import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_PRETRAINING_MAPPING,
      MegatronBertForCausalLM,
      MegatronBertForMaskedLM,
      MegatronBertForMultipleChoice,
      MegatronBertForNextSentencePrediction,
      MegatronBertForPreTraining,
      MegatronBertForQuestionAnswering,
      MegatronBertForSequenceClassification,
      MegatronBertForTokenClassification,
      MegatronBertModel,
  )
class  A_			:

  def __init__(		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   str=13  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=7  ,				__SCREAMING_SNAKE_CASE			:   Tuple=True  ,				__SCREAMING_SNAKE_CASE			:   List[str]=True  ,				__SCREAMING_SNAKE_CASE			:   Tuple=True  ,				__SCREAMING_SNAKE_CASE			:   int=True  ,				__SCREAMING_SNAKE_CASE			:   List[str]=99  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=64  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=32  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=5  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=4  ,				__SCREAMING_SNAKE_CASE			:   int=37  ,				__SCREAMING_SNAKE_CASE			:   List[str]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0.1  ,				__SCREAMING_SNAKE_CASE			:   Dict=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=5_12  ,				__SCREAMING_SNAKE_CASE			:   int=16  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=2  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=3  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=None  ,				):
   __a							=	parent
   __a							=	batch_size
   __a							=	seq_length
   __a							=	is_training
   __a							=	use_input_mask
   __a							=	use_token_type_ids
   __a							=	use_labels
   __a							=	vocab_size
   __a							=	hidden_size
   __a							=	embedding_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	intermediate_size
   __a							=	hidden_act
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	max_position_embeddings
   __a							=	type_vocab_size
   __a							=	type_sequence_label_size
   __a							=	initializer_range
   __a							=	num_labels
   __a							=	num_choices
   __a							=	scope

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	ids_tensor([self.batch_size, self.seq_length]  ,				self.vocab_size							)

   __a							=	None
   if self.use_input_mask:
    __a							=	random_attention_mask([self.batch_size, self.seq_length]							)

   __a							=	None
   if self.use_token_type_ids:
    __a							=	ids_tensor([self.batch_size, self.seq_length]  ,				self.type_vocab_size							)

   __a							=	None
   __a							=	None
   __a							=	None
   if self.use_labels:
    __a							=	ids_tensor([self.batch_size]  ,				self.type_sequence_label_size							)
    __a							=	ids_tensor([self.batch_size, self.seq_length]  ,				self.num_labels							)
    __a							=	ids_tensor([self.batch_size]  ,				self.num_choices							)

   __a							=	self.get_config()

   return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

  def 		_UpperCAmelCase (		self			:   str							):
   return MegatronBertConfig(
       vocab_size=self.vocab_size  ,				hidden_size=self.hidden_size  ,				num_hidden_layers=self.num_hidden_layers  ,				num_attention_heads=self.num_attention_heads  ,				intermediate_size=self.intermediate_size  ,				embedding_size=self.embedding_size  ,				hidden_act=self.hidden_act  ,				hidden_dropout_prob=self.hidden_dropout_prob  ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,				max_position_embeddings=self.max_position_embeddings  ,				type_vocab_size=self.type_vocab_size  ,				is_decoder=__SCREAMING_SNAKE_CASE  ,				initializer_range=self.initializer_range  ,				)

  def 		_UpperCAmelCase (		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   int							):
   __a							=	MegatronBertModel(config=__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	model(__SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE							)
   __a							=	model(__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE							)
   __a							=	model(__SCREAMING_SNAKE_CASE							)

   self.parent.assertEqual(result.last_hidden_state.shape  ,				(self.batch_size, self.seq_length, self.hidden_size)							)
   self.parent.assertEqual(result.pooler_output.shape  ,				(self.batch_size, self.hidden_size)							)

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Tuple							):
   __a							=	MegatronBertForMaskedLM(config=__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	model(__SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE  ,				labels=__SCREAMING_SNAKE_CASE							)
   self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.seq_length, self.vocab_size)							)

  def 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]							):
   __a							=	MegatronBertForCausalLM(config=__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	model(__SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE  ,				labels=__SCREAMING_SNAKE_CASE							)
   self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.seq_length, self.vocab_size)							)

  def 		_UpperCAmelCase (		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   str							):
   __a							=	MegatronBertForNextSentencePrediction(config=__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	model(
       __SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE  ,				labels=__SCREAMING_SNAKE_CASE  ,				)
   self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, 2)							)

