



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__			(		_UpperCamelCase			: str=None ,_UpperCamelCase			: int=None							):
   return field(default_factory=lambda: default ,metadata=_UpperCamelCase							)



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





def 		a__			(		_UpperCamelCase			: Dict							):







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





def 		a__			(		_UpperCamelCase			: List[str]							):







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



class  __lowerCAmelCase     :




 def __init__(						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    args
    __lowerCamelCase													=    defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    plt.subplots()
    __lowerCamelCase													=    '''Time usage''' if self.args.is_time else '''Memory usage'''
    __lowerCamelCase													=    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													=    sorted(set(self.result_dict[model_name]['''bsz''']				)				)
       __lowerCamelCase													=    sorted(set(self.result_dict[model_name]['''seq_len''']				)				)
       __lowerCamelCase													=    self.result_dict[model_name]['''result''']

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

       __lowerCamelCase													=    (
           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													=    np.asarray(
                 [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]	,				dtype=_lowerCAmelCase	,				)
          else:
             __lowerCamelCase													=    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))													=    (
              ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
          )

          __lowerCamelCase													=    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."""

    __lowerCamelCase													=    title_str[:-4]
    __lowerCamelCase													=    '''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__			(		):
   __lowerCamelCase													=    HfArgumentParser(_UpperCamelCase							)
   __lowerCamelCase													=    parser.parse_args_into_dataclasses()[0]
   __lowerCamelCase													=    Plot(args=_UpperCamelCase							)
   plot.plot()


if __name__ == "__main__":
  main()






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a_			=					False



class  __lowerCAmelCase     (	unittest.TestCase   ):
 pass



@nightly
@require_torch_gpu
class  __lowerCAmelCase     (	unittest.TestCase   ):




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt='''first prompt'''	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=2	,				output_type='''numpy'''	,				).images

    with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(__UpperCAmelCase				)
       __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained(__UpperCAmelCase	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    generator.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt='''first prompt'''	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=2	,				output_type='''numpy'''	,				).images

    assert np.abs(image - new_image				).sum() < 1E-5, "Models don't have the same forward pass"




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    '''cyberpunk 2077'''
    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt=__UpperCAmelCase	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=50	,				output_type='''numpy'''	,				).images

    __lowerCamelCase													=    image[0, 253:256, 253:256, -1]

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1

    __lowerCamelCase													=    '''A painting of a squirrel eating a burger '''
    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.text_to_image(
        prompt=__UpperCAmelCase	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=50	,				output_type='''numpy'''				).images

    __lowerCamelCase													=    image[0, 253:256, 253:256, -1]

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1

    __lowerCamelCase													=    pipe.image_variation(__UpperCAmelCase	,				generator=__UpperCAmelCase	,				output_type='''numpy'''				).images

    __lowerCamelCase													=    image[0, 253:256, 253:256, -1]

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1






