



"""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       _snake_case						(						UpperCamelCase						:		List[Any]=None			,		UpperCamelCase						:		List[Any]=None						):
 return field(default_factory=lambda: default			,		metadata=__lowercase						)



@dataclass
class       SCREAMING_SNAKE_CASE__  :
   __lowerCAmelCase  :   str						=						field(
       metadata={'help': 'The csv file to plot.'}      ,			)
   __lowerCAmelCase  :   bool						=						field(
       default=UpperCAmelCase__      ,			metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}      ,			)
   __lowerCAmelCase  :   bool						=						field(
       default=UpperCAmelCase__      ,			metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}      ,			)
   __lowerCAmelCase  :   bool						=						field(
       default=UpperCAmelCase__      ,			metadata={'help': 'Disable logarithmic scale when plotting'}      ,			)
   __lowerCAmelCase  :   bool						=						field(
       default=UpperCAmelCase__      ,			metadata={
           'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
       }      ,			)
   __lowerCAmelCase  :   Optional[str]						=						field(
       default=UpperCAmelCase__      ,			metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}      ,			)
   __lowerCAmelCase  :   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       _snake_case						(						UpperCamelCase						:		Tuple						):
 try:
  int(__lowercase						)
  return True
 except ValueError:
  return False

def       _snake_case						(						UpperCamelCase						:		Tuple						):
 try:
  float(__lowercase						)
  return True
 except ValueError:
  return False



class       SCREAMING_SNAKE_CASE__  :

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	args
    UpperCAmelCase	:					Any	=	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

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




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




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

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

     UpperCAmelCase	:					str	=	(
         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:
       UpperCAmelCase	:					Union[str, Any]	=	np.asarray(
           [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,     dtype=_SCREAMING_SNAKE_CASE      ,     )
      else:
       UpperCAmelCase	:					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      ,     )

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

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

     title_str += F" {label_model_name} vs."

    UpperCAmelCase	:					List[str]	=	title_str[:-4]
    UpperCAmelCase	:					Tuple	=	"""Time in s""" if self.args.is_time else """Memory in MB"""

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

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

def       _snake_case						(						):
 UpperCAmelCase	:					str	=	HfArgumentParser(__lowercase						)
 UpperCAmelCase	:					int	=	parser.parse_args_into_dataclasses()[0]
 UpperCAmelCase	:					Dict	=	Plot(args=__lowercase						)
 plot.plot()


if __name__ == "__main__":
      main()


import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_lowerCamelCase       :				List[Any]       =			HfApi()