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]							):
   __a							=	MegatronBertForPreTraining(config=__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	model(
       __SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE  ,				labels=__SCREAMING_SNAKE_CASE  ,				next_sentence_label=__SCREAMING_SNAKE_CASE  ,				)
   self.parent.assertEqual(result.prediction_logits.shape  ,				(self.batch_size, self.seq_length, self.vocab_size)							)
   self.parent.assertEqual(result.seq_relationship_logits.shape  ,				(self.batch_size, 2)							)

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]							):
   __a							=	MegatronBertForQuestionAnswering(config=__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	model(
       __SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE  ,				start_positions=__SCREAMING_SNAKE_CASE  ,				end_positions=__SCREAMING_SNAKE_CASE  ,				)
   self.parent.assertEqual(result.start_logits.shape  ,				(self.batch_size, self.seq_length)							)
   self.parent.assertEqual(result.end_logits.shape  ,				(self.batch_size, self.seq_length)							)

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[str]							):
   __a							=	self.num_labels
   __a							=	MegatronBertForSequenceClassification(__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	model(__SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE  ,				labels=__SCREAMING_SNAKE_CASE							)
   self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.num_labels)							)

  def 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[str]							):
   __a							=	self.num_labels
   __a							=	MegatronBertForTokenClassification(config=__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	model(__SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE  ,				labels=__SCREAMING_SNAKE_CASE							)
   self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.seq_length, self.num_labels)							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   List[str]							):
   __a							=	self.num_choices
   __a							=	MegatronBertForMultipleChoice(config=__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	input_ids.unsqueeze(1							).expand(-1  ,				self.num_choices  ,				-1							).contiguous()
   __a							=	token_type_ids.unsqueeze(1							).expand(-1  ,				self.num_choices  ,				-1							).contiguous()
   __a							=	input_mask.unsqueeze(1							).expand(-1  ,				self.num_choices  ,				-1							).contiguous()
   __a							=	model(
       __SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE  ,				token_type_ids=__SCREAMING_SNAKE_CASE  ,				labels=__SCREAMING_SNAKE_CASE  ,				)
   self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.num_choices)							)

  def 		_UpperCAmelCase (		self			:   List[str]							):
   __a							=	self.prepare_config_and_inputs()
   (
       (
       __a
   )							,  (
       __a
   )							,  (
       __a
   )							,  (
       __a
   )							,  (
       __a
   )							,  (
       __a
   )							,  (
       __a
   )							,  
   )							=	config_and_inputs
   __a							=	{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
   return config, inputs_dict
@require_torch
class  A_			(							a_      ,							a_      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      (
      (
          MegatronBertModel,
          MegatronBertForMaskedLM,
          MegatronBertForCausalLM,
          MegatronBertForMultipleChoice,
          MegatronBertForNextSentencePrediction,
          MegatronBertForPreTraining,
          MegatronBertForQuestionAnswering,
          MegatronBertForSequenceClassification,
          MegatronBertForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  _SCREAMING_SNAKE_CASE     					=      (
      {
          """feature-extraction""": MegatronBertModel,
          """fill-mask""": MegatronBertForMaskedLM,
          """question-answering""": MegatronBertForQuestionAnswering,
          """text-classification""": MegatronBertForSequenceClassification,
          """text-generation""": MegatronBertForCausalLM,
          """token-classification""": MegatronBertForTokenClassification,
          """zero-shot""": MegatronBertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  _SCREAMING_SNAKE_CASE     					=      True
  # test_resize_embeddings = False
  _SCREAMING_SNAKE_CASE     					=      False

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=False							):
   __a							=	super()._prepare_for_class(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				return_labels=__SCREAMING_SNAKE_CASE							)

   if return_labels:
    if model_class in get_values(__SCREAMING_SNAKE_CASE							):
     __a							=	torch.zeros(
         (self.model_tester.batch_size, self.model_tester.seq_length)  ,				dtype=torch.long  ,				device=__SCREAMING_SNAKE_CASE							)
     __a							=	torch.zeros(
         self.model_tester.batch_size  ,				dtype=torch.long  ,				device=__SCREAMING_SNAKE_CASE							)
   return inputs_dict

