

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				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='char'
			lowerCamelCase__							     ='bpe'
			lowerCamelCase__							     ='wp'

a_      =     (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     =['image_processor', 'char_tokenizer']
			lowerCamelCase__							     ='ViTImageProcessor'
			lowerCamelCase__							     ='MgpstrTokenizer'
			def __init__(      self				:			Any   ,   a				:			Any=None   ,   a				:			str=None   ,   **a				:			List[Any]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							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   ,   )
					SCREAMING_SNAKE_CASE :   Dict					  =							kwargs.pop("feature_extractor"						)

				SCREAMING_SNAKE_CASE :   int					  =							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`."						)

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

				super().__init__(a   ,   a						)
			def __call__(      self				:			Optional[Any]   ,   a				:			List[Any]=None   ,   a				:			List[Any]=None   ,   a				:			Union[str, Any]=None   ,   **a				:			int						)				->							List[Any]:

				"""simple docstring"""

				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:
					SCREAMING_SNAKE_CASE :   Tuple					  =							self.image_processor(a   ,   return_tensors=a   ,   **a						)
				if text is not None:
					SCREAMING_SNAKE_CASE :   List[str]					  =							self.char_tokenizer(a   ,   return_tensors=a   ,   **a						)

				if text is None:
					return inputs
				elif images is None:
					return encodings
				else:
					SCREAMING_SNAKE_CASE :   Tuple					  =							encodings["input_ids"]
					return inputs
			def 				__UpperCamelCase			(      self				:			Union[str, Any]   ,   a				:			List[Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[str]					  =							sequences
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							char_preds.size(0						)

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							self._decode_helper(a   ,   "char"						)
				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							self._decode_helper(a   ,   "bpe"						)
				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self._decode_helper(a   ,   "wp"						)

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

				SCREAMING_SNAKE_CASE :   int					  =							{}
				SCREAMING_SNAKE_CASE :   List[str]					  =							final_strs
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							final_scores
				SCREAMING_SNAKE_CASE :   Any					  =							char_strs
				SCREAMING_SNAKE_CASE :   Tuple					  =							bpe_strs
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							wp_strs
				return out
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			str   ,   a				:			Optional[Any]						)				->							Dict:

				"""simple docstring"""

				if format == DecodeType.CHARACTER:
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.char_decode
					SCREAMING_SNAKE_CASE :   int					  =							1
					SCREAMING_SNAKE_CASE :   Tuple					  =							"[s]"
				elif format == DecodeType.BPE:
					SCREAMING_SNAKE_CASE :   Any					  =							self.bpe_decode
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							2
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							"#"
				elif format == DecodeType.WORDPIECE:
					SCREAMING_SNAKE_CASE :   Any					  =							self.wp_decode
					SCREAMING_SNAKE_CASE :   Tuple					  =							102
					SCREAMING_SNAKE_CASE :   List[str]					  =							"[SEP]"
				else:
					raise ValueError(F"Format {format} is not supported."						)

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

				for index in range(a						):
					SCREAMING_SNAKE_CASE :   List[Any]					  =							preds_str[index].find(a						)
					SCREAMING_SNAKE_CASE :   Tuple					  =							preds_str[index][:pred_eos]
					SCREAMING_SNAKE_CASE :   int					  =							preds_index[index].cpu().tolist()
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							pred_index.index(a						) if eos_token in pred_index else -1
					SCREAMING_SNAKE_CASE :   int					  =							preds_max_prob[index][: pred_eos_index + 1]
					SCREAMING_SNAKE_CASE :   List[Any]					  =							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				:			int   ,   a				:			str						)				->							List[Any]:

				"""simple docstring"""

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

				"""simple docstring"""

				return self.bpe_tokenizer.batch_decode(a						)






			def 				__UpperCamelCase			(      self				:			Union[str, Any]   ,   a				:			str						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							[seq.replace(" "   ,   ""						) for seq in self.wp_tokenizer.batch_decode(a						)]
				return decode_strs


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_      =     {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'VanForImageClassification',
			    'VanModel',
			    'VanPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_van import (
						    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    VanForImageClassification,
						    VanModel,
						    VanPreTrainedModel,
						)

else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure)