


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__     :  Optional[Any]=None				,							UpperCamelCase__     :  int=None						):
       return field(default_factory=lambda: default				,							metadata=UpperCamelCase__						)







@dataclass
class      lowerCAmelCase__       :




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




def 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]						):
       try:
              int(UpperCamelCase__						)
              return True
       except ValueError:
              return False
def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]						):
       try:
              float(UpperCamelCase__						)
              return True
       except ValueError:
              return False







class      lowerCAmelCase__       :




     '''simple docstring'''


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

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


     def  _lowerCamelCase (						self)			->				int:
            _A    ,			_A      :       Union[str, Any]				    = plt.subplots()
            _A      :       str				    = "Time usage" if self.args.is_time else "Memory usage"
            _A      :       Dict				    = 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()):
                   _A      :       List[str]				    = sorted(set(self.result_dict[model_name]["bsz"]))
                   _A      :       int				    = sorted(set(self.result_dict[model_name]["seq_len"]))
                   _A      :       List[str]				    = self.result_dict[model_name]["result"]

                   ((_A)    ,			(_A))      :       Dict				    = (
                       (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                   )

                   _A      :       Any				    = (
                       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:
                                 _A      :       Optional[int]				    = np.asarray(
                                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,						dtype=__lowerCamelCase ,						)
                          else:
                                 _A      :       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 ,						)

                          ((_A)    ,			(_A))      :       Union[str, Any]				    = (
                              ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                          )

                          _A      :       int				    = 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."

            _A      :       str				    = title_str[:-4]
            _A      :       int				    = "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 							_UpperCAmelCase				():
       _A      :       List[str]				    = HfArgumentParser(UpperCamelCase__						)
       _A      :       Union[str, Any]				    = parser.parse_args_into_dataclasses()[0]
       _A      :       Any				    = Plot(args=UpperCamelCase__						)
       plot.plot()


if __name__ == "__main__":
    main()




import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()







class      lowerCAmelCase__       (				a     ,    a     ,    unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VQModel
     __SCREAMING_SNAKE_CASE  			= "sample"


     @property
     def  _lowerCamelCase (						self ,						__lowerCamelCase=(3_2, 3_2))			->				Optional[Any]:
            _A      :       Optional[int]				    = 4
            _A      :       Tuple				    = 3

            _A      :       List[Any]				    = floats_tensor((batch_size, num_channels) + sizes).to(__lowerCamelCase)

            return {"sample": image}


     @property
     def  _lowerCamelCase (						self)			->				int:
            return (3, 3_2, 3_2)


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


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       List[Any]				    = {
                "block_out_channels": [3_2, 6_4],
                "in_channels": 3,
                "out_channels": 3,
                "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
                "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
                "latent_channels": 3,
            }
            _A      :       int				    = self.dummy_input
            return init_dict, inputs_dict


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            pass


     def  _lowerCamelCase (						self)			->				Any:
            pass


     def  _lowerCamelCase (						self)			->				Any:
            _A    ,			_A      :       List[Any]				    = VQModel.from_pretrained("fusing/vqgan-dummy" ,						output_loading_info=__lowerCamelCase)
            self.assertIsNotNone(__lowerCamelCase)
            self.assertEqual(len(loading_info["missing_keys"]) ,						0)

            model.to(__lowerCamelCase)
            _A      :       str				    = model(**self.dummy_input)

            assert image is not None, "Make sure output is not None"


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Optional[Any]				    = VQModel.from_pretrained("fusing/vqgan-dummy")
            model.to(__lowerCamelCase).eval()

            torch.manual_seed(0)
            if torch.cuda.is_available():
                   torch.cuda.manual_seed_all(0)

            _A      :       Tuple				    = torch.randn(1 ,						model.config.in_channels ,						model.config.sample_size ,						model.config.sample_size)
            _A      :       Optional[int]				    = image.to(__lowerCamelCase)
            with torch.no_grad():
                   _A      :       List[str]				    = model(__lowerCamelCase).sample

            _A      :       int				    = output[0, -1, -3:, -3:].flatten().cpu()
            # fmt: off
            _A      :       Optional[Any]				    = torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3])
            # fmt: on
            self.assertTrue(torch.allclose(__lowerCamelCase ,						__lowerCamelCase ,						atol=1e-3))
