
'''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


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

transformers_logging.set_verbosity_info()





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[int]):
				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    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							int       ,							UpperCAmelCase__							:							Union[str, Any]):
				return max(metric_fn(UpperCAmelCase__       ,							UpperCAmelCase__) for gt in ground_truths)





def    UpperCAmelCase							(  UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							List[Any]):
				lowerCamelCase					:			Dict									= [line.strip() for line in open(UpperCAmelCase__       ,							'r').readlines()]
				lowerCamelCase					:			List[str]									= []

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

				lowerCamelCase					:			Union[str, Any]									= 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__)

				lowerCamelCase					:			Any									= 1_0_0.0 * em / total
				lowerCamelCase					:			Any									= 1_0_0.0 * fa / total

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





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							List[Any]       ,							UpperCAmelCase__							:							Tuple):
				lowerCamelCase					:			Optional[Any]									= args.k
				lowerCamelCase					:			Optional[int]									= [line.strip() for line in open(UpperCAmelCase__       ,							'r').readlines()]
				lowerCamelCase					:			Union[str, Any]									= [line.strip() for line in open(UpperCAmelCase__       ,							'r').readlines()]

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

				lowerCamelCase					:			Dict									= 1_0_0.0 * em / total
				logger.info(F'''Precision@{k}: {em: .2f}''')





def    UpperCAmelCase							(  UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							Optional[Any]):
				def strip_title(UpperCAmelCase__							:							Union[str, Any]):
								if title.startswith('"'):
												lowerCamelCase					:			str									= title[1:]
								if title.endswith('"'):
												lowerCamelCase					:			List[str]									= title[:-1]
								return title

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

				lowerCamelCase					:			List[Any]									= rag_model.rag.question_encoder(UpperCAmelCase__)
				lowerCamelCase					:			Any									= question_enc_outputs[0]

				lowerCamelCase					:			Union[str, Any]									= 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'       ,							)
				lowerCamelCase					:			Optional[int]									= rag_model.retriever.index.get_doc_dicts(result.doc_ids)
				lowerCamelCase					:			Union[str, Any]									= []
				for docs in all_docs:
								lowerCamelCase					:			Optional[Any]									= [strip_title(UpperCAmelCase__) for title in docs['title']]
								provenance_strings.append('\t'.join(UpperCAmelCase__))
				return provenance_strings





def    UpperCAmelCase							(  UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							Union[str, Any]):
				with torch.no_grad():
								lowerCamelCase					:			List[str]									= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    UpperCAmelCase__       ,							return_tensors='pt'       ,							padding=UpperCAmelCase__       ,							truncation=UpperCAmelCase__)

								lowerCamelCase					:			int									= inputs_dict.input_ids.to(args.device)
								lowerCamelCase					:			Optional[Any]									= inputs_dict.attention_mask.to(args.device)
								lowerCamelCase					:			Optional[Any]									= 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]]       ,							)
								lowerCamelCase					:			Union[str, Any]									= 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    UpperCAmelCase							(  ):
				lowerCamelCase					:			List[str]									= 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.'       ,							)
				lowerCamelCase					:			Optional[int]									= parser.parse_args()
				lowerCamelCase					:			Optional[Any]									= torch.device('cuda' if torch.cuda.is_available() else 'cpu')
				return args





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

				lowerCamelCase					:			Dict									= (
				    [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__)

				lowerCamelCase					:			List[str]									= get_scores if args.eval_mode == 'e2e' else get_precision_at_k
				lowerCamelCase					:			Tuple									= 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'):
												lowerCamelCase					:			str									= RagRetriever.from_pretrained(UpperCAmelCase__       ,							**UpperCAmelCase__)
												lowerCamelCase					:			Any									= model_class.from_pretrained(UpperCAmelCase__       ,							retriever=UpperCAmelCase__       ,							**UpperCAmelCase__)
												model.retriever.init_retrieval()
								else:
												lowerCamelCase					:			Dict									= 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:
												lowerCamelCase					:			List[str]									= []
												for line in tqdm(UpperCAmelCase__):
																questions.append(line.strip())
																if len(UpperCAmelCase__) == args.eval_batch_size:
																				lowerCamelCase					:			str									= evaluate_batch_fn(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
																				preds_file.write('\n'.join(UpperCAmelCase__) + '\n')
																				preds_file.flush()
																				lowerCamelCase					:			Tuple									= []
												if len(UpperCAmelCase__) > 0:
																lowerCamelCase					:			Union[str, Any]									= 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__":
					A			      =      get_args()
					main(args)


'''simple docstring'''






import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


A			      =      logging.get_logger(__name__)




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''AutoTokenizer'''
					_lowerCAmelCase			     =	['''tokenizer''']

					_lowerCAmelCase			     =	{
					    '''semantic_prompt''': 1,
					    '''coarse_prompt''': 2,
					    '''fine_prompt''': 2,
					}
					def __init__(     self,    A,    A=None      ):

									"""simple docstring"""


									super().__init__(A      )

									lowerCamelCase					:			Union[str, Any]									= speaker_embeddings
					@classmethod
					def     UpperCAmelCase_							(     cls,    A,    A="speaker_embeddings_path.json",    **A      ):

