





'''simple docstring'''




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


__SCREAMING_SNAKE_CASE     :str       =				logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				{
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
__SCREAMING_SNAKE_CASE     :List[str]       =				{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__SCREAMING_SNAKE_CASE     :int       =				sorted(arg_to_scheduler.keys())
__SCREAMING_SNAKE_CASE     :Tuple       =				'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''







class   A_	(  pl.LightningModule			):




 def __init__(					self       :    Dict	,			snake_case_       :    Optional[Any]	,			snake_case_       :    Any=None	,			snake_case_       :    Optional[Any]="base"	,			snake_case_       :    List[str]=None	,			snake_case_       :    str=None	,			snake_case_       :    Any=None	,			**snake_case_       :    Optional[int]	,			):
      super().__init__()
      # TODO: move to self.save_hyperparameters()
      # self.save_hyperparameters()
      # can also expand arguments into trainer signature for easier reading

      self.save_hyperparameters(snake_case_    )
      _UpperCAmelCase     							=   0
      _UpperCAmelCase     							=   Path(self.hparams.output_dir    )
      _UpperCAmelCase     							=   self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
           _UpperCAmelCase     							=   AutoConfig.from_pretrained(
               self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path	,			**({"num_labels": num_labels} if num_labels is not None else {})	,			cache_dir=snake_case_	,			**snake_case_	,			)
      else:
           _UpperCAmelCase     							=   config

      _UpperCAmelCase     							=   ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
      for p in extra_model_params:
           if getattr(self.hparams	,			snake_case_	,			snake_case_    ):
                assert hasattr(self.config	,			snake_case_    ), f'model config doesn\'t have a `{p}` attribute'
                setattr(self.config	,			snake_case_	,			getattr(self.hparams	,			snake_case_    )    )

      if tokenizer is None:
           _UpperCAmelCase     							=   AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path	,			cache_dir=snake_case_	,			)
      else:
           _UpperCAmelCase     							=   tokenizer
      _UpperCAmelCase     							=   MODEL_MODES[mode]
      if model is None:
           _UpperCAmelCase     							=   self.model_type.from_pretrained(
               self.hparams.model_name_or_path	,			from_tf=bool(".ckpt" in self.hparams.model_name_or_path    )	,			config=self.config	,			cache_dir=snake_case_	,			)
      else:
           _UpperCAmelCase     							=   model




 def      lowercase	(					self       :    Any	,			*snake_case_       :    Union[str, Any]	,			**snake_case_       :    List[str]    ):
      _UpperCAmelCase     							=   self.model_type.from_pretrained(*snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   arg_to_scheduler[self.hparams.lr_scheduler]
      _UpperCAmelCase     							=   get_schedule_func(
          self.opt	,			num_warmup_steps=self.hparams.warmup_steps	,			num_training_steps=self.total_steps()    )
      _UpperCAmelCase     							=   {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
      return scheduler




 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   self.model
      _UpperCAmelCase     							=   ["""bias""", """LayerNorm.weight"""]
      _UpperCAmelCase     							=   [
          {
              """params""": [
                  p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay    )
              ],  # check this named paramters
              """weight_decay""": self.hparams.weight_decay,
          },
          {
              """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay    )],
              """weight_decay""": 0.0,
          },
      ]
      if self.hparams.adafactor:
           _UpperCAmelCase     							=   Adafactor(
               snake_case_	,			lr=self.hparams.learning_rate	,			scale_parameter=snake_case_	,			relative_step=snake_case_    )

      else:
           _UpperCAmelCase     							=   AdamW(
               snake_case_	,			lr=self.hparams.learning_rate	,			eps=self.hparams.adam_epsilon    )
      _UpperCAmelCase     							=   optimizer

      _UpperCAmelCase     							=   self.get_lr_scheduler()

      return [optimizer], [scheduler]




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    List[str]	,			snake_case_       :    Optional[int]    ):
      return self.validation_step(snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    List[Any]    ):
      return self.validation_end(snake_case_    )




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   max(1	,			self.hparams.gpus    )  # TODO: consider num_tpu_cores
      _UpperCAmelCase     							=   self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
      return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




 def      lowercase	(					self       :    int	,			snake_case_       :    List[Any]    ):
      if stage == "test":
           _UpperCAmelCase     							=   len(self.test_dataloader().dataset    )
      else:
           _UpperCAmelCase     							=   self.get_dataloader("train"	,			self.hparams.train_batch_size	,			shuffle=snake_case_    )
           _UpperCAmelCase     							=   len(self.train_dataloader().dataset    )




