



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


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






@require_torch
class      UpperCamelCase    (						unittest.TestCase     ):



	def 			__A							(       self			,	UpperCAmelCase__		):
							return FSMTTokenizer.from_pretrained(UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							# 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(UpperCAmelCase__		)
							A__							=       self.get_model(UpperCAmelCase__		)

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

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










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


UpperCAmelCase_							:      Dict      =  logging.get_logger(__name__)

UpperCAmelCase_							:      List[Any]      =  {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   int				=	"""gpt_neox"""



	def __init__(       self			,	UpperCAmelCase__=50_432			,	UpperCAmelCase__=6_144			,	UpperCAmelCase__=44			,	UpperCAmelCase__=64			,	UpperCAmelCase__=24_576			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=0.25			,	UpperCAmelCase__=10_000			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=2_048			,	UpperCAmelCase__=0.02			,	UpperCAmelCase__=1e-5			,	UpperCAmelCase__=True			,	UpperCAmelCase__=0			,	UpperCAmelCase__=2			,	UpperCAmelCase__=False			,	UpperCAmelCase__=True			,	UpperCAmelCase__=None			,	**UpperCAmelCase__			,	):
							super().__init__(bos_token_id=UpperCAmelCase__			,	eos_token_id=UpperCAmelCase__			,	**UpperCAmelCase__		)
							A__							=       vocab_size
							A__							=       max_position_embeddings
							A__							=       hidden_size
							A__							=       num_hidden_layers
							A__							=       num_attention_heads
							A__							=       intermediate_size
							A__							=       hidden_act
							A__							=       rotary_pct
							A__							=       rotary_emb_base
							A__							=       attention_dropout
							A__							=       hidden_dropout
							A__							=       classifier_dropout
							A__							=       initializer_range
							A__							=       layer_norm_eps
							A__							=       use_cache
							A__							=       tie_word_embeddings
							A__							=       use_parallel_residual
							A__							=       rope_scaling
							self._rope_scaling_validation()

							if self.hidden_size % self.num_attention_heads != 0:
													raise ValueError(
													    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"		)



	def 			__A							(       self		):
							if self.rope_scaling is None:
													return

							if not isinstance(self.rope_scaling			,	UpperCAmelCase__		) or len(self.rope_scaling		) != 2:
													raise ValueError(
													    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
													    F"""got {self.rope_scaling}"""		)
							A__							=       self.rope_scaling.get("type"			,	UpperCAmelCase__		)
							A__							=       self.rope_scaling.get("factor"			,	UpperCAmelCase__		)
							if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
													raise ValueError(
													    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""		)
							if rope_scaling_factor is None or not isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) or rope_scaling_factor <= 1.0:
													raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""		)