									"""simple docstring"""


									if speaker_embeddings_dict_path is not None:
													lowerCamelCase					:			List[Any]									= get_file_from_repo(
													    A,    A,    subfolder=kwargs.pop('subfolder',    A      ),    cache_dir=kwargs.pop('cache_dir',    A      ),    force_download=kwargs.pop('force_download',    A      ),    proxies=kwargs.pop('proxies',    A      ),    resume_download=kwargs.pop('resume_download',    A      ),    local_files_only=kwargs.pop('local_files_only',    A      ),    use_auth_token=kwargs.pop('use_auth_token',    A      ),    revision=kwargs.pop('revision',    A      ),    )
													if speaker_embeddings_path is None:
																	logger.warning(
																	    F'''`{os.path.join(A,    A      )}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'''      )
																	lowerCamelCase					:			Union[str, Any]									= None
													else:
																	with open(A      ) as speaker_embeddings_json:
																					lowerCamelCase					:			Union[str, Any]									= json.load(A      )
									else:
													lowerCamelCase					:			Dict									= None

									lowerCamelCase					:			int									= AutoTokenizer.from_pretrained(A,    **A      )

									return cls(tokenizer=A,    speaker_embeddings=A      )
					def     UpperCAmelCase_							(     self,    A,    A="speaker_embeddings_path.json",    A="speaker_embeddings",    A = False,    **A,    ):

									"""simple docstring"""


									if self.speaker_embeddings is not None:
													os.makedirs(os.path.join(A,    A,    'v2'      ),    exist_ok=A      )

													lowerCamelCase					:			Any									= {}

													lowerCamelCase					:			Tuple									= save_directory

													for prompt_key in self.speaker_embeddings:
																	if prompt_key != "repo_or_path":
																					lowerCamelCase					:			Optional[int]									= self._load_voice_preset(A      )

																					lowerCamelCase					:			Optional[Any]									= {}
																					for key in self.speaker_embeddings[prompt_key]:
																									np.save(
																									    os.path.join(
																									        embeddings_dict['repo_or_path'],    A,    F'''{prompt_key}_{key}'''      ),    voice_preset[key],    allow_pickle=A,    )
																									lowerCamelCase					:			Tuple									= os.path.join(A,    F'''{prompt_key}_{key}.npy'''      )

																					lowerCamelCase					:			Optional[Any]									= tmp_dict

													with open(os.path.join(A,    A      ),    'w'      ) as fp:
																	json.dump(A,    A      )

									super().save_pretrained(A,    A,    **A      )
					def     UpperCAmelCase_							(     self,    A = None,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.speaker_embeddings[voice_preset]

									lowerCamelCase					:			Optional[Any]									= {}
									for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
													if key not in voice_preset_paths:
																	raise ValueError(
																	    F'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'''      )

													lowerCamelCase					:			Optional[Any]									= get_file_from_repo(
													    self.speaker_embeddings.get('repo_or_path',    '/'      ),    voice_preset_paths[key],    subfolder=kwargs.pop('subfolder',    A      ),    cache_dir=kwargs.pop('cache_dir',    A      ),    force_download=kwargs.pop('force_download',    A      ),    proxies=kwargs.pop('proxies',    A      ),    resume_download=kwargs.pop('resume_download',    A      ),    local_files_only=kwargs.pop('local_files_only',    A      ),    use_auth_token=kwargs.pop('use_auth_token',    A      ),    revision=kwargs.pop('revision',    A      ),    )
													if path is None:
																	raise ValueError(
																	    F'''`{os.path.join(self.speaker_embeddings.get('repo_or_path',    '/'      ),    voice_preset_paths[key]      )}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.'''      )

													lowerCamelCase					:			Tuple									= np.load(A      )

									return voice_preset_dict
					def     UpperCAmelCase_							(     self,    A = None      ):

									"""simple docstring"""


									for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
													if key not in voice_preset:
																	raise ValueError(F'''Voice preset unrecognized, missing {key} as a key.'''      )

													if not isinstance(voice_preset[key],    np.ndarray      ):
																	raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]      )}D ndarray.'''      )

													if len(voice_preset[key].shape      ) != self.preset_shape[key]:
																	raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]      )}D ndarray.'''      )




					def __call__(     self,    A=None,    A=None,    A="pt",    A=256,    A=False,    A=True,    A=False,    **A,    ):

									"""simple docstring"""


									if voice_preset is not None and not isinstance(A,    A      ):
													if (
													    isinstance(A,    A      )
													    and self.speaker_embeddings is not None
													    and voice_preset in self.speaker_embeddings
													):
																	lowerCamelCase					:			Dict									= self._load_voice_preset(A      )

													else:
																	if isinstance(A,    A      ) and not voice_preset.endswith('.npz'      ):
																					lowerCamelCase					:			List[Any]									= voice_preset + '.npz'

																	lowerCamelCase					:			Tuple									= np.load(A      )

									if voice_preset is not None:
													self._validate_voice_preset_dict(A,    **A      )
													lowerCamelCase					:			List[Any]									= BatchFeature(data=A,    tensor_type=A      )

									lowerCamelCase					:			str									= self.tokenizer(
									    A,    return_tensors=A,    padding='max_length',    max_length=A,    return_attention_mask=A,    return_token_type_ids=A,    add_special_tokens=A,    **A,    )

									if voice_preset is not None:
													lowerCamelCase					:			Dict									= voice_preset

									return encoded_text