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    Any	,			snake_case_       :    Optional[int]	,			snake_case_       :    Tuple = False    ):
      raise NotImplementedError("You must implement this for your task"    )




 def      lowercase	(					self       :    Dict    ):
      return self.train_loader




 def      lowercase	(					self       :    List[str]    ):
      return self.get_dataloader("dev"	,			self.hparams.eval_batch_size	,			shuffle=snake_case_    )




 def      lowercase	(					self       :    List[Any]    ):
      return self.get_dataloader("test"	,			self.hparams.eval_batch_size	,			shuffle=snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    List[str]    ):
      return os.path.join(
          self.hparams.data_dir	,			"cached_{}_{}_{}".format(
              snake_case_	,			list(filter(snake_case_	,			self.hparams.model_name_or_path.split("/"    )    )    ).pop()	,			str(self.hparams.max_seq_length    )	,			)	,			)




 @pl.utilities.rank_zero_only
 def      lowercase	(					self       :    Any	,			snake_case_       :    List[Any]    ):
      _UpperCAmelCase     							=   self.output_dir.joinpath("best_tfmr"    )
      _UpperCAmelCase     							=   self.step_count
      self.model.save_pretrained(snake_case_    )
      self.tokenizer.save_pretrained(snake_case_    )




 @staticmethod
 def      lowercase	(					snake_case_       :    int	,			snake_case_       :    Optional[Any]    ):
      parser.add_argument(
          "--model_name_or_path"	,			default=snake_case_	,			type=snake_case_	,			required=snake_case_	,			help="Path to pretrained model or model identifier from huggingface.co/models"	,			)
      parser.add_argument(
          "--config_name"	,			default=""	,			type=snake_case_	,			help="Pretrained config name or path if not the same as model_name"    )
      parser.add_argument(
          "--tokenizer_name"	,			default=snake_case_	,			type=snake_case_	,			help="Pretrained tokenizer name or path if not the same as model_name"	,			)
      parser.add_argument(
          "--cache_dir"	,			default=str(Path(snake_case_    ).parent / "test_run" / "cache"    )	,			type=snake_case_	,			help="Where do you want to store the pre-trained models downloaded from huggingface.co"	,			)
      parser.add_argument(
          "--encoder_layerdrop"	,			type=snake_case_	,			help="Encoder layer dropout probability (Optional). Goes into model.config"	,			)
      parser.add_argument(
          "--decoder_layerdrop"	,			type=snake_case_	,			help="Decoder layer dropout probability (Optional). Goes into model.config"	,			)
      parser.add_argument(
          "--dropout"	,			type=snake_case_	,			help="Dropout probability (Optional). Goes into model.config"	,			)
      parser.add_argument(
          "--attention_dropout"	,			type=snake_case_	,			help="Attention dropout probability (Optional). Goes into model.config"	,			)
      parser.add_argument("--learning_rate"	,			default=5e-5	,			type=snake_case_	,			help="The initial learning rate for Adam."    )
      parser.add_argument(
          "--lr_scheduler"	,			default="linear"	,			choices=snake_case_	,			metavar=snake_case_	,			type=snake_case_	,			help="Learning rate scheduler"	,			)
      parser.add_argument("--weight_decay"	,			default=0.0	,			type=snake_case_	,			help="Weight decay if we apply some."    )
      parser.add_argument("--adam_epsilon"	,			default=1e-8	,			type=snake_case_	,			help="Epsilon for Adam optimizer."    )
      parser.add_argument("--warmup_steps"	,			default=0	,			type=snake_case_	,			help="Linear warmup over warmup_steps."    )
      parser.add_argument("--num_workers"	,			default=4	,			type=snake_case_	,			help="kwarg passed to DataLoader"    )
      parser.add_argument("--num_train_epochs"	,			dest="max_epochs"	,			default=3	,			type=snake_case_    )
      parser.add_argument("--train_batch_size"	,			default=3_2	,			type=snake_case_    )
      parser.add_argument("--eval_batch_size"	,			default=3_2	,			type=snake_case_    )
      parser.add_argument("--adafactor"	,			action="store_true"    )







class   A_	(  pl.Callback			):




