





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    A__  (			__snake_case     ):





      @slow
      @require_torch
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"			,				"prajjwal1/bert-tiny"  )
       UpperCamelCase      :   Union[str, Any]			      =  BertTokenizer.from_pretrained("bert-base-uncased"  )

       UpperCamelCase      :   int			      =  bertabert.config.encoder.vocab_size
       UpperCamelCase      :   str			      =  tokenizer.sep_token_id
       UpperCamelCase      :   Dict			      =  tokenizer.cls_token_id
       UpperCamelCase      :   Dict			      =  128

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

       UpperCamelCase      :   Tuple			      =  train_dataset.select(range(32  )  )
       UpperCamelCase      :   int			      =  val_dataset.select(range(16  )  )

       UpperCamelCase      :   List[str]			      =  4

       def _map_to_encoder_decoder_inputs(A_  ):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        UpperCamelCase      :   str			      =  tokenizer(batch["article"]			,				padding="max_length"			,				truncation=A_			,				max_length=512  )
        UpperCamelCase      :   Dict			      =  tokenizer(batch["highlights"]			,				padding="max_length"			,				truncation=A_			,				max_length=128  )
        UpperCamelCase      :   str			      =  inputs.input_ids
        UpperCamelCase      :   str			      =  inputs.attention_mask

        UpperCamelCase      :   Dict			      =  outputs.input_ids
        UpperCamelCase      :   str			      =  outputs.input_ids.copy()
        UpperCamelCase      :   Union[str, Any]			      =  [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
        ]
        UpperCamelCase      :   List[Any]			      =  outputs.attention_mask

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

        return batch

       def _compute_metrics(A_  ):
        UpperCamelCase      :   Optional[int]			      =  pred.label_ids
        UpperCamelCase      :   List[str]			      =  pred.predictions

        # all unnecessary tokens are removed
        UpperCamelCase      :   Any			      =  tokenizer.batch_decode(A_			,				skip_special_tokens=A_  )
        UpperCamelCase      :   Dict			      =  tokenizer.batch_decode(A_			,				skip_special_tokens=A_  )

        UpperCamelCase      :   List[str]			      =  sum([int(pred_str[i] == label_str[i]  ) for i in range(len(A_  )  )]  ) / len(A_  )

        return {"accuracy": accuracy}

       # map train dataset
       UpperCamelCase      :   Tuple			      =  train_dataset.map(
           _map_to_encoder_decoder_inputs			,				batched=A_			,				batch_size=A_			,				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
       UpperCamelCase      :   Union[str, Any]			      =  val_dataset.map(
           _map_to_encoder_decoder_inputs			,				batched=A_			,				batch_size=A_			,				remove_columns=["article", "highlights"]			,				)
       val_dataset.set_format(
           type="torch"			,				columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]			,				)

       UpperCamelCase      :   Any			      =  self.get_auto_remove_tmp_dir()

       UpperCamelCase      :   Dict			      =  SeqaSeqTrainingArguments(
           output_dir=A_			,				per_device_train_batch_size=A_			,				per_device_eval_batch_size=A_			,				predict_with_generate=A_			,				evaluation_strategy="steps"			,				do_train=A_			,				do_eval=A_			,				warmup_steps=0			,				eval_steps=2			,				logging_steps=2			,				)

       # instantiate trainer
       UpperCamelCase      :   Any			      =  SeqaSeqTrainer(
           model=A_			,				args=A_			,				compute_metrics=_compute_metrics			,				train_dataset=A_			,				eval_dataset=A_			,				tokenizer=A_			,				)

       # start training
       trainer.train()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase			:	List[Any]					    =	{
    """configuration_chinese_clip""": [
        """CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ChineseCLIPConfig""",
        """ChineseCLIPOnnxConfig""",
        """ChineseCLIPTextConfig""",
        """ChineseCLIPVisionConfig""",
    ],
    """processing_chinese_clip""": ["""ChineseCLIPProcessor"""],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase			:	Tuple					    =	["""ChineseCLIPFeatureExtractor"""]
    __lowerCamelCase			:	List[str]					    =	["""ChineseCLIPImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase			:	str					    =	[
        """CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ChineseCLIPModel""",
        """ChineseCLIPPreTrainedModel""",
        """ChineseCLIPTextModel""",
        """ChineseCLIPVisionModel""",
    ]

if TYPE_CHECKING:
    from .configuration_chinese_clip import (
        CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ChineseCLIPConfig,
        ChineseCLIPOnnxConfig,
        ChineseCLIPTextConfig,
        ChineseCLIPVisionConfig,
    )
    from .processing_chinese_clip import ChineseCLIPProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_chinese_clip import (
            CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            ChineseCLIPModel,
            ChineseCLIPPreTrainedModel,
            ChineseCLIPTextModel,
            ChineseCLIPVisionModel,
        )

else:
    import sys

    __lowerCamelCase			:	Any					    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
