





'''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 (						_lowercase  ):
		UpperCAmelCase					:	List[str] 			=		'''char'''
		UpperCAmelCase					:	Dict 			=		'''bpe'''
		UpperCAmelCase					:	List[str] 			=		'''wp'''




__lowerCamelCase : Dict											=			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Dict 			=		['''image_processor''', '''char_tokenizer''']
		UpperCAmelCase					:	List[Any] 			=		'''ViTImageProcessor'''
		UpperCAmelCase					:	Tuple 			=		'''MgpstrTokenizer'''




		def __init__(self      :	List[str]							,			A__      :	Optional[int]=None							,			A__      :	Dict=None							,			**A__      :	Optional[int]				)					-> Dict:
						lowercase         = 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__							,			)
										lowercase         = kwargs.pop("feature_extractor"				)

						lowercase         = 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`."				)

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

						super().__init__(A__							,			A__				)




		def __call__(self      :	Tuple							,			A__      :	Tuple=None							,			A__      :	str=None							,			A__      :	Optional[Any]=None							,			**A__      :	Dict				)					-> str:
						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:
										lowercase         = self.image_processor(A__							,			return_tensors=A__							,			**A__				)
						if text is not None:
										lowercase         = self.char_tokenizer(A__							,			return_tensors=A__							,			**A__				)

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




		def 				UpperCAmelCase__   (self      :	str							,			A__      :	Union[str, Any]				)					-> List[str]:
						lowercase   ,							lowercase   ,							lowercase         = sequences
						lowercase         = char_preds.size(0				)

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

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

						lowercase         = {}
						lowercase         = final_strs
						lowercase         = final_scores
						lowercase         = char_strs
						lowercase         = bpe_strs
						lowercase         = wp_strs
						return out




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

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

						for index in range(A__				):
										lowercase         = preds_str[index].find(A__				)
										lowercase         = preds_str[index][:pred_eos]
										lowercase         = preds_index[index].cpu().tolist()
										lowercase         = pred_index.index(A__				) if eos_token in pred_index else -1
										lowercase         = preds_max_prob[index][: pred_eos_index + 1]
										lowercase         = 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 				UpperCAmelCase__   (self      :	Dict							,			A__      :	Any				)					-> Optional[Any]:
						lowercase         = [seq.replace(" "							,			""				) for seq in self.char_tokenizer.batch_decode(A__				)]
						return decode_strs




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	Tuple				)					-> List[str]:
						return self.bpe_tokenizer.batch_decode(A__				)





		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	int				)					-> Any:
						lowercase         = [seq.replace(" "							,			""				) for seq in self.wp_tokenizer.batch_decode(A__				)]
						return decode_strs











'''simple docstring'''

from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return DownloadCommand(args.model		,							args.cache_dir		,							args.force		,							args.trust_remote_code							)


class       UpperCAmelCase (						_lowercase  ):




		@staticmethod
		def 				UpperCAmelCase__   (A__      :	ArgumentParser				)					-> List[Any]:
						lowercase         = parser.add_parser("download"				)
						download_parser.add_argument(
						    "--cache-dir"							,			type=A__							,			default=A__							,			help="Path to location to store the models"				)
						download_parser.add_argument(
						    "--force"							,			action="store_true"							,			help="Force the model to be download even if already in cache-dir"				)
						download_parser.add_argument(
						    "--trust-remote-code"							,			action="store_true"							,			help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"							,			)
						download_parser.add_argument("model"							,			type=A__							,			help="Name of the model to download"				)
						download_parser.set_defaults(func=A__				)




		def __init__(self      :	Tuple							,			A__      :	str							,			A__      :	str							,			A__      :	bool							,			A__      :	bool				)					-> str:
						lowercase         = model
						lowercase         = cache
						lowercase         = force
						lowercase         = trust_remote_code





		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Dict:
						from ..models.auto import AutoModel, AutoTokenizer

						AutoModel.from_pretrained(
						    self._model							,			cache_dir=self._cache							,			force_download=self._force							,			trust_remote_code=self._trust_remote_code				)
						AutoTokenizer.from_pretrained(
						    self._model							,			cache_dir=self._cache							,			force_download=self._force							,			trust_remote_code=self._trust_remote_code				)




