

"""simple docstring"""




from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
						import datasets





class       lowerCamelCase  (UpperCAmelCase__	):

			@slow
			@require_torch
			def     SCREAMING_SNAKE_CASE (    self :  Tuple				)      ->			List[Any]:
						SCREAMING_SNAKE_CASE__									=					EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""			, """prajjwal1/bert-tiny"""				)
						SCREAMING_SNAKE_CASE__									=					BertTokenizer.from_pretrained("""bert-base-uncased"""				)

						SCREAMING_SNAKE_CASE__									=					bertabert.config.encoder.vocab_size
						SCREAMING_SNAKE_CASE__									=					tokenizer.sep_token_id
						SCREAMING_SNAKE_CASE__									=					tokenizer.cls_token_id
						SCREAMING_SNAKE_CASE__									=					1_2_8

						SCREAMING_SNAKE_CASE__									=					datasets.load_dataset("""cnn_dailymail"""			, """3.0.0"""			, split="""train[:1%]"""				)
						SCREAMING_SNAKE_CASE__									=					datasets.load_dataset("""cnn_dailymail"""			, """3.0.0"""			, split="""validation[:1%]"""				)

						SCREAMING_SNAKE_CASE__									=					train_dataset.select(range(3_2				)				)
						SCREAMING_SNAKE_CASE__									=					val_dataset.select(range(1_6				)				)

						SCREAMING_SNAKE_CASE__									=					4

						def _map_to_encoder_decoder_inputs(__UpperCAmelCase :  List[str]				):
									# Tokenizer will automatically set [BOS] <text> [EOS]
									SCREAMING_SNAKE_CASE__									=					tokenizer(batch["""article"""]			, padding="""max_length"""			, truncation=UpperCAmelCase__			, max_length=5_1_2				)
									SCREAMING_SNAKE_CASE__									=					tokenizer(batch["""highlights"""]			, padding="""max_length"""			, truncation=UpperCAmelCase__			, max_length=1_2_8				)
									SCREAMING_SNAKE_CASE__									=					inputs.input_ids
									SCREAMING_SNAKE_CASE__									=					inputs.attention_mask

									SCREAMING_SNAKE_CASE__									=					outputs.input_ids
									SCREAMING_SNAKE_CASE__									=					outputs.input_ids.copy()
									SCREAMING_SNAKE_CASE__									=					[
									    [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
									]
									SCREAMING_SNAKE_CASE__									=					outputs.attention_mask

									assert all(len(UpperCAmelCase__				) == 5_1_2 for x in inputs.input_ids				)
									assert all(len(UpperCAmelCase__				) == 1_2_8 for x in outputs.input_ids				)

									return batch

						def _compute_metrics(__UpperCAmelCase :  Tuple				):
									SCREAMING_SNAKE_CASE__									=					pred.label_ids
									SCREAMING_SNAKE_CASE__									=					pred.predictions

									# all unnecessary tokens are removed
									SCREAMING_SNAKE_CASE__									=					tokenizer.batch_decode(UpperCAmelCase__			, skip_special_tokens=UpperCAmelCase__				)
									SCREAMING_SNAKE_CASE__									=					tokenizer.batch_decode(UpperCAmelCase__			, skip_special_tokens=UpperCAmelCase__				)

									SCREAMING_SNAKE_CASE__									=					sum([int(pred_str[i] == label_str[i]				) for i in range(len(UpperCAmelCase__				)				)]				) / len(UpperCAmelCase__				)

									return {"accuracy": accuracy}

						# map train dataset
						SCREAMING_SNAKE_CASE__									=					train_dataset.map(
						    _map_to_encoder_decoder_inputs			, batched=UpperCAmelCase__			, batch_size=UpperCAmelCase__			, remove_columns=["""article""", """highlights"""]			, )
						train_dataset.set_format(
						    type="""torch"""			, columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]			, )

						# same for validation dataset
						SCREAMING_SNAKE_CASE__									=					val_dataset.map(
						    _map_to_encoder_decoder_inputs			, batched=UpperCAmelCase__			, batch_size=UpperCAmelCase__			, remove_columns=["""article""", """highlights"""]			, )
						val_dataset.set_format(
						    type="""torch"""			, columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]			, )

