


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__						(UpperCamelCase_=None							,UpperCamelCase_=None    ):



     """simple docstring"""






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

@dataclass
class   A__ :







    """simple docstring"""

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






def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     try:
          int(UpperCamelCase_    )
          return True
     except ValueError:
          return False
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






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

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case   ):
         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(__snake_case   )
              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 		a_      (	self   ):
         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=__snake_case      , )
                   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(__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.'''

         snake_case					=    title_str[:-4]
         snake_case					=    '''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       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    HfArgumentParser(UpperCamelCase_    )
     snake_case					=    parser.parse_args_into_dataclasses()[0]
     snake_case					=    Plot(args=UpperCamelCase_    )
     plot.plot()


if __name__ == "__main__":
 main()








import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort

class   A__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
    def 		a_      (	self      , __snake_case=0   ):
         snake_case					=    floats_tensor((1, 3, 1_2_8, 1_2_8)      , rng=random.Random(__snake_case   )   )
         snake_case					=    np.random.RandomState(__snake_case   )
         snake_case					=    {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''strength''': 0.75,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087]   )
         assert np.abs(image_slice - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    PNDMScheduler.from_config(pipe.scheduler.config      , skip_prk_steps=__snake_case   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         # warmup pass to apply optimizations
         snake_case					=    pipe(**self.get_dummy_inputs()   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]   )

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




    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552]   )

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






@nightly
@require_onnxruntime
@require_torch_gpu
class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    @property
    def 		a_      (	self   ):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )
    @property
    def 		a_      (	self   ):
         snake_case					=    ort.SessionOptions()
         snake_case					=    False
         return options
    def 		a_      (	self   ):
         snake_case					=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''   )
         snake_case					=    init_image.resize((7_6_8, 5_1_2)   )
         # using the PNDM scheduler by default
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''      , revision='''onnx'''      , safety_checker=__snake_case      , feature_extractor=__snake_case      , provider=self.gpu_provider      , sess_options=self.gpu_options      , )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    '''A fantasy landscape, trending on artstation'''

         snake_case					=    np.random.RandomState(0   )
         snake_case					=    pipe(
             prompt=__snake_case      , image=__snake_case      , strength=0.75      , guidance_scale=7.5      , num_inference_steps=1_0      , generator=__snake_case      , output_type='''np'''      , )
         snake_case					=    output.images
         snake_case					=    images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         snake_case					=    np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]   )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2E-2




    def 		a_      (	self   ):
         snake_case					=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''   )
         snake_case					=    init_image.resize((7_6_8, 5_1_2)   )
         snake_case					=    LMSDiscreteScheduler.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''      , subfolder='''scheduler'''      , revision='''onnx'''   )
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''      , revision='''onnx'''      , scheduler=__snake_case      , safety_checker=__snake_case      , feature_extractor=__snake_case      , provider=self.gpu_provider      , sess_options=self.gpu_options      , )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    '''A fantasy landscape, trending on artstation'''

         snake_case					=    np.random.RandomState(0   )
         snake_case					=    pipe(
             prompt=__snake_case      , image=__snake_case      , strength=0.75      , guidance_scale=7.5      , num_inference_steps=2_0      , generator=__snake_case      , output_type='''np'''      , )
         snake_case					=    output.images
         snake_case					=    images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         snake_case					=    np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]   )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2E-2




