code: 
  
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  import unittest
  import warnings
  
  from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
  from transformers.utils import cached_property
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
   import tensorflow as tf
  
   from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel
  
  
  
  
  
  
  @require_tf
  class 			UpperCAmelCase_						:
        __SCREAMING_SNAKE_CASE		:     List[Any]				  =				MarianConfig
        __SCREAMING_SNAKE_CASE		:     Dict				  =				{}
        __SCREAMING_SNAKE_CASE		:     List[str]				  =				'gelu'
  
  
  
        def __init__(  self  :			Tuple  ,			A  :			int  ,			A  :			Dict=1_3  ,			A  :			Union[str, Any]=7  ,			A  :			Tuple=True  ,			A  :			str=False  ,			A  :			Tuple=9_9  ,			A  :			List[str]=3_2  ,			A  :			int=2  ,			A  :			Union[str, Any]=4  ,			A  :			List[Any]=3_7  ,			A  :			List[Any]=0.1  ,			A  :			str=0.1  ,			A  :			str=2_0  ,			A  :			Optional[Any]=2  ,			A  :			int=1  ,			A  :			Optional[Any]=0  ,			):
             _UpperCAmelCase    :			Union[str, Any]              =					parent
             _UpperCAmelCase    :			int              =					batch_size
             _UpperCAmelCase    :			int              =					seq_length
             _UpperCAmelCase    :			Any              =					is_training
             _UpperCAmelCase    :			Dict              =					use_labels
             _UpperCAmelCase    :			List[Any]              =					vocab_size
             _UpperCAmelCase    :			Any              =					hidden_size
             _UpperCAmelCase    :			int              =					num_hidden_layers
             _UpperCAmelCase    :			Optional[Any]              =					num_attention_heads
             _UpperCAmelCase    :			str              =					intermediate_size
  
             _UpperCAmelCase    :			Tuple              =					hidden_dropout_prob
             _UpperCAmelCase    :			List[Any]              =					attention_probs_dropout_prob
             _UpperCAmelCase    :			Dict              =					max_position_embeddings
             _UpperCAmelCase    :			Union[str, Any]              =					eos_token_id
             _UpperCAmelCase    :			List[str]              =					pad_token_id
             _UpperCAmelCase    :			List[str]              =					bos_token_id
  
  
  
        def 		snake_case_      (  self  :			Tuple		):
             _UpperCAmelCase    :			List[str]              =					ids_tensor([self.batch_size, self.seq_length - 1]  ,			self.vocab_size		)
             _UpperCAmelCase    :			List[str]              =					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)  ,			1		)
             _UpperCAmelCase    :			str              =					tf.concat([input_ids, eos_tensor]  ,			axis=1		)
  
             _UpperCAmelCase    :			Tuple              =					ids_tensor([self.batch_size, self.seq_length]  ,			self.vocab_size		)
  
             _UpperCAmelCase    :			str              =					self.config_cls(
                 vocab_size=self.vocab_size  ,			d_model=self.hidden_size  ,			encoder_layers=self.num_hidden_layers  ,			decoder_layers=self.num_hidden_layers  ,			encoder_attention_heads=self.num_attention_heads  ,			decoder_attention_heads=self.num_attention_heads  ,			encoder_ffn_dim=self.intermediate_size  ,			decoder_ffn_dim=self.intermediate_size  ,			dropout=self.hidden_dropout_prob  ,			attention_dropout=self.attention_probs_dropout_prob  ,			max_position_embeddings=self.max_position_embeddings  ,			eos_token_ids=[2]  ,			bos_token_id=self.bos_token_id  ,			pad_token_id=self.pad_token_id  ,			decoder_start_token_id=self.pad_token_id  ,			**self.config_updates  ,			)
             _UpperCAmelCase    :			Optional[Any]              =					prepare_marian_inputs_dict(lowercase_  ,			lowercase_  ,			lowercase_		)
             return config, inputs_dict
  
  
  
        def 		snake_case_      (  self  :			Union[str, Any]  ,			A  :			Dict  ,			A  :			Optional[Any]		):
             _UpperCAmelCase    :			Any              =					TFMarianModel(config=lowercase_		).get_decoder()
             _UpperCAmelCase    :			int              =					inputs_dict['''input_ids''']
  