_lowerCamelCase       :				Dict       =			{}
# fmt: off
_lowerCamelCase       :				List[Any]       =			torch.tensor([
    -0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7_4_6_7,
    1.2_3_4_2, -2.2_4_8_5, 0.4_6_3_6, 0.8_0_7_6, -0.7_9_9_1, 0.3_9_6_9, 0.8_4_9_8, 0.9_1_8_9,
    -1.8_8_8_7, -3.3_5_2_2, 0.7_6_3_9, 0.2_0_4_0, 0.6_2_7_1, -2.7_1_4_8, -1.6_3_1_6, 3.0_8_3_9,
    0.3_1_8_6, 0.2_7_2_1, -0.9_7_5_9, -1.2_4_6_1, 2.6_2_5_7, 1.3_5_5_7
])
_lowerCamelCase       :				int       =			torch.tensor([
    -2.3_6_3_9, -2.5_3_4_4, 0.0_0_5_4, -0.6_6_7_4, 1.5_9_9_0, 1.0_1_5_8, 0.3_1_2_4, -2.1_4_3_6,
    1.8_7_9_5, -2.5_4_2_9, -0.1_5_6_6, -0.3_9_7_3, 1.2_4_9_0, 2.6_4_4_7, 1.2_2_8_3, -0.5_2_0_8,
    -2.8_1_5_4, -3.5_1_1_9, 2.3_8_3_8, 1.2_0_3_3, 1.7_2_0_1, -2.1_2_5_6, -1.4_5_7_6, 2.7_9_4_8,
    2.4_2_0_4, -0.9_7_5_2, -1.2_5_4_6, 0.8_0_2_7, 3.2_7_5_8, 3.1_3_6_5
])
_lowerCamelCase       :				Optional[int]       =			torch.tensor([
    -0.6_5_3_1, -0.6_8_9_1, -0.3_1_7_2, -0.5_3_7_5, -0.9_1_4_0, -0.5_3_6_7, -0.1_1_7_5, -0.7_8_6_9,
    -0.3_8_0_8, -0.4_5_1_3, -0.2_0_9_8, -0.0_0_8_3, 0.3_1_8_3, 0.5_1_4_0, 0.2_2_4_7, -0.1_3_0_4,
    -0.1_3_0_2, -0.2_8_0_2, -0.2_0_8_4, -0.2_0_2_5, -0.4_9_6_7, -0.4_8_7_3, -0.0_8_6_1, 0.6_9_2_5,
    0.0_2_5_0, 0.1_2_9_0, -0.1_5_4_3, 0.6_3_1_6, 1.0_4_6_0, 1.4_9_4_3
])
_lowerCamelCase       :				Dict       =			torch.tensor([
    0.0_9_1_1, 0.1_1_0_7, 0.0_1_8_2, 0.0_4_3_5, -0.0_8_0_5, -0.0_6_0_8, 0.0_3_8_1, 0.2_1_7_2,
    -0.0_2_8_0, 0.1_3_2_7, -0.0_2_9_9, -0.0_2_5_5, -0.0_0_5_0, -0.1_1_7_0, -0.1_0_4_6, 0.0_3_0_9,
    0.1_3_6_7, 0.1_7_2_8, -0.0_5_3_3, -0.0_7_4_8, -0.0_5_3_4, 0.1_6_2_4, 0.0_3_8_4, -0.1_8_0_5,
    -0.0_7_0_7, 0.0_6_4_2, 0.0_2_2_0, -0.0_1_3_4, -0.1_3_3_3, -0.1_5_0_5
])
_lowerCamelCase       :				Dict       =			torch.tensor([
    0.1_3_2_1, 0.1_3_3_7, 0.0_4_4_0, 0.0_6_2_2, -0.0_5_9_1, -0.0_3_7_0, 0.0_5_0_3, 0.2_1_3_3,
    -0.0_1_7_7, 0.1_4_1_5, -0.0_1_1_6, -0.0_1_1_2, 0.0_0_4_4, -0.0_9_8_0, -0.0_7_8_9, 0.0_3_9_5,
    0.1_5_0_2, 0.1_7_8_5, -0.0_4_8_8, -0.0_5_1_4, -0.0_4_0_4, 0.1_5_3_9, 0.0_4_5_4, -0.1_5_5_9,
    -0.0_6_6_5, 0.0_6_5_9, 0.0_3_8_3, -0.0_0_0_5, -0.1_2_6_6, -0.1_3_8_6
])
_lowerCamelCase       :				List[Any]       =			torch.tensor([
    0.1_1_5_4, 0.1_2_1_8, 0.0_3_0_7, 0.0_5_2_6, -0.0_7_1_1, -0.0_5_4_1, 0.0_3_6_6, 0.2_0_7_8,
    -0.0_2_6_7, 0.1_3_1_7, -0.0_2_2_6, -0.0_1_9_3, -0.0_0_1_4, -0.1_0_5_5, -0.0_9_0_2, 0.0_3_3_0,
    0.1_3_9_1, 0.1_7_0_9, -0.0_5_6_2, -0.0_6_9_3, -0.0_5_6_0, 0.1_4_8_2, 0.0_3_8_1, -0.1_6_8_3,
