




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_ (      _A=None      ,   _A=None  )			->			Union[str, Any]:







				"""simple docstring"""
				return field(default_factory=lambda: default      ,   metadata=_A  )


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


def      a_ (      _A  )			->			Optional[Any]:







				"""simple docstring"""







				try:
								int(_A  )
								return True
				except ValueError:
								return False
def      a_ (      _A  )			->			Union[str, Any]:







				"""simple docstring"""







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


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      List[str]       ,	UpperCamelCase:      List[Any]						)		->		Optional[Any]:
											snake_case__									=     args
											snake_case__									=     defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

											with open(self.args.csv_file       ,	newline=''						) as csv_file:
															snake_case__									=     csv.DictReader(_lowerCAmelCase						)
															for row in reader:
																			snake_case__									=     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
																							snake_case__									=     int(row['result']						)
																			elif can_convert_to_float(row['result']						):
																							# value is not None
																							snake_case__									=     float(row['result']						)


							def lowerCAmelCase_ (     self:      List[str]						)		->		Optional[int]:
											snake_case__							,						snake_case__									=     plt.subplots()
											snake_case__									=     'Time usage' if self.args.is_time else 'Memory usage'
											snake_case__									=     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()						):
															snake_case__									=     sorted(set(self.result_dict[model_name]['bsz']						)						)
															snake_case__									=     sorted(set(self.result_dict[model_name]['seq_len']						)						)
															snake_case__									=     self.result_dict[model_name]['result']

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

															snake_case__									=     (
															    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:
																							snake_case__									=     np.asarray(
																							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,	dtype=_lowerCAmelCase       ,	)
																			else:
																							snake_case__									=     np.asarray(
																							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,	dtype=np.floataa       ,	)

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

																			snake_case__									=     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.'''

											snake_case__									=     title_str[:-4]
											snake_case__									=     '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_ (      )			->			List[Any]:







				"""simple docstring"""
				snake_case__									=     HfArgumentParser(_A  )
				snake_case__									=     parser.parse_args_into_dataclasses()[0]
				snake_case__									=     Plot(args=_A  )
				plot.plot()


if __name__ == "__main__":
							main()












import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       StableUnCLIPImgaImgPipeline
							_UpperCAmelCase     =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS
							_UpperCAmelCase     =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							_UpperCAmelCase     =       frozenset(
							    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							_UpperCAmelCase     =       frozenset([]	)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Union[str, Any]:
											snake_case__									=     32
											snake_case__									=     embedder_hidden_size

											# image encoding components

											snake_case__									=     CLIPImageProcessor(crop_size=32       ,	size=32						)

											torch.manual_seed(0						)
											snake_case__									=     CLIPVisionModelWithProjection(
											    CLIPVisionConfig(
											        hidden_size=UpperCamelCase       ,	projection_dim=UpperCamelCase       ,	num_hidden_layers=5       ,	num_attention_heads=4       ,	image_size=32       ,	intermediate_size=37       ,	patch_size=1       ,	)						)

											# regular denoising components

											torch.manual_seed(0						)
											snake_case__									=     StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase						)
											snake_case__									=     DDPMScheduler(beta_schedule='squaredcos_cap_v2'						)

											torch.manual_seed(0						)
											snake_case__									=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

											torch.manual_seed(0						)
											snake_case__									=     CLIPTextModel(
											    CLIPTextConfig(
											        bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=UpperCamelCase       ,	projection_dim=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       ,	)						)

											torch.manual_seed(0						)
											snake_case__									=     UNetaDConditionModel(
											    sample_size=32       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')       ,	up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')       ,	block_out_channels=(32, 64)       ,	attention_head_dim=(2, 4)       ,	class_embed_type='projection'       ,	projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,	cross_attention_dim=UpperCamelCase       ,	layers_per_block=1       ,	upcast_attention=UpperCamelCase       ,	use_linear_projection=UpperCamelCase       ,	)

											torch.manual_seed(0						)
											snake_case__									=     DDIMScheduler(
											    beta_schedule='scaled_linear'       ,	beta_start=0.00_085       ,	beta_end=0.012       ,	prediction_type='v_prediction'       ,	set_alpha_to_one=UpperCamelCase       ,	steps_offset=1       ,	)

											torch.manual_seed(0						)
											snake_case__									=     AutoencoderKL()

											snake_case__									=     {
											    # image encoding components
											    'feature_extractor': feature_extractor,
											    'image_encoder': image_encoder.eval(),
											    # image noising components
											    'image_normalizer': image_normalizer.eval(),
											    'image_noising_scheduler': image_noising_scheduler,
											    # regular denoising components
											    'tokenizer': tokenizer,
											    'text_encoder': text_encoder.eval(),
											    'unet': unet.eval(),
											    'scheduler': scheduler,
											    'vae': vae.eval(),
											}

