



'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase:				str       =	logging.get_logger(__name__)

_lowercase:				Union[str, Any]       =	{
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}



class 			_lowercase							(     a__			):





	"""simple docstring"""
	__A					=							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A					=							"squeezebert"




	def __init__(self	, lowerCamelCase_=30522	, lowerCamelCase_=768	, lowerCamelCase_=12	, lowerCamelCase_=12	, lowerCamelCase_=3072	, lowerCamelCase_="gelu"	, lowerCamelCase_=0.1	, lowerCamelCase_=0.1	, lowerCamelCase_=512	, lowerCamelCase_=2	, lowerCamelCase_=0.02	, lowerCamelCase_=1E-1_2	, lowerCamelCase_=0	, lowerCamelCase_=768	, lowerCamelCase_=4	, lowerCamelCase_=4	, lowerCamelCase_=4	, lowerCamelCase_=1	, lowerCamelCase_=4	, lowerCamelCase_=4	, **lowerCamelCase_	, ):
						"""simple docstring"""

						super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_	, **SCREAMING_SNAKE_CASE_	)

						a        =     vocab_size
						a        =     hidden_size
						a        =     num_hidden_layers
						a        =     num_attention_heads
						a        =     hidden_act
						a        =     intermediate_size
						a        =     hidden_dropout_prob
						a        =     attention_probs_dropout_prob
						a        =     max_position_embeddings
						a        =     type_vocab_size
						a        =     initializer_range
						a        =     layer_norm_eps
						a        =     embedding_size
						a        =     q_groups
						a        =     k_groups
						a        =     v_groups
						a        =     post_attention_groups
						a        =     intermediate_groups
						a        =     output_groups




class 			_lowercase							(     a__			):





	"""simple docstring"""




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						if self.task == "multiple-choice":
											a        =     {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											a        =     {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						        ("token_type_ids", dynamic_axis),
						    ]	)





# 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 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls




	@slow
	@require_torch_gpu
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.get_auto_remove_tmp_dir()
						a        =     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
						a        =     get_gpu_count()
						a        =     F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
						a        =     [sys.executable] + distributed_args + args
						execute_subprocess_async(lowerCamelCase_	, env=self.get_env()	)

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