
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  UpperCamelCase__		(    __lowerCamelCase   ):







	'''simple docstring'''





	@slow
	@require_torch
	def   __snake_case				(			self		):
					A__  :	Optional[Any]         =							EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''				,       '''prajjwal1/bert-tiny'''		)
					A__  :	int         =							BertTokenizer.from_pretrained('''bert-base-uncased'''		)

					A__  :	int         =							bertabert.config.encoder.vocab_size
					A__  :	Union[str, Any]         =							tokenizer.sep_token_id
					A__  :	Tuple         =							tokenizer.cls_token_id
					A__  :	Union[str, Any]         =							128

					A__  :	Tuple         =							datasets.load_dataset('''cnn_dailymail'''				,       '''3.0.0'''				,       split='''train[:1%]'''		)
					A__  :	Optional[Any]         =							datasets.load_dataset('''cnn_dailymail'''				,       '''3.0.0'''				,       split='''validation[:1%]'''		)

					A__  :	str         =							train_dataset.select(range(32		)		)
					A__  :	int         =							val_dataset.select(range(16		)		)

					A__  :	str         =							4

					def _map_to_encoder_decoder_inputs(UpperCamelCase__		):
									# Tokenizer will automatically set [BOS] <text> [EOS]
									A__  :	Dict         =							tokenizer(batch['''article''']				,       padding='''max_length'''				,       truncation=UpperCamelCase_				,       max_length=512		)
									A__  :	Optional[Any]         =							tokenizer(batch['''highlights''']				,       padding='''max_length'''				,       truncation=UpperCamelCase_				,       max_length=128		)
									A__  :	Any         =							inputs.input_ids
									A__  :	int         =							inputs.attention_mask

									A__  :	List[str]         =							outputs.input_ids
									A__  :	List[Any]         =							outputs.input_ids.copy()
									A__  :	Optional[Any]         =							[
									    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
									]
									A__  :	int         =							outputs.attention_mask

									assert all(len(UpperCamelCase_		) == 512 for x in inputs.input_ids		)
									assert all(len(UpperCamelCase_		) == 128 for x in outputs.input_ids		)

									return batch

					def _compute_metrics(UpperCamelCase__		):
									A__  :	List[str]         =							pred.label_ids
									A__  :	Tuple         =							pred.predictions

									# all unnecessary tokens are removed
									A__  :	Tuple         =							tokenizer.batch_decode(UpperCamelCase_				,       skip_special_tokens=UpperCamelCase_		)
									A__  :	int         =							tokenizer.batch_decode(UpperCamelCase_				,       skip_special_tokens=UpperCamelCase_		)

									A__  :	Union[str, Any]         =							sum([int(pred_str[i] == label_str[i]		) for i in range(len(UpperCamelCase_		)		)]		) / len(UpperCamelCase_		)

									return {"accuracy": accuracy}

					# map train dataset
					A__  :	Optional[Any]         =							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
					A__  :	List[Any]         =							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''']				,       )

					A__  :	str         =							self.get_auto_remove_tmp_dir()

					A__  :	Tuple         =							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
					A__  :	Dict         =							SeqaSeqTrainer(
					    model=UpperCamelCase_				,       args=UpperCamelCase_				,       compute_metrics=_compute_metrics				,       train_dataset=UpperCamelCase_				,       eval_dataset=UpperCamelCase_				,       tokenizer=UpperCamelCase_				,       )

					# start training
					trainer.train()

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
					from .pipeline_kandinsky import KandinskyPipeline
					from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
					from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
					from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
					from .text_encoder import MultilingualCLIP