


"""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__      (    SCREAMING_SNAKE_CASE   :   str=None    ,				SCREAMING_SNAKE_CASE   :   str=None    ):


  '''simple docstring'''







  return field(default_factory=lambda: default    ,				metadata=SCREAMING_SNAKE_CASE    )


@dataclass
class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


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

def  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ):


  '''simple docstring'''







  try:
    int(SCREAMING_SNAKE_CASE    )
    return True
  except ValueError:
    return False
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







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


class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self		,					snake_case__						):






         """simple docstring"""





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

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







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase					:					List[Any]								=							plt.subplots()
         lowerCAmelCase					:					Any								=							"Time usage" if self.args.is_time else "Memory usage"
         lowerCAmelCase					:					str								=							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()						):
           lowerCAmelCase					:					Tuple								=							sorted(set(self.result_dict[model_name]["bsz"]						)						)
           lowerCAmelCase					:					Dict								=							sorted(set(self.result_dict[model_name]["seq_len"]						)						)
           lowerCAmelCase					:					int								=							self.result_dict[model_name]["result"]

           ((lowerCAmelCase)						,  (lowerCAmelCase))					:					List[Any]								=							(
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

           lowerCAmelCase					:					Dict								=							(
               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:
               lowerCAmelCase					:					Optional[Any]								=							np.asarray(
                   [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,					dtype=snake_case__		,					)
             else:
               lowerCAmelCase					:					List[str]								=							np.asarray(
                   [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,					dtype=np.floataa		,					)

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

             lowerCAmelCase					:					int								=							np.asarray(snake_case__		,					snake_case__						)[: len(snake_case__						)]
             plt.scatter(
                 snake_case__		,					snake_case__		,					label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""						)
             plt.plot(snake_case__		,					snake_case__		,					"--"						)

           title_str += f""" {label_model_name} vs."""

         lowerCAmelCase					:					Dict								=							title_str[:-4]
         lowerCAmelCase					:					Optional[int]								=							"Time in s" if self.args.is_time else "Memory in MB"

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

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

def  a__      (    ):


  '''simple docstring'''







  lowerCAmelCase					:					Any								=							HfArgumentParser(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					List[Any]								=							parser.parse_args_into_dataclasses()[0]
  lowerCAmelCase					:					Dict								=							Plot(args=SCREAMING_SNAKE_CASE    )
  plot.plot()


if __name__ == "__main__":
     main()









"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 				SCREAMING_SNAKE_CASE__    (		lowercase       ,   unittest.TestCase    ):
       """simple docstring"""


       a  :    Optional[Any]       =KandinskyVaaImgaImgPipeline
       a  :    Optional[int]       =["image_embeds", "negative_image_embeds", "image"]
       a  :    Optional[int]       =[
           "image_embeds",
           "negative_image_embeds",
           "image",
       ]
       a  :    str       =[
           "generator",
           "height",
           "width",
           "strength",
           "guidance_scale",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       a  :    Dict       =False

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return 32

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return 32

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return self.time_input_dim

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return self.time_input_dim * 4

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return 100

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         torch.manual_seed(0						)

         lowerCAmelCase					:					List[str]								=							{
             "in_channels": 4,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 8,
             "addition_embed_type": "image",
             "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
             "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
             "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "layers_per_block": 1,
             "encoder_hid_dim": self.text_embedder_hidden_size,
             "encoder_hid_dim_type": "image_proj",
             "cross_attention_dim": self.cross_attention_dim,
             "attention_head_dim": 4,
             "resnet_time_scale_shift": "scale_shift",
             "class_embed_type": None,
         }

         lowerCAmelCase					:					int								=							UNetaDConditionModel(**snake_case__						)
         return model

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         torch.manual_seed(0						)
         lowerCAmelCase					:					Optional[int]								=							VQModel(**self.dummy_movq_kwargs						)
         return model

