



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__  =			logging.get_logger(__name__)

UpperCAmelCase__  =			{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}





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







			'''simple docstring'''
			for attribute in key.split('.'       ):
						_UpperCAmelCase									=  getattr(_UpperCAmelCase				,  _UpperCAmelCase       )

			if weight_type is not None:
						_UpperCAmelCase									=  getattr(_UpperCAmelCase				,  _UpperCAmelCase       ).shape
			else:
						_UpperCAmelCase									=  hf_pointer.shape

			assert hf_shape == value.shape, (
			    F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
			    F" {value.shape} for {full_name}"
			)

			if weight_type == "weight":
						_UpperCAmelCase									=  value
			elif weight_type == "weight_g":
						_UpperCAmelCase									=  value
			elif weight_type == "weight_v":
						_UpperCAmelCase									=  value
			elif weight_type == "bias":
						_UpperCAmelCase									=  value
			else:
						_UpperCAmelCase									=  value

			logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."       )





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







			'''simple docstring'''
			_UpperCAmelCase									=  []
			_UpperCAmelCase									=  fairseq_model.state_dict()

			_UpperCAmelCase									=  hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						_UpperCAmelCase									=  False
						if "conv_layers" in name:
									load_conv_layer(
									    _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  hf_model.config.feat_extract_norm == 'group'				,  )
									_UpperCAmelCase									=  True
						else:
									for key, mapped_key in MAPPING.items():
												_UpperCAmelCase									=  'sew_d.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

												if key in name or key.split('w2v_model.'       )[-1] == name.split('.'       )[0]:
															_UpperCAmelCase									=  True
															if "*" in mapped_key:
																		_UpperCAmelCase									=  name.split(_UpperCAmelCase       )[0].split('.'       )[-2]
																		if not layer_index.isnumeric():
																					continue
																		_UpperCAmelCase									=  mapped_key.replace('*'				,  _UpperCAmelCase       )
															if "weight_g" in name:
																		_UpperCAmelCase									=  'weight_g'
															elif "weight_v" in name:
																		_UpperCAmelCase									=  'weight_v'
															elif "weight" in name:
																		_UpperCAmelCase									=  'weight'
															elif "bias" in name:
																		_UpperCAmelCase									=  'bias'
															else:
																		_UpperCAmelCase									=  None
															set_recursively(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )
												continue
						if not is_used:
									unused_weights.append(_UpperCAmelCase       )

			logger.warning(F"Unused weights: {unused_weights}"       )





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







			'''simple docstring'''
			_UpperCAmelCase									=  full_name.split('conv_layers.'       )[-1]
			_UpperCAmelCase									=  name.split('.'       )
			_UpperCAmelCase									=  int(items[0]       )
			_UpperCAmelCase									=  int(items[1]       )

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    F"{full_name} has size {value.shape}, but"
									    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
									)
									_UpperCAmelCase									=  value
									logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."       )
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    F"{full_name} has size {value.shape}, but"
									    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
									)
									_UpperCAmelCase									=  value
									logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."       )
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
									    " found."
									)
									_UpperCAmelCase									=  value
									logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    F"{full_name} has size {value.shape}, but"
									    F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
									)
									_UpperCAmelCase									=  value
									logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
			else:
						unused_weights.append(_UpperCAmelCase       )





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







			'''simple docstring'''
			_UpperCAmelCase									=  SEWDConfig()
			if is_finetuned:
						_UpperCAmelCase									=  model.wav_encoder.wav_model.cfg
			else:
						_UpperCAmelCase									=  model.cfg

			_UpperCAmelCase									=  fs_config.conv_bias
			_UpperCAmelCase									=  eval(fs_config.conv_feature_layers       )
			_UpperCAmelCase									=  [x[0] for x in conv_layers]
			_UpperCAmelCase									=  [x[1] for x in conv_layers]
			_UpperCAmelCase									=  [x[2] for x in conv_layers]
			_UpperCAmelCase									=  'gelu'
			_UpperCAmelCase									=  'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
			_UpperCAmelCase									=  0.0
			_UpperCAmelCase									=  fs_config.activation_fn.name
			_UpperCAmelCase									=  fs_config.encoder_embed_dim
			_UpperCAmelCase									=  0.02
			_UpperCAmelCase									=  fs_config.encoder_ffn_embed_dim
			_UpperCAmelCase									=  1E-5
			_UpperCAmelCase									=  fs_config.encoder_layerdrop
			_UpperCAmelCase									=  fs_config.encoder_attention_heads
			_UpperCAmelCase									=  fs_config.conv_pos_groups
			_UpperCAmelCase									=  fs_config.conv_pos
			_UpperCAmelCase									=  len(_UpperCAmelCase       )
			_UpperCAmelCase									=  fs_config.encoder_layers
			_UpperCAmelCase									=  fs_config.squeeze_factor
			# DeBERTa-specific parameters:
			_UpperCAmelCase									=  fs_config.max_position_embeddings
			_UpperCAmelCase									=  fs_config.position_buckets
			_UpperCAmelCase									=  fs_config.share_att_key
			_UpperCAmelCase									=  fs_config.relative_attention
			_UpperCAmelCase									=  fs_config.position_biased_input
			_UpperCAmelCase									=  tuple(fs_config.pos_att_type.split('|'       )       )
			_UpperCAmelCase									=  fs_config.norm_rel_ebd

