





"""simple docstring"""






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


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

lowerCamelCase__ :       int						     =			{
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """marian"""
	UpperCamelCase				=  ["""past_key_values"""]
	UpperCamelCase				=  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





	def __init__(						self    :Dict		, lowerCamelCase_    :Optional[int]=5_81_01		, lowerCamelCase_    :List[str]=None		, lowerCamelCase_    :Optional[Any]=10_24		, lowerCamelCase_    :Optional[Any]=12		, lowerCamelCase_    :int=40_96		, lowerCamelCase_    :Dict=16		, lowerCamelCase_    :Optional[Any]=12		, lowerCamelCase_    :str=40_96		, lowerCamelCase_    :str=16		, lowerCamelCase_    :List[Any]=0.0		, lowerCamelCase_    :int=0.0		, lowerCamelCase_    :Union[str, Any]=True		, lowerCamelCase_    :Any=True		, lowerCamelCase_    :Union[str, Any]="gelu"		, lowerCamelCase_    :Union[str, Any]=10_24		, lowerCamelCase_    :Optional[int]=0.1		, lowerCamelCase_    :Optional[Any]=0.0		, lowerCamelCase_    :Any=0.0		, lowerCamelCase_    :Any=0.0_2		, lowerCamelCase_    :List[str]=5_81_00		, lowerCamelCase_    :Union[str, Any]=False		, lowerCamelCase_    :int=5_81_00		, lowerCamelCase_    :Any=0		, lowerCamelCase_    :List[str]=0		, lowerCamelCase_    :Tuple=True		, **lowerCamelCase_    :Dict		, )							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= vocab_size
			SCREAMING_SNAKE_CASE       :     Dict				= decoder_vocab_size or vocab_size
			SCREAMING_SNAKE_CASE       :     Any				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     Tuple				= d_model
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= encoder_ffn_dim
			SCREAMING_SNAKE_CASE       :     List[Any]				= encoder_layers
			SCREAMING_SNAKE_CASE       :     Dict				= encoder_attention_heads
			SCREAMING_SNAKE_CASE       :     int				= decoder_ffn_dim
			SCREAMING_SNAKE_CASE       :     List[Any]				= decoder_layers
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= decoder_attention_heads
			SCREAMING_SNAKE_CASE       :     Dict				= dropout
			SCREAMING_SNAKE_CASE       :     str				= attention_dropout
			SCREAMING_SNAKE_CASE       :     Any				= activation_dropout
			SCREAMING_SNAKE_CASE       :     int				= activation_function
			SCREAMING_SNAKE_CASE       :     Any				= init_std
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= encoder_layerdrop
			SCREAMING_SNAKE_CASE       :     Optional[int]				= decoder_layerdrop
			SCREAMING_SNAKE_CASE       :     List[Any]				= use_cache
			SCREAMING_SNAKE_CASE       :     List[Any]				= encoder_layers
			SCREAMING_SNAKE_CASE       :     int				= scale_embedding  # scale factor will be sqrt(d_model) if True
			SCREAMING_SNAKE_CASE       :     List[str]				= share_encoder_decoder_embeddings
			super().__init__(
			    pad_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, is_encoder_decoder=lowerCamelCase_		, decoder_start_token_id=lowerCamelCase_		, forced_eos_token_id=lowerCamelCase_		, **lowerCamelCase_		, )





class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	@property
	# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Mapping[str, Mapping[int, str]]:







			'''simple docstring'''




			if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE       :     List[str]				= OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
					    ]					)

					if self.use_past:
							SCREAMING_SNAKE_CASE       :     List[str]				= {0: '''batch'''}
							SCREAMING_SNAKE_CASE       :     Any				= {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
					else:
							SCREAMING_SNAKE_CASE       :     List[str]				= {0: '''batch''', 1: '''decoder_sequence'''}
							SCREAMING_SNAKE_CASE       :     Optional[int]				= {0: '''batch''', 1: '''decoder_sequence'''}

					if self.use_past:
							self.fill_with_past_key_values_(lowerCamelCase_		, direction='''inputs'''					)
			elif self.task == "causal-lm":
					# TODO: figure this case out.
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
					    ]					)
					if self.use_past:
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[int]				= self.num_layers
							for i in range(lowerCamelCase_					):
									SCREAMING_SNAKE_CASE       :     Dict				= {0: '''batch''', 2: '''past_sequence + sequence'''}
									SCREAMING_SNAKE_CASE       :     List[Any]				= {0: '''batch''', 2: '''past_sequence + sequence'''}
			else:
					SCREAMING_SNAKE_CASE       :     int				= OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
					        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
					        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
					    ]					)

			return common_inputs





	@property
	# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
	def   __lowerCAmelCase (						self    :List[Any]					)							->			Mapping[str, Mapping[int, str]]:







