



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__					(  _a       ):
				A_   :     int								=     'char'
				A_   :     Dict								=     'bpe'
				A_   :     Any								=     'wp'





__snake_case     :Optional[int]			       =(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)



class        lowerCAmelCase__					(  _a       ):
				A_   :     Optional[int]								=     ['image_processor', 'char_tokenizer']
				A_   :     Optional[Any]								=     'ViTImageProcessor'
				A_   :     Tuple								=     'MgpstrTokenizer'


				def __init__(							self      :  Dict						, __UpperCamelCase      :  Optional[Any]=None						, __UpperCamelCase      :  str=None						, **__UpperCamelCase      :  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.'						, lowerCAmelCase_						, )
																		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__(lowerCAmelCase_						, lowerCAmelCase_       )


				def __call__(							self      :  str						, __UpperCamelCase      :  List[Any]=None						, __UpperCamelCase      :  Union[str, Any]=None						, __UpperCamelCase      :  Any=None						, **__UpperCamelCase      :  Tuple       )		->     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(lowerCAmelCase_						, return_tensors=lowerCAmelCase_						, **lowerCAmelCase_       )
											if text is not None:
																		A      						=    self.char_tokenizer(lowerCAmelCase_						, return_tensors=lowerCAmelCase_						, **lowerCAmelCase_       )

											if text is None:
																		return inputs
											elif images is None:
																		return encodings
											else:
																		A      						=    encodings['input_ids']
																		return inputs


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

											A						,   A      						=    self._decode_helper(lowerCAmelCase_						, 'char'       )
											A						,   A      						=    self._decode_helper(lowerCAmelCase_						, 'bpe'       )
											A						,   A      						=    self._decode_helper(lowerCAmelCase_						, 'wp'       )

											A      						=    []
											A      						=    []
											for i in range(lowerCAmelCase_       ):
																		A      						=    [char_scores[i], bpe_scores[i], wp_scores[i]]
																		A      						=    [char_strs[i], bpe_strs[i], wp_strs[i]]
																		A      						=    scores.index(max(lowerCAmelCase_       )       )
																		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      :  Optional[int]						, __UpperCamelCase      :  Dict						, __UpperCamelCase      :  Optional[Any]       )		->     Dict:


											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      						=    102
																		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=lowerCAmelCase_						, sorted=lowerCAmelCase_       )
											A      						=    preds_index.view(-1						, lowerCAmelCase_       )[:, 1:]
											A      						=    decoder(lowerCAmelCase_       )
											A						,   A      						=    torch.nn.functional.softmax(lowerCAmelCase_						, dim=2       ).max(dim=2       )
											A      						=    preds_max_prob[:, 1:]

											for index in range(lowerCAmelCase_       ):
																		A      						=    preds_str[index].find(lowerCAmelCase_       )
																		A      						=    preds_str[index][:pred_eos]
																		A      						=    preds_index[index].cpu().tolist()
																		A      						=    pred_index.index(lowerCAmelCase_       ) 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(lowerCAmelCase_       )
																		conf_scores.append(lowerCAmelCase_       )

											return dec_strs, conf_scores


				def __UpperCamelCase						(							self      :  Optional[Any]						, __UpperCamelCase      :  Optional[int]       )		->     Union[str, Any]:
											A      						=    [seq.replace(' '						, ''       ) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase_       )]
											return decode_strs


				def __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  Optional[Any]       )		->     Tuple:
											return self.bpe_tokenizer.batch_decode(lowerCAmelCase_       )


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  Dict       )		->     Optional[Any]:
											A      						=    [seq.replace(' '						, ''       ) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase_       )]
											return decode_strs





'''simple docstring'''


import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
				_snake_case : Tuple  				=						{
				    'linear': PIL.Image.Resampling.BILINEAR,
				    'bilinear': PIL.Image.Resampling.BILINEAR,
				    'bicubic': PIL.Image.Resampling.BICUBIC,
				    'lanczos': PIL.Image.Resampling.LANCZOS,
				    'nearest': PIL.Image.Resampling.NEAREST,
				}
else:
				_snake_case : Any  				=						{
				    'linear': PIL.Image.LINEAR,
				    'bilinear': PIL.Image.BILINEAR,
				    'bicubic': PIL.Image.BICUBIC,
				    'lanczos': PIL.Image.LANCZOS,
				    'nearest': PIL.Image.NEAREST,
				}
def 						snake_case_       (UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							_a      	=							(images / 2 + 0.5).clamp(0					,		1 )
							_a      	=							images.cpu().permute(0					,		2					,		3					,		1 ).float().numpy()
							_a      	=							numpy_to_pil(UpperCamelCase )
							return images

def 						snake_case_       (UpperCamelCase			:    str ):





							'''simple docstring'''


							if images.ndim == 3:
														_a      	=							images[None, ...]
							_a      	=							(images * 255).round().astype('''uint8''' )
							if images.shape[-1] == 1:
														# special case for grayscale (single channel) images
														_a      	=							[Image.fromarray(image.squeeze()					,		mode='''L''' ) for image in images]
							else:
														_a      	=							[Image.fromarray(UpperCamelCase ) for image in images]

							return pil_images