			# take care of any params that are overridden by the Wav2VecCtc model
			if is_finetuned:
						_UpperCAmelCase									=  model.cfg
						_UpperCAmelCase									=  fs_config.final_dropout
						_UpperCAmelCase									=  fs_config.layerdrop
			_UpperCAmelCase									=  fs_config.activation_dropout
			_UpperCAmelCase									=  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
			_UpperCAmelCase									=  fs_config.attention_dropout
			_UpperCAmelCase									=  fs_config.dropout_input
			_UpperCAmelCase									=  fs_config.dropout
			_UpperCAmelCase									=  fs_config.mask_channel_length
			_UpperCAmelCase									=  fs_config.mask_channel_prob
			_UpperCAmelCase									=  fs_config.mask_length
			_UpperCAmelCase									=  fs_config.mask_prob

			_UpperCAmelCase									=  'Wav2Vec2FeatureExtractor'
			_UpperCAmelCase									=  'Wav2Vec2CTCTokenizer'

			return config





@torch.no_grad()
def 				A      (	_UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  str=None				,  _UpperCAmelCase							:  Any=None				,  _UpperCAmelCase							:  Union[str, Any]=True       )							->					List[Any]:







			'''simple docstring'''

			if is_finetuned:
						_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]				,  arg_overrides={'data': '/'.join(dict_path.split('/'       )[:-1]       )}       )
			else:
						_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

			if config_path is not None:
						_UpperCAmelCase									=  SEWDConfig.from_pretrained(_UpperCAmelCase       )
			else:
						_UpperCAmelCase									=  convert_config(model[0]				,  _UpperCAmelCase       )
			_UpperCAmelCase									=  model[0].eval()

			_UpperCAmelCase									=  True if config.feat_extract_norm == 'layer' else False
			_UpperCAmelCase									=  WavaVecaFeatureExtractor(
			    feature_size=1				,  sampling_rate=16_000				,  padding_value=0				,  do_normalize=_UpperCAmelCase				,  return_attention_mask=_UpperCAmelCase				,  )

			if is_finetuned:
						if dict_path:
									_UpperCAmelCase									=  Dictionary.load(_UpperCAmelCase       )

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									_UpperCAmelCase									=  target_dict.pad_index
									_UpperCAmelCase									=  target_dict.bos_index
									_UpperCAmelCase									=  target_dict.pad_index
									_UpperCAmelCase									=  target_dict.bos_index
									_UpperCAmelCase									=  target_dict.eos_index
									_UpperCAmelCase									=  len(target_dict.symbols       )
									_UpperCAmelCase									=  os.path.join(_UpperCAmelCase				,  'vocab.json'       )
									if not os.path.isdir(_UpperCAmelCase       ):
												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_UpperCAmelCase       )       )
												return
									os.makedirs(_UpperCAmelCase				,  exist_ok=_UpperCAmelCase       )
									with open(_UpperCAmelCase				,  'w'				,  encoding='utf-8'       ) as vocab_handle:
												json.dump(target_dict.indices				,  _UpperCAmelCase       )
									_UpperCAmelCase									=  WavaVecaCTCTokenizer(
									    _UpperCAmelCase				,  unk_token=target_dict.unk_word				,  pad_token=target_dict.pad_word				,  bos_token=target_dict.bos_word				,  eos_token=target_dict.eos_word				,  word_delimiter_token='|'				,  do_lower_case=_UpperCAmelCase				,  )
									_UpperCAmelCase									=  WavaVecaProcessor(feature_extractor=_UpperCAmelCase				,  tokenizer=_UpperCAmelCase       )
									processor.save_pretrained(_UpperCAmelCase       )

						_UpperCAmelCase									=  SEWDForCTC(_UpperCAmelCase       )
			else:
						_UpperCAmelCase									=  SEWDModel(_UpperCAmelCase       )
						feature_extractor.save_pretrained(_UpperCAmelCase       )

			recursively_load_weights(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )

			hf_model.save_pretrained(_UpperCAmelCase       )


if __name__ == "__main__":
							UpperCAmelCase__  =			argparse.ArgumentParser()
							parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
							parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
							parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
							parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
							parser.add_argument(
							    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
							)
							UpperCAmelCase__  =			parser.parse_args()
							convert_sew_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
							)





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							:  Optional[Any]       )							->					str:







			'''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[Any]				,  _UpperCAmelCase							:  List[Any]				,  _UpperCAmelCase							:  Optional[Any]       )							->					Dict:







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





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







			'''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							:  Any				,  _UpperCAmelCase							:  List[Any]				,  _UpperCAmelCase							:  Any       )							->					List[Any]:







			'''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							:  Any				,  _UpperCAmelCase							:  int				,  _UpperCAmelCase							:  Union[str, Any]       )							->					List[Any]:







			'''simple docstring'''
			def strip_title(_UpperCAmelCase							:  List[Any]       ):
						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							:  Any				,  _UpperCAmelCase							:  List[str]				,  _UpperCAmelCase							:  Optional[Any]       )							->					str:







			'''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      (	)							->					Optional[Any]:







			'''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							:  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)
