


import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger




class       lowercase_					(lowercase__   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_)  -> Tuple:
											a__      =params

											a__      =np.array(lowercase_)
											a__      =np.array([len(lowercase_) for t in data])

											self.check()
											self.remove_long_sequences()
											self.remove_empty_sequences()
											self.remove_unknown_sequences()
											self.check()
											self.print_statistics()





				def __getitem__(				self       ,  lowercase_)  -> List[str]:
											return (self.token_ids[index], self.lengths[index])





				def __len__(				self)  -> Any:
											return len(self.lengths)





				def 						__UpperCamelCase    (				self)  -> Any:
											assert len(self.token_ids) == len(self.lengths)
											assert all(self.lengths[i] == len(self.token_ids[i]) for i in range(len(self.lengths)))





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =self.params.max_model_input_size
											a__      =self.lengths > max_len
											logger.info(F"""Splitting {sum(lowercase_)} too long sequences.""")

											def divide_chunks(lowercase_       ,  lowercase_):
																		return [l[i : i + n] for i in range(0       ,  len(lowercase_)       ,  lowercase_)]

											a__      =[]
											a__      =[]
											if self.params.mlm:
																		a__    ,     a__      =self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
											else:
																		a__    ,     a__      =self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']

											for seq_, len_ in zip(self.token_ids       ,  self.lengths):
																		assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
																		if len_ <= max_len:
																									new_tok_ids.append(seq_)
																									new_lengths.append(len_)
																		else:
																									a__      =[]
																									for sub_s in divide_chunks(seq_       ,  max_len - 2):
																																if sub_s[0] != cls_id:
																																							a__      =np.insert(lowercase_       ,  0       ,  lowercase_)
																																if sub_s[-1] != sep_id:
																																							a__      =np.insert(lowercase_       ,  len(lowercase_)       ,  lowercase_)
																																assert len(lowercase_) <= max_len
																																assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																																sub_seqs.append(lowercase_)

																									new_tok_ids.extend(lowercase_)
																									new_lengths.extend([len(lowercase_) for l in sub_seqs])

											a__      =np.array(lowercase_)
											a__      =np.array(lowercase_)





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =len(self)
											a__      =self.lengths > 11
											a__      =self.token_ids[indices]
											a__      =self.lengths[indices]
											a__      =len(self)
											logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences.""")





				def 						__UpperCamelCase    (				self)  -> Any:
											if "unk_token" not in self.params.special_tok_ids:
																		return
											else:
																		a__      =self.params.special_tok_ids['unk_token']
											a__      =len(self)
											a__      =np.array([np.count_nonzero(a == unk_token_id) for a in self.token_ids])
											a__      =(unk_occs / self.lengths) < 0.5
											a__      =self.token_ids[indices]
											a__      =self.lengths[indices]
											a__      =len(self)
											logger.info(F"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).""")





				def 						__UpperCamelCase    (				self)  -> List[str]:
											if not self.params.is_master:
																		return
											logger.info(F"""{len(self)} sequences""")
											# data_len = sum(self.lengths)
											# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
											# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

											# unk_idx = self.params.special_tok_ids['unk_token']
											# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
											# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> str:
											a__      =[t[0] for t in batch]
											a__      =[t[1] for t in batch]
											assert len(lowercase_) == len(lowercase_)

											# Max for paddings
											a__      =max(lowercase_)

											# Pad token ids
											if self.params.mlm:
																		a__      =self.params.special_tok_ids['pad_token']
											else:
																		a__      =self.params.special_tok_ids['unk_token']
											a__      =[list(t.astype(lowercase_)) + [pad_idx] * (max_seq_len_ - len(lowercase_)) for t in token_ids]
											assert len(tk_) == len(lowercase_)
											assert all(len(lowercase_) == max_seq_len_ for t in tk_)

											a__      =torch.tensor(tk_)  # (bs, max_seq_len_)
											a__      =torch.tensor(lowercase_)  # (bs)
											return tk_t, lg_t





from typing import Dict, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
	import torch


_lowerCAmelCase:						List[Any]   				=       logging.get_logger(__name__)




class       lowercase_					(lowercase__   ):
				snake_case	    =['pixel_values']





				def __init__(				self       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = PILImageResampling.BILINEAR       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = True       ,  lowercase_ = 1 / 255       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> None:
											super().__init__(**lowercase_)
											a__      =size if size is not None else {'shortest_edge': 256}
											a__      =get_size_dict(lowercase_       ,  default_to_square=lowercase_)
											a__      =crop_size if crop_size is not None else {'height': 224, 'width': 224}
											a__      =get_size_dict(lowercase_       ,  param_name='crop_size')
											a__      =do_resize
											a__      =size
											a__      =resample
											a__      =do_center_crop
											a__      =crop_size
											a__      =do_rescale
											a__      =rescale_factor
											a__      =do_normalize
											a__      =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											a__      =image_std if image_std is not None else IMAGENET_STANDARD_STD





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_ = PILImageResampling.BICUBIC       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> np.ndarray:
											a__      =get_size_dict(lowercase_       ,  default_to_square=lowercase_)
											if "shortest_edge" not in size:
																		raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
											a__      =get_resize_output_image_size(lowercase_       ,  size=size['shortest_edge']       ,  default_to_square=lowercase_)
											return resize(lowercase_       ,  size=lowercase_       ,  resample=lowercase_       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> np.ndarray:
											a__      =get_size_dict(lowercase_)
											if "height" not in size or "width" not in size:
																		raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}""")
											return center_crop(lowercase_       ,  size=(size['height'], size['width'])       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  **lowercase_)  -> np.ndarray:
											return rescale(lowercase_       ,  scale=lowercase_       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> np.ndarray:
											return normalize(lowercase_       ,  mean=lowercase_       ,  std=lowercase_       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = ChannelDimension.FIRST       ,  **lowercase_       ,  )  -> Tuple:
											a__      =do_resize if do_resize is not None else self.do_resize
											a__      =size if size is not None else self.size
											a__      =get_size_dict(lowercase_       ,  default_to_square=lowercase_)
											a__      =resample if resample is not None else self.resample
											a__      =do_center_crop if do_center_crop is not None else self.do_center_crop
											a__      =crop_size if crop_size is not None else self.crop_size
											a__      =get_size_dict(lowercase_       ,  param_name='crop_size')
											a__      =do_rescale if do_rescale is not None else self.do_rescale
											a__      =rescale_factor if rescale_factor is not None else self.rescale_factor
											a__      =do_normalize if do_normalize is not None else self.do_normalize
											a__      =image_mean if image_mean is not None else self.image_mean
											a__      =image_std if image_std is not None else self.image_std

											a__      =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:
																		raise ValueError('Size must be specified if do_resize is True.')

											if do_center_crop and crop_size is None:
																		raise ValueError('Crop size must be specified if do_center_crop 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.
											a__      =[to_numpy_array(lowercase_) for image in images]

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

											if do_center_crop:
																		a__      =[self.center_crop(image=lowercase_       ,  size=lowercase_) for image in images]

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

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

											a__      =[to_channel_dimension_format(lowercase_       ,  lowercase_) for image in images]

											a__      ={'pixel_values': images}
											return BatchFeature(data=lowercase_       ,  tensor_type=lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None)  -> str:
											a__      =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_):
																									a__      =target_sizes.numpy()

																		a__      =[]

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

											return semantic_segmentation

