






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







@require_torch
class    lowercase__			(							unittest.TestCase   ):


			def 	UpperCAmelCase__						(      self     :  int   ,       snake_case__     :  List[str]					):
										return FSMTTokenizer.from_pretrained(lowerCamelCase__					)


			def 	UpperCAmelCase__						(      self     :  str   ,       snake_case__     :  Optional[Any]					):
										lowerCamelCase_       :							Any									=FSMTForConditionalGeneration.from_pretrained(lowerCamelCase__					).to(lowerCamelCase__					)
										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 	UpperCAmelCase__						(      self     :  Optional[Any]   ,       snake_case__     :  str   ,       snake_case__     :  List[str]					):
										# 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
										lowerCamelCase_       :							Any									=F"""facebook/wmt19-{pair}"""
										lowerCamelCase_       :							int									=self.get_tokenizer(lowerCamelCase__					)
										lowerCamelCase_       :							int									=self.get_model(lowerCamelCase__					)

										lowerCamelCase_       :							Tuple									=bleu_data[pair]["""src"""]
										lowerCamelCase_       :							Optional[int]									=bleu_data[pair]["""tgt"""]

										lowerCamelCase_       :							str									=tokenizer(lowerCamelCase__   ,       return_tensors="pt"   ,       truncation=lowerCamelCase__   ,       padding="longest"					).to(lowerCamelCase__					)
										lowerCamelCase_       :							Any									=model.generate(
										    input_ids=batch.input_ids   ,       num_beams=8   ,       )
										lowerCamelCase_       :							Optional[Any]									=tokenizer.batch_decode(
										    lowerCamelCase__   ,       skip_special_tokens=lowerCamelCase__   ,       clean_up_tokenization_spaces=lowerCamelCase__					)
										lowerCamelCase_       :							Union[str, Any]									=calculate_bleu(lowerCamelCase__   ,       lowerCamelCase__					)
										print(lowerCamelCase__					)
										self.assertGreaterEqual(scores["bleu"]   ,       lowerCamelCase__					)








from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_:Optional[Any]             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      "camembert"






    def __init__(       self,		lowerCamelCase__=3_0522,		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-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       List[Any]          =   vocab_size
        A :       Dict          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[Any]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Tuple          =   intermediate_size
        A :       Tuple          =   hidden_dropout_prob
        A :       List[Any]          =   attention_probs_dropout_prob
        A :       Optional[int]          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Tuple          =   position_embedding_type
        A :       str          =   use_cache
        A :       Any          =   classifier_dropout






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @property
    def 			_lowerCAmelCase					(       self		):
        if self.task == "multiple-choice":
            A :       int          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A :       Tuple          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