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Any	,			snake_case_       :    Optional[int]    ):
      if (
          trainer.is_global_zero and trainer.global_rank == 0
      ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
           pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class   A_	(  pl.Callback			):




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Dict	,			snake_case_       :    int    ):
      # print(pl_module.model.rag)
      for name, param in pl_module.model.rag.named_parameters():
           if param.grad is None:
                print(snake_case_    )







class   A_	(  pl.Callback			):




 def      lowercase	(					self       :    Any	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    int    ):
      _UpperCAmelCase     							=   trainer.lr_schedulers[0]["""scheduler"""]
      _UpperCAmelCase     							=   {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
      pl_module.logger.log_metrics(snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    List[str]	,			snake_case_       :    Dict    ):
      rank_zero_info("***** Validation results *****"    )
      _UpperCAmelCase     							=   trainer.callback_metrics
      # Log results
      for key in sorted(snake_case_    ):
           if key not in ["log", "progress_bar"]:
                rank_zero_info("{} = {}\n".format(snake_case_	,			str(metrics[key]    )    )    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    Any    ):
      rank_zero_info("***** Test results *****"    )
      _UpperCAmelCase     							=   trainer.callback_metrics
      # Log and save results to file
      _UpperCAmelCase     							=   os.path.join(pl_module.hparams.output_dir	,			"test_results.txt"    )
      with open(snake_case_	,			"w"    ) as writer:
           for key in sorted(snake_case_    ):
                if key not in ["log", "progress_bar"]:
                     rank_zero_info("{} = {}\n".format(snake_case_	,			str(metrics[key]    )    )    )
                     writer.write("{} = {}\n".format(snake_case_	,			str(metrics[key]    )    )    )





def 	UpperCAmelCase_							(					__lowercase							:      List[Any]			,     __lowercase							:      Optional[Any]       )		->       Optional[int]:


     '''simple docstring'''



     parser.add_argument(
         "--output_dir"			,     default=str(Path(lowerCamelCase__       ).parent / "test_run" / "model_checkpoints"       )			,     type=lowerCamelCase__			,     help="The output directory where the model predictions and checkpoints will be written."			,     )
     parser.add_argument(
         "--fp16"			,     action="store_true"			,     help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"			,     )

     parser.add_argument(
         "--fp16_opt_level"			,     type=lowerCamelCase__			,     default="O2"			,     help=(
             "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
             "See details at https://nvidia.github.io/apex/amp.html"
         )			,     )
     parser.add_argument("--n_tpu_cores"			,     dest="tpu_cores"			,     type=lowerCamelCase__       )
     parser.add_argument("--max_grad_norm"			,     dest="gradient_clip_val"			,     default=1.0			,     type=lowerCamelCase__			,     help="Max gradient norm"       )
     parser.add_argument("--do_train"			,     action="store_true"			,     help="Whether to run training."       )
     parser.add_argument("--do_predict"			,     action="store_true"			,     help="Whether to run predictions on the test set."       )
     parser.add_argument(
         "--gradient_accumulation_steps"			,     dest="accumulate_grad_batches"			,     type=lowerCamelCase__			,     default=1			,     help="Number of updates steps to accumulate before performing a backward/update pass."			,     )
     parser.add_argument("--seed"			,     type=lowerCamelCase__			,     default=42			,     help="random seed for initialization"       )
     parser.add_argument(
         "--data_dir"			,     default=str(Path(lowerCamelCase__       ).parent / "test_run" / "dummy-train-data"       )			,     type=lowerCamelCase__			,     help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."			,     )






def 	UpperCAmelCase_							(					__lowercase							:      Optional[int]			,     __lowercase							:      Tuple			,     __lowercase							:      List[Any]=None			,     __lowercase							:      Any=True			,     __lowercase							:      Optional[int]=[]			,     __lowercase							:      Any=None			,     __lowercase							:      Optional[int]=None			,     **__lowercase							:      Dict			,     )		->       Union[str, Any]:


     '''simple docstring'''



     pl.seed_everything(args.seed       )

     # init model
     _UpperCAmelCase     							=   Path(model.hparams.output_dir       )
     odir.mkdir(exist_ok=lowerCamelCase__       )

     # add custom checkpoints
     if checkpoint_callback is None:
          _UpperCAmelCase     							=   pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir			,     prefix="checkpoint"			,     monitor="val_loss"			,     mode="min"			,     save_top_k=1       )
     if early_stopping_callback:
          extra_callbacks.append(lowerCamelCase__       )
     if logging_callback is None:
          _UpperCAmelCase     							=   LoggingCallback()

     _UpperCAmelCase     							=   {}

     if args.fpaa:
          _UpperCAmelCase     							=   16

     if args.gpus > 1:
          _UpperCAmelCase     							=   """auto"""
          _UpperCAmelCase     							=   """ddp"""

     _UpperCAmelCase     							=   args.accumulate_grad_batches
     _UpperCAmelCase     							=   None
     _UpperCAmelCase     							=   """auto"""

     _UpperCAmelCase     							=   pl.Trainer.from_argparse_args(
         lowerCamelCase__			,     weights_summary=lowerCamelCase__			,     callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]			,     logger=lowerCamelCase__			,     val_check_interval=1			,     num_sanity_val_steps=2			,     **lowerCamelCase__			,     )

     if args.do_train:
          trainer.fit(lowerCamelCase__       )

     else:
          print("RAG modeling tests with new set functions successfuly executed!"       )
     return trainer









'''simple docstring'''




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		Any					=					['image_processor', 'tokenizer']
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					'BlipImageProcessor'
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					('BertTokenizer', 'BertTokenizerFast')


	def __init__(self       ,      lowercase       ,      lowercase							):
				A_	:						List[Any]	=     False
				super().__init__(lowercase       ,      lowercase							)
				A_	:						Tuple	=     self.image_processor


	def __call__(self       ,      lowercase = None       ,      lowercase = None       ,      lowercase = True       ,      lowercase = False       ,      lowercase = None       ,      lowercase = None       ,      lowercase = 0       ,      lowercase = None       ,      lowercase = None       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = True       ,      lowercase = None       ,      **lowercase       ,      ):
				if images is None and text is None:
							raise ValueError("""You have to specify either images or text."""							)

				# Get only text
				if images is None:
							A_	:						Optional[Any]	=     self.tokenizer
							A_	:						Tuple	=     self.tokenizer(
							    text=lowercase       ,      add_special_tokens=lowercase       ,      padding=lowercase       ,      truncation=lowercase       ,      max_length=lowercase       ,      stride=lowercase       ,      pad_to_multiple_of=lowercase       ,      return_attention_mask=lowercase       ,      return_overflowing_tokens=lowercase       ,      return_special_tokens_mask=lowercase       ,      return_offsets_mapping=lowercase       ,      return_token_type_ids=lowercase       ,      return_length=lowercase       ,      verbose=lowercase       ,      return_tensors=lowercase       ,      **lowercase       ,      )
							return text_encoding

				# add pixel_values
				A_	:						int	=     self.image_processor(lowercase       ,      return_tensors=lowercase							)

				if text is not None:
							A_	:						Optional[Any]	=     self.tokenizer(
							    text=lowercase       ,      add_special_tokens=lowercase       ,      padding=lowercase       ,      truncation=lowercase       ,      max_length=lowercase       ,      stride=lowercase       ,      pad_to_multiple_of=lowercase       ,      return_attention_mask=lowercase       ,      return_overflowing_tokens=lowercase       ,      return_special_tokens_mask=lowercase       ,      return_offsets_mapping=lowercase       ,      return_token_type_ids=lowercase       ,      return_length=lowercase       ,      verbose=lowercase       ,      return_tensors=lowercase       ,      **lowercase       ,      )
				else:
							A_	:						List[str]	=     None

				if text_encoding is not None:
							encoding_image_processor.update(lowercase							)

				return encoding_image_processor


	def     _a	(self       ,      *lowercase       ,      **lowercase							):
				return self.tokenizer.batch_decode(*lowercase       ,      **lowercase							)


	def     _a	(self       ,      *lowercase       ,      **lowercase							):
				return self.tokenizer.decode(*lowercase       ,      **lowercase							)


	@property
	def     _a	(self							):
				A_	:						int	=     self.tokenizer.model_input_names
				A_	:						Any	=     self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)