			'''simple docstring'''




			if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= super().outputs
			else:
					SCREAMING_SNAKE_CASE       :     Dict				= super(lowerCamelCase_		, self					).outputs
					if self.use_past:
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     str				= self.num_layers
							for i in range(lowerCamelCase_					):
									SCREAMING_SNAKE_CASE       :     List[Any]				= {0: '''batch''', 2: '''past_sequence + sequence'''}
									SCREAMING_SNAKE_CASE       :     Optional[Any]				= {0: '''batch''', 2: '''past_sequence + sequence'''}
			return common_outputs





	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :Any		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :List[Any] = -1		, lowerCamelCase_    :Union[str, Any] = False		, lowerCamelCase_    :Any = None		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= self._generate_dummy_inputs_for_encoder_and_decoder(
			    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)

			# Generate decoder inputs
			SCREAMING_SNAKE_CASE       :     int				= seq_length if not self.use_past else 1
			SCREAMING_SNAKE_CASE       :     Optional[int]				= self._generate_dummy_inputs_for_encoder_and_decoder(
			    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= dict(**lowerCamelCase_		, **lowerCamelCase_					)

			if self.use_past:
					if not is_torch_available():
							raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
					else:
							import torch
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     int				= common_inputs['''input_ids'''].shape
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= common_inputs['''decoder_input_ids'''].shape[1]
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= self.num_attention_heads
					SCREAMING_SNAKE_CASE       :     int				= (
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= decoder_seq_length + 3
					SCREAMING_SNAKE_CASE       :     Tuple				= (
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)

					SCREAMING_SNAKE_CASE       :     List[Any]				= torch.cat(
					    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCamelCase_		, lowerCamelCase_					)]		, dim=1					)

					SCREAMING_SNAKE_CASE       :     Optional[Any]				= []
					# If the number of encoder and decoder layers are present in the model configuration, both are considered
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.num_layers
					SCREAMING_SNAKE_CASE       :     Optional[int]				= min(lowerCamelCase_		, lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     int				= max(lowerCamelCase_		, lowerCamelCase_					) - min_num_layers
					SCREAMING_SNAKE_CASE       :     List[str]				= '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

					for _ in range(lowerCamelCase_					):
							common_inputs["past_key_values"].append(
							    (
							        torch.zeros(lowerCamelCase_					),
							        torch.zeros(lowerCamelCase_					),
							        torch.zeros(lowerCamelCase_					),
							        torch.zeros(lowerCamelCase_					),
							    )					)
					# TODO: test this.
					SCREAMING_SNAKE_CASE       :     Tuple				= encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
					for _ in range(lowerCamelCase_		, lowerCamelCase_					):
							common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase_					), torch.zeros(lowerCamelCase_					))					)
			return common_inputs





	def   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Union[str, Any] = -1		, lowerCamelCase_    :Union[str, Any] = -1		, lowerCamelCase_    :List[str] = False		, lowerCamelCase_    :Any = None		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self._generate_dummy_inputs_for_encoder_and_decoder(
			    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)

			if self.use_past:
					if not is_torch_available():
							raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
					else:
							import torch
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Dict				= common_inputs['''input_ids'''].shape
					# Not using the same length for past_key_values
					SCREAMING_SNAKE_CASE       :     Dict				= seqlen + 2
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     str				= self.num_layers
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[int]				= self.num_attention_heads
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= (
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)

					SCREAMING_SNAKE_CASE       :     List[Any]				= common_inputs['''attention_mask'''].dtype
					SCREAMING_SNAKE_CASE       :     Any				= torch.cat(
					    [common_inputs['''attention_mask'''], torch.ones(lowerCamelCase_		, lowerCamelCase_		, dtype=lowerCamelCase_					)]		, dim=1					)
					SCREAMING_SNAKE_CASE       :     Any				= [
					    (torch.zeros(lowerCamelCase_					), torch.zeros(lowerCamelCase_					)) for _ in range(lowerCamelCase_					)
					]
			return common_inputs





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Dict = -1		, lowerCamelCase_    :Optional[int] = -1		, lowerCamelCase_    :Union[str, Any] = False		, lowerCamelCase_    :str = None		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= compute_effective_axis_dimension(
			    lowerCamelCase_		, fixed_dimension=OnnxConfig.default_fixed_batch		, num_token_to_add=0					)

			# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= tokenizer.num_special_tokens_to_add(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     str				= compute_effective_axis_dimension(
			    lowerCamelCase_		, fixed_dimension=OnnxConfig.default_fixed_sequence		, num_token_to_add=lowerCamelCase_					)

			# Generate dummy inputs according to compute batch and sequence
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= [''' '''.join([tokenizer.unk_token]					) * seq_length] * batch_size
			SCREAMING_SNAKE_CASE       :     Any				= dict(tokenizer(lowerCamelCase_		, return_tensors=lowerCamelCase_					)					)
			return common_inputs





	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Optional[Any] = -1		, lowerCamelCase_    :str = -1		, lowerCamelCase_    :List[str] = False		, lowerCamelCase_    :Any = None		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
					    lowerCamelCase_		, batch_size=lowerCamelCase_		, seq_length=lowerCamelCase_		, is_pair=lowerCamelCase_		, framework=lowerCamelCase_					)

			else:
					SCREAMING_SNAKE_CASE       :     str				= self._generate_dummy_inputs_for_causal_lm(
					    lowerCamelCase_		, batch_size=lowerCamelCase_		, seq_length=lowerCamelCase_		, is_pair=lowerCamelCase_		, framework=lowerCamelCase_					)

			return common_inputs





	def   __lowerCAmelCase (						self    :Optional[Any]		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :int		, lowerCamelCase_    :int		, lowerCamelCase_    :Tuple					)							->			str:







			'''simple docstring'''




			if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= super()._flatten_past_key_values_(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)
			else:
					SCREAMING_SNAKE_CASE       :     Tuple				= super(lowerCamelCase_		, self					)._flatten_past_key_values_(
					    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)







	@property
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			float:







			'''simple docstring'''




			return 1E-4







"""simple docstring"""






import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowerCamelCase__ :       Optional[Any]						     =			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def __A						(   a_ :							Optional[int]     )->							Dict:




		'''simple docstring'''






		if "token" in model_name_or_path:
				return "rag_token"
		if "sequence" in model_name_or_path:
				return "rag_sequence"
		if "bart" in model_name_or_path:
				return "bart"
		return None





def __A						(   a_ :							List[Any]		,		a_ :							Optional[int]		,		a_ :							Optional[int]     )->							Dict:




		'''simple docstring'''






		return max(metric_fn(a_		,		a_     ) for gt in ground_truths     )





def __A						(   a_ :							List[Any]		,		a_ :							Union[str, Any]		,		a_ :							str     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= []

		if args.gold_data_mode == "qa":
				SCREAMING_SNAKE_CASE       :     List[Any]				= pd.read_csv(a_		,		sep='''\t'''		,		header=a_     )
				for answer_list in data[1]:
						SCREAMING_SNAKE_CASE       :     str				= ast.literal_eval(a_     )
						answers.append(a_     )
		else:
				SCREAMING_SNAKE_CASE       :     Any				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
				SCREAMING_SNAKE_CASE       :     Dict				= [[reference] for reference in references]

		SCREAMING_SNAKE_CASE       :     Dict				= 0
		for prediction, ground_truths in zip(a_		,		a_     ):
				total += 1
				em += metric_max_over_ground_truths(a_		,		a_		,		a_     )
				fa += metric_max_over_ground_truths(a_		,		a_		,		a_     )

		SCREAMING_SNAKE_CASE       :     Any				= 100.0 * em / total
		SCREAMING_SNAKE_CASE       :     Optional[int]				= 100.0 * fa / total

		logger.info(F"F1: {fa:.2f}"     )
		logger.info(F"EM: {em:.2f}"     )





def __A						(   a_ :							Any		,		a_ :							Any		,		a_ :							List[Any]     )->							Tuple:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= args.k
		SCREAMING_SNAKE_CASE       :     Tuple				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]

		SCREAMING_SNAKE_CASE       :     Dict				= 0
		for hypo, reference in zip(a_		,		a_     ):
				SCREAMING_SNAKE_CASE       :     Optional[int]				= set(hypo.split('''\t'''     )[:k]     )
				SCREAMING_SNAKE_CASE       :     List[str]				= set(reference.split('''\t'''     )     )
				total += 1
				em += len(hypo_provenance & ref_provenance     ) / k

		SCREAMING_SNAKE_CASE       :     Dict				= 100.0 * em / total
		logger.info(F"Precision@{k}: {em: .2f}"     )





def __A						(   a_ :							Any		,		a_ :							List[str]		,		a_ :							str     )->							int:




