
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 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'char'
	_A			=  'bpe'
	_A			=  'wp'
lowercase						:      Any									= (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  ['image_processor', 'char_tokenizer']
	_A			=  'ViTImageProcessor'
	_A			=  'MgpstrTokenizer'







	def __init__(      self	:Union[str, Any]  ,     a	:int=None  ,     a	:Tuple=None  ,     **a	:Dict		)	->				Optional[Any]:
								__UpperCamelCase			:     Optional[Any]         =      None
								if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead."  ,     a  ,     )
															__UpperCamelCase			:     Dict         =      kwargs.pop("feature_extractor"		)

								__UpperCamelCase			:     Any         =      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`."		)

								__UpperCamelCase			:     List[str]         =      tokenizer
								__UpperCamelCase			:     Optional[Any]         =      AutoTokenizer.from_pretrained("gpt2"		)
								__UpperCamelCase			:     Dict         =      AutoTokenizer.from_pretrained("bert-base-uncased"		)

								super().__init__(a  ,     a		)







	def __call__(      self	:Optional[Any]  ,     a	:str=None  ,     a	:Union[str, Any]=None  ,     a	:Any=None  ,     **a	:List[Any]		)	->				List[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:
															__UpperCamelCase			:     List[str]         =      self.image_processor(a  ,     return_tensors=a  ,     **a		)
								if text is not None:
															__UpperCamelCase			:     int         =      self.char_tokenizer(a  ,     return_tensors=a  ,     **a		)

								if text is None:
															return inputs
								elif images is None:
															return encodings
								else:
															__UpperCamelCase			:     int         =      encodings["input_ids"]
															return inputs







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:Optional[Any]		)	->				Union[str, Any]:
								__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      sequences
								__UpperCamelCase			:     List[str]         =      char_preds.size(0		)

								__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      self._decode_helper(a  ,     "char"		)
								__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      self._decode_helper(a  ,     "bpe"		)
								__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      self._decode_helper(a  ,     "wp"		)

								__UpperCamelCase			:     Tuple         =      []
								__UpperCamelCase			:     Optional[Any]         =      []
								for i in range(a		):
															__UpperCamelCase			:     List[str]         =      [char_scores[i], bpe_scores[i], wp_scores[i]]
															__UpperCamelCase			:     Tuple         =      [char_strs[i], bpe_strs[i], wp_strs[i]]
															__UpperCamelCase			:     Optional[Any]         =      scores.index(max(a		)		)
															final_strs.append(strs[max_score_index]		)
															final_scores.append(scores[max_score_index]		)

								__UpperCamelCase			:     Any         =      {}
								__UpperCamelCase			:     Optional[int]         =      final_strs
								__UpperCamelCase			:     List[Any]         =      final_scores
								__UpperCamelCase			:     Dict         =      char_strs
								__UpperCamelCase			:     Any         =      bpe_strs
								__UpperCamelCase			:     Tuple         =      wp_strs
								return out







	def      _lowerCamelCase       (      self	:Dict  ,     a	:str  ,     a	:str		)	->				Dict:
								if format == DecodeType.CHARACTER:
															__UpperCamelCase			:     int         =      self.char_decode
															__UpperCamelCase			:     List[Any]         =      1
															__UpperCamelCase			:     List[str]         =      "[s]"
								elif format == DecodeType.BPE:
															__UpperCamelCase			:     int         =      self.bpe_decode
															__UpperCamelCase			:     Tuple         =      2
															__UpperCamelCase			:     Union[str, Any]         =      "#"
								elif format == DecodeType.WORDPIECE:
															__UpperCamelCase			:     List[Any]         =      self.wp_decode
															__UpperCamelCase			:     Union[str, Any]         =      1_0_2
															__UpperCamelCase			:     List[Any]         =      "[SEP]"
								else:
															raise ValueError(f'Format {format} is not supported.'		)

								__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      [], []
								__UpperCamelCase			:     str         =      pred_logits.size(0		)
								__UpperCamelCase			:     Dict         =      pred_logits.size(1		)
								__UpperCamelCase     ,		__UpperCamelCase			:     Optional[Any]         =      pred_logits.topk(1  ,     dim=-1  ,     largest=a  ,     sorted=a		)
								__UpperCamelCase			:     Any         =      preds_index.view(-1  ,     a		)[:, 1:]
								__UpperCamelCase			:     str         =      decoder(a		)
								__UpperCamelCase     ,		__UpperCamelCase			:     Optional[Any]         =      torch.nn.functional.softmax(a  ,     dim=2		).max(dim=2		)
								__UpperCamelCase			:     Tuple         =      preds_max_prob[:, 1:]

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

								return dec_strs, conf_scores







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:List[Any]		)	->				Any:
								__UpperCamelCase			:     List[Any]         =      [seq.replace(" "  ,     ""		) for seq in self.char_tokenizer.batch_decode(a		)]
								return decode_strs







	def      _lowerCamelCase       (      self	:int  ,     a	:str		)	->				List[Any]:
								return self.bpe_tokenizer.batch_decode(a		)







	def      _lowerCamelCase       (      self	:Dict  ,     a	:str		)	->				Union[str, Any]:
								__UpperCamelCase			:     Optional[Any]         =      [seq.replace(" "  ,     ""		) for seq in self.wp_tokenizer.batch_decode(a		)]
								return decode_strs

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  ComputeEnvironment.AMAZON_SAGEMAKER
	_A			=  True
	_A			=  'ml.p3.2xlarge'
	_A			=  'accelerate_sagemaker_execution_role'
	_A			=  'hf-sm'
	_A			=  'us-east-1'
	_A			=  1
	_A			=  'accelerate-sagemaker-1'
	_A			=  '1.6'
	_A			=  '4.4'
	_A			=  'train.py'
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Tuple		)	->				str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase			:     str         =      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args		)
								assert isinstance(converted_args["model_name_or_path"]  ,     a		)
								assert isinstance(converted_args["do_train"]  ,     a		)
								assert isinstance(converted_args["epochs"]  ,     a		)
								assert isinstance(converted_args["learning_rate"]  ,     a		)
								assert isinstance(converted_args["max_steps"]  ,     a		)

								with pytest.raises(a		):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args		)