


"""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):

	"""simple docstring"""





	snake_case__       :						Optional[int]					      =    "char"
	snake_case__       :						Any					      =    "bpe"
	snake_case__       :						Any					      =    "wp"


a__       :							List[Any]        =  (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						str					      =    ["image_processor", "char_tokenizer"]
	snake_case__       :						Any					      =    "ViTImageProcessor"
	snake_case__       :						Optional[Any]					      =    "MgpstrTokenizer"




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

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

						super().__init__(UpperCAmelCase__    ,    UpperCAmelCase__     )




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

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




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       Any     ) ->			Dict:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       sequences
						__SCREAMING_SNAKE_CASE		     =       char_preds.size(0     )

						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self._decode_helper(UpperCAmelCase__    ,    "char"     )
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self._decode_helper(UpperCAmelCase__    ,    "bpe"     )
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self._decode_helper(UpperCAmelCase__    ,    "wp"     )

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

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




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       List[str]    ,    UpperCAmelCase__    :       Any     ) ->			Tuple:
						if format == DecodeType.CHARACTER:
											__SCREAMING_SNAKE_CASE		     =       self.char_decode
											__SCREAMING_SNAKE_CASE		     =       1
											__SCREAMING_SNAKE_CASE		     =       "[s]"
						elif format == DecodeType.BPE:
											__SCREAMING_SNAKE_CASE		     =       self.bpe_decode
											__SCREAMING_SNAKE_CASE		     =       2
											__SCREAMING_SNAKE_CASE		     =       "#"
						elif format == DecodeType.WORDPIECE:
											__SCREAMING_SNAKE_CASE		     =       self.wp_decode
											__SCREAMING_SNAKE_CASE		     =       1_0_2
											__SCREAMING_SNAKE_CASE		     =       "[SEP]"
						else:
											raise ValueError(F"""Format {format} is not supported."""     )

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

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

						return dec_strs, conf_scores




	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       Union[str, Any]     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       [seq.replace(" "    ,    ""     ) for seq in self.char_tokenizer.batch_decode(UpperCAmelCase__     )]
						return decode_strs




	def 						UpperCAmelCase_   (				self    :       Optional[Any]    ,    UpperCAmelCase__    :       int     ) ->			List[Any]:
						return self.bpe_tokenizer.batch_decode(UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Optional[Any]    ,    UpperCAmelCase__    :       Tuple     ) ->			Union[str, Any]:
						__SCREAMING_SNAKE_CASE		     =       [seq.replace(" "    ,    ""     ) for seq in self.wp_tokenizer.batch_decode(UpperCAmelCase__     )]
						return decode_strs




"""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


a__       :							Dict        =  logging.get_logger(__name__)




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					def constraint_to_multiple_of(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=0						,		lowerCAmelCase_=None     ):
										__SCREAMING_SNAKE_CASE		     =       round(val / multiple     ) * multiple

										if max_val is not None and x > max_val:
															__SCREAMING_SNAKE_CASE		     =       math.floor(val / multiple     ) * multiple

										if x < min_val:
															__SCREAMING_SNAKE_CASE		     =       math.ceil(val / multiple     ) * multiple

										return x

					__SCREAMING_SNAKE_CASE		     =       (output_size, output_size) if isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) else output_size

					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       get_image_size(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       output_size

					# determine new height and width
					__SCREAMING_SNAKE_CASE		     =       output_height / input_height
					__SCREAMING_SNAKE_CASE		     =       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		     =       scale_width
										else:
															# fit height
															__SCREAMING_SNAKE_CASE		     =       scale_height

					__SCREAMING_SNAKE_CASE		     =       constraint_to_multiple_of(scale_height * input_height						,		multiple=lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       constraint_to_multiple_of(scale_width * input_width						,		multiple=lowerCAmelCase_     )

					return (new_height, new_width)




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						List[Any]					      =    ["pixel_values"]




	def __init__(				self    :       int    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Dict[str, int] = None    ,    UpperCAmelCase__    :       PILImageResampling = PILImageResampling.BILINEAR    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       int = 1    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Union[int, float] = 1 / 2_5_5    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    **UpperCAmelCase__    :       Optional[Any]    ,    ) ->			None:
						super().__init__(**UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       size if size is not None else {"height": 3_8_4, "width": 3_8_4}
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       do_resize
						__SCREAMING_SNAKE_CASE		     =       size
						__SCREAMING_SNAKE_CASE		     =       keep_aspect_ratio
						__SCREAMING_SNAKE_CASE		     =       ensure_multiple_of
						__SCREAMING_SNAKE_CASE		     =       resample
						__SCREAMING_SNAKE_CASE		     =       do_rescale
						__SCREAMING_SNAKE_CASE		     =       rescale_factor
						__SCREAMING_SNAKE_CASE		     =       do_normalize
						__SCREAMING_SNAKE_CASE		     =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__SCREAMING_SNAKE_CASE		     =       image_std if image_std is not None else IMAGENET_STANDARD_STD




