

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 ):

     @slow
     @require_torch
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

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

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

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

            _UpperCamelCase              =						4

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

                   _UpperCamelCase              =						outputs.input_ids
                   _UpperCamelCase              =						outputs.input_ids.copy()
                   _UpperCamelCase              =						[
                       [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
                   ]
                   _UpperCamelCase              =						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              =						pred.label_ids
                   _UpperCamelCase              =						pred.predictions

                   # all unnecessary tokens are removed
                   _UpperCamelCase              =						tokenizer.batch_decode(A,       skip_special_tokens=A      )
                   _UpperCamelCase              =						tokenizer.batch_decode(A,       skip_special_tokens=A      )

                   _UpperCamelCase              =						sum([int(pred_str[i] == label_str[i]      ) for i in range(len(A      )      )]      ) / len(A      )

                   return {"accuracy": accuracy}

            # map train dataset
            _UpperCamelCase              =						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              =						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              =						self.get_auto_remove_tmp_dir()

            _UpperCamelCase              =						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              =						SeqaSeqTrainer(
                model=A,       args=A,       compute_metrics=_compute_metrics,       train_dataset=A,       eval_dataset=A,       tokenizer=A,       )

            # start training
            trainer.train()













import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


lowercase    : Optional[int]    	=  logging.get_logger(__name__)

lowercase    : List[Any]    	=  {
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class  __A(						__UpperCAmelCase ):
     __A	  =       "detr"
     __A	  =       ["past_key_values"]
     __A	  =       {
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }

     def __init__(      self,       A=True,       A=None,       A=3,       A=100,       A=6,       A=2048,       A=8,       A=6,       A=2048,       A=8,       A=0.0,       A=0.0,       A=True,       A="relu",       A=256,       A=0.1,       A=0.0,       A=0.0,       A=0.02,       A=1.0,       A=False,       A="sine",       A="resnet50",       A=True,       A=False,       A=1,       A=5,       A=2,       A=1,       A=1,       A=5,       A=2,       A=0.1,       **A,       ):





            """simple docstring"""

            if backbone_config is not None and use_timm_backbone:
                   raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''      )

            if not use_timm_backbone:
                   if backbone_config is None:
                          logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''      )
                          _UpperCamelCase              =						CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']      )
                   elif isinstance(A,       A      ):
                          _UpperCamelCase              =						backbone_config.get('''model_type'''      )
                          _UpperCamelCase              =						CONFIG_MAPPING[backbone_model_type]
                          _UpperCamelCase              =						config_class.from_dict(A      )
                   # set timm attributes to None
                   _UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						None, None, None

            _UpperCamelCase              =						use_timm_backbone
            _UpperCamelCase              =						backbone_config
            _UpperCamelCase              =						num_channels
            _UpperCamelCase              =						num_queries
            _UpperCamelCase              =						d_model
            _UpperCamelCase              =						encoder_ffn_dim
            _UpperCamelCase              =						encoder_layers
            _UpperCamelCase              =						encoder_attention_heads
            _UpperCamelCase              =						decoder_ffn_dim
            _UpperCamelCase              =						decoder_layers
            _UpperCamelCase              =						decoder_attention_heads
            _UpperCamelCase              =						dropout
            _UpperCamelCase              =						attention_dropout
            _UpperCamelCase              =						activation_dropout
            _UpperCamelCase              =						activation_function
            _UpperCamelCase              =						init_std
            _UpperCamelCase              =						init_xavier_std
            _UpperCamelCase              =						encoder_layerdrop
            _UpperCamelCase              =						decoder_layerdrop
            _UpperCamelCase              =						encoder_layers
            _UpperCamelCase              =						auxiliary_loss
            _UpperCamelCase              =						position_embedding_type
            _UpperCamelCase              =						backbone
            _UpperCamelCase              =						use_pretrained_backbone
            _UpperCamelCase              =						dilation
            # Hungarian matcher
            _UpperCamelCase              =						class_cost
            _UpperCamelCase              =						bbox_cost
            _UpperCamelCase              =						giou_cost
            # Loss coefficients
            _UpperCamelCase              =						mask_loss_coefficient
            _UpperCamelCase              =						dice_loss_coefficient
            _UpperCamelCase              =						bbox_loss_coefficient
            _UpperCamelCase              =						giou_loss_coefficient
            _UpperCamelCase              =						eos_coefficient
            super().__init__(is_encoder_decoder=A,       **A      )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.encoder_attention_heads

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.d_model

     @classmethod
     def 	_UpperCamelCase     (      cls,       A,       **A      ):





            """simple docstring"""

            return cls(backbone_config=A,       **A      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						copy.deepcopy(self.__dict__      )
            if output["backbone_config"] is not None:
                   _UpperCamelCase              =						self.backbone_config.to_dict()
            _UpperCamelCase              =						self.__class__.model_type
            return output






class  __A(						__UpperCAmelCase ):
     __A	  =       version.parse("1.11" )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 1E-5



     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 12






