



"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
   import torch

if is_vision_available():
   import PIL


_UpperCAmelCase   =	logging.get_logger(__name__)





def    __magic_name__ (							lowercase							,	lowercase							,	lowercase							,	lowercase       ):
   def constraint_to_multiple_of(lowercase							,	lowercase							,	lowercase=0							,	lowercase=None       ):
      SCREAMING_SNAKE_CASE_:       List[str]   	=round(val / multiple       ) * multiple

      if max_val is not None and x > max_val:
         SCREAMING_SNAKE_CASE_:       List[str]   	=math.floor(val / multiple       ) * multiple

      if x < min_val:
         SCREAMING_SNAKE_CASE_:       Optional[Any]   	=math.ceil(val / multiple       ) * multiple

      return x

   SCREAMING_SNAKE_CASE_:       int   	=(output_size, output_size) if isinstance(_lowerCamelCase							,	_lowerCamelCase       ) else output_size

   SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       List[str]   	=get_image_size(_lowerCamelCase       )
   SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_:       int   	=output_size

   # determine new height and width
   SCREAMING_SNAKE_CASE_:       int   	=output_height / input_height
   SCREAMING_SNAKE_CASE_:       List[str]   	=output_width / input_width

   if keep_aspect_ratio:
      # scale as little as possible
      if abs(1 - scale_width       ) < abs(1 - scale_height       ):
         # fit width
         SCREAMING_SNAKE_CASE_:       Any   	=scale_width
      else:
         # fit height
         SCREAMING_SNAKE_CASE_:       List[Any]   	=scale_height

   SCREAMING_SNAKE_CASE_:       List[Any]   	=constraint_to_multiple_of(scale_height * input_height							,	multiple=_lowerCamelCase       )
   SCREAMING_SNAKE_CASE_:       Dict   	=constraint_to_multiple_of(scale_width * input_width							,	multiple=_lowerCamelCase       )

   return (new_height, new_width)
class     a		(     UpperCamelCase__				):
  UpperCamelCase		: List[Any]     =    ['pixel_values']



  def __init__(  self       :    List[str]				,						lowerCAmelCase       :    bool = True				,						lowerCAmelCase       :    Dict[str, int] = None				,						lowerCAmelCase       :    PILImageResampling = PILImageResampling.BILINEAR				,						lowerCAmelCase       :    bool = False				,						lowerCAmelCase       :    int = 1				,						lowerCAmelCase       :    bool = True				,						lowerCAmelCase       :    Union[int, float] = 1 / 255				,						lowerCAmelCase       :    bool = True				,						lowerCAmelCase       :    Optional[Union[float, List[float]]] = None				,						lowerCAmelCase       :    Optional[Union[float, List[float]]] = None				,						**lowerCAmelCase       :    Optional[Any]				,						)   ->    None:


     '''simple docstring'''




     super().__init__(**lowercase_						)
     SCREAMING_SNAKE_CASE_:       Dict   	=size if size is not None else {"""height""": 384, """width""": 384}
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=get_size_dict(lowercase_						)
     SCREAMING_SNAKE_CASE_:       int   	=do_resize
     SCREAMING_SNAKE_CASE_:       List[str]   	=size
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=keep_aspect_ratio
     SCREAMING_SNAKE_CASE_:       Dict   	=ensure_multiple_of
     SCREAMING_SNAKE_CASE_:       int   	=resample
     SCREAMING_SNAKE_CASE_:       List[str]   	=do_rescale
     SCREAMING_SNAKE_CASE_:       List[Any]   	=rescale_factor
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=do_normalize
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     SCREAMING_SNAKE_CASE_:       List[Any]   	=image_std if image_std is not None else IMAGENET_STANDARD_STD



  def 		lowerCamelCase__				(  self       :    List[Any]				,						lowerCAmelCase       :    np.ndarray				,						lowerCAmelCase       :    Dict[str, int]				,						lowerCAmelCase       :    bool = False				,						lowerCAmelCase       :    int = 1				,						lowerCAmelCase       :    PILImageResampling = PILImageResampling.BICUBIC				,						lowerCAmelCase       :    Optional[Union[str, ChannelDimension]] = None				,						**lowerCAmelCase       :    Tuple				,						)   ->    np.ndarray:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       List[str]   	=get_size_dict(lowercase_						)
     if "height" not in size or "width" not in size:
        raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''						)
     SCREAMING_SNAKE_CASE_:       Tuple   	=get_resize_output_image_size(
         lowercase_				,						output_size=(size["""height"""], size["""width"""])				,						keep_aspect_ratio=lowercase_				,						multiple=lowercase_				,						)
     return resize(lowercase_				,						size=lowercase_				,						resample=lowercase_				,						data_format=lowercase_				,						**lowercase_						)



  def 		lowerCamelCase__				(  self       :    Any				,						lowerCAmelCase       :    np.ndarray				,						lowerCAmelCase       :    Union[int, float]				,						lowerCAmelCase       :    Optional[Union[str, ChannelDimension]] = None				,						**lowerCAmelCase       :    int				,						)   ->    int:


