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# ###########################################################################
#
# CLOUDERA APPLIED MACHINE LEARNING PROTOTYPE (AMP)
# (C) Cloudera, Inc. 2022
# All rights reserved.
#
# Applicable Open Source License: Apache 2.0
#
# NOTE: Cloudera open source products are modular software products
# made up of hundreds of individual components, each of which was
# individually copyrighted. Each Cloudera open source product is a
# collective work under U.S. Copyright Law. Your license to use the
# collective work is as provided in your written agreement with
# Cloudera. Used apart from the collective work, this file is
# licensed for your use pursuant to the open source license
# identified above.
#
# This code is provided to you pursuant a written agreement with
# (i) Cloudera, Inc. or (ii) a third-party authorized to distribute
# this code. If you do not have a written agreement with Cloudera nor
# with an authorized and properly licensed third party, you do not
# have any rights to access nor to use this code.
#
# Absent a written agreement with Cloudera, Inc. (“Cloudera”) to the
# contrary, A) CLOUDERA PROVIDES THIS CODE TO YOU WITHOUT WARRANTIES OF ANY
# KIND; (B) CLOUDERA DISCLAIMS ANY AND ALL EXPRESS AND IMPLIED
# WARRANTIES WITH RESPECT TO THIS CODE, INCLUDING BUT NOT LIMITED TO
# IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY AND
# FITNESS FOR A PARTICULAR PURPOSE; (C) CLOUDERA IS NOT LIABLE TO YOU,
# AND WILL NOT DEFEND, INDEMNIFY, NOR HOLD YOU HARMLESS FOR ANY CLAIMS
# ARISING FROM OR RELATED TO THE CODE; AND (D)WITH RESPECT TO YOUR EXERCISE
# OF ANY RIGHTS GRANTED TO YOU FOR THE CODE, CLOUDERA IS NOT LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, PUNITIVE OR
# CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES
# RELATED TO LOST REVENUE, LOST PROFITS, LOSS OF INCOME, LOSS OF
# BUSINESS ADVANTAGE OR UNAVAILABILITY, OR LOSS OR CORRUPTION OF
# DATA.
#
# ###########################################################################
from apps.data_utils import DATA_PACKET
from src.style_transfer import StyleTransfer
from src.style_classification import StyleIntensityClassifier
from src.content_preservation import ContentPreservationScorer
def load_and_cache_HF_models(style_data_packet):
"""
This utility function is used to download and cache models needed for all style
attributes in `apps.data_utils.DATA_PACKET`
Args:
style_data_packet (dict)
"""
for style_data in style_data_packet.keys():
try:
st = StyleTransfer(model_identifier=style_data.seq2seq_model_path)
sic = StyleIntensityClassifier(style_data.cls_model_path)
cps = ContentPreservationScorer(
cls_model_identifier=style_data.cls_model_path,
sbert_model_identifier=style_data.sbert_model_path,
)
del st, sic, cps
except Exception as e:
print(e)
if __name__=="__main__":
load_and_cache_HF_models(DATA_PACKET) |