



import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

a_			=					logging.getLogger()





def 		a__			(		):
   __lowerCamelCase													=    argparse.ArgumentParser()
   parser.add_argument('''-f'''							)
   __lowerCamelCase													=    parser.parse_args()
   return args.f





class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    logging.StreamHandler(sys.stdout				)
    logger.addHandler(__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    get_gpu_count()

    if n_gpu > 1:
       pass
       # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
       # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
       # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
       # cmd = [sys.executable] + distributed_args + args
       # execute_subprocess_async(cmd, env=self.get_env())
       # XXX: test the results - need to save them first into .json file
    else:
       args.insert(0	,				'''run_glue_deebert.py'''				)
       with patch.object(__UpperCAmelCase	,				'''argv'''	,				__UpperCAmelCase				):
          __lowerCamelCase													=    run_glue_deebert.main()
          for value in result.values():
             self.assertGreaterEqual(__UpperCAmelCase	,				0.666				)




 @slow
 @require_torch_non_multi_gpu
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
    self.run_and_check(__UpperCAmelCase				)

    __lowerCamelCase													=    '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
    self.run_and_check(__UpperCAmelCase				)

    __lowerCamelCase													=    '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
    self.run_and_check(__UpperCAmelCase				)











import torch

from diffusers import StableDiffusionPipeline


a_			=					"""path-to-your-trained-model"""
a_			=					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

a_			=					"""A photo of sks dog in a bucket"""
a_			=					pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






