

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__								=		logging.getLogger()

lowerCAmelCase__								=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		def 		A__					(				self	,       __snake_case):
			os.makedirs(__snake_case	,       exist_ok=__snake_case)
			_UpperCamelCase   :		Dict			=						{'source': 'What is love ?', 'target': 'life'}
			_UpperCamelCase   :		List[str]			=						{'train': 12, 'val': 2, 'test': 2}
			for split in ["train", "test", "val"]:
				for field in ["source", "target"]:
					_UpperCamelCase   :		str			=						'\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 		A__					(				self	,       __snake_case	,       __snake_case = "pytorch"):
			_UpperCamelCase   :		Optional[Any]			=						self.get_auto_remove_tmp_dir()
			_UpperCamelCase   :		int			=						os.path.join(__snake_case	,       'output')
			_UpperCamelCase   :		Optional[Any]			=						os.path.join(__snake_case	,       'data')
			self._create_dummy_data(data_dir=__snake_case)

			_UpperCamelCase   :		Optional[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   :		List[str]			=						[sys.executable, str(Path(finetune_rag.__file__).resolve())] + testargs
			execute_subprocess_async(__snake_case	,       env=self.get_env())

			_UpperCamelCase   :		Dict			=						os.path.join(__snake_case	,       'metrics.json')
			with open(__snake_case) as f:
				_UpperCamelCase   :		Optional[Any]			=						json.load(__snake_case)
			return result


		@require_torch_gpu
		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						self._run_finetune(gpus=1)
			self.assertGreaterEqual(result['test'][0]['test_avg_em']	,       0.2)


		@require_torch_multi_gpu
		def 		A__					(				self):
			_UpperCamelCase   :		str			=						self._run_finetune(gpus=2)
			self.assertGreaterEqual(result['test'][0]['test_avg_em']	,       0.2)


		@require_torch_gpu
		@require_ray
		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						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 		A__					(				self):
			_UpperCamelCase   :		Any			=						self._run_finetune(gpus=1	,       distributed_retriever='ray')
			self.assertGreaterEqual(result['test'][0]['test_avg_em']	,       0.2)







from ...processing_utils import ProcessorMixin







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  ["image_processor", "feature_extractor"]
		a__					       =  "TvltImageProcessor"
		a__					       =  "TvltFeatureExtractor"


		def __init__(				self	,       __snake_case	,       __snake_case):
			super().__init__(image_processor=__snake_case	,       feature_extractor=__snake_case)

			_UpperCamelCase   :		List[str]			=						image_processor
			_UpperCamelCase   :		Dict			=						feature_extractor


		def __call__(				self	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=False	,       __snake_case=False	,       *__snake_case	,       **__snake_case	,       ):

			if images is None and audio is None:
				raise ValueError('You need to specify either an `images` or `audio` input to process.')

			_UpperCamelCase   :		Union[str, Any]			=						None
			if images is not None:
				_UpperCamelCase   :		Tuple			=						self.image_processor(__snake_case	,       mask_pixel=__snake_case	,       *__snake_case	,       **__snake_case)
			if images_mixed is not None:
				_UpperCamelCase   :		Union[str, Any]			=						self.image_processor(__snake_case	,       is_mixed=__snake_case	,       *__snake_case	,       **__snake_case)
			if audio is not None:
				_UpperCamelCase   :		Tuple			=						self.feature_extractor(
				    __snake_case	,       *__snake_case	,       sampling_rate=__snake_case	,       mask_audio=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		Tuple			=						{}
			if audio is not None:
				output_dict.update(__snake_case)
			if images is not None:
				output_dict.update(__snake_case)
			if images_mixed_dict is not None:
				output_dict.update(__snake_case)
			return output_dict






		@property
		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						self.image_processor.model_input_names
			_UpperCamelCase   :		List[Any]			=						self.feature_extractor.model_input_names
			return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))




