
"""simple docstring"""






# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json

class 				__a    (   __a					):



							'''simple docstring'''







							@classmethod
							def      SCREAMING_SNAKE_CASE	(    cls					) ->       str:




										'''simple docstring'''
										return cls





							@slow
							@require_torch_gpu
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					self.get_auto_remove_tmp_dir()
										__lowercase						      =					f'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

										# we want this test to run even if there is only one GPU, but if there are more we use them all
										__lowercase						      =					get_gpu_count()
										__lowercase						      =					f'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
										__lowercase						      =					[sys.executable] + distributed_args + args
										execute_subprocess_async(_lowerCamelCase     ,						env=self.get_env()					)

										__lowercase						      =					os.path.join(_lowerCamelCase     ,						"test_bleu.json"					)
										__lowercase						      =					load_json(_lowerCamelCase					)
										# print(metrics)
										self.assertGreaterEqual(metrics["bleu"]     ,						25					)







"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowercase							 =							logging.get_logger(__name__)

_lowercase							 =							{
    '''facebook/convnext-tiny-224''': '''https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json''',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}

class 				__a    (   __a				,      __a					):



							'''simple docstring'''







							_lowerCamelCase				: List[Any]					      =      """convnext"""
							def __init__(    self     ,						_lowerCamelCase=3     ,						_lowerCamelCase=4     ,						_lowerCamelCase=4     ,						_lowerCamelCase=None     ,						_lowerCamelCase=None     ,						_lowerCamelCase="gelu"     ,						_lowerCamelCase=0.02     ,						_lowerCamelCase=1e-12     ,						_lowerCamelCase=1e-6     ,						_lowerCamelCase=0.0     ,						_lowerCamelCase=224     ,						_lowerCamelCase=None     ,						_lowerCamelCase=None     ,						**_lowerCamelCase     ,						) ->       List[str]:




										'''simple docstring'''
										super().__init__(**_lowerCamelCase					)

										__lowercase						      =					num_channels
										__lowercase						      =					patch_size
										__lowercase						      =					num_stages
										__lowercase						      =					[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
										__lowercase						      =					[3, 3, 9, 3] if depths is None else depths
										__lowercase						      =					hidden_act
										__lowercase						      =					initializer_range
										__lowercase						      =					layer_norm_eps
										__lowercase						      =					layer_scale_init_value
										__lowercase						      =					drop_path_rate
										__lowercase						      =					image_size
										__lowercase						      =					["stem"] + [f'''stage{idx}''' for idx in range(1     ,						len(self.depths					) + 1					)]
										__lowercase      ,				__lowercase						      =					get_aligned_output_features_output_indices(
										    out_features=_lowerCamelCase     ,						out_indices=_lowerCamelCase     ,						stage_names=self.stage_names					)





class 				__a    (   __a					):



							'''simple docstring'''







							_lowerCamelCase				: str					      =      version.parse("""1.11"""					)
							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Mapping[str, Mapping[int, str]]:




										'''simple docstring'''
										return OrderedDict(
										    [
										        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
										    ]					)





							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       float:




										'''simple docstring'''
										return 1e-5





