






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





@require_torch
class     SCREAMING_SNAKE_CASE					(			unittest.TestCase       ):





							"""simple docstring"""

							def    __A			(	self: int		,  __A: Dict )     ->					Union[str, Any]:
												return FSMTTokenizer.from_pretrained(lowerCamelCase__ )

							def    __A			(	self: int		,  __A: Any )     ->					List[str]:
												_A										=			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    __A			(	self: Tuple		,  __A: Any		,  __A: List[Any] )     ->					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
												_A										=			f"""facebook/wmt19-{pair}"""
												_A										=			self.get_tokenizer(lowerCamelCase__ )
												_A										=			self.get_model(lowerCamelCase__ )

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

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




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


a  :      List[Any]            =    logging.get_logger(__name__)

a  :      Any            =    {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				int          =    'openai-gpt'
    SCREAMING_SNAKE_CASE:				List[str]          =    {
        'max_position_embeddings': 'n_positions',
        'hidden_size': 'n_embd',
        'num_attention_heads': 'n_head',
        'num_hidden_layers': 'n_layer',
    }




    def __init__(    self       ,						lowerCamelCase__=40_478       ,						lowerCamelCase__=512       ,						lowerCamelCase__=768       ,						lowerCamelCase__=12       ,						lowerCamelCase__=12       ,						lowerCamelCase__="gelu"       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=1E-5       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__="cls_index"       ,						lowerCamelCase__=True       ,						lowerCamelCase__=None       ,						lowerCamelCase__=True       ,						lowerCamelCase__=0.1       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							Union[str, Any]       =	vocab_size
       lowerCAmelCase_:							List[Any]       =	n_positions
       lowerCAmelCase_:							Tuple       =	n_embd
       lowerCAmelCase_:							Optional[int]       =	n_layer
       lowerCAmelCase_:							Optional[int]       =	n_head
       lowerCAmelCase_:							int       =	afn
       lowerCAmelCase_:							str       =	resid_pdrop
       lowerCAmelCase_:							Optional[int]       =	embd_pdrop
       lowerCAmelCase_:							Optional[int]       =	attn_pdrop
       lowerCAmelCase_:							Dict       =	layer_norm_epsilon
       lowerCAmelCase_:							List[Any]       =	initializer_range
       lowerCAmelCase_:							Union[str, Any]       =	summary_type
       lowerCAmelCase_:							Any       =	summary_use_proj
       lowerCAmelCase_:							Dict       =	summary_activation
       lowerCAmelCase_:							Dict       =	summary_first_dropout
       lowerCAmelCase_:							List[Any]       =	summary_proj_to_labels
       super().__init__(**lowerCamelCase__ )