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import os
import pandas as pd
from huggingface_hub import hf_hub_download, upload_file
from huggingface_hub.utils import EntryNotFoundError
REPO_ID = "diffusers/benchmarks"
def has_previous_benchmark() -> str:
from run_all import FINAL_CSV_FILENAME
csv_path = None
try:
csv_path = hf_hub_download(repo_id=REPO_ID, repo_type="dataset", filename=FINAL_CSV_FILENAME)
except EntryNotFoundError:
csv_path = None
return csv_path
def filter_float(value):
if isinstance(value, str):
return float(value.split()[0])
return value
def push_to_hf_dataset():
from run_all import FINAL_CSV_FILENAME, GITHUB_SHA
csv_path = has_previous_benchmark()
if csv_path is not None:
current_results = pd.read_csv(FINAL_CSV_FILENAME)
previous_results = pd.read_csv(csv_path)
numeric_columns = current_results.select_dtypes(include=["float64", "int64"]).columns
for column in numeric_columns:
# get previous values as floats, aligned to current index
prev_vals = previous_results[column].map(filter_float).reindex(current_results.index)
# get current values as floats
curr_vals = current_results[column].astype(float)
# stringify the current values
curr_str = curr_vals.map(str)
# build an appendage only when prev exists and differs
append_str = prev_vals.where(prev_vals.notnull() & (prev_vals != curr_vals), other=pd.NA).map(
lambda x: f" ({x})" if pd.notnull(x) else ""
)
# combine
current_results[column] = curr_str + append_str
os.remove(FINAL_CSV_FILENAME)
current_results.to_csv(FINAL_CSV_FILENAME, index=False)
commit_message = f"upload from sha: {GITHUB_SHA}" if GITHUB_SHA is not None else "upload benchmark results"
upload_file(
repo_id=REPO_ID,
path_in_repo=FINAL_CSV_FILENAME,
path_or_fileobj=FINAL_CSV_FILENAME,
repo_type="dataset",
commit_message=commit_message,
)
upload_file(
repo_id="diffusers/benchmark-analyzer",
path_in_repo=FINAL_CSV_FILENAME,
path_or_fileobj=FINAL_CSV_FILENAME,
repo_type="space",
commit_message=commit_message,
)
if __name__ == "__main__":
push_to_hf_dataset()
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