



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


UpperCAmelCase__       =	logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def 							A     (					_UpperCAmelCase    :       List[Any]   )    ->			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     (					_UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       str							,       _UpperCAmelCase    :       List[Any]   )    ->			List[str]:







			'''simple docstring'''
			return max(metric_fn(_UpperCAmelCase							,       _UpperCAmelCase   ) for gt in ground_truths   )





def 							A     (					_UpperCAmelCase    :       str							,       _UpperCAmelCase    :       str							,       _UpperCAmelCase    :       Optional[Any]   )    ->			int:







			'''simple docstring'''
			_UpperCAmelCase			=       [line.strip() for line in open(_UpperCAmelCase							,       'r'   ).readlines()]
			_UpperCAmelCase			=       []

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

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

			_UpperCAmelCase			=       100.0 * em / total
			_UpperCAmelCase			=       100.0 * fa / total

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





def 							A     (					_UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       List[Any]   )    ->			Dict:







			'''simple docstring'''
			_UpperCAmelCase			=       args.k
			_UpperCAmelCase			=       [line.strip() for line in open(_UpperCAmelCase							,       'r'   ).readlines()]
			_UpperCAmelCase			=       [line.strip() for line in open(_UpperCAmelCase							,       'r'   ).readlines()]

			_UpperCAmelCase			=       _UpperCAmelCase			=       0
			for hypo, reference in zip(_UpperCAmelCase							,       _UpperCAmelCase   ):
						_UpperCAmelCase			=       set(hypo.split('\t'   )[:k]   )
						_UpperCAmelCase			=       set(reference.split('\t'   )   )
						total += 1
						em += len(hypo_provenance & ref_provenance   ) / k

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





def 							A     (					_UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       int   )    ->			Tuple:







			'''simple docstring'''
			def strip_title(_UpperCAmelCase    :       int   ):
						if title.startswith('"'   ):
									_UpperCAmelCase			=       title[1:]
						if title.endswith('"'   ):
									_UpperCAmelCase			=       title[:-1]
						return title

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

			_UpperCAmelCase			=       rag_model.rag.question_encoder(_UpperCAmelCase   )
			_UpperCAmelCase			=       question_enc_outputs[0]

			_UpperCAmelCase			=       rag_model.retriever(
			    _UpperCAmelCase							,       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'							,       )
			_UpperCAmelCase			=       rag_model.retriever.index.get_doc_dicts(result.doc_ids   )
			_UpperCAmelCase			=       []
			for docs in all_docs:
						_UpperCAmelCase			=       [strip_title(_UpperCAmelCase   ) for title in docs['title']]
						provenance_strings.append('\t'.join(_UpperCAmelCase   )   )
			return provenance_strings





def 							A     (					_UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       Union[str, Any]							,       _UpperCAmelCase    :       Union[str, Any]   )    ->			List[Any]:







			'''simple docstring'''
			with torch.no_grad():
						_UpperCAmelCase			=       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    _UpperCAmelCase							,       return_tensors='pt'							,       padding=_UpperCAmelCase							,       truncation=_UpperCAmelCase   )

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

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

						return answers





def 							A     (					)    ->			Dict:







			'''simple docstring'''
			_UpperCAmelCase			=       argparse.ArgumentParser()
			parser.add_argument(
			    '--model_type'							,       choices=['rag_sequence', 'rag_token', 'bart']							,       type=_UpperCAmelCase							,       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=_UpperCAmelCase							,       choices=['exact', 'compressed', 'legacy']							,       type=_UpperCAmelCase							,       help='RAG model retriever type'							,       )
			parser.add_argument(
			    '--index_path'							,       default=_UpperCAmelCase							,       type=_UpperCAmelCase							,       help='Path to the retrieval index'							,       )
			parser.add_argument('--n_docs'							,       default=5							,       type=_UpperCAmelCase							,       help='Number of retrieved docs'   )
			parser.add_argument(
			    '--model_name_or_path'							,       default=_UpperCAmelCase							,       type=_UpperCAmelCase							,       required=_UpperCAmelCase							,       help='Path to pretrained checkpoints or model identifier from huggingface.co/models'							,       )
			parser.add_argument(
			    '--eval_mode'							,       choices=['e2e', 'retrieval']							,       default='e2e'							,       type=_UpperCAmelCase							,       help=(
			        'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
			        ' precision@k.'
			    )							,       )
			parser.add_argument('--k'							,       default=1							,       type=_UpperCAmelCase							,       help='k for the precision@k calculation'   )
			parser.add_argument(
			    '--evaluation_set'							,       default=_UpperCAmelCase							,       type=_UpperCAmelCase							,       required=_UpperCAmelCase							,       help='Path to a file containing evaluation samples'							,       )
			parser.add_argument(
			    '--gold_data_path'							,       default=_UpperCAmelCase							,       type=_UpperCAmelCase							,       required=_UpperCAmelCase							,       help='Path to a tab-separated file with gold samples'							,       )
			parser.add_argument(
			    '--gold_data_mode'							,       default='qa'							,       type=_UpperCAmelCase							,       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=_UpperCAmelCase							,       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=_UpperCAmelCase							,       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=_UpperCAmelCase							,       help='Number of beams to be used when generating answers'							,       )
			parser.add_argument('--min_length'							,       default=1							,       type=_UpperCAmelCase							,       help='Min length of the generated answers'   )
			parser.add_argument('--max_length'							,       default=50							,       type=_UpperCAmelCase							,       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.'							,       )
			_UpperCAmelCase			=       parser.parse_args()
			_UpperCAmelCase			=       torch.device('cuda' if torch.cuda.is_available() else 'cpu'   )
			return args





