


'''simple docstring'''

import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

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


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


lowerCamelCase							:       Optional[int]  						=     logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

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






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A	)	->	Tuple:






       """simple docstring"""







       lowercase__				      =			[line.strip() for line in open(A  ,			'''r'''	).readlines()]
       lowercase__				      =			[]

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

       lowercase__				      =			lowercase__				      =			lowercase__				      =			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	)

       lowercase__				      =			100.0 * em / total
       lowercase__				      =			100.0 * fa / total

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

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






       """simple docstring"""







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

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

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

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






       """simple docstring"""







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

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

       lowercase__				      =			rag_model.rag.question_encoder(A	)
       lowercase__				      =			question_enc_outputs[0]

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

def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A	)	->	Dict:






       """simple docstring"""







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

              lowercase__				      =			inputs_dict.input_ids.to(args.device	)
              lowercase__				      =			inputs_dict.attention_mask.to(args.device	)
              lowercase__				      =			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]]  ,			)
              lowercase__				      =			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 _SCREAMING_SNAKE_CASE				()	->	int:






       """simple docstring"""







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

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

def _SCREAMING_SNAKE_CASE				(A	)	->	Any:






       """simple docstring"""







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

       lowercase__				      =			(
           [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	)

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

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


if __name__ == "__main__":
    lowerCamelCase							:       str  						=     get_args()
    main(args)








'''simple docstring'''

import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase							:       Dict  						=     logging.get_logger(__name__)

def _SCREAMING_SNAKE_CASE				(A	)	->	Optional[Any]:






       """simple docstring"""







       lowercase__				      =			MobileNetVaConfig(layer_norm_eps=0.001	)

       if "quant" in model_name:
              raise ValueError('''Quantized models are not supported.'''	)

       lowercase__				      =			re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''  ,			A	)
       if matches:
              lowercase__				      =			float(matches[1]	)
              lowercase__				      =			int(matches[2]	)

       if model_name.startswith('''deeplabv3_'''	):
              lowercase__				      =			8
              lowercase__				      =			21
              lowercase__				      =			'''pascal-voc-id2label.json'''
       else:
              # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
              # of the usual 1000. The first class (index 0) is "background".
              lowercase__				      =			1_001
              lowercase__				      =			'''imagenet-1k-id2label.json'''

       lowercase__				      =			'''huggingface/label-files'''
       lowercase__				      =			json.load(open(hf_hub_download(A  ,			A  ,			repo_type='''dataset'''	)  ,			'''r'''	)	)

       if config.num_labels == 1_001:
              lowercase__				      =			{int(A	) + 1: v for k, v in idalabel.items()}
              lowercase__				      =			'''background'''
       else:
              lowercase__				      =			{int(A	): v for k, v in idalabel.items()}

       lowercase__				      =			idalabel
       lowercase__				      =			{v: k for k, v in idalabel.items()}

       return config

def _SCREAMING_SNAKE_CASE				()	->	List[Any]:






       """simple docstring"""







       lowercase__				      =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       lowercase__				      =			Image.open(requests.get(A  ,			stream=A	).raw	)
       return im

@torch.no_grad()
def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A=False	)	->	Any:






       """simple docstring"""







       lowercase__				      =			get_mobilenet_va_config(A	)

       # Load 🤗 model
       if model_name.startswith('''deeplabv3_'''	):
              lowercase__				      =			MobileNetVaForSemanticSegmentation(A	).eval()
       else:
              lowercase__				      =			MobileNetVaForImageClassification(A	).eval()

       # Load weights from TensorFlow checkpoint
       load_tf_weights_in_mobilenet_va(A  ,			A  ,			A	)

       # Check outputs on an image, prepared by MobileNetV2ImageProcessor
       lowercase__				      =			MobileNetVaImageProcessor(
           crop_size={'''width''': config.image_size, '''height''': config.image_size}  ,			size={'''shortest_edge''': config.image_size + 32}  ,			)
       lowercase__				      =			image_processor(images=prepare_img()  ,			return_tensors='''pt'''	)
       lowercase__				      =			model(**A	)
       lowercase__				      =			outputs.logits

       if model_name.startswith('''deeplabv3_'''	):
              assert logits.shape == (1, 21, 65, 65)

              if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                     lowercase__				      =			torch.tensor(
                         [
                             [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
                             [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
                             [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
                         ]	)

              else:
                     raise ValueError(f"Unknown model name: {model_name}"	)

              assert torch.allclose(logits[0, :3, :3, :3]  ,			A  ,			atol=1E-4	)
       else:
              assert logits.shape == (1, 1_001)

              if model_name == "mobilenet_v2_1.4_224":
                     lowercase__				      =			torch.tensor([0.0_181, -1.0_015, 0.4_688]	)
              elif model_name == "mobilenet_v2_1.0_224":
                     lowercase__				      =			torch.tensor([0.2_445, -1.1_993, 0.1_905]	)
              elif model_name == "mobilenet_v2_0.75_160":
                     lowercase__				      =			torch.tensor([0.2_482, 0.4_136, 0.6_669]	)
              elif model_name == "mobilenet_v2_0.35_96":
                     lowercase__				      =			torch.tensor([0.1_451, -0.4_624, 0.7_192]	)
              else:
                     lowercase__				      =			None

              if expected_logits is not None:
                     assert torch.allclose(logits[0, :3]  ,			A  ,			atol=1E-4	)

       Path(A	).mkdir(exist_ok=A	)
       print(f"Saving model {model_name} to {pytorch_dump_folder_path}"	)
       model.save_pretrained(A	)
       print(f"Saving image processor to {pytorch_dump_folder_path}"	)
       image_processor.save_pretrained(A	)

       if push_to_hub:
              print('''Pushing to the hub...'''	)
              lowercase__				      =			'''google/''' + model_name
              image_processor.push_to_hub(A	)
              model.push_to_hub(A	)


if __name__ == "__main__":
 lowerCamelCase							:       int  						=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--model_name',
     default='mobilenet_v2_1.0_224',
     type=str,
     help='Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<depth>_<size>\'.',
 )
 parser.add_argument(
     '--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
 )
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
 )

 lowerCamelCase							:       int  						=     parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )




