


'''simple docstring'''



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


__A	=logging.get_logger(__name__)


class 		_snake_case					(     __lowercase	):




					def __init__(      self							,							_lowerCamelCase):
								super().__init__()
								UpperCAmelCase__  :					Union[str, Any]        =     nn.ModuleList(_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = False							,							_lowerCamelCase = True							,							):
								for i, (image, scale, controlnet) in enumerate(zip(_lowerCamelCase							,							_lowerCamelCase							,							self.nets)):
											UpperCAmelCase__  :					List[str]        =     controlnet(
											    _lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							)

											# merge samples
											if i == 0:
														UpperCAmelCase__  :					Dict        =     down_samples, mid_sample
											else:
														UpperCAmelCase__  :					Tuple        =     [
														    samples_prev + samples_curr
														    for samples_prev, samples_curr in zip(_lowerCamelCase							,							_lowerCamelCase)
														]
														mid_block_res_sample += mid_sample

								return down_block_res_samples, mid_block_res_sample




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							_lowerCamelCase = False							,							_lowerCamelCase = None							,							):
								UpperCAmelCase__  :					List[Any]        =     0
								UpperCAmelCase__  :					str        =     save_directory
								for controlnet in self.nets:
											controlnet.save_pretrained(
											    _lowerCamelCase							,							is_main_process=_lowerCamelCase							,							save_function=_lowerCamelCase							,							safe_serialization=_lowerCamelCase							,							variant=_lowerCamelCase							,							)

											idx += 1
											UpperCAmelCase__  :					List[Any]        =     model_path_to_save + f'''_{idx}'''




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							**_lowerCamelCase):
								UpperCAmelCase__  :					Dict        =     0
								UpperCAmelCase__  :					Union[str, Any]        =     []

								# load controlnet and append to list until no controlnet directory exists anymore
								# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
								# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
								UpperCAmelCase__  :					Optional[int]        =     pretrained_model_path
								while os.path.isdir(_lowerCamelCase):
											UpperCAmelCase__  :					Optional[int]        =     ControlNetModel.from_pretrained(_lowerCamelCase							,							**_lowerCamelCase)
											controlnets.append(_lowerCamelCase)

											idx += 1
											UpperCAmelCase__  :					Optional[int]        =     pretrained_model_path + f'''_{idx}'''

								logger.info(f'''{len(_lowerCamelCase)} controlnets loaded from {pretrained_model_path}.''')

								if len(_lowerCamelCase) == 0:
											raise ValueError(
											    f'''No ControlNets found under {os.path.dirname(_lowerCamelCase)}. Expected at least {pretrained_model_path + '_0'}.''')

								return cls(_lowerCamelCase)



'''simple docstring'''



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     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[Any]        =     argparse.ArgumentParser()
			parser.add_argument("""-f"""					)
			UpperCAmelCase__  :					Optional[Any]        =     parser.parse_args()
			return args.f


class 		_snake_case					(     a__	):




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     logging.StreamHandler(sys.stdout)
								logger.addHandler(_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Any        =     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(_lowerCamelCase							,							"""argv"""							,							_lowerCamelCase):
														UpperCAmelCase__  :					Union[str, Any]        =     run_glue_deebert.main()
														for value in result.values():
																	self.assertGreaterEqual(_lowerCamelCase							,							0.666)




					@slow
					@require_torch_non_multi_gpu
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     """
            --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(_lowerCamelCase)

								UpperCAmelCase__  :					Dict        =     """
            --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(_lowerCamelCase)

								UpperCAmelCase__  :					Any        =     """
            --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(_lowerCamelCase)