
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     a						(							snake_case__: Dict ):


				'''simple docstring'''
				if "token" in model_name_or_path:
								return "rag_token"
				if "sequence" in model_name_or_path:
								return "rag_sequence"
				if "bart" in model_name_or_path:
								return "bart"
				return None




def     a						(							snake_case__: str						,       snake_case__: Dict						,       snake_case__: str ):


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




def     a						(							snake_case__: Dict						,       snake_case__: Any						,       snake_case__: List[Any] ):


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

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

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

				lowercase_					  = 1_0_0.0 * em / total
				lowercase_					  = 1_0_0.0 * fa / total

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




def     a						(							snake_case__: Optional[int]						,       snake_case__: Any						,       snake_case__: Any ):


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

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

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




def     a						(							snake_case__: List[Any]						,       snake_case__: Union[str, Any]						,       snake_case__: Union[str, Any] ):


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

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

				lowercase_					  = rag_model.rag.question_encoder(snake_case__ )
				lowercase_					  = question_enc_outputs[0]

				lowercase_					  = rag_model.retriever(
				    snake_case__						,       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(snake_case__ ) for title in docs['''title''']]
								provenance_strings.append('''\t'''.join(snake_case__ ) )
				return provenance_strings




def     a						(							snake_case__: Dict						,       snake_case__: Dict						,       snake_case__: int ):


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

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

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

								return answers




def     a						(							):


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

				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(snake_case__						,       args.predictions_path						,       args.gold_data_path )
												continue

								logger.info('''***** Running evaluation for {} *****'''.format(snake_case__ ) )
								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(snake_case__						,       **snake_case__ )
												lowercase_					  = model_class.from_pretrained(snake_case__						,       retriever=snake_case__						,       **snake_case__ )
												model.retriever.init_retrieval()
								else:
												lowercase_					  = model_class.from_pretrained(snake_case__						,       **snake_case__ )
								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(snake_case__ ):
																questions.append(line.strip() )
																if len(snake_case__ ) == args.eval_batch_size:
																				lowercase_					  = evaluate_batch_fn(snake_case__						,       snake_case__						,       snake_case__ )
																				preds_file.write('''\n'''.join(snake_case__ ) + '''\n''' )
																				preds_file.flush()
																				lowercase_					  = []
												if len(snake_case__ ) > 0:
																lowercase_					  = evaluate_batch_fn(snake_case__						,       snake_case__						,       snake_case__ )
																preds_file.write('''\n'''.join(snake_case__ ) )
																preds_file.flush()

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


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






import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__a        = [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-generation',
        'text-classification',
        'token-classification',
        'language-modeling',
        'multiple-choice',
        'question-answering',
        'summarization',
        'translation',
        'image-classification',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
						import run_clm
						import run_image_classification
						import run_mlm
						import run_ner
						import run_qa as run_squad
						import run_summarization
						import run_swag
						import run_text_classification
						import run_translation


logging.basicConfig(level=logging.DEBUG)

__a        = logging.getLogger()




def     a						(							):


				'''simple docstring'''
				lowercase_					  = argparse.ArgumentParser()
				parser.add_argument('''-f''' )
				lowercase_					  = parser.parse_args()
				return args.f




def     a						(							snake_case__: List[Any] ):


				'''simple docstring'''
				lowercase_					  = {}
				lowercase_					  = os.path.join(snake_case__						,       '''all_results.json''' )
				if os.path.exists(snake_case__ ):
								with open(snake_case__						,       '''r''' ) as f:
												lowercase_					  = json.load(snake_case__ )
				else:
								raise ValueError(F'''can\'t find {path}''' )
				return results




def     a						(							):


				'''simple docstring'''
				return bool(tf.config.list_physical_devices('''GPU''' ) )


__a        = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	@skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''	)
	def 						_lowercase  (	self  :      Tuple	)	->					List[Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

					if is_cuda_available():
									testargs.append('''--fp16'''	)

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_text_classification.main()
									# Reset the mixed precision policy so we don't break other tests
									tf.keras.mixed_precision.set_global_policy('''float32'''	)
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''eval_accuracy''']	,							0.75	)
	def 						_lowercase  (	self  :      Optional[Any]	)	->					int:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

					if len(tf.config.list_physical_devices('''GPU'''	)	) > 1:
									# Skipping because there are not enough batches to train the model + would need a drop_last to work.
									return

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_clm.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertLess(result['''eval_perplexity''']	,							1_0_0	)
	def 						_lowercase  (	self  :      Any	)	->					Union[str, Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_mlm.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertLess(result['''eval_perplexity''']	,							4_2	)
	def 						_lowercase  (	self  :      Any	)	->					str:
					# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
					lowercase_					  = 7 if get_gpu_count() > 1 else 2

					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_ner.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''accuracy''']	,							0.75	)
	def 						_lowercase  (	self  :      Any	)	->					Optional[Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_squad.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''f1''']	,							3_0	)
									self.assertGreaterEqual(result['''exact''']	,							3_0	)
	def 						_lowercase  (	self  :      str	)	->					Dict:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_swag.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''val_accuracy''']	,							0.8	)
	@slow
	def 						_lowercase  (	self  :      int	)	->					int:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_summarization.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''rouge1''']	,							1_0	)
									self.assertGreaterEqual(result['''rouge2''']	,							2	)
									self.assertGreaterEqual(result['''rougeL''']	,							7	)
									self.assertGreaterEqual(result['''rougeLsum''']	,							7	)
	@slow
	def 						_lowercase  (	self  :      Dict	)	->					Any:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_translation.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''bleu''']	,							3_0	)




	def 						_lowercase  (	self  :      int	)	->					List[str]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_image_classification.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''accuracy''']	,							0.7	)




