

"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


A_				:      List[Any]           =			get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
						A_				:      Union[str, Any]           =			json.load(f)





@require_torch
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    List[Any]							) ->     Union[str, Any]:
						return FSMTTokenizer.from_pretrained(__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any     ,					__UpperCAmelCase					:    str							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   FSMTForConditionalGeneration.from_pretrained(__UpperCAmelCase							).to(__UpperCAmelCase							)
						if torch_device == "cuda":
									model.half()
						return model





			@parameterized.expand(
			    [
			        ["""en-ru""", 26.0],
			        ["""ru-en""", 22.0],
			        ["""en-de""", 22.0],
			        ["""de-en""", 29.0],
			    ]							)
			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    Dict							) ->     int:
						# note: this test is not testing the best performance since it only evals a small batch
						# but it should be enough to detect a regression in the output quality
						SCREAMING_SNAKE_CASE__										=   F"""facebook/wmt19-{pair}"""
						SCREAMING_SNAKE_CASE__										=   self.get_tokenizer(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   self.get_model(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   bleu_data[pair]["""src"""]
						SCREAMING_SNAKE_CASE__										=   bleu_data[pair]["""tgt"""]

						SCREAMING_SNAKE_CASE__										=   tokenizer(__UpperCAmelCase     ,					return_tensors="""pt"""     ,					truncation=__UpperCAmelCase     ,					padding="""longest"""							).to(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model.generate(
						    input_ids=batch.input_ids     ,					num_beams=8     ,					)
						SCREAMING_SNAKE_CASE__										=   tokenizer.batch_decode(
						    __UpperCAmelCase     ,					skip_special_tokens=__UpperCAmelCase     ,					clean_up_tokenization_spaces=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   calculate_bleu(__UpperCAmelCase     ,					__UpperCAmelCase							)
						print(__UpperCAmelCase							)
						self.assertGreaterEqual(scores["""bleu"""]     ,					__UpperCAmelCase							)





"""simple docstring"""




from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


A_				:      Union[str, Any]           =			{
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		int       		=				'ernie_m'
			lowerCamelCase__ :		Dict[str, str]       		=				{"dropout": "classifier_dropout", "num_classes": "num_labels"}

			def __init__(       self					:    List[str]     ,					__UpperCAmelCase					:    int = 2_5_0_0_0_2     ,					__UpperCAmelCase					:    int = 7_6_8     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 3_0_7_2     ,					__UpperCAmelCase					:    str = "gelu"     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    int = 5_1_4     ,					__UpperCAmelCase					:    float = 0.02     ,					__UpperCAmelCase					:    int = 1     ,					__UpperCAmelCase					:    float = 1e-05     ,					__UpperCAmelCase					:    Union[str, Any]=None     ,					__UpperCAmelCase					:    List[Any]=False     ,					__UpperCAmelCase					:    Union[str, Any]=0.0     ,					**__UpperCAmelCase					:    Any     ,					) ->     Optional[Any]:
						super().__init__(pad_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   classifier_dropout
						SCREAMING_SNAKE_CASE__										=   is_decoder
						SCREAMING_SNAKE_CASE__										=   act_dropout