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.dummy_unet
         lowerCAmelCase					:					Optional[int]								=							self.dummy_movq

         lowerCAmelCase					:					List[str]								=							{
             "num_train_timesteps": 1_000,
             "beta_schedule": "linear",
             "beta_start": 0.00085,
             "beta_end": 0.012,
             "clip_sample": False,
             "set_alpha_to_one": False,
             "steps_offset": 0,
             "prediction_type": "epsilon",
             "thresholding": False,
         }

         lowerCAmelCase					:					Tuple								=							DDIMScheduler(**snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							{
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }

         return components

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__=0						):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							floats_tensor((1, self.text_embedder_hidden_size)		,					rng=random.Random(snake_case__						)						).to(snake_case__						)
         lowerCAmelCase					:					Any								=							floats_tensor((1, self.text_embedder_hidden_size)		,					rng=random.Random(seed + 1						)						).to(
             snake_case__						)
         # create init_image
         lowerCAmelCase					:					List[Any]								=							floats_tensor((1, 3, 64, 64)		,					rng=random.Random(snake_case__						)						).to(snake_case__						)
         lowerCAmelCase					:					List[str]								=							image.cpu().permute(0		,					2		,					3		,					1						)[0]
         lowerCAmelCase					:					List[str]								=							Image.fromarray(np.uinta(snake_case__						)						).convert("RGB"						).resize((256, 256)						)

         if str(snake_case__						).startswith("mps"						):
           lowerCAmelCase					:					Optional[int]								=							torch.manual_seed(snake_case__						)
         else:
           lowerCAmelCase					:					Optional[int]								=							torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
         lowerCAmelCase					:					List[str]								=							{
             "image": init_image,
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "generator": generator,
             "height": 64,
             "width": 64,
             "num_inference_steps": 10,
             "guidance_scale": 7.0,
             "strength": 0.2,
             "output_type": "np",
         }
         return inputs







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							"cpu"

         lowerCAmelCase					:					Dict								=							self.get_dummy_components()

         lowerCAmelCase					:					Union[str, Any]								=							self.pipeline_class(**snake_case__						)
         lowerCAmelCase					:					Optional[int]								=							pipe.to(snake_case__						)

         pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase					:					int								=							pipe(**self.get_dummy_inputs(snake_case__						)						)
         lowerCAmelCase					:					Union[str, Any]								=							output.images

         lowerCAmelCase					:					Union[str, Any]								=							pipe(
             **self.get_dummy_inputs(snake_case__						)		,					return_dict=snake_case__		,					)[0]

         lowerCAmelCase					:					Optional[Any]								=							image[0, -3:, -3:, -1]
         lowerCAmelCase					:					Optional[int]								=							image_from_tuple[0, -3:, -3:, -1]

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

         lowerCAmelCase					:					int								=							np.array(
             [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]						)
         assert (
             np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
         ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
         ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""


@slow
@require_torch_gpu
class 				SCREAMING_SNAKE_CASE__    (		unittest.TestCase    ):
       """simple docstring"""


       def 	lowercase__							(						self						):






         """simple docstring"""





         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/kandinskyv22_img2img_frog.npy"						)

         lowerCAmelCase					:					Tuple								=							load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"						)
         lowerCAmelCase					:					Optional[Any]								=							"A red cartoon frog, 4k"

         lowerCAmelCase					:					int								=							KandinskyVaaPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-prior"		,					torch_dtype=torch.floataa						)
         pipe_prior.to(snake_case__						)

         lowerCAmelCase					:					List[Any]								=							KandinskyVaaImgaImgPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-decoder"		,					torch_dtype=torch.floataa						)
         lowerCAmelCase					:					Tuple								=							pipeline.to(snake_case__						)

         pipeline.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase					:					str								=							torch.Generator(device="cpu"						).manual_seed(0						)
         lowerCAmelCase						,  lowerCAmelCase					:					Optional[Any]								=							pipe_prior(
             snake_case__		,					generator=snake_case__		,					num_inference_steps=5		,					negative_prompt=""		,					).to_tuple()

         lowerCAmelCase					:					Tuple								=							pipeline(
             image=snake_case__		,					image_embeds=snake_case__		,					negative_image_embeds=snake_case__		,					generator=snake_case__		,					num_inference_steps=100		,					height=768		,					width=768		,					strength=0.2		,					output_type="np"		,					)

         lowerCAmelCase					:					Optional[Any]								=							output.images[0]

         assert image.shape == (768, 768, 3)

         assert_mean_pixel_difference(snake_case__		,					snake_case__						)