    -0.0_6_8_1, 0.0_6_6_1, 0.0_3_3_1, -0.0_0_4_6, -0.1_2_6_8, -0.1_4_3_1
])
_lowerCamelCase       :				Dict       =			torch.tensor([
    0.1_1_9_2, 0.1_2_4_0, 0.0_4_1_4, 0.0_6_0_6, -0.0_5_5_7, -0.0_4_1_2, 0.0_4_3_0, 0.2_0_4_2,
    -0.0_2_0_0, 0.1_3_8_5, -0.0_1_1_5, -0.0_1_3_2, 0.0_0_1_7, -0.0_9_6_5, -0.0_8_0_2, 0.0_3_9_8,
    0.1_4_3_3, 0.1_7_4_7, -0.0_4_5_8, -0.0_5_3_3, -0.0_4_0_7, 0.1_5_4_5, 0.0_4_1_9, -0.1_5_7_4,
    -0.0_6_4_5, 0.0_6_2_6, 0.0_3_4_1, -0.0_0_1_0, -0.1_1_9_9, -0.1_3_9_0
])
_lowerCamelCase       :				int       =			torch.tensor([
    0.1_0_7_5, 0.1_0_7_4, 0.0_2_0_5, 0.0_4_3_1, -0.0_7_7_4, -0.0_6_0_7, 0.0_2_9_8, 0.2_0_4_2,
    -0.0_3_2_0, 0.1_2_6_7, -0.0_2_8_1, -0.0_2_5_0, -0.0_0_6_4, -0.1_0_9_1, -0.0_9_4_6, 0.0_2_9_0,
    0.1_3_2_8, 0.1_6_5_0, -0.0_5_8_0, -0.0_7_3_8, -0.0_5_8_6, 0.1_4_4_0, 0.0_3_3_7, -0.1_7_4_6,
    -0.0_7_1_2, 0.0_6_0_5, 0.0_2_5_0, -0.0_0_9_9, -0.1_3_1_6, -0.1_4_7_3
])
_lowerCamelCase       :				int       =			torch.tensor([
    -1.4_5_7_2, -2.0_4_8_1, -0.0_4_1_4, -0.6_0_0_5, 1.4_1_3_6, 0.5_8_4_8, 0.4_0_2_8, -2.7_3_3_0,
    1.2_2_1_2, -2.1_2_2_8, 0.2_1_5_5, 0.4_0_3_9, 0.7_6_6_2, 2.0_5_3_5, 0.7_4_7_7, -0.3_2_4_3,
    -2.1_7_5_8, -2.7_6_4_8, 1.6_9_4_7, 0.7_0_2_6, 1.2_3_3_8, -1.6_0_7_8, -0.8_6_8_2, 2.2_8_1_0,
    1.8_5_7_4, -0.5_7_1_8, -0.5_5_8_6, -0.0_1_8_6, 2.3_4_1_5, 2.1_2_5_1])
_lowerCamelCase       :				Tuple       =			torch.tensor([
    -1.3_6_9_0, -1.9_7_2_0, -0.4_0_9_0, -0.6_9_6_6, 1.4_6_6_0, 0.9_9_3_8, -0.1_3_8_5, -2.7_3_2_4,
    0.7_7_3_6, -1.8_9_1_7, 0.2_9_2_3, 0.4_2_9_3, 0.1_6_9_3, 1.4_1_1_2, 1.1_8_8_7, -0.3_1_8_1,
    -2.2_1_6_0, -2.6_3_8_1, 1.3_1_7_0, 0.8_1_6_3, 0.9_2_4_0, -1.6_5_4_4, -0.6_0_9_9, 2.5_2_5_9,
    1.6_4_3_0, -0.9_0_9_0, -0.9_3_9_2, -0.0_1_2_6, 2.4_2_6_8, 2.3_2_6_6
])
_lowerCamelCase       :				List[str]       =			torch.tensor([
    -1.3_5_2_5, -1.9_6_2_8, -0.3_9_5_6, -0.6_8_6_0, 1.4_6_6_4, 1.0_0_1_4, -0.1_2_5_9, -2.7_2_1_2,
    0.7_7_7_2, -1.8_8_1_1, 0.2_9_9_6, 0.4_3_8_8, 0.1_7_0_4, 1.4_0_2_9, 1.1_7_0_1, -0.3_0_2_7,
    -2.2_0_5_3, -2.6_2_8_7, 1.3_3_5_0, 0.8_1_3_1, 0.9_2_7_4, -1.6_2_9_2, -0.6_0_9_8, 2.5_1_3_1,
    1.6_5_0_5, -0.8_9_5_8, -0.9_2_9_8, -0.0_1_5_1, 2.4_2_5_7, 2.3_3_5_5
])
_lowerCamelCase       :				int       =			torch.tensor([
    -2.0_5_8_5, -2.7_8_9_7, -0.2_8_5_0, -0.8_9_4_0, 1.9_0_5_2, 0.5_7_0_2, 0.6_3_4_5, -3.8_9_5_9,
    1.5_9_3_2, -3.2_3_1_9, 0.1_9_7_4, 0.0_2_8_7, 1.7_5_6_6, 2.6_5_4_3, 0.8_3_8_7, -0.5_3_5_1,
    -3.2_7_3_6, -4.3_3_7_5, 2.9_0_2_9, 1.6_3_9_0, 1.4_6_4_0, -2.1_7_0_1, -1.9_0_1_3, 2.9_3_4_1,
