




"""simple docstring"""





import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY

@is_pipeline_test
class  __lowercase    (     unittest.TestCase		):


				'''simple docstring'''


				_lowerCAmelCase		     =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				_lowerCAmelCase		     =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

				def  _lowerCamelCase   (				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ):
										if isinstance(model.config ,SCREAMING_SNAKE_CASE_ ):
																snake_case__    :   Tuple							     =   list(tokenizer.lang_code_to_id.keys() )[:2]
																snake_case__    :   int							     =   TranslationPipeline(model=SCREAMING_SNAKE_CASE_ ,tokenizer=SCREAMING_SNAKE_CASE_ ,src_lang=SCREAMING_SNAKE_CASE_ ,tgt_lang=SCREAMING_SNAKE_CASE_ )
										else:
																snake_case__    :   Tuple							     =   TranslationPipeline(model=SCREAMING_SNAKE_CASE_ ,tokenizer=SCREAMING_SNAKE_CASE_ )
										return translator, ["Some string", "Some other text"]

				def  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       str ):
										snake_case__    :   str							     =   translator('''Some string''' )
										self.assertEqual(SCREAMING_SNAKE_CASE_ ,[{'''translation_text''': ANY(SCREAMING_SNAKE_CASE_ )}] )

										snake_case__    :   Optional[Any]							     =   translator(['''Some string'''] )
										self.assertEqual(SCREAMING_SNAKE_CASE_ ,[{'''translation_text''': ANY(SCREAMING_SNAKE_CASE_ )}] )

										snake_case__    :   Optional[int]							     =   translator(['''Some string''', '''other string'''] )
										self.assertEqual(SCREAMING_SNAKE_CASE_ ,[{'''translation_text''': ANY(SCREAMING_SNAKE_CASE_ )}, {'''translation_text''': ANY(SCREAMING_SNAKE_CASE_ )}] )

				@require_torch
				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
										snake_case__    :   List[str]							     =   pipeline('''translation_en_to_ro''' ,model='''patrickvonplaten/t5-tiny-random''' ,framework='''pt''' )
										snake_case__    :   Tuple							     =   translator('''This is a test string''' ,max_length=2_0 )
										self.assertEqual(
										    SCREAMING_SNAKE_CASE_ ,[
										        {
										            '''translation_text''': (
										                '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
										                ''' Beide Beide'''
										            )
										        }
										    ] ,)

				@require_tf
				def  _lowerCamelCase   (				self				:       List[str] ):
										snake_case__    :   Dict							     =   pipeline('''translation_en_to_ro''' ,model='''patrickvonplaten/t5-tiny-random''' ,framework='''tf''' )
										snake_case__    :   Optional[Any]							     =   translator('''This is a test string''' ,max_length=2_0 )
										self.assertEqual(
										    SCREAMING_SNAKE_CASE_ ,[
										        {
										            '''translation_text''': (
										                '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
										                ''' Beide Beide'''
										            )
										        }
										    ] ,)

				@require_torch
				def  _lowerCamelCase   (				self				:       int ):
										snake_case__    :   List[str]							     =   pipeline('''translation_en_to_de''' ,model='''patrickvonplaten/t5-tiny-random''' ,framework='''pt''' )
										snake_case__    :   int							     =   translator('''This is a test string''' ,max_length=2_0 )
										self.assertEqual(
										    SCREAMING_SNAKE_CASE_ ,[
										        {
										            '''translation_text''': (
										                '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
										                ''' urine urine urine urine urine urine urine'''
										            )
										        }
										    ] ,)





				@require_tf
				def  _lowerCamelCase   (				self				:       Any ):
										snake_case__    :   Tuple							     =   pipeline('''translation_en_to_de''' ,model='''patrickvonplaten/t5-tiny-random''' ,framework='''tf''' )
										snake_case__    :   Optional[Any]							     =   translator('''This is a test string''' ,max_length=2_0 )
										self.assertEqual(
										    SCREAMING_SNAKE_CASE_ ,[
										        {
										            '''translation_text''': (
										                '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
										                ''' urine urine urine urine urine urine urine'''
										            )
										        }
										    ] ,)







class  __lowercase    (     unittest.TestCase		):


				'''simple docstring'''


				@require_torch
				@slow
				def  _lowerCamelCase   (				self				:       Optional[Any] ):
										# We don't provide a default for this pair
										with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
																pipeline(task='''translation_cn_to_ar''' )

										# but we do for this one
										snake_case__    :   Dict							     =   pipeline(task='''translation_en_to_de''' )
										self.assertEqual(translator._preprocess_params['''src_lang'''] ,'''en''' )
										self.assertEqual(translator._preprocess_params['''tgt_lang'''] ,'''de''' )

				@require_torch
				@slow
				def  _lowerCamelCase   (				self				:       List[str] ):
										snake_case__    :   Optional[int]							     =   MBartForConditionalGeneration.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt''' )
										snake_case__    :   str							     =   MBartaaTokenizerFast.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt''' )

										snake_case__    :   Optional[int]							     =   pipeline(task='''translation''' ,model=SCREAMING_SNAKE_CASE_ ,tokenizer=SCREAMING_SNAKE_CASE_ )
										# Missing src_lang, tgt_lang
										with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
																translator('''This is a test''' )

										snake_case__    :   Optional[int]							     =   translator('''This is a test''' ,src_lang='''en_XX''' ,tgt_lang='''ar_AR''' )
										self.assertEqual(SCREAMING_SNAKE_CASE_ ,[{'''translation_text''': '''هذا إختبار'''}] )

										snake_case__    :   List[str]							     =   translator('''This is a test''' ,src_lang='''en_XX''' ,tgt_lang='''hi_IN''' )
										self.assertEqual(SCREAMING_SNAKE_CASE_ ,[{'''translation_text''': '''यह एक परीक्षण है'''}] )

