

import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase    				=     logging.get_logger(__name__)

__UpperCAmelCase    				=     {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__UpperCAmelCase    				=     {
    'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
    'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}

__UpperCAmelCase    				=     {
    'ctrl': 2_5_6,
}

__UpperCAmelCase    				=     {
    'Pregnancy': 1_6_8_6_2_9,
    'Christianity': 7_6_7_5,
    'Explain': 1_0_6_4_2_3,
    'Fitness': 6_3_4_4_0,
    'Saving': 6_3_1_6_3,
    'Ask': 2_7_1_7_1,
    'Ass': 9_5_9_8_5,
    'Joke': 1_6_3_5_0_9,
    'Questions': 4_5_6_2_2,
    'Thoughts': 4_9_6_0_5,
    'Retail': 5_2_3_4_2,
    'Feminism': 1_6_4_3_3_8,
    'Writing': 1_1_9_9_2,
    'Atheism': 1_9_2_2_6_3,
    'Netflix': 4_8_6_1_6,
    'Computing': 3_9_6_3_9,
    'Opinion': 4_3_2_1_3,
    'Alone': 4_4_9_6_7,
    'Funny': 5_8_9_1_7,
    'Gaming': 4_0_3_5_8,
    'Human': 4_0_8_8,
    'India': 1_3_3_1,
    'Joker': 7_7_1_3_8,
    'Diet': 3_6_2_0_6,
    'Legal': 1_1_8_5_9,
    'Norman': 4_9_3_9,
    'Tip': 7_2_6_8_9,
    'Weight': 5_2_3_4_3,
    'Movies': 4_6_2_7_3,
    'Running': 2_3_4_2_5,
    'Science': 2_0_9_0,
    'Horror': 3_7_7_9_3,
    'Confession': 6_0_5_7_2,
    'Finance': 1_2_2_5_0,
    'Politics': 1_6_3_6_0,
    'Scary': 1_9_1_9_8_5,
    'Support': 1_2_6_5_4,
    'Technologies': 3_2_5_1_6,
    'Teenage': 6_6_1_6_0,
    'Event': 3_2_7_6_9,
    'Learned': 6_7_4_6_0,
    'Notion': 1_8_2_7_7_0,
    'Wikipedia': 3_7_5_8_3,
    'Books': 6_6_6_5,
    'Extract': 7_6_0_5_0,
    'Confessions': 1_0_2_7_0_1,
    'Conspiracy': 7_5_9_3_2,
    'Links': 6_3_6_7_4,
    'Narcissus': 1_5_0_4_2_5,
    'Relationship': 5_4_7_6_6,
    'Relationships': 1_3_4_7_9_6,
    'Reviews': 4_1_6_7_1,
    'News': 4_2_5_6,
    'Translation': 2_6_8_2_0,
    'multilingual': 1_2_8_4_0_6,
}







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    set()
     _lowerCAmelCase   :					List[str]        =    word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)				)
          _lowerCAmelCase   :					List[str]        =    char

     _lowerCAmelCase   :					Tuple        =    set(SCREAMING_SNAKE_CASE				)
     return pairs





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Optional[int]      =						VOCAB_FILES_NAMES
 _lowercase     :					Optional[int]      =						PRETRAINED_VOCAB_FILES_MAP
 _lowercase     :					Union[str, Any]      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowercase     :					Union[str, Any]      =						CONTROL_CODES

 def __init__(		self :	Tuple     ,						A_ :	Dict     ,						A_ :	Optional[Any]     ,						A_ :	Dict="<unk>"     ,						**A_ :	Union[str, Any]       ):






      '''simple docstring'''




      super().__init__(unk_token=A_     ,						**A_       )

      with open(A_     ,						encoding="utf-8"       ) as vocab_handle:
           _lowerCAmelCase   :					List[Any]        =    json.load(A_       )
      _lowerCAmelCase   :					str        =    {v: k for k, v in self.encoder.items()}
      with open(A_     ,						encoding="utf-8"       ) as merges_handle:
           _lowerCAmelCase   :					Union[str, Any]        =    merges_handle.read().split("\n"       )[1:-1]
      _lowerCAmelCase   :					Union[str, Any]        =    [tuple(merge.split()       ) for merge in merges]
      _lowerCAmelCase   :					List[Any]        =    dict(zip(A_     ,						range(len(A_       )       )       )       )
      _lowerCAmelCase   :					Any        =    {}

 @property
 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      return len(self.encoder       )

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      return dict(self.encoder     ,						**self.added_tokens_encoder       )

 def    __magic_name__       (		self :	List[str]     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      if token in self.cache:
           return self.cache[token]
      _lowerCAmelCase   :					Tuple        =    tuple(A_       )
      _lowerCAmelCase   :					Optional[int]        =    tuple(list(word[:-1]       ) + [word[-1] + "</w>"]       )
      _lowerCAmelCase   :					Optional[int]        =    get_pairs(A_       )

      if not pairs:
           return token

      while True:
           _lowerCAmelCase   :					Tuple        =    min(A_     ,						key=lambda A_       : self.bpe_ranks.get(A_     ,						float("inf"       )       )       )
           if bigram not in self.bpe_ranks:
                break
           _lowerCAmelCase      ,			_lowerCAmelCase   :					List[Any]        =    bigram
           _lowerCAmelCase   :					Optional[Any]        =    []
           _lowerCAmelCase   :					List[Any]        =    0
           while i < len(A_       ):
                try:
                     _lowerCAmelCase   :					Optional[int]        =    word.index(A_     ,						A_       )
                except ValueError:
                     new_word.extend(word[i:]       )
                     break
                else:
                     new_word.extend(word[i:j]       )
                     _lowerCAmelCase   :					List[Any]        =    j

                if word[i] == first and i < len(A_       ) - 1 and word[i + 1] == second:
                     new_word.append(first + second       )
                     i += 2
                else:
                     new_word.append(word[i]       )
                     i += 1
           _lowerCAmelCase   :					str        =    tuple(A_       )
           _lowerCAmelCase   :					Dict        =    new_word
           if len(A_       ) == 1:
                break
           else:
                _lowerCAmelCase   :					Any        =    get_pairs(A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    "@@ ".join(A_       )
      _lowerCAmelCase   :					str        =    word[:-4]
      _lowerCAmelCase   :					List[Any]        =    word
      return word

 def    __magic_name__       (		self :	Tuple     ,						A_ :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    []