def 							A     (					_UpperCAmelCase    :       Union[str, Any]   )    ->			int:







			'''simple docstring'''
			_UpperCAmelCase			=       {}
			if args.model_type is None:
						_UpperCAmelCase			=       infer_model_type(args.model_name_or_path   )
						assert args.model_type is not None
			if args.model_type.startswith('rag'   ):
						_UpperCAmelCase			=       RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
						_UpperCAmelCase			=       args.n_docs
						if args.index_name is not None:
									_UpperCAmelCase			=       args.index_name
						if args.index_path is not None:
									_UpperCAmelCase			=       args.index_path
			else:
						_UpperCAmelCase			=       BartForConditionalGeneration

			_UpperCAmelCase			=       (
			    [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'							,       _UpperCAmelCase   )

			_UpperCAmelCase			=       get_scores if args.eval_mode == 'e2e' else get_precision_at_k
			_UpperCAmelCase			=       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(_UpperCAmelCase							,       args.predictions_path							,       args.gold_data_path   )
									continue

						logger.info('***** Running evaluation for {} *****'.format(_UpperCAmelCase   )   )
						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'   ):
									_UpperCAmelCase			=       RagRetriever.from_pretrained(_UpperCAmelCase							,       **_UpperCAmelCase   )
									_UpperCAmelCase			=       model_class.from_pretrained(_UpperCAmelCase							,       retriever=_UpperCAmelCase							,       **_UpperCAmelCase   )
									model.retriever.init_retrieval()
						else:
									_UpperCAmelCase			=       model_class.from_pretrained(_UpperCAmelCase							,       **_UpperCAmelCase   )
						model.to(args.device   )

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

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


if __name__ == "__main__":
							UpperCAmelCase__       =	get_args()
							main(args)





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


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''decision_transformer'''
			UpperCamelCase        =					['''past_key_values''']
			UpperCamelCase        =					{
			    '''max_position_embeddings''': '''n_positions''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}



			def __init__(						self       :  Any			,		A       :  Optional[int]=17			,		A       :  List[str]=4			,		A       :  int=1_28			,		A       :  Union[str, Any]=40_96			,		A       :  Any=True			,		A       :  Any=1			,		A       :  List[Any]=10_24			,		A       :  List[Any]=3			,		A       :  Tuple=1			,		A       :  Any=None			,		A       :  Optional[int]="relu"			,		A       :  Union[str, Any]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=1E-5			,		A       :  List[Any]=0.0_2			,		A       :  Tuple=True			,		A       :  Union[str, Any]=True			,		A       :  str=5_02_56			,		A       :  Union[str, Any]=5_02_56			,		A       :  List[Any]=False			,		A       :  Optional[int]=False			,		**A       :  int			,		)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       state_dim
						_UpperCAmelCase			=       act_dim
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       max_ep_len
						_UpperCAmelCase			=       action_tanh
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       n_positions
						_UpperCAmelCase			=       n_layer
						_UpperCAmelCase			=       n_head
						_UpperCAmelCase			=       n_inner
						_UpperCAmelCase			=       activation_function
						_UpperCAmelCase			=       resid_pdrop
						_UpperCAmelCase			=       embd_pdrop
						_UpperCAmelCase			=       attn_pdrop
						_UpperCAmelCase			=       layer_norm_epsilon
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       scale_attn_weights
						_UpperCAmelCase			=       use_cache
						_UpperCAmelCase			=       scale_attn_by_inverse_layer_idx
						_UpperCAmelCase			=       reorder_and_upcast_attn

						_UpperCAmelCase			=       bos_token_id
						_UpperCAmelCase			=       eos_token_id

						super().__init__(bos_token_id=A			,		eos_token_id=A			,		**A)