		'''simple docstring'''






		def strip_title(a_ :							Optional[Any]     ):
				if title.startswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Tuple				= title[1:]
				if title.endswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Any				= title[:-1]
				return title

		SCREAMING_SNAKE_CASE       :     Tuple				= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    a_		,		return_tensors='''pt'''		,		padding=a_		,		truncation=a_		,		)['''input_ids'''].to(args.device     )

		SCREAMING_SNAKE_CASE       :     Any				= rag_model.rag.question_encoder(a_     )
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= question_enc_outputs[0]

		SCREAMING_SNAKE_CASE       :     Dict				= rag_model.retriever(
		    a_		,		question_enc_pool_output.cpu().detach().to(torch.floataa     ).numpy()		,		prefix=rag_model.rag.generator.config.prefix		,		n_docs=rag_model.config.n_docs		,		return_tensors='''pt'''		,		)
		SCREAMING_SNAKE_CASE       :     Any				= rag_model.retriever.index.get_doc_dicts(result.doc_ids     )
		SCREAMING_SNAKE_CASE       :     Dict				= []
		for docs in all_docs:
				SCREAMING_SNAKE_CASE       :     List[Any]				= [strip_title(a_     ) for title in docs['''title''']]
				provenance_strings.append('''\t'''.join(a_     )     )
		return provenance_strings





def __A						(   a_ :							List[Any]		,		a_ :							int		,		a_ :							str     )->							Tuple:




		'''simple docstring'''






		with torch.no_grad():
				SCREAMING_SNAKE_CASE       :     Optional[int]				= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    a_		,		return_tensors='''pt'''		,		padding=a_		,		truncation=a_     )

				SCREAMING_SNAKE_CASE       :     Dict				= inputs_dict.input_ids.to(args.device     )
				SCREAMING_SNAKE_CASE       :     Any				= inputs_dict.attention_mask.to(args.device     )
				SCREAMING_SNAKE_CASE       :     Tuple				= rag_model.generate(  # rag_model overwrites generate
				    a_		,		attention_mask=a_		,		num_beams=args.num_beams		,		min_length=args.min_length		,		max_length=args.max_length		,		early_stopping=a_		,		num_return_sequences=1		,		bad_words_ids=[[0, 0]]		,		)
				SCREAMING_SNAKE_CASE       :     Dict				= rag_model.retriever.generator_tokenizer.batch_decode(a_		,		skip_special_tokens=a_     )

				if args.print_predictions:
						for q, a in zip(a_		,		a_     ):
								logger.info('''Q: {} - A: {}'''.format(a_		,		a_     )     )

				return answers





def __A						(   )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= argparse.ArgumentParser()
		parser.add_argument(
		    '''--model_type'''		,		choices=['''rag_sequence''', '''rag_token''', '''bart''']		,		type=a_		,		help=(
		        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
		        ''' model_name_or_path'''
		    )		,		)
		parser.add_argument(
		    '''--index_name'''		,		default=a_		,		choices=['''exact''', '''compressed''', '''legacy''']		,		type=a_		,		help='''RAG model retriever type'''		,		)
		parser.add_argument(
		    '''--index_path'''		,		default=a_		,		type=a_		,		help='''Path to the retrieval index'''		,		)
		parser.add_argument('''--n_docs'''		,		default=5		,		type=a_		,		help='''Number of retrieved docs'''     )
		parser.add_argument(
		    '''--model_name_or_path'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''		,		)
		parser.add_argument(
		    '''--eval_mode'''		,		choices=['''e2e''', '''retrieval''']		,		default='''e2e'''		,		type=a_		,		help=(
		        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
		        ''' precision@k.'''
		    )		,		)
		parser.add_argument('''--k'''		,		default=1		,		type=a_		,		help='''k for the precision@k calculation'''     )
		parser.add_argument(
		    '''--evaluation_set'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to a file containing evaluation samples'''		,		)
		parser.add_argument(
		    '''--gold_data_path'''		,		default=a_		,		type=a_		,		required=a_		,		help='''Path to a tab-separated file with gold samples'''		,		)
		parser.add_argument(
		    '''--gold_data_mode'''		,		default='''qa'''		,		type=a_		,		choices=['''qa''', '''ans''']		,		help=(
		        '''Format of the gold data file'''
		        '''qa - a single line in the following format: question [tab] answer_list'''
		        '''ans - a single line of the gold file contains the expected answer string'''
		    )		,		)
		parser.add_argument(
		    '''--predictions_path'''		,		type=a_		,		default='''predictions.txt'''		,		help='''Name of the predictions file, to be stored in the checkpoints directory'''		,		)
		parser.add_argument(
		    '''--eval_all_checkpoints'''		,		action='''store_true'''		,		help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''		,		)
		parser.add_argument(
		    '''--eval_batch_size'''		,		default=8		,		type=a_		,		help='''Batch size per GPU/CPU for evaluation.'''		,		)
		parser.add_argument(
		    '''--recalculate'''		,		help='''Recalculate predictions even if the prediction file exists'''		,		action='''store_true'''		,		)
		parser.add_argument(
		    '''--num_beams'''		,		default=4		,		type=a_		,		help='''Number of beams to be used when generating answers'''		,		)
		parser.add_argument('''--min_length'''		,		default=1		,		type=a_		,		help='''Min length of the generated answers'''     )
		parser.add_argument('''--max_length'''		,		default=50		,		type=a_		,		help='''Max length of the generated answers'''     )

