


"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 				UpperCamelCase_			(				UpperCamelCase    ,						unittest.TestCase):

 """simple docstring"""





 snake_case__       :						int					      =    KandinskyInpaintPipeline
 snake_case__       :						List[Any]					      =    ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
 snake_case__       :						Union[str, Any]					      =    [
     "prompt",
     "negative_prompt",
     "image_embeds",
     "negative_image_embeds",
     "image",
     "mask_image",
 ]
 snake_case__       :						List[Any]					      =    [
     "generator",
     "height",
     "width",
     "latents",
     "guidance_scale",
     "negative_prompt",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 snake_case__       :						Optional[int]					      =    False




 @property
 def 						UpperCAmelCase_   (				self    :       Any     ) ->			str:
      return 3_2




 @property
 def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Any:
      return 3_2




 @property
 def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			List[Any]:
      return self.time_input_dim




 @property
 def 						UpperCAmelCase_   (				self    :       int     ) ->			int:
      return self.time_input_dim * 4




 @property
 def 						UpperCAmelCase_   (				self    :       str     ) ->			Any:
      return 1_0_0




 @property
 def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			List[Any]:
      __SCREAMING_SNAKE_CASE		     =       XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"     )
      return tokenizer




 @property
 def 						UpperCAmelCase_   (				self    :       Any     ) ->			List[Any]:
      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE		     =       MCLIPConfig(
          numDims=self.cross_attention_dim    ,    transformerDimensions=self.text_embedder_hidden_size    ,    hidden_size=self.text_embedder_hidden_size    ,    intermediate_size=3_7    ,    num_attention_heads=4    ,    num_hidden_layers=5    ,    vocab_size=1_0_0_5    ,    )

      __SCREAMING_SNAKE_CASE		     =       MultilingualCLIP(UpperCAmelCase__     )
      __SCREAMING_SNAKE_CASE		     =       text_encoder.eval()

      return text_encoder




 @property
 def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Optional[Any]:
      torch.manual_seed(0     )

      __SCREAMING_SNAKE_CASE		     =       {
          "in_channels": 9,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "text_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": "text_image_proj",
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": None,
      }

      __SCREAMING_SNAKE_CASE		     =       UNetaDConditionModel(**UpperCAmelCase__     )
      return model




 @property
 def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			Any:
      return {
          "block_out_channels": [3_2, 6_4],
          "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": 1_2,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }




 @property
 def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Optional[Any]:
      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE		     =       VQModel(**self.dummy_movq_kwargs     )
      return model




 def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Optional[Any]:
      __SCREAMING_SNAKE_CASE		     =       self.dummy_text_encoder
      __SCREAMING_SNAKE_CASE		     =       self.dummy_tokenizer
      __SCREAMING_SNAKE_CASE		     =       self.dummy_unet
      __SCREAMING_SNAKE_CASE		     =       self.dummy_movq

      __SCREAMING_SNAKE_CASE		     =       DDIMScheduler(
          num_train_timesteps=1_0_0_0    ,    beta_schedule="linear"    ,    beta_start=0.00_085    ,    beta_end=0.012    ,    clip_sample=UpperCAmelCase__    ,    set_alpha_to_one=UpperCAmelCase__    ,    steps_offset=1    ,    prediction_type="epsilon"    ,    thresholding=UpperCAmelCase__    ,    )

      __SCREAMING_SNAKE_CASE		     =       {
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }

      return components




 def 						UpperCAmelCase_   (				self    :       Optional[Any]    ,    UpperCAmelCase__    :       Tuple    ,    UpperCAmelCase__    :       Union[str, Any]=0     ) ->			Dict:
      __SCREAMING_SNAKE_CASE		     =       floats_tensor((1, self.cross_attention_dim)    ,    rng=random.Random(UpperCAmelCase__     )     ).to(UpperCAmelCase__     )
      __SCREAMING_SNAKE_CASE		     =       floats_tensor((1, self.cross_attention_dim)    ,    rng=random.Random(seed + 1     )     ).to(UpperCAmelCase__     )
      # create init_image
      __SCREAMING_SNAKE_CASE		     =       floats_tensor((1, 3, 6_4, 6_4)    ,    rng=random.Random(UpperCAmelCase__     )     ).to(UpperCAmelCase__     )
      __SCREAMING_SNAKE_CASE		     =       image.cpu().permute(0    ,    2    ,    3    ,    1     )[0]
      __SCREAMING_SNAKE_CASE		     =       Image.fromarray(np.uinta(UpperCAmelCase__     )     ).convert("RGB"     ).resize((2_5_6, 2_5_6)     )
      # create mask
      __SCREAMING_SNAKE_CASE		     =       np.ones((6_4, 6_4)    ,    dtype=np.floataa     )
      __SCREAMING_SNAKE_CASE		     =       0

      if str(UpperCAmelCase__     ).startswith("mps"     ):
           __SCREAMING_SNAKE_CASE		     =       torch.manual_seed(UpperCAmelCase__     )
      else:
           __SCREAMING_SNAKE_CASE		     =       torch.Generator(device=UpperCAmelCase__     ).manual_seed(UpperCAmelCase__     )
      __SCREAMING_SNAKE_CASE		     =       {
          "prompt": "horse",
          "image": init_image,
          "mask_image": mask,
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "generator": generator,
          "height": 6_4,
          "width": 6_4,
          "num_inference_steps": 2,
          "guidance_scale": 4.0,
          "output_type": "np",
      }
      return inputs




 def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Optional[int]:
      __SCREAMING_SNAKE_CASE		     =       "cpu"