             _UpperCAmelCase    :			Optional[Any]              =					input_ids[:1, :]
             _UpperCAmelCase    :			int              =					inputs_dict['''attention_mask'''][:1, :]
             _UpperCAmelCase    :			Tuple              =					inputs_dict['''head_mask''']
             _UpperCAmelCase    :			List[Any]              =					1
  
             # first forward pass
             _UpperCAmelCase    :			int              =					model(lowercase_  ,			attention_mask=lowercase_  ,			head_mask=lowercase_  ,			use_cache=lowercase_		)
  
             _UpperCAmelCase    :			Optional[int]              =					outputs.to_tuple()
  
             # create hypothetical next token and extent to next_input_ids
             _UpperCAmelCase    :			Union[str, Any]              =					ids_tensor((self.batch_size, 3)  ,			config.vocab_size		)
             _UpperCAmelCase    :			Union[str, Any]              =					tf.cast(ids_tensor((self.batch_size, 3)  ,			2		)  ,			tf.inta		)
  
             # append to next input_ids and
             _UpperCAmelCase    :			Dict              =					tf.concat([input_ids, next_tokens]  ,			axis=-1		)
             _UpperCAmelCase    :			Optional[int]              =					tf.concat([attention_mask, next_attn_mask]  ,			axis=-1		)
  
             _UpperCAmelCase    :			List[Any]              =					model(lowercase_  ,			attention_mask=lowercase_		)[0]
             _UpperCAmelCase    :			str              =					model(lowercase_  ,			attention_mask=lowercase_  ,			past_key_values=lowercase_		)[0]
  
             self.parent.assertEqual(next_tokens.shape[1]  ,			output_from_past.shape[1]		)
  
             # select random slice
             _UpperCAmelCase    :			int              =					int(ids_tensor((1,)  ,			output_from_past.shape[-1]		)		)
             _UpperCAmelCase    :			Union[str, Any]              =					output_from_no_past[:, -3:, random_slice_idx]
             _UpperCAmelCase    :			str              =					output_from_past[:, :, random_slice_idx]
  
             # test that outputs are equal for slice
             tf.debugging.assert_near(lowercase_  ,			lowercase_  ,			rtol=1e-3		)
  
  
  
  
  
  
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Any		,						SCREAMING_SNAKE_CASE__			:		Optional[int]		,						SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		List[Any]=None		,						SCREAMING_SNAKE_CASE__			:		Union[str, Any]=None		,						SCREAMING_SNAKE_CASE__			:		Optional[int]=None		,						SCREAMING_SNAKE_CASE__			:		Tuple=None		,						SCREAMING_SNAKE_CASE__			:		Optional[int]=None		,						)						->     Optional[int]:
  
  
  
  
  
       '''simple docstring'''
  
       if attention_mask is None:
            _UpperCAmelCase    :			str              =					tf.cast(tf.math.not_equal(lowercase_		,						config.pad_token_id       )		,						tf.inta       )
       if decoder_attention_mask is None:
            _UpperCAmelCase    :			Any              =					tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape		,						dtype=tf.inta       ),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,						config.pad_token_id       )		,						tf.inta       ),
                ]		,						axis=-1		,						)
       if head_mask is None:
            _UpperCAmelCase    :			List[Any]              =					tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
       if decoder_head_mask is None:
            _UpperCAmelCase    :			Union[str, Any]              =					tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
       if cross_attn_head_mask is None:
            _UpperCAmelCase    :			List[Any]              =					tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }
  
  
  
  
  
  
  @require_tf
  class 			UpperCAmelCase_						(       __UpperCAmelCase ,    __UpperCAmelCase ,    unittest.TestCase		):
        __SCREAMING_SNAKE_CASE		:     List[Any]				  =				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
        __SCREAMING_SNAKE_CASE		:     Dict				  =				(TFMarianMTModel,) if is_tf_available() else ()
        __SCREAMING_SNAKE_CASE		:     str				  =				(
            {
                'conversational': TFMarianMTModel,
                'feature-extraction': TFMarianModel,
                'summarization': TFMarianMTModel,
                'text2text-generation': TFMarianMTModel,
                'translation': TFMarianMTModel,
            }
            if is_tf_available()
            else {}
        )
        __SCREAMING_SNAKE_CASE		:     Optional[int]				  =				True
        __SCREAMING_SNAKE_CASE		:     Optional[Any]				  =				False
        __SCREAMING_SNAKE_CASE		:     Union[str, Any]				  =				False
  