    3.4_9_8_1, -0.6_2_5_5, -1.1_6_4_4, -0.1_5_9_1, 3.7_0_9_7, 3.2_0_6_6
])
_lowerCamelCase       :				Tuple       =			torch.tensor([
    -2.3_1_3_9, -2.5_5_9_4, -0.0_1_9_7, -0.6_7_8_5, 1.7_0_0_1, 1.1_6_0_6, 0.3_0_7_5, -2.1_7_4_0,
    1.8_0_7_1, -2.5_6_3_0, -0.0_9_2_6, -0.3_8_1_1, 1.2_1_1_6, 2.6_2_4_6, 1.2_7_3_1, -0.5_3_9_8,
    -2.8_1_5_3, -3.6_1_4_0, 2.3_8_9_3, 1.3_2_6_2, 1.6_2_5_8, -2.1_8_5_6, -1.3_2_6_7, 2.8_3_9_5,
    2.3_7_7_9, -1.0_6_2_3, -1.2_4_6_8, 0.8_9_5_9, 3.3_3_6_7, 3.2_2_4_3
])
_lowerCamelCase       :				int       =			torch.tensor([
    -2.0_6_2_8, -2.7_6_6_7, -0.2_0_8_9, -0.8_2_6_3, 2.0_5_3_9, 0.5_9_9_2, 0.6_4_9_5, -3.8_3_3_6,
    1.6_0_2_5, -3.2_8_1_7, 0.1_7_2_1, -0.0_6_3_3, 1.7_5_1_6, 2.7_0_3_9, 0.8_1_0_0, -0.5_9_0_8,
    -3.2_1_1_3, -4.4_3_4_3, 2.9_2_5_7, 1.3_6_3_2, 1.5_5_6_2, -2.1_4_8_9, -1.9_8_9_4, 3.0_5_6_0,
    3.3_3_9_6, -0.7_3_2_8, -1.0_4_1_7, 0.0_3_8_3, 3.7_0_9_3, 3.2_3_4_3
])
_lowerCamelCase       :				List[Any]       =			torch.tensor([
    -1.4_5_7_4, -2.0_5_6_9, -0.0_4_7_3, -0.6_1_1_7, 1.4_0_1_8, 0.5_7_6_9, 0.4_1_2_9, -2.7_3_4_4,
    1.2_2_4_1, -2.1_3_9_7, 0.2_0_0_0, 0.3_9_3_7, 0.7_6_1_6, 2.0_4_5_3, 0.7_3_2_4, -0.3_3_9_1,
    -2.1_7_4_6, -2.7_7_4_4, 1.6_9_6_3, 0.6_9_2_1, 1.2_1_8_7, -1.6_1_7_2, -0.8_8_7_7, 2.2_4_3_9,
    1.8_4_7_1, -0.5_8_3_9, -0.5_6_0_5, -0.0_4_6_4, 2.3_2_5_0, 2.1_2_1_9
])
# fmt: on

_lowerCamelCase       :				List[str]       =			api.list_models(filter='''diffusers''')
for mod in models:
			if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
						_lowerCamelCase       :				Any       =			'''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]

						print(F'Started running {mod.modelId}!!!')

						if mod.modelId.startswith('''CompVis'''):
									_lowerCamelCase       :				Optional[Any]       =			UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
						else:
									_lowerCamelCase       :				int       =			UNetaDModel.from_pretrained(local_checkpoint)

						torch.manual_seed(0)
						random.seed(0)

						_lowerCamelCase       :				Union[str, Any]       =			torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
						_lowerCamelCase       :				int       =			torch.tensor([10] * noise.shape[0])
						with torch.no_grad():
									_lowerCamelCase       :				int       =			model(noise, time_step).sample

						assert torch.allclose(
						    logits[0, 0, 0, :30], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1E-3
						)
						print(F'{mod.modelId} has passed successfully!!!')