

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				(				__A ):
			'''simple docstring'''







			@slow
			@require_torch
			def 				__UpperCamelCase			(      self				:			Any						)				->							Any:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE :   int					  =							bertabert.config.encoder.vocab_size
				SCREAMING_SNAKE_CASE :   Dict					  =							tokenizer.sep_token_id
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							tokenizer.cls_token_id
				SCREAMING_SNAKE_CASE :   List[Any]					  =							128

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

				SCREAMING_SNAKE_CASE :   List[Any]					  =							train_dataset.select(range(32						)						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							val_dataset.select(range(16						)						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							4

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

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

					# all unnecessary tokens are removed
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							tokenizer.batch_decode(a   ,   skip_special_tokens=a						)
					SCREAMING_SNAKE_CASE :   str					  =							tokenizer.batch_decode(a   ,   skip_special_tokens=a						)

					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							sum([int(pred_str[i] == label_str[i]						) for i in range(len(a						)						)]						) / len(a						)

					return {"accuracy": accuracy}

				# map train dataset
				SCREAMING_SNAKE_CASE :   Dict					  =							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
				SCREAMING_SNAKE_CASE :   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"]   ,   )

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.get_auto_remove_tmp_dir()

				SCREAMING_SNAKE_CASE :   List[str]					  =							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
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							SeqaSeqTrainer(
				    model=a   ,   args=a   ,   compute_metrics=_compute_metrics   ,   train_dataset=a   ,   eval_dataset=a   ,   tokenizer=a   ,   )

				# start training
				trainer.train()

'''simple docstring'''
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""swin"""

		lowerCamelCase     							=						{
		    """num_attention_heads""": """num_heads""",
		    """num_hidden_layers""": """num_layers""",
		}



		def __init__(      self				:			Tuple  ,	UpperCamelCase__				:			int=224  ,	UpperCamelCase__				:			Optional[int]=4  ,	UpperCamelCase__				:			str=3  ,	UpperCamelCase__				:			str=96  ,	UpperCamelCase__				:			List[Any]=[2, 2, 6, 2]  ,	UpperCamelCase__				:			Optional[Any]=[3, 6, 12, 24]  ,	UpperCamelCase__				:			Optional[Any]=7  ,	UpperCamelCase__				:			Tuple=4.0  ,	UpperCamelCase__				:			Tuple=True  ,	UpperCamelCase__				:			str=0.0  ,	UpperCamelCase__				:			Optional[Any]=0.0  ,	UpperCamelCase__				:			List[str]=0.1  ,	UpperCamelCase__				:			Optional[Any]="gelu"  ,	UpperCamelCase__				:			Optional[Any]=False  ,	UpperCamelCase__				:			Tuple=0.02  ,	UpperCamelCase__				:			List[Any]=1e-5  ,	UpperCamelCase__				:			Any=32  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			int=None  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			Optional[int]:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				snake_case  :       Any     			=						image_size
				snake_case  :       Optional[int]     			=						patch_size
				snake_case  :       List[Any]     			=						num_channels
				snake_case  :       Union[str, Any]     			=						embed_dim
				snake_case  :       str     			=						depths
				snake_case  :       str     			=						len(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						num_heads
				snake_case  :       List[Any]     			=						window_size
				snake_case  :       Optional[int]     			=						mlp_ratio
				snake_case  :       Union[str, Any]     			=						qkv_bias
				snake_case  :       Optional[int]     			=						hidden_dropout_prob
				snake_case  :       Optional[int]     			=						attention_probs_dropout_prob
				snake_case  :       Optional[int]     			=						drop_path_rate
				snake_case  :       List[Any]     			=						hidden_act
				snake_case  :       int     			=						use_absolute_embeddings
				snake_case  :       str     			=						layer_norm_eps
				snake_case  :       Optional[Any]     			=						initializer_range
				snake_case  :       Any     			=						encoder_stride
				# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				snake_case  :       str     			=						int(embed_dim * 2 ** (len(UpperCamelCase__      ) - 1)      )
				snake_case  :       int     			=						['''stem'''] + [f'stage{idx}' for idx in range(1  ,	len(UpperCamelCase__      ) + 1      )]
				snake_case			,snake_case  :       List[Any]     			=						get_aligned_output_features_output_indices(
				    out_features=UpperCamelCase__  ,	out_indices=UpperCamelCase__  ,	stage_names=self.stage_names      )







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						version.parse("""1.11"""					)



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






				"""simple docstring"""







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



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			float:






				"""simple docstring"""







				return 1e-4