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Dict[str, int]    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       int = 1    ,    UpperCAmelCase__    :       PILImageResampling = PILImageResampling.BICUBIC    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Dict    ,    ) ->			np.ndarray:
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__     )
						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		     =       get_resize_output_image_size(
						    UpperCAmelCase__    ,    output_size=(size["height"], size["width"])    ,    keep_aspect_ratio=UpperCAmelCase__    ,    multiple=UpperCAmelCase__    ,    )
						return resize(UpperCAmelCase__    ,    size=UpperCAmelCase__    ,    resample=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Union[int, float]    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Union[str, Any]    ,    ) ->			Any:
						return rescale(UpperCAmelCase__    ,    scale=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Union[float, List[float]]    ,    UpperCAmelCase__    :       Union[float, List[float]]    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Optional[Any]    ,    ) ->			np.ndarray:
						return normalize(UpperCAmelCase__    ,    mean=UpperCAmelCase__    ,    std=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       ImageInput    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       int = None    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       int = None    ,    UpperCAmelCase__    :       PILImageResampling = None    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       float = None    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[str, TensorType]] = None    ,    UpperCAmelCase__    :       ChannelDimension = ChannelDimension.FIRST    ,    **UpperCAmelCase__    :       int    ,    ) ->			PIL.Image.Image:
						__SCREAMING_SNAKE_CASE		     =       do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE		     =       size if size is not None else self.size
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						__SCREAMING_SNAKE_CASE		     =       ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						__SCREAMING_SNAKE_CASE		     =       resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE		     =       do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE		     =       rescale_factor if rescale_factor is not None else self.rescale_factor
						__SCREAMING_SNAKE_CASE		     =       do_normalize if do_normalize is not None else self.do_normalize
						__SCREAMING_SNAKE_CASE		     =       image_mean if image_mean is not None else self.image_mean
						__SCREAMING_SNAKE_CASE		     =       image_std if image_std is not None else self.image_std

						__SCREAMING_SNAKE_CASE		     =       make_list_of_images(UpperCAmelCase__     )

						if not valid_images(UpperCAmelCase__     ):
											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		     =       [to_numpy_array(UpperCAmelCase__     ) for image in images]

						if do_resize:
											__SCREAMING_SNAKE_CASE		     =       [self.resize(image=UpperCAmelCase__    ,    size=UpperCAmelCase__    ,    resample=UpperCAmelCase__     ) for image in images]

						if do_rescale:
											__SCREAMING_SNAKE_CASE		     =       [self.rescale(image=UpperCAmelCase__    ,    scale=UpperCAmelCase__     ) for image in images]

						if do_normalize:
											__SCREAMING_SNAKE_CASE		     =       [self.normalize(image=UpperCAmelCase__    ,    mean=UpperCAmelCase__    ,    std=UpperCAmelCase__     ) for image in images]

						__SCREAMING_SNAKE_CASE		     =       [to_channel_dimension_format(UpperCAmelCase__    ,    UpperCAmelCase__     ) for image in images]

						__SCREAMING_SNAKE_CASE		     =       {"pixel_values": images}
						return BatchFeature(data=UpperCAmelCase__    ,    tensor_type=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       List[str]    ,    UpperCAmelCase__    :       List[Tuple] = None     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       outputs.logits

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

											if is_torch_tensor(UpperCAmelCase__     ):
																__SCREAMING_SNAKE_CASE		     =       target_sizes.numpy()

											__SCREAMING_SNAKE_CASE		     =       []

											for idx in range(len(UpperCAmelCase__     )     ):
																__SCREAMING_SNAKE_CASE		     =       torch.nn.functional.interpolate(
																    logits[idx].unsqueeze(dim=0     )    ,    size=target_sizes[idx]    ,    mode="bilinear"    ,    align_corners=UpperCAmelCase__     )
																__SCREAMING_SNAKE_CASE		     =       resized_logits[0].argmax(dim=0     )
																semantic_segmentation.append(UpperCAmelCase__     )
						else:
											__SCREAMING_SNAKE_CASE		     =       logits.argmax(dim=1     )
											__SCREAMING_SNAKE_CASE		     =       [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]     )]

						return semantic_segmentation
