


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A			:						Any        =  logging.get_logger(__name__)

A			:						Tuple        =  {
    'sail/poolformer_s12': 'https://huggingface.co/sail/poolformer_s12/resolve/main/config.json',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''poolformer'''

					def __init__(self       :		Dict ,   _UpperCAmelCase       :		Union[str, Any]=3 ,   _UpperCAmelCase       :		str=16 ,   _UpperCAmelCase       :		Any=16 ,   _UpperCAmelCase       :		Union[str, Any]=3 ,   _UpperCAmelCase       :		Union[str, Any]=4.0 ,   _UpperCAmelCase       :		str=[2, 2, 6, 2] ,   _UpperCAmelCase       :		int=[64, 128, 320, 512] ,   _UpperCAmelCase       :		Union[str, Any]=[7, 3, 3, 3] ,   _UpperCAmelCase       :		List[Any]=[4, 2, 2, 2] ,   _UpperCAmelCase       :		Union[str, Any]=[2, 1, 1, 1] ,   _UpperCAmelCase       :		Optional[Any]=4 ,   _UpperCAmelCase       :		Dict=0.0 ,   _UpperCAmelCase       :		Union[str, Any]="gelu" ,   _UpperCAmelCase       :		Optional[Any]=True ,   _UpperCAmelCase       :		Tuple=1E-5 ,   _UpperCAmelCase       :		Tuple=0.02 ,   **_UpperCAmelCase       :		Union[str, Any] ,   )		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						num_channels
							lowercase__	     =						patch_size
							lowercase__	     =						stride
							lowercase__	     =						padding
							lowercase__	     =						pool_size
							lowercase__	     =						hidden_sizes
							lowercase__	     =						mlp_ratio
							lowercase__	     =						depths
							lowercase__	     =						patch_sizes
							lowercase__	     =						strides
							lowercase__	     =						num_encoder_blocks
							lowercase__	     =						drop_path_rate
							lowercase__	     =						hidden_act
							lowercase__	     =						use_layer_scale
							lowercase__	     =						layer_scale_init_value
							lowercase__	     =						initializer_range
							super().__init__(**_UpperCAmelCase						)






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					version.parse('''1.11'''				)

					@property
					def     lowerCamelCase__	(self       :		List[str]						)		->  Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							return OrderedDict(
							    [
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]						)


					@property
					def     lowerCamelCase__	(self       :		Dict						)		->  float:




							"""simple docstring"""


							return 2E-3





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       (			UpperCAmelCase__				):
					'''simple docstring'''






					@slow
					@require_torch
					def     lowerCamelCase__	(self       :		int						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,   """prajjwal1/bert-tiny"""						)
							lowercase__	     =						BertTokenizer.from_pretrained("""bert-base-uncased"""						)

							lowercase__	     =						bertabert.config.encoder.vocab_size
							lowercase__	     =						tokenizer.sep_token_id
							lowercase__	     =						tokenizer.cls_token_id
							lowercase__	     =						128

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

							lowercase__	     =						train_dataset.select(range(32						)						)
							lowercase__	     =						val_dataset.select(range(16						)						)

							lowercase__	     =						4

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

									lowercase__	     =						outputs.input_ids
									lowercase__	     =						outputs.input_ids.copy()
									lowercase__	     =						[
									    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
									]
									lowercase__	     =						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       :		List[Any]						):
									lowercase__	     =						pred.label_ids
									lowercase__	     =						pred.predictions

									# all unnecessary tokens are removed
									lowercase__	     =						tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)
									lowercase__	     =						tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)

									lowercase__	     =						sum([int(pred_str[i] == label_str[i]						) for i in range(len(_UpperCAmelCase						)						)]						) / len(_UpperCAmelCase						)

									return {"accuracy": accuracy}

							# map train dataset
							lowercase__	     =						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
							lowercase__	     =						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"""] ,   )

							lowercase__	     =						self.get_auto_remove_tmp_dir()

							lowercase__	     =						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
							lowercase__	     =						SeqaSeqTrainer(
							    model=_UpperCAmelCase ,   args=_UpperCAmelCase ,   compute_metrics=_compute_metrics ,   train_dataset=_UpperCAmelCase ,   eval_dataset=_UpperCAmelCase ,   tokenizer=_UpperCAmelCase ,   )

							# start training
							trainer.train()