      __SCREAMING_SNAKE_CASE		     =       self.get_dummy_components()

      __SCREAMING_SNAKE_CASE		     =       self.pipeline_class(**UpperCAmelCase__     )
      __SCREAMING_SNAKE_CASE		     =       pipe.to(UpperCAmelCase__     )

      pipe.set_progress_bar_config(disable=UpperCAmelCase__     )

      __SCREAMING_SNAKE_CASE		     =       pipe(**self.get_dummy_inputs(UpperCAmelCase__     )     )
      __SCREAMING_SNAKE_CASE		     =       output.images

      __SCREAMING_SNAKE_CASE		     =       pipe(
          **self.get_dummy_inputs(UpperCAmelCase__     )    ,    return_dict=UpperCAmelCase__    ,    )[0]

      __SCREAMING_SNAKE_CASE		     =       image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE		     =       image_from_tuple[0, -3:, -3:, -1]

      print(F"""image.shape {image.shape}"""     )

      assert image.shape == (1, 6_4, 6_4, 3)

      __SCREAMING_SNAKE_CASE		     =       np.array(
          [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]     )

      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()}"""




 def 						UpperCAmelCase_   (				self    :       int     ) ->			Optional[int]:
      super().test_inference_batch_single_identical(expected_max_diff=3E-3     )




@slow
@require_torch_gpu
class 				UpperCamelCase_			(				unittest.TestCase):

 """simple docstring"""





 def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			str:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




 def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Tuple:
      __SCREAMING_SNAKE_CASE		     =       load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"     )

      __SCREAMING_SNAKE_CASE		     =       load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"     )
      __SCREAMING_SNAKE_CASE		     =       np.ones((7_6_8, 7_6_8)    ,    dtype=np.floataa     )
      __SCREAMING_SNAKE_CASE		     =       0

      __SCREAMING_SNAKE_CASE		     =       "a hat"

      __SCREAMING_SNAKE_CASE		     =       KandinskyPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-1-prior"    ,    torch_dtype=torch.floataa     )
      pipe_prior.to(UpperCAmelCase__     )

      __SCREAMING_SNAKE_CASE		     =       KandinskyInpaintPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-1-inpaint"    ,    torch_dtype=torch.floataa     )
      __SCREAMING_SNAKE_CASE		     =       pipeline.to(UpperCAmelCase__     )
      pipeline.set_progress_bar_config(disable=UpperCAmelCase__     )

      __SCREAMING_SNAKE_CASE		     =       torch.Generator(device="cpu"     ).manual_seed(0     )
      __SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       pipe_prior(
          UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    num_inference_steps=5    ,    negative_prompt=""    ,    ).to_tuple()

      __SCREAMING_SNAKE_CASE		     =       pipeline(
          UpperCAmelCase__    ,    image=UpperCAmelCase__    ,    mask_image=UpperCAmelCase__    ,    image_embeds=UpperCAmelCase__    ,    negative_image_embeds=UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    num_inference_steps=1_0_0    ,    height=7_6_8    ,    width=7_6_8    ,    output_type="np"    ,    )

      __SCREAMING_SNAKE_CASE		     =       output.images[0]

      assert image.shape == (7_6_8, 7_6_8, 3)

      assert_mean_pixel_difference(UpperCAmelCase__    ,    UpperCAmelCase__     )




"""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      UpperCAmelCase__ (lowerCAmelCase_=None						,		lowerCAmelCase_=None     ):




					'''simple docstring'''


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




@dataclass
class 				UpperCamelCase_			:

	"""simple docstring"""





	snake_case__       :						str					      =    field(
	    metadata={"help": "The csv file to plot."}    ,						)
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}    ,						)
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}    ,						)
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Disable logarithmic scale when plotting"}    ,						)
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={
	        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
	    }    ,						)
	snake_case__       :						Optional[str]					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}    ,						)
	snake_case__       :						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      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					try:
										int(lowerCAmelCase_     )
										return True
					except ValueError:
										return False




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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




class 				UpperCamelCase_			:

	"""simple docstring"""





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

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




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       plt.subplots()
						__SCREAMING_SNAKE_CASE		     =       "Time usage" if self.args.is_time else "Memory usage"
						__SCREAMING_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()     ):
											__SCREAMING_SNAKE_CASE		     =       sorted(set(self.result_dict[model_name]["bsz"]     )     )
											__SCREAMING_SNAKE_CASE		     =       sorted(set(self.result_dict[model_name]["seq_len"]     )     )
											__SCREAMING_SNAKE_CASE		     =       self.result_dict[model_name]["result"]

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

											__SCREAMING_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:
																					__SCREAMING_SNAKE_CASE		     =       np.asarray(
																					    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]    ,    dtype=UpperCAmelCase__    ,    )
																else:
																					__SCREAMING_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    ,    )

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

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

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

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

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

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




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       HfArgumentParser(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       parser.parse_args_into_dataclasses()[0]
					__SCREAMING_SNAKE_CASE		     =       Plot(args=lowerCAmelCase_     )
					plot.plot()


if __name__ == "__main__":
		main()