  
  
        def 		snake_case_      (  self  :			int		):
             _UpperCAmelCase    :			Union[str, Any]              =					TFMarianModelTester(self		)
             _UpperCAmelCase    :			List[Any]              =					ConfigTester(self  ,			config_class=lowercase_		)
  
  
  
        def 		snake_case_      (  self  :			Optional[int]		):
             self.config_tester.run_common_tests()
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]		):
             _UpperCAmelCase    :			Optional[int]              =					self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*lowercase_		)
  
  
  
  
  
  
  
  @require_tf
  class 			UpperCAmelCase_						(       unittest.TestCase		):
        __SCREAMING_SNAKE_CASE		:     Optional[int]				  =				1_0_0_0  # show more chars for failing integration tests
  
  
  
        @classmethod
        def 		snake_case_      (  cls  :			Optional[int]		):
             _UpperCAmelCase    :			str              =					f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
             return cls
  
  
  
        @cached_property
        def 		snake_case_      (  self  :			Tuple		):
             return AutoTokenizer.from_pretrained(self.model_name		)
  
  
  
        @property
        def 		snake_case_      (  self  :			int		):
             return self.tokenizer.eos_token_id
  
  
  
        @cached_property
        def 		snake_case_      (  self  :			str		):
             warnings.simplefilter("error"		)
             _UpperCAmelCase    :			TFMarianMTModel              =					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
             assert isinstance(lowercase_  ,			lowercase_		)
             _UpperCAmelCase    :			Dict              =					model.config
             self.assertListEqual(c.bad_words_ids  ,			[[c.pad_token_id]]		)
             self.assertEqual(c.max_length  ,			5_1_2		)
             self.assertEqual(c.decoder_start_token_id  ,			c.pad_token_id		)
             return model
  
  
  
        def 		snake_case_      (  self  :			Dict  ,			**A  :			int		):
             _UpperCAmelCase    :			Dict              =					self.translate_src_text(**lowercase_		)
             self.assertListEqual(self.expected_text  ,			lowercase_		)
  
  
  
        def 		snake_case_      (  self  :			List[Any]  ,			**A  :			Dict		):
             _UpperCAmelCase    :			Any              =					self.tokenizer(self.src_text  ,			**lowercase_  ,			padding=lowercase_  ,			return_tensors="tf"		)
             _UpperCAmelCase    :			str              =					self.model.generate(
                 model_inputs.input_ids  ,			attention_mask=model_inputs.attention_mask  ,			num_beams=2  ,			max_length=1_2_8		)
             _UpperCAmelCase    :			str              =					self.tokenizer.batch_decode(generated_ids.numpy()  ,			skip_special_tokens=lowercase_		)
             return generated_words
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 			UpperCAmelCase_						(       __UpperCAmelCase		):
        __SCREAMING_SNAKE_CASE		:     Optional[int]				  =				'mt'
        __SCREAMING_SNAKE_CASE		:     Tuple				  =				'en'
        __SCREAMING_SNAKE_CASE		:     Union[str, Any]				  =				['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
        __SCREAMING_SNAKE_CASE		:     int				  =				['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']
  
  
  
        @unittest.skip("Skipping until #12647 is resolved."		)
        @slow
        def 		snake_case_      (  self  :			List[Any]		):
             self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 			UpperCAmelCase_						(       __UpperCAmelCase		):
        __SCREAMING_SNAKE_CASE		:     List[Any]				  =				'en'
        __SCREAMING_SNAKE_CASE		:     Dict				  =				'zh'
        __SCREAMING_SNAKE_CASE		:     List[Any]				  =				['My name is Wolfgang and I live in Berlin']
        __SCREAMING_SNAKE_CASE		:     List[str]				  =				['我叫沃尔夫冈 我住在柏林']
  
  
  
        @unittest.skip("Skipping until #12647 is resolved."		)
        @slow
        def 		snake_case_      (  self  :			Tuple		):
             self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 			UpperCAmelCase_						(       __UpperCAmelCase		):
        __SCREAMING_SNAKE_CASE		:     Tuple				  =				'en'
        __SCREAMING_SNAKE_CASE		:     List[str]				  =				'ROMANCE'
        __SCREAMING_SNAKE_CASE		:     Tuple				  =				[
            '>>fr<< Don\'t spend so much time watching TV.',
            '>>pt<< Your message has been sent.',
            '>>es<< He\'s two years older than me.',
        ]
        __SCREAMING_SNAKE_CASE		:     Any				  =				[
            'Ne passez pas autant de temps à regarder la télé.',
            'A sua mensagem foi enviada.',
            'Es dos años más viejo que yo.',
        ]
  
