






"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase     :     int            =	logging.get_logger(__name__)

_lowerCAmelCase     :     str            =	{
    """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"""
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      ='''cpmant'''
   def __init__(						self	:     List[str]				,       snake_case__	:     int = 3_0_7_2_0				,       snake_case__	:     int = 4_0_9_6				,       snake_case__	:     int = 3_2				,       snake_case__	:     int = 1_2_8				,       snake_case__	:     int = 1_0_2_4_0				,       snake_case__	:     int = 4_8				,       snake_case__	:     int = 0.0				,       snake_case__	:     int = 5_1_2				,       snake_case__	:     int = 2_0_4_8				,       snake_case__	:     int = 1e-6				,       snake_case__	:     float = 1.0				,       snake_case__	:     int = 3_2				,       snake_case__	:     int = 3_2				,       snake_case__	:     int = 3_2				,       snake_case__	:     bool = True				,       **snake_case__	:     List[Any]				,       ):


        '''simple docstring'''
        super().__init__(**snake_case__				)
        UpperCAmelCase__		:  Dict				=				prompt_types
        UpperCAmelCase__		:  List[str]				=				prompt_length
        UpperCAmelCase__		:  Optional[int]				=				segment_types
        UpperCAmelCase__		:  Tuple				=				hidden_size
        UpperCAmelCase__		:  Dict				=				num_attention_heads
        UpperCAmelCase__		:  Any				=				dim_head
        UpperCAmelCase__		:  Any				=				dim_ff
        UpperCAmelCase__		:  Tuple				=				num_hidden_layers
        UpperCAmelCase__		:  str				=				position_bias_num_buckets
        UpperCAmelCase__		:  str				=				position_bias_max_distance
        UpperCAmelCase__		:  Optional[int]				=				dropout_p
        UpperCAmelCase__		:  Optional[Any]				=				eps
        UpperCAmelCase__		:  List[str]				=				use_cache
        UpperCAmelCase__		:  List[Any]				=				vocab_size
        UpperCAmelCase__		:  List[Any]				=				init_std






# 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  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							@classmethod
							def  UpperCamelCase__			(	cls    ):





										"""simple docstring"""




										return cls






							@slow
							@require_torch_gpu
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE_						:			Tuple       						= 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
										SCREAMING_SNAKE_CASE_						:			Dict       						= get_gpu_count()
										SCREAMING_SNAKE_CASE_						:			int       						= F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= [sys.executable] + distributed_args + args
										execute_subprocess_async(lowerCAmelCase__       ,		env=self.get_env()    )

										SCREAMING_SNAKE_CASE_						:			List[str]       						= os.path.join(lowerCAmelCase__       ,		'test_bleu.json'    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= load_json(lowerCAmelCase__    )
										# print(metrics)
										self.assertGreaterEqual(metrics['bleu']       ,		2_5    )