						SCREAMING_SNAKE_CASE__									=					self.get_auto_remove_tmp_dir()

						SCREAMING_SNAKE_CASE__									=					SeqaSeqTrainingArguments(
						    output_dir=UpperCAmelCase__			, per_device_train_batch_size=UpperCAmelCase__			, per_device_eval_batch_size=UpperCAmelCase__			, predict_with_generate=UpperCAmelCase__			, evaluation_strategy="""steps"""			, do_train=UpperCAmelCase__			, do_eval=UpperCAmelCase__			, warmup_steps=0			, eval_steps=2			, logging_steps=2			, )

						# instantiate trainer
						SCREAMING_SNAKE_CASE__									=					SeqaSeqTrainer(
						    model=UpperCAmelCase__			, args=UpperCAmelCase__			, compute_metrics=_compute_metrics			, train_dataset=UpperCAmelCase__			, eval_dataset=UpperCAmelCase__			, tokenizer=UpperCAmelCase__			, )

						# start training
						trainer.train()








import os
import sys
import unittest


_lowerCamelCase	:		Optional[Any]							       =   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


_lowerCamelCase	:		Any							       =   os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
_lowerCamelCase	:		str							       =   os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")


class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Tuple:





           '''simple docstring'''

           A__       =    get_test_to_tester_mapping(UpperCAmelCase__)
           A__       =    get_test_to_tester_mapping(UpperCAmelCase__)

           A__       =    {'''BertModelTest''': '''BertModelTester'''}

           A__       =    {
               '''BlipModelTest''': '''BlipModelTester''',
               '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
               '''BlipTextModelTest''': '''BlipTextModelTester''',
               '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
               '''BlipVQAModelTest''': '''BlipVQAModelTester''',
               '''BlipVisionModelTest''': '''BlipVisionModelTester''',
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->List[Any]:





           '''simple docstring'''

           A__       =    get_model_to_test_mapping(UpperCAmelCase__)
           A__       =    get_model_to_test_mapping(UpperCAmelCase__)

           A__       =    {
               '''BertForMaskedLM''': ['''BertModelTest'''],
               '''BertForMultipleChoice''': ['''BertModelTest'''],
               '''BertForNextSentencePrediction''': ['''BertModelTest'''],
               '''BertForPreTraining''': ['''BertModelTest'''],
               '''BertForQuestionAnswering''': ['''BertModelTest'''],
               '''BertForSequenceClassification''': ['''BertModelTest'''],
               '''BertForTokenClassification''': ['''BertModelTest'''],
               '''BertLMHeadModel''': ['''BertModelTest'''],
               '''BertModel''': ['''BertModelTest'''],
           }

           A__       =    {
               '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
               '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
               '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
               '''BlipModel''': ['''BlipModelTest'''],
               '''BlipTextModel''': ['''BlipTextModelTest'''],
               '''BlipVisionModel''': ['''BlipVisionModelTest'''],
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->str:





           '''simple docstring'''

           A__       =    get_model_to_tester_mapping(UpperCAmelCase__)
           A__       =    get_model_to_tester_mapping(UpperCAmelCase__)

           A__       =    {
               '''BertForMaskedLM''': ['''BertModelTester'''],
               '''BertForMultipleChoice''': ['''BertModelTester'''],
               '''BertForNextSentencePrediction''': ['''BertModelTester'''],
               '''BertForPreTraining''': ['''BertModelTester'''],
               '''BertForQuestionAnswering''': ['''BertModelTester'''],
               '''BertForSequenceClassification''': ['''BertModelTester'''],
               '''BertForTokenClassification''': ['''BertModelTester'''],
               '''BertLMHeadModel''': ['''BertModelTester'''],
               '''BertModel''': ['''BertModelTester'''],
           }

           A__       =    {
               '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
               '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
               '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
               '''BlipModel''': ['''BlipModelTester'''],
               '''BlipTextModel''': ['''BlipTextModelTester'''],
               '''BlipVisionModel''': ['''BlipVisionModelTester'''],
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)