  
  
        @unittest.skip("Skipping until #12647 is resolved."		)
        @slow
        def 		snake_case_      (  self  :			int		):
             self._assert_generated_batch_equal_expected()
  
  
  
        @unittest.skip("Skipping until #12647 is resolved."		)
        @slow
        def 		snake_case_      (  self  :			str		):
             _UpperCAmelCase    :			Dict              =					TranslationPipeline(self.model  ,			self.tokenizer  ,			framework="tf"		)
             _UpperCAmelCase    :			Optional[Any]              =					pipeline(self.src_text		)
             self.assertEqual(self.expected_text  ,			[x["translation_text"] for x in output]		)
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]       )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       def remove_articles(SCREAMING_SNAKE_CASE__			:		Tuple       ):
            return re.sub(R"\b(a|an|the)\b"		,						" "		,						SCREAMING_SNAKE_CASE__       )
  
       def white_space_fix(SCREAMING_SNAKE_CASE__			:		Any       ):
            return " ".join(text.split()       )
  
       def remove_punc(SCREAMING_SNAKE_CASE__			:		Optional[int]       ):
            _UpperCAmelCase    :			List[str]              =					set(string.punctuation       )
            return "".join(ch for ch in text if ch not in exclude       )
  
       def lower(SCREAMING_SNAKE_CASE__			:		List[str]       ):
            return text.lower()
  
       return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__       )       )       )       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		str       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			List[Any]              =					normalize_answer(SCREAMING_SNAKE_CASE__       ).split()
       _UpperCAmelCase    :			Tuple              =					normalize_answer(SCREAMING_SNAKE_CASE__       ).split()
       _UpperCAmelCase    :			List[Any]              =					Counter(SCREAMING_SNAKE_CASE__       ) & Counter(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			List[str]              =					sum(common.values()       )
       if num_same == 0:
            return 0
       _UpperCAmelCase    :			Union[str, Any]              =					1.0 * num_same / len(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			Union[str, Any]              =					1.0 * num_same / len(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			Union[str, Any]              =					(2 * precision * recall) / (precision + recall)
       return fa
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		Dict       )						->     str:
  
  
  
  
  
       '''simple docstring'''
  
       return normalize_answer(SCREAMING_SNAKE_CASE__       ) == normalize_answer(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		Dict		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]       )						->     str:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			int              =					[]
       for ground_truth in ground_truths:
            _UpperCAmelCase    :			Dict              =					metric_fn(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
            scores_for_ground_truths.append(SCREAMING_SNAKE_CASE__       )
       return max(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Tuple:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			str              =					0
       _UpperCAmelCase    :			int              =					[]
       for passage in dataset:
            for qa in passage["qas"]:
                 total += 1
                 if qa["id"] not in predictions:
                      _UpperCAmelCase    :			List[Any]              =					f'Unanswered question {qa["id"]} will receive score 0.'
                      print(SCREAMING_SNAKE_CASE__		,						file=sys.stderr       )
                      continue
  
                 _UpperCAmelCase    :			Union[str, Any]              =					[x["text"] for x in qa["answers"]]
                 _UpperCAmelCase    :			Optional[int]              =					predictions[qa["id"]]
  
                 _UpperCAmelCase    :			int              =					metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
                 if int(_exact_match       ) == 1:
                      correct_ids.append(qa["id"]       )
                 exact_match += _exact_match
  
                 fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
  
       _UpperCAmelCase    :			List[str]              =					exact_match / total
       _UpperCAmelCase    :			str              =					fa / total
  
       return {"exact_match": exact_match, "f1": fa}, correct_ids
  
  
  if __name__ == "__main__":
   _lowerCAmelCase       :      Any							=							"1.0"
   _lowerCAmelCase       :      Any							=							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.")
   _lowerCAmelCase       :      Tuple							=							parser.parse_args()
  
   with open(args.data_file) as data_file:
    _lowerCAmelCase       :      int							=							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,
     )
    _lowerCAmelCase       :      List[str]							=							dataset_json["data"]
  
   with open(args.pred_file) as pred_file:
    _lowerCAmelCase       :      str							=							json.load(pred_file)
  
   _lowerCAmelCase,		_lowerCAmelCase       :      List[Any]							=							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)
  