



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__):







       @slow
       @require_torch
       def      snake_case__   (     self):

              '''simple docstring'''

              _lowerCAmelCase  :							Optional[Any]				     =					EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny",    "prajjwal1/bert-tiny")
              _lowerCAmelCase  :							Optional[Any]				     =					BertTokenizer.from_pretrained("bert-base-uncased")

              _lowerCAmelCase  :							Any				     =					bertabert.config.encoder.vocab_size
              _lowerCAmelCase  :							Tuple				     =					tokenizer.sep_token_id
              _lowerCAmelCase  :							Tuple				     =					tokenizer.cls_token_id
              _lowerCAmelCase  :							List[str]				     =					128

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

              _lowerCAmelCase  :							Dict				     =					train_dataset.select(range(32))
              _lowerCAmelCase  :							int				     =					val_dataset.select(range(16))

              _lowerCAmelCase  :							Dict				     =					4

              def _map_to_encoder_decoder_inputs(__a):
                     # Tokenizer will automatically set [BOS] <text> [EOS]
                     _lowerCAmelCase  :							Optional[Any]				     =					tokenizer(batch["article"],    padding="max_length",    truncation=_SCREAMING_SNAKE_CASE,    max_length=512)
                     _lowerCAmelCase  :							Optional[int]				     =					tokenizer(batch["highlights"],    padding="max_length",    truncation=_SCREAMING_SNAKE_CASE,    max_length=128)
                     _lowerCAmelCase  :							List[Any]				     =					inputs.input_ids
                     _lowerCAmelCase  :							Tuple				     =					inputs.attention_mask

                     _lowerCAmelCase  :							str				     =					outputs.input_ids
                     _lowerCAmelCase  :							Optional[int]				     =					outputs.input_ids.copy()
                     _lowerCAmelCase  :							Tuple				     =					[
                         [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
                     ]
                     _lowerCAmelCase  :							Tuple				     =					outputs.attention_mask

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

                     return batch

              def _compute_metrics(__a):
                     _lowerCAmelCase  :							Dict				     =					pred.label_ids
                     _lowerCAmelCase  :							List[str]				     =					pred.predictions

                     # all unnecessary tokens are removed
                     _lowerCAmelCase  :							Union[str, Any]				     =					tokenizer.batch_decode(_SCREAMING_SNAKE_CASE,    skip_special_tokens=_SCREAMING_SNAKE_CASE)
                     _lowerCAmelCase  :							Optional[int]				     =					tokenizer.batch_decode(_SCREAMING_SNAKE_CASE,    skip_special_tokens=_SCREAMING_SNAKE_CASE)

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

                     return {"accuracy": accuracy}

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

              _lowerCAmelCase  :							List[str]				     =					self.get_auto_remove_tmp_dir()

              _lowerCAmelCase  :							str				     =					SeqaSeqTrainingArguments(
                  output_dir=_SCREAMING_SNAKE_CASE,    per_device_train_batch_size=_SCREAMING_SNAKE_CASE,    per_device_eval_batch_size=_SCREAMING_SNAKE_CASE,    predict_with_generate=_SCREAMING_SNAKE_CASE,    evaluation_strategy="steps",    do_train=_SCREAMING_SNAKE_CASE,    do_eval=_SCREAMING_SNAKE_CASE,    warmup_steps=0,    eval_steps=2,    logging_steps=2,    )

              # instantiate trainer
              _lowerCAmelCase  :							Tuple				     =					SeqaSeqTrainer(
                  model=_SCREAMING_SNAKE_CASE,    args=_SCREAMING_SNAKE_CASE,    compute_metrics=_compute_metrics,    train_dataset=_SCREAMING_SNAKE_CASE,    eval_dataset=_SCREAMING_SNAKE_CASE,    tokenizer=_SCREAMING_SNAKE_CASE,    )

              # start training
              trainer.train()





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}



class 						UpperCAmelCase_					( a     ,      a):
							lowerCamelCase__         =	'focalnet'







							def __init__(     self,    __a=224,    __a=4,    __a=3,    __a=96,    __a=False,    __a=[192, 384, 768, 768],    __a=[2, 2, 6, 2],    __a=[2, 2, 2, 2],    __a=[3, 3, 3, 3],    __a="gelu",    __a=4.0,    __a=0.0,    __a=0.1,    __a=False,    __a=1E-4,    __a=False,    __a=False,    __a=False,    __a=0.02,    __a=1E-5,    __a=32,    __a=None,    __a=None,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase  :							str				     =					image_size
														_lowerCAmelCase  :							List[str]				     =					patch_size
														_lowerCAmelCase  :							List[Any]				     =					num_channels
														_lowerCAmelCase  :							Tuple				     =					embed_dim
														_lowerCAmelCase  :							List[Any]				     =					use_conv_embed
														_lowerCAmelCase  :							Any				     =					hidden_sizes
														_lowerCAmelCase  :							Tuple				     =					depths
														_lowerCAmelCase  :							Dict				     =					focal_levels
														_lowerCAmelCase  :							Optional[Any]				     =					focal_windows
														_lowerCAmelCase  :							str				     =					hidden_act
														_lowerCAmelCase  :							Union[str, Any]				     =					mlp_ratio
														_lowerCAmelCase  :							Any				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Dict				     =					drop_path_rate
														_lowerCAmelCase  :							str				     =					use_layerscale
														_lowerCAmelCase  :							str				     =					layerscale_value
														_lowerCAmelCase  :							Union[str, Any]				     =					use_post_layernorm
														_lowerCAmelCase  :							Optional[int]				     =					use_post_layernorm_in_modulation
														_lowerCAmelCase  :							str				     =					normalize_modulator
														_lowerCAmelCase  :							Any				     =					initializer_range
														_lowerCAmelCase  :							Union[str, Any]				     =					layer_norm_eps
														_lowerCAmelCase  :							Any				     =					encoder_stride
														_lowerCAmelCase  :							List[str]				     =					["stem"] + [f"stage{idx}" for idx in range(1,    len(self.depths) + 1)]
														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					get_aligned_output_features_output_indices(
														    out_features=__a,    out_indices=__a,    stage_names=self.stage_names)
