:
																											lowercase__							 =       np.asarray(
																											    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,     dtype=lowerCamelCase__       ,     )
																					else:
																											lowercase__							 =       np.asarray(
																											    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,     dtype=np.floataa       ,     )

																					((lowercase__) ,						(lowercase__))							 =       (
																					    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																					)

																					lowercase__							 =       np.asarray(lowerCamelCase__       ,     lowerCamelCase__       )[: len(lowerCamelCase__       )]
																					plt.scatter(
																					    lowerCamelCase__       ,     lowerCamelCase__       ,     label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''       )
																					plt.plot(lowerCamelCase__       ,     lowerCamelCase__       ,     """--"""       )

															title_str += F''' {label_model_name} vs.'''

									lowercase__							 =       title_str[:-4]
									lowercase__							 =       """Time in s""" if self.args.is_time else """Memory in MB"""

									# plot
									plt.title(lowerCamelCase__       )
									plt.xlabel(lowerCamelCase__       )
									plt.ylabel(lowerCamelCase__       )
									plt.legend()

									if self.args.figure_png_file is not None:
															plt.savefig(self.args.figure_png_file       )
									else:
															plt.show()





def  _A		(							):
						lowercase__							 =       HfArgumentParser(lowercase__	)
						lowercase__							 =       parser.parse_args_into_dataclasses()[0]
						lowercase__							 =       Plot(args=lowercase__	)
						plot.plot()


if __name__ == "__main__":
				main()






