'''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


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

transformers_logging.set_verbosity_info()



def 							UpperCamelCase__			(			a__      ):


						'''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 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






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



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=[line.strip() for line in open(a__			,      'r'      ).readlines()]
						_lowerCAmelCase										=[]

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

						_lowerCAmelCase										=_lowerCAmelCase										=_lowerCAmelCase										=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__      )

						_lowerCAmelCase										=100.0 * em / total
						_lowerCAmelCase										=100.0 * fa / total

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



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=args.k
						_lowerCAmelCase										=[line.strip() for line in open(a__			,      'r'      ).readlines()]
						_lowerCAmelCase										=[line.strip() for line in open(a__			,      'r'      ).readlines()]

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

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



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						def strip_title(a__      ):
												if title.startswith('"'      ):
																		_lowerCAmelCase										=title[1:]
												if title.endswith('"'      ):
																		_lowerCAmelCase										=title[:-1]
												return title

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

						_lowerCAmelCase										=rag_model.rag.question_encoder(a__      )
						_lowerCAmelCase										=question_enc_outputs[0]

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



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						with torch.no_grad():
												_lowerCAmelCase										=rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
												    a__			,      return_tensors='pt'			,      padding=a__			,      truncation=a__      )

												_lowerCAmelCase										=inputs_dict.input_ids.to(args.device      )
												_lowerCAmelCase										=inputs_dict.attention_mask.to(args.device      )
												_lowerCAmelCase										=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]]			,      )
												_lowerCAmelCase										=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 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=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=5_0			,      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.'			,      )
						_lowerCAmelCase										=parser.parse_args()
						_lowerCAmelCase										=torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
						return args



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






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

						_lowerCAmelCase										=(
						    [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__      )

						_lowerCAmelCase										=get_scores if args.eval_mode == 'e2e' else get_precision_at_k
						_lowerCAmelCase										=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'      ):
																		_lowerCAmelCase										=RagRetriever.from_pretrained(a__			,      **a__      )
																		_lowerCAmelCase										=model_class.from_pretrained(a__			,      retriever=a__			,      **a__      )
																		model.retriever.init_retrieval()
												else:
																		_lowerCAmelCase										=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:
																		_lowerCAmelCase										=[]
																		for line in tqdm(a__      ):
																								questions.append(line.strip()      )
																								if len(a__      ) == args.eval_batch_size:
																														_lowerCAmelCase										=evaluate_batch_fn(a__			,      a__			,      a__      )
																														preds_file.write('\n'.join(a__      ) + '\n'      )
																														preds_file.flush()
																														_lowerCAmelCase										=[]
																		if len(a__      ) > 0:
																								_lowerCAmelCase										=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__":
							lowercase_     		= get_args()
							main(args)






'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=os.path.join(args.tf_model_dir			,      'parameters.json'      )
						_lowerCAmelCase										=json.loads(open(a__      ).read()      )
						if not params:
												raise ValueError(
												    F'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'''      )
						if not args.output.endswith('.pt'      ):
												_lowerCAmelCase										=args.output + '.pt'
						_lowerCAmelCase										=OrderedDict()
						with tf.device('/CPU:0'      ):
												_lowerCAmelCase										=tf.train.load_checkpoint(args.tf_model_dir      )
												_lowerCAmelCase										=reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		_lowerCAmelCase										=reader.get_tensor(a__      ).astype(np.floataa      )
																		if key_name.endswith('/adam_m'      ) or key_name.endswith('/adam_v'      ):
																								continue
																		if key_name.startswith('pasts/'      ):
																								if key_name.startswith('pasts/mlp'      ):
																														_lowerCAmelCase										=int(key_name[9]      )
																								elif key_name.startswith('pasts/out'      ):
																														_lowerCAmelCase										=8
																								_lowerCAmelCase										='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/moe'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/switch_gating/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/softmlp/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/wo/kernel'      ) or key_name.endswith('/wi/kernel'      ):
																														_lowerCAmelCase										=key_name[-9:-7]
																														for i in range(1_6      ):
																																				_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
																																				_lowerCAmelCase										=(
																																				    vnp[i].transpose([1, 0]      ).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/mlp'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/p1/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wi.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p1/bias'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wi.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p2/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wo.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p2/bias'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wo.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/ln'      ):
																								_lowerCAmelCase										=int(key_name[8:].split('/'      )[0]      )
																								if key_name.endswith('/b'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.norm.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/g'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.norm.weight' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/att'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/qkv/kernel'      ):
																														_lowerCAmelCase										=vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														_lowerCAmelCase										=state[:, 0, :, :]
																														_lowerCAmelCase										=state[:, 1, :, :]
																														_lowerCAmelCase										=state[:, 2, :, :]
																														_lowerCAmelCase										=(
																														    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=(
																														    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=(
																														    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/o/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
																														_lowerCAmelCase										=(
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]      ).transpose([1, 0]      ).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/an'      ):
																								_lowerCAmelCase										=int(key_name[8:].split('/'      )[0]      )
																								if key_name.endswith('/b'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.norm.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/g'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.norm.weight' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif (
																		    key_name.startswith('model/wte'      )
																		    or key_name.startswith('model/wpe'      )
																		    or key_name.startswith('model/ete'      )
																		):
																								_lowerCAmelCase										={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
																								    key_name[-3:]
																								]
																								_lowerCAmelCase										='model.%s.weight' % nlayer
																								_lowerCAmelCase										=vnp.copy()  # same in embedded
																								_lowerCAmelCase										=torch.tensor(a__      )
																								if key_name.startswith('model/wte'      ):
																														_lowerCAmelCase										='lm_head.weight'
																														_lowerCAmelCase										=vnp.copy()  # same in embedded
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/wob'      ):
																								_lowerCAmelCase										='final_logits_bias'
																								_lowerCAmelCase										=vnp.copy()  # same in embedded
																								_lowerCAmelCase										=state.reshape((1, -1)      )
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name == "model/dense/kernel":
																								_lowerCAmelCase										='model.last_project.weight'
																								_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name == "model/dense_1/bias":
																								_lowerCAmelCase										='model.last_project.bias'
																								_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																								_lowerCAmelCase										=torch.tensor(a__      )
						torch.save(a__			,      args.output      )


if __name__ == "__main__":
							lowercase_     		= argparse.ArgumentParser(
							    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
							)
							parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
							parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
							lowercase_     		= parser.parse_args()
							convert_tf_gptsan_to_pt(args)





