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


lowerCamelCase_						: Tuple         =  logging.get_logger(__name__)

lowerCamelCase_						: Union[str, Any]         =  {
    """uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						Dict					      = """visual_bert"""

			def __init__(       self :       Any						,	snake_case_ :       List[Any]=3_0522						,	snake_case_ :       int=768						,	snake_case_ :       List[Any]=512						,	snake_case_ :       int=12						,	snake_case_ :       Tuple=12						,	snake_case_ :       Optional[Any]=3072						,	snake_case_ :       Union[str, Any]="gelu"						,	snake_case_ :       Optional[Any]=0.1						,	snake_case_ :       Any=0.1						,	snake_case_ :       Optional[Any]=512						,	snake_case_ :       Tuple=2						,	snake_case_ :       Union[str, Any]=0.02						,	snake_case_ :       Optional[int]=1e-12						,	snake_case_ :       Any=False						,	snake_case_ :       Any=True						,	snake_case_ :       Union[str, Any]=1						,	snake_case_ :       int=0						,	snake_case_ :       str=2						,	**snake_case_ :       Optional[int]						,	):
							super().__init__(pad_token_id=snake_case_						,	bos_token_id=snake_case_						,	eos_token_id=snake_case_						,	**snake_case_      )

							UpperCamelCase_:							List[str]	=						vocab_size
							UpperCamelCase_:							List[str]	=						max_position_embeddings
							UpperCamelCase_:							List[str]	=						hidden_size
							UpperCamelCase_:							List[str]	=						visual_embedding_dim
							UpperCamelCase_:							str	=						num_hidden_layers
							UpperCamelCase_:							Tuple	=						num_attention_heads
							UpperCamelCase_:							List[str]	=						intermediate_size
							UpperCamelCase_:							str	=						hidden_act
							UpperCamelCase_:							int	=						hidden_dropout_prob
							UpperCamelCase_:							Optional[int]	=						attention_probs_dropout_prob
							UpperCamelCase_:							Tuple	=						initializer_range
							UpperCamelCase_:							List[Any]	=						type_vocab_size
							UpperCamelCase_:							int	=						layer_norm_eps
							UpperCamelCase_:							Union[str, Any]	=						bypass_transformer
							UpperCamelCase_:							Optional[int]	=						special_visual_initialize








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)
lowerCamelCase_						: Union[str, Any]         =  logging.getLogger()

lowerCamelCase_						: List[str]         =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Dict      ):
							os.makedirs(snake_case_						,	exist_ok=snake_case_      )
							UpperCamelCase_:							int	=						{"""source""": """What is love ?""", """target""": """life"""}
							UpperCamelCase_:							Tuple	=						{"""train""": 12, """val""": 2, """test""": 2}
							for split in ["train", "test", "val"]:
											for field in ["source", "target"]:
															UpperCamelCase_:							Tuple	=						"""\n""".join([contents[field]] * n_lines[split]      )
															with open(os.path.join(snake_case_						,	f'''{split}.{field}'''      )						,	"""w"""      ) as f:
																			f.write(snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       int						,	snake_case_ :       str = "pytorch"      ):
							UpperCamelCase_:							Optional[Any]	=						self.get_auto_remove_tmp_dir()
							UpperCamelCase_:							Dict	=						os.path.join(snake_case_						,	"""output"""      )
							UpperCamelCase_:							Any	=						os.path.join(snake_case_						,	"""data"""      )
							self._create_dummy_data(data_dir=snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						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"""      )

							UpperCamelCase_:							Optional[Any]	=						[sys.executable, str(Path(finetune_rag.__file__      ).resolve()      )] + testargs
							execute_subprocess_async(snake_case_						,	env=self.get_env()      )

							UpperCamelCase_:							Optional[int]	=						os.path.join(snake_case_						,	"""metrics.json"""      )
							with open(snake_case_      ) as f:
											UpperCamelCase_:							Any	=						json.load(snake_case_      )
							return result

			@require_torch_gpu
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							List[str]	=						self._run_finetune(gpus=1      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )

			@require_torch_multi_gpu
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						self._run_finetune(gpus=2      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )

			@require_torch_gpu
			@require_ray
			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							List[Any]	=						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       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							List[Any]	=						self._run_finetune(gpus=1						,	distributed_retriever="""ray"""      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )






