"""simple docstring"""






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowerCAmelCase__  :  List[Any]						     =	logging.get_logger(__name__)


if is_torch_available():
						import torch
						from torch import nn
						from torch.utils.data import Dataset

						from transformers import Trainer
						class 				snake_case       ( __UpperCAmelCase				):




											"""simple docstring"""

											def __init__(				self   :	Optional[int]    ,lowerCamelCase__   :	int = 101			):
															UpperCAmelCase__             =							length







											def __len__(				self   :	Union[str, Any]			):
															return self.length







											def __getitem__(				self   :	str    ,lowerCamelCase__   :	Tuple			):
															return i






						class 				snake_case       :




											"""simple docstring"""

											def __call__(				self   :	int    ,lowerCamelCase__   :	Tuple			):
															return {"input_ids": torch.tensor(lowerCamelCase__			), "labels": torch.tensor(lowerCamelCase__			)}






						class 				snake_case       ( nn.Module				):




											"""simple docstring"""

											def __init__(				self   :	Any			):
															super().__init__()
															# Add some (unused) params otherwise DDP will complain.
															UpperCAmelCase__             =							nn.Linear(120    ,80			)







											def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	List[str]    ,lowerCamelCase__   :	Union[str, Any]=None			):
															if labels is not None:
																			return torch.tensor(0.0    ,device=input_ids.device			), input_ids
															else:
																			return input_ids
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					@require_torch_neuroncore
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							f'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
									UpperCAmelCase__             =							self.get_auto_remove_tmp_dir()
									UpperCAmelCase__             =							f'''--output_dir {output_dir}'''.split()
									UpperCAmelCase__             =							['torchrun'] + distributed_args + args
									execute_subprocess_async(lowerCamelCase__    ,env=self.get_env()			)
									# successful return here == success - any errors would have caused an error in the sub-call






class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					@require_torch_multi_gpu
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							f'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
									UpperCAmelCase__             =							self.get_auto_remove_tmp_dir()
									UpperCAmelCase__             =							f'''--output_dir {output_dir}'''.split()
									UpperCAmelCase__             =							['torchrun'] + distributed_args + args
									execute_subprocess_async(lowerCamelCase__    ,env=self.get_env()			)
									# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
						# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
						#
						# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

						lowerCAmelCase__  :  List[Any]						     =	HfArgumentParser((TrainingArguments,))
						lowerCAmelCase__  :  Any						     =	parser.parse_args_into_dataclasses()[0]

						logger.warning(
						    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
						    F"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
						)

						# Essentially, what we want to verify in the distributed case is that we get all samples back,
						# in the right order. (this is crucial for prediction for instance)
						for dataset_length in [101, 40, 7]:
												lowerCAmelCase__  :  str						     =	DummyDataset(dataset_length)



												def   a_						(					lowerCamelCase       ):
																UpperCAmelCase__             =							list(range(len(lowerCamelCase       )       )       )
																UpperCAmelCase__             =							p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																if not success and training_args.local_rank == 0:
																				logger.warning(
																				    'Predictions and/or labels do not match expected results:\n  - predictions: '
																				    f'''{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'''       )
																return {"success": success}

												lowerCAmelCase__  :  Union[str, Any]						     =	Trainer(
												    model=DummyModel(),
												    args=training_args,
												    data_collator=DummyDataCollator(),
												    eval_dataset=dataset,
												    compute_metrics=compute_metrics,
												)
												lowerCAmelCase__  :  str						     =	trainer.evaluate()
												logger.info(metrics)
												if metrics["eval_success"] is not True:
																		logger.error(metrics)
																		exit(1)

												lowerCAmelCase__  :  Optional[Any]						     =	trainer.predict(dataset)
												logger.info(p.metrics)
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics)
																		exit(1)

												lowerCAmelCase__  :  List[Any]						     =	2

												lowerCAmelCase__  :  Optional[Any]						     =	trainer.evaluate()
												logger.info(metrics)
												if metrics["eval_success"] is not True:
																		logger.error(metrics)
																		exit(1)

												lowerCAmelCase__  :  List[str]						     =	trainer.predict(dataset)
												logger.info(p.metrics)
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics)
																		exit(1)







												lowerCAmelCase__  :  List[str]						     =	None










'''simple docstring'''

import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
  import torch






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""char"""
 __lowercase   		=				"""bpe"""
 __lowercase   		=				"""wp"""

__lowerCAmelCase							:	Union[str, Any]			=(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				["""image_processor""", """char_tokenizer"""]
 __lowercase   		=				"""ViTImageProcessor"""
 __lowercase   		=				"""MgpstrTokenizer"""
 def __init__(				self      :int						,       lowercase_      :int=None						,       lowercase_      :List[str]=None						,       **lowercase_      :List[Any]							)->							Optional[Any]:
     A__				=			None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."						,       lowercase_						,       )
         A__				=			kwargs.pop("feature_extractor"							)

     A__				=			image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."							)
     if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."							)

     A__				=			tokenizer
     A__				=			AutoTokenizer.from_pretrained("gpt2"							)
     A__				=			AutoTokenizer.from_pretrained("bert-base-uncased"							)

     super().__init__(lowercase_						,       lowercase_							)
 def __call__(				self      :Optional[Any]						,       lowercase_      :Any=None						,       lowercase_      :Tuple=None						,       lowercase_      :List[str]=None						,       **lowercase_      :Union[str, Any]							)->							Optional[Any]:
     if images is None and text is None:
         raise ValueError("You need to specify either an `images` or `text` input to process."							)

     if images is not None:
         A__				=			self.image_processor(lowercase_						,       return_tensors=lowercase_						,       **lowercase_							)
     if text is not None:
         A__				=			self.char_tokenizer(lowercase_						,       return_tensors=lowercase_						,       **lowercase_							)

     if text is None:
         return inputs
     elif images is None:
         return encodings
     else:
         A__				=			encodings["input_ids"]
         return inputs
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :int							)->							int:
     A__,	A__,	A__				=			sequences
     A__				=			char_preds.size(0							)

     A__,	A__				=			self._decode_helper(lowercase_						,       "char"							)
     A__,	A__				=			self._decode_helper(lowercase_						,       "bpe"							)
     A__,	A__				=			self._decode_helper(lowercase_						,       "wp"							)

     A__				=			[]
     A__				=			[]
     for i in range(lowercase_							):
         A__				=			[char_scores[i], bpe_scores[i], wp_scores[i]]
         A__				=			[char_strs[i], bpe_strs[i], wp_strs[i]]
         A__				=			scores.index(max(lowercase_							)							)
         final_strs.append(strs[max_score_index]							)
         final_scores.append(scores[max_score_index]							)

     A__				=			{}
     A__				=			final_strs
     A__				=			final_scores
     A__				=			char_strs
     A__				=			bpe_strs
     A__				=			wp_strs
     return out
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :List[str]						,       lowercase_      :str							)->							Optional[Any]:
     if format == DecodeType.CHARACTER:
         A__				=			self.char_decode
         A__				=			1
         A__				=			"[s]"
     elif format == DecodeType.BPE:
         A__				=			self.bpe_decode
         A__				=			2
         A__				=			"#"
     elif format == DecodeType.WORDPIECE:
         A__				=			self.wp_decode
         A__				=			1_02
         A__				=			"[SEP]"
     else:
         raise ValueError(F"Format {format} is not supported."							)

     A__,	A__				=			[], []
     A__				=			pred_logits.size(0							)
     A__				=			pred_logits.size(1							)
     A__,	A__				=			pred_logits.topk(1						,       dim=-1						,       largest=lowercase_						,       sorted=lowercase_							)
     A__				=			preds_index.view(-1						,       lowercase_							)[:, 1:]
     A__				=			decoder(lowercase_							)
     A__,	A__				=			torch.nn.functional.softmax(lowercase_						,       dim=2							).max(dim=2							)
     A__				=			preds_max_prob[:, 1:]

     for index in range(lowercase_							):
         A__				=			preds_str[index].find(lowercase_							)
         A__				=			preds_str[index][:pred_eos]
         A__				=			preds_index[index].cpu().tolist()
         A__				=			pred_index.index(lowercase_							) if eos_token in pred_index else -1
         A__				=			preds_max_prob[index][: pred_eos_index + 1]
         A__				=			pred_max_prob.cumprod(dim=0							)[-1] if pred_max_prob.nelement() != 0 else 0.0
         dec_strs.append(lowercase_							)
         conf_scores.append(lowercase_							)

     return dec_strs, conf_scores
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]							)->							int:
     A__				=			[seq.replace(" "						,       ""							) for seq in self.char_tokenizer.batch_decode(lowercase_							)]
     return decode_strs
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :Optional[Any]							)->							List[str]:
     return self.bpe_tokenizer.batch_decode(lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :List[str]							)->							Union[str, Any]:
     A__				=			[seq.replace(" "						,       ""							) for seq in self.wp_tokenizer.batch_decode(lowercase_							)]
     return decode_strs