		parser.add_argument(
		    '''--print_predictions'''		,		action='''store_true'''		,		help='''If True, prints predictions while evaluating.'''		,		)
		parser.add_argument(
		    '''--print_docs'''		,		action='''store_true'''		,		help='''If True, prints docs retried while generating.'''		,		)
		SCREAMING_SNAKE_CASE       :     List[str]				= parser.parse_args()
		SCREAMING_SNAKE_CASE       :     Dict				= torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''     )
		return args





def __A						(   a_ :							Optional[Any]     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= {}
		if args.model_type is None:
				SCREAMING_SNAKE_CASE       :     List[str]				= infer_model_type(args.model_name_or_path     )
				assert args.model_type is not None
		if args.model_type.startswith('''rag'''     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= args.n_docs
				if args.index_name is not None:
						SCREAMING_SNAKE_CASE       :     Tuple				= args.index_name
				if args.index_path is not None:
						SCREAMING_SNAKE_CASE       :     List[Any]				= args.index_path
		else:
				SCREAMING_SNAKE_CASE       :     str				= BartForConditionalGeneration

		SCREAMING_SNAKE_CASE       :     Optional[int]				= (
		    [f.path for f in os.scandir(args.model_name_or_path     ) if f.is_dir()]
		    if args.eval_all_checkpoints
		    else [args.model_name_or_path]
		)

		logger.info('''Evaluate the following checkpoints: %s'''		,		a_     )

		SCREAMING_SNAKE_CASE       :     int				= get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
		SCREAMING_SNAKE_CASE       :     str				= evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

		for checkpoint in checkpoints:
				if os.path.exists(args.predictions_path     ) and (not args.recalculate):
						logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path     )     )
						score_fn(a_		,		args.predictions_path		,		args.gold_data_path     )
						continue

				logger.info('''***** Running evaluation for {} *****'''.format(a_     )     )
				logger.info('''  Batch size = %d'''		,		args.eval_batch_size     )
				logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path     )     )

				if args.model_type.startswith('''rag'''     ):
						SCREAMING_SNAKE_CASE       :     Dict				= RagRetriever.from_pretrained(a_		,		**a_     )
						SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_class.from_pretrained(a_		,		retriever=a_		,		**a_     )
						model.retriever.init_retrieval()
				else:
						SCREAMING_SNAKE_CASE       :     str				= model_class.from_pretrained(a_		,		**a_     )
				model.to(args.device     )

				with open(args.evaluation_set		,		'''r'''     ) as eval_file, open(args.predictions_path		,		'''w'''     ) as preds_file:
						SCREAMING_SNAKE_CASE       :     Dict				= []
						for line in tqdm(a_     ):
								questions.append(line.strip()     )
								if len(a_     ) == args.eval_batch_size:
										SCREAMING_SNAKE_CASE       :     str				= evaluate_batch_fn(a_		,		a_		,		a_     )
										preds_file.write('''\n'''.join(a_     ) + '''\n'''     )
										preds_file.flush()
										SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
						if len(a_     ) > 0:
								SCREAMING_SNAKE_CASE       :     str				= evaluate_batch_fn(a_		,		a_		,		a_     )
								preds_file.write('''\n'''.join(a_     )     )
								preds_file.flush()

						score_fn(a_		,		args.predictions_path		,		args.gold_data_path     )


if __name__ == "__main__":
					lowerCamelCase__ :       List[str]						     =			get_args()
					main(args)