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   __a							=	MegatronBertModelTester(self							)
   __a							=	ConfigTester(self  ,				config_class=__SCREAMING_SNAKE_CASE  ,				hidden_size=37							)

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   self.config_tester.run_common_tests()

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_megatron_bert_model(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[str]							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_megatron_bert_for_pretraining(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_megatron_bert_for_question_answering(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_megatron_bert_for_token_classification(*__SCREAMING_SNAKE_CASE							)



def 	__A		(      _A       ):



 """simple docstring"""


 return torch.tensor(
     _A				,					dtype=torch.long				,					device=_A				,					)


SCREAMING_SNAKE_CASE			:						int				  =		1e-4






@require_torch
@require_sentencepiece
@require_tokenizers
class  A_			(							unittest.TestCase			):

  @slow
  @unittest.skip("Model is not available."							)
  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	"nvidia/megatron-bert-uncased-345m"
   if "MYDIR" in os.environ:
    __a							=	os.path.join(os.environ["MYDIR"]  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	MegatronBertModel.from_pretrained(__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.half()
   __a							=	_long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]							)
   with torch.no_grad():
    __a							=	model(__SCREAMING_SNAKE_CASE							)[0]
   __a							=	torch.Size((1, 9, 10_24)							)
   self.assertEqual(output.shape  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	[-0.60_40, -0.25_17, -0.10_25, 0.34_20, -0.67_58, -0.00_17, -0.10_89, -0.19_90, 0.57_28]
   for ii in range(3							):
    for jj in range(3							):
     __a							=	output[0, ii, jj]
     __a							=	expected[3 * ii + jj]
     __a							=	"ii={} jj={} a={} b={}".format(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
     self.assertTrue(math.isclose(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				rel_tol=__SCREAMING_SNAKE_CASE  ,				abs_tol=__SCREAMING_SNAKE_CASE							)  ,				msg=__SCREAMING_SNAKE_CASE							)



import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.getLogger()

SCREAMING_SNAKE_CASE			:						List[str]				  =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class  A_			(							a_			):

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int							):
   os.makedirs(__SCREAMING_SNAKE_CASE  ,				exist_ok=__SCREAMING_SNAKE_CASE							)
   __a							=	{"source": "What is love ?", "target": "life"}
   __a							=	{"train": 12, "val": 2, "test": 2}
   for split in ["train", "test", "val"]:
    for field in ["source", "target"]:
     __a							=	"\n".join([contents[field]] * n_lines[split]							)
     with open(os.path.join(__SCREAMING_SNAKE_CASE  ,				f"""{split}.{field}"""							)  ,				"w"							) as f:
      f.write(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   str = "pytorch"							):
   __a							=	self.get_auto_remove_tmp_dir()
   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"output"							)
   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"data"							)
   self._create_dummy_data(data_dir=__SCREAMING_SNAKE_CASE							)

   __a							=	f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

   if gpus > 0:
    testargs.append(f"""--gpus={gpus}"""							)
    if is_apex_available():
     testargs.append("--fp16"							)
   else:
    testargs.append("--gpus=0"							)
    testargs.append("--distributed_backend=ddp_cpu"							)
    testargs.append("--num_processes=2"							)

   __a							=	[sys.executable, str(Path(finetune_rag.__file__							).resolve()							)] + testargs
   execute_subprocess_async(__SCREAMING_SNAKE_CASE  ,				env=self.get_env()							)

   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"metrics.json"							)
   with open(__SCREAMING_SNAKE_CASE							) as f:
    __a							=	json.load(__SCREAMING_SNAKE_CASE							)
   return result

  @require_torch_gpu
  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	self._run_finetune(gpus=1							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_multi_gpu
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	self._run_finetune(gpus=2							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_gpu
  @require_ray
  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	self._run_finetune(gpus=1  ,				distributed_retriever="ray"							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_multi_gpu
  @require_ray
  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self._run_finetune(gpus=1  ,				distributed_retriever="ray"							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)