											return components
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Tuple=0       ,	UpperCamelCase:      Union[str, Any]=True						)		->		Any:
											if str(UpperCamelCase						).startswith('mps'						):
															snake_case__									=     torch.manual_seed(UpperCamelCase						)
											else:
															snake_case__									=     torch.Generator(device=UpperCamelCase						).manual_seed(UpperCamelCase						)

											snake_case__									=     floats_tensor((1, 3, 32, 32)       ,	rng=random.Random(UpperCamelCase						)						).to(UpperCamelCase						)

											if pil_image:
															snake_case__									=     input_image * 0.5 + 0.5
															snake_case__									=     input_image.clamp(0       ,	1						)
															snake_case__									=     input_image.cpu().permute(0       ,	2       ,	3       ,	1						).float().numpy()
															snake_case__									=     DiffusionPipeline.numpy_to_pil(UpperCamelCase						)[0]

											return {
											    "prompt": "An anime racoon running a marathon",
											    "image": input_image,
											    "generator": generator,
											    "num_inference_steps": 2,
											    "output_type": "np",
											}
							@skip_mps
							def lowerCAmelCase_ (     self:      Dict						)		->		Optional[Any]:
											snake_case__									=     'cpu'  # ensure determinism for the device-dependent torch.Generator
											snake_case__									=     self.get_dummy_components()
											snake_case__									=     StableUnCLIPImgaImgPipeline(**UpperCamelCase						)
											snake_case__									=     sd_pipe.to(UpperCamelCase						)
											sd_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     self.get_dummy_inputs(UpperCamelCase						)
											inputs.update({'image_embeds': None}						)
											snake_case__									=     sd_pipe(**UpperCamelCase						).images
											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case__									=     np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Union[str, Any]:
											snake_case__									=     torch_device in ['cpu', 'mps']

											self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		int:
											snake_case__									=     torch_device in ['cpu', 'mps']

											self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase						)


							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()       ,	reason='XFormers attention is only available with CUDA and `xformers` installed'       ,	)
							def lowerCAmelCase_ (     self:      str						)		->		Optional[int]:
											self._test_xformers_attention_forwardGenerator_pass(test_max_difference=UpperCamelCase						)


@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[int]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Dict:
											snake_case__									=     load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'						)

											snake_case__									=     load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'						)

											snake_case__									=     StableUnCLIPImgaImgPipeline.from_pretrained(
											    'fusing/stable-unclip-2-1-l-img2img'       ,	torch_dtype=torch.floataa						)
											pipe.to(UpperCamelCase						)
											pipe.set_progress_bar_config(disable=UpperCamelCase						)
											# stable unclip will oom when integration tests are run on a V100,
											# so turn on memory savings
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											snake_case__									=     torch.Generator(device='cpu'						).manual_seed(0						)
											snake_case__									=     pipe(UpperCamelCase       ,	'anime turle'       ,	generator=UpperCamelCase       ,	output_type='np'						)

											snake_case__									=     output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Dict:
											snake_case__									=     load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'						)

											snake_case__									=     load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'						)

											snake_case__									=     StableUnCLIPImgaImgPipeline.from_pretrained(
											    'fusing/stable-unclip-2-1-h-img2img'       ,	torch_dtype=torch.floataa						)
											pipe.to(UpperCamelCase						)
											pipe.set_progress_bar_config(disable=UpperCamelCase						)
											# stable unclip will oom when integration tests are run on a V100,
											# so turn on memory savings
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											snake_case__									=     torch.Generator(device='cpu'						).manual_seed(0						)
											snake_case__									=     pipe(UpperCamelCase       ,	'anime turle'       ,	generator=UpperCamelCase       ,	output_type='np'						)

											snake_case__									=     output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(UpperCamelCase       ,	UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		str:
											snake_case__									=     load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'						)

											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											snake_case__									=     StableUnCLIPImgaImgPipeline.from_pretrained(
											    'fusing/stable-unclip-2-1-h-img2img'       ,	torch_dtype=torch.floataa						)
											snake_case__									=     pipe.to(UpperCamelCase						)
											pipe.set_progress_bar_config(disable=UpperCamelCase						)
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											snake_case__									=     pipe(
											    UpperCamelCase       ,	'anime turtle'       ,	num_inference_steps=2       ,	output_type='np'       ,	)

											snake_case__									=     torch.cuda.max_memory_allocated()
											# make sure that less than 7 GB is allocated
											assert mem_bytes < 7 * 10**9