      _lowerCAmelCase   :					List[Any]        =    re.findall(r"\S+\n?"     ,						A_       )

      for token in words:
           split_tokens.extend(list(self.bpe(A_       ).split(" "       )       )       )
      return split_tokens

 def    __magic_name__       (		self :	str     ,						A_ :	Optional[Any]       ):






      '''simple docstring'''




      return self.encoder.get(A_     ,						self.encoder.get(self.unk_token       )       )

 def    __magic_name__       (		self :	str     ,						A_ :	Any       ):






      '''simple docstring'''




      return self.decoder.get(A_     ,						self.unk_token       )

 def    __magic_name__       (		self :	List[str]     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    " ".join(A_       ).replace("@@ "     ,						""       ).strip()
      return out_string


 def    __magic_name__       (		self :	str     ,						A_ :	str     ,						A_ :	Optional[str] = None       ):






      '''simple docstring'''




      if not os.path.isdir(A_       ):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
           return
      _lowerCAmelCase   :					int        =    os.path.join(
          A_     ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
      _lowerCAmelCase   :					Any        =    os.path.join(
          A_     ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]       )

      with open(A_     ,						"w"     ,						encoding="utf-8"       ) as f:
           f.write(json.dumps(self.encoder     ,						indent=2     ,						sort_keys=A_     ,						ensure_ascii=A_       ) + "\n"       )

      _lowerCAmelCase   :					str        =    0
      with open(A_     ,						"w"     ,						encoding="utf-8"       ) as writer:
           writer.write("#version: 0.2\n"       )
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,						key=lambda A_       : kv[1]       ):
                if index != token_index:
                     logger.warning(
                         F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                         " Please check that the tokenizer is not corrupted!"       )
                     _lowerCAmelCase   :					Union[str, Any]        =    token_index
                writer.write(" ".join(A_       ) + "\n"       )
                index += 1

      return vocab_file, merge_file

 # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
 #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
 #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
 #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
 #     return ''.join(tokens_generated_so_far)









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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
  import PIL

if is_torch_available():
  import torch


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[Any]      =						['''pixel_values''']

 def __init__(		self :	Tuple     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	bool = True     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = True     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					Optional[int]        =    size if size is not None else {"shortest_edge": 2_2_4}
      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Any        =    crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
      _lowerCAmelCase   :					str        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Any        =    do_resize
      _lowerCAmelCase   :					Optional[Any]        =    size
      _lowerCAmelCase   :					str        =    resample
      _lowerCAmelCase   :					Optional[Any]        =    do_rescale
      _lowerCAmelCase   :					Dict        =    rescale_factor
      _lowerCAmelCase   :					Any        =    do_center_crop
      _lowerCAmelCase   :					List[Any]        =    crop_size
      _lowerCAmelCase   :					List[Any]        =    do_flip_channel_order

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PIL.Image.BILINEAR     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_     ,						default_to_square=A_       )
      if "shortest_edge" not in size:
           raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''       )
      _lowerCAmelCase   :					Union[str, Any]        =    get_resize_output_image_size(A_     ,						size=size["shortest_edge"]     ,						default_to_square=A_       )
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    get_size_dict(A_       )
      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()}'''       )
      return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Union[int, float]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Any     ,						):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	np.ndarray     ,						A_ :	Optional[Union[str, ChannelDimension]] = None       ):






      '''simple docstring'''




      return flip_channel_order(A_     ,						data_format=A_       )

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	float = None     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Tuple     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					Dict        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					str        =    do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase   :					List[Any]        =    rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase   :					Tuple        =    do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowerCAmelCase   :					str        =    (
          do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
      )

      _lowerCAmelCase   :					str        =    size if size is not None else self.size
      _lowerCAmelCase   :					str        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Optional[Any]        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Dict        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           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_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."       )

      if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."       )

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

      if do_resize:
           _lowerCAmelCase   :					Any        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      if do_center_crop:
           _lowerCAmelCase   :					Tuple        =    [self.center_crop(image=A_     ,						size=A_       ) for image in images]

      if do_rescale:
           _lowerCAmelCase   :					Any        =    [self.rescale(image=A_     ,						scale=A_       ) for image in images]

      # the pretrained checkpoints assume images are BGR, not RGB
      if do_flip_channel_order:
           _lowerCAmelCase   :					Dict        =    [self.flip_channel_order(image=A_       ) for image in images]

      _lowerCAmelCase   :					Optional[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )


 def    __magic_name__       (		self :	List[Any]     ,						A_ :	Union[str, Any]     ,						A_ :	List[Tuple] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    outputs.logits

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

           if is_torch_tensor(A_       ):
                _lowerCAmelCase   :					Dict        =    target_sizes.numpy()

           _lowerCAmelCase   :					List[Any]        =    []

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

      return semantic_segmentation