										# src_lang, tgt_lang can be defined at pipeline call time
										snake_case__    :   int							     =   pipeline(task='''translation''' ,model=SCREAMING_SNAKE_CASE_ ,tokenizer=SCREAMING_SNAKE_CASE_ ,src_lang='''en_XX''' ,tgt_lang='''ar_AR''' )
										snake_case__    :   Union[str, Any]							     =   translator('''This is a test''' )
										self.assertEqual(SCREAMING_SNAKE_CASE_ ,[{'''translation_text''': '''هذا إختبار'''}] )

				@require_torch
				def  _lowerCamelCase   (				self				:       Optional[Any] ):
										snake_case__    :   Optional[int]							     =   '''patrickvonplaten/t5-tiny-random'''
										snake_case__    :   Optional[Any]							     =   pipeline(task='''translation_cn_to_ar''' ,model=SCREAMING_SNAKE_CASE_ )
										self.assertEqual(translator._preprocess_params['''src_lang'''] ,'''cn''' )
										self.assertEqual(translator._preprocess_params['''tgt_lang'''] ,'''ar''' )

				@require_torch
				def  _lowerCamelCase   (				self				:       List[Any] ):
										snake_case__    :   Optional[Any]							     =   '''patrickvonplaten/t5-tiny-random'''
										with pytest.warns(SCREAMING_SNAKE_CASE_ ,match=r'''.*translation_en_to_de.*''' ):
																snake_case__    :   List[Any]							     =   pipeline(task='''translation''' ,model=SCREAMING_SNAKE_CASE_ )
										self.assertEqual(translator.task ,'''translation_en_to_de''' )
										self.assertEqual(translator._preprocess_params['''src_lang'''] ,'''en''' )
										self.assertEqual(translator._preprocess_params['''tgt_lang'''] ,'''de''' )





				@require_torch
				def  _lowerCamelCase   (				self				:       Optional[int] ):
										with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
																pipeline(task='''translation''' )






"""simple docstring"""





import argparse
import json
import re
import string
import sys
from collections import Counter

def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						def remove_articles(lowerCamelCase						):
												return re.sub(R'''\b(a|an|the)\b''' ,     ''' ''' ,     lowerCamelCase						)

						def white_space_fix(lowerCamelCase						):
												return " ".join(text.split()						)

						def remove_punc(lowerCamelCase						):
												snake_case__    :   Tuple							     =   set(string.punctuation						)
												return "".join(ch for ch in text if ch not in exclude						)

						def lower(lowerCamelCase						):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase						)						)						)						)

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Dict							     =   normalize_answer(lowerCamelCase						).split()
						snake_case__    :   Dict							     =   normalize_answer(lowerCamelCase						).split()
						snake_case__    :   List[Any]							     =   Counter(lowerCamelCase						) & Counter(lowerCamelCase						)
						snake_case__    :   List[str]							     =   sum(common.values()						)
						if num_same == 0:
												return 0
						snake_case__    :   int							     =   1.0 * num_same / len(lowerCamelCase						)
						snake_case__    :   str							     =   1.0 * num_same / len(lowerCamelCase						)
						snake_case__    :   Union[str, Any]							     =   (2 * precision * recall) / (precision + recall)
						return fa

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						return normalize_answer(lowerCamelCase						) == normalize_answer(lowerCamelCase						)

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   int							     =   []
						for ground_truth in ground_truths:
												snake_case__    :   Optional[Any]							     =   metric_fn(lowerCamelCase ,     lowerCamelCase						)
												scores_for_ground_truths.append(lowerCamelCase						)
						return max(lowerCamelCase						)

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Union[str, Any]							     =   0
						snake_case__    :   Any							     =   []
						for passage in dataset:
												for qa in passage["qas"]:
																		total += 1
																		if qa["id"] not in predictions:
																								snake_case__    :   int							     =   f'''Unanswered question {qa['id']} will receive score 0.'''
																								print(lowerCamelCase ,     file=sys.stderr						)
																								continue

																		snake_case__    :   Any							     =   [x['''text'''] for x in qa['''answers''']]
																		snake_case__    :   Dict							     =   predictions[qa['''id''']]

																		snake_case__    :   Union[str, Any]							     =   metric_max_over_ground_truths(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)
																		if int(_exact_match						) == 1:
																								correct_ids.append(qa['''id''']						)
																		exact_match += _exact_match

																		fa += metric_max_over_ground_truths(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)

						snake_case__    :   str							     =   exact_match / total
						snake_case__    :   Optional[int]							     =   fa / total

						return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
							_lowercase		 =      """1.0"""
							_lowercase		 =      argparse.ArgumentParser("""Official evaluation script for ReCoRD v1.0.""")
							parser.add_argument("""data_file""", help="""The dataset file in JSON format.""")
							parser.add_argument("""pred_file""", help="""The model prediction file in JSON format.""")
							parser.add_argument("""--output_correct_ids""", action="""store_true""", help="""Output the correctly answered query IDs.""")
							_lowercase		 =      parser.parse_args()

							with open(args.data_file) as data_file:
														_lowercase		 =      json.load(data_file)
														if dataset_json["version"] != expected_version:
																					print(
																					    f"""Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json["version"]}""",
																					    file=sys.stderr,
																					)
														_lowercase		 =      dataset_json["""data"""]

							with open(args.pred_file) as pred_file:
														_lowercase		 =      json.load(pred_file)

							_lowercase ,_lowercase		 =      evaluate(dataset, predictions)

							if args.output_correct_ids:
														print(f"""Output {len(correct_ids)} correctly answered question IDs.""")
														with open("""correct_ids.json""", """w""") as f:
																					json.dump(correct_ids, f)