     '''simple docstring'''




     return rescale(lowercase_				,						scale=lowercase_				,						data_format=lowercase_				,						**lowercase_						)



  def 		lowerCamelCase__				(  self       :    Dict				,						lowerCAmelCase       :    np.ndarray				,						lowerCAmelCase       :    Union[float, List[float]]				,						lowerCAmelCase       :    Union[float, List[float]]				,						lowerCAmelCase       :    Optional[Union[str, ChannelDimension]] = None				,						**lowerCAmelCase       :    Optional[Any]				,						)   ->    np.ndarray:


     '''simple docstring'''




     return normalize(lowercase_				,						mean=lowercase_				,						std=lowercase_				,						data_format=lowercase_				,						**lowercase_						)



  def 		lowerCamelCase__				(  self       :    List[Any]				,						lowerCAmelCase       :    ImageInput				,						lowerCAmelCase       :    bool = None				,						lowerCAmelCase       :    int = None				,						lowerCAmelCase       :    bool = None				,						lowerCAmelCase       :    int = None				,						lowerCAmelCase       :    PILImageResampling = None				,						lowerCAmelCase       :    bool = None				,						lowerCAmelCase       :    float = None				,						lowerCAmelCase       :    bool = None				,						lowerCAmelCase       :    Optional[Union[float, List[float]]] = None				,						lowerCAmelCase       :    Optional[Union[float, List[float]]] = None				,						lowerCAmelCase       :    Optional[Union[str, TensorType]] = None				,						lowerCAmelCase       :    ChannelDimension = ChannelDimension.FIRST				,						**lowerCAmelCase       :    Optional[Any]				,						)   ->    PIL.Image.Image:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[int]   	=do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE_:       Tuple   	=size if size is not None else self.size
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=get_size_dict(lowercase_						)
     SCREAMING_SNAKE_CASE_:       Any   	=keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
     SCREAMING_SNAKE_CASE_:       List[str]   	=resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE_:       List[str]   	=do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE_:       Optional[int]   	=make_list_of_images(lowercase_						)

     if not valid_images(lowercase_						):
        raise ValueError(
            """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
            """torch.Tensor, tf.Tensor or jax.ndarray."""						)

     if do_resize and size is None or resample is None:
        raise ValueError("""Size and resample must be specified if do_resize is True."""						)

     if do_rescale and rescale_factor is None:
        raise ValueError("""Rescale factor must be specified if do_rescale is True."""						)

     if do_normalize and (image_mean is None or image_std is None):
        raise ValueError("""Image mean and std must be specified if do_normalize is True."""						)

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=[to_numpy_array(lowercase_						) for image in images]

     if do_resize:
        SCREAMING_SNAKE_CASE_:       List[Any]   	=[self.resize(image=lowercase_				,						size=lowercase_				,						resample=lowercase_						) for image in images]

     if do_rescale:
        SCREAMING_SNAKE_CASE_:       Tuple   	=[self.rescale(image=lowercase_				,						scale=lowercase_						) for image in images]

     if do_normalize:
        SCREAMING_SNAKE_CASE_:       int   	=[self.normalize(image=lowercase_				,						mean=lowercase_				,						std=lowercase_						) for image in images]

     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=[to_channel_dimension_format(lowercase_				,						lowercase_						) for image in images]

     SCREAMING_SNAKE_CASE_:       int   	={"""pixel_values""": images}
     return BatchFeature(data=lowercase_				,						tensor_type=lowercase_						)





  def 		lowerCamelCase__				(  self       :    Any				,						lowerCAmelCase       :    Dict				,						lowerCAmelCase       :    List[Tuple] = None						)   ->    List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[int]   	=outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
        if len(lowercase_						) != len(lowercase_						):
           raise ValueError(
               """Make sure that you pass in as many target sizes as the batch dimension of the logits"""						)

        if is_torch_tensor(lowercase_						):
           SCREAMING_SNAKE_CASE_:       int   	=target_sizes.numpy()

        SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=[]

        for idx in range(len(lowercase_						)						):
           SCREAMING_SNAKE_CASE_:       Any   	=torch.nn.functional.interpolate(
               logits[idx].unsqueeze(dim=0						)				,						size=target_sizes[idx]				,						mode="""bilinear"""				,						align_corners=lowercase_						)
           SCREAMING_SNAKE_CASE_:       Optional[int]   	=resized_logits[0].argmax(dim=0						)
           semantic_segmentation.append(lowercase_						)
     else:
        SCREAMING_SNAKE_CASE_:       List[str]   	=logits.argmax(dim=1						)
        SCREAMING_SNAKE_CASE_:       Any   	=[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

     return semantic_segmentation










'''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



