






"""simple docstring"""



import json
import os
from typing import Optional, Tuple

import regex as re

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


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

_UpperCamelCase		:  Union[str, Any]						=		{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

_UpperCamelCase		:  Optional[int]						=		{
    '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		:  List[Any]						=		{
    'ctrl': 2_5_6,
}

_UpperCamelCase		:  str						=		{
    '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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str    ):





  '''simple docstring'''





  lowercase							    =       set()
  lowercase							    =       word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)    )
    lowercase							    =       char

  lowercase							    =       set(__snake_case    )
  return pairs



class        a     (   a_	):
    UpperCAmelCase_ :     Any	     =VOCAB_FILES_NAMES
    UpperCAmelCase_ :     Any	     =PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_ :     List[Any]	     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_ :     Tuple	     =CONTROL_CODES







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase="<unk>"							,		**_lowerCamelCase       ):
      super().__init__(unk_token=_lowerCamelCase							,		**_lowerCamelCase       )

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







    @property
    def 			UpperCamelCase_      (			self       ):
      return len(self.encoder       )







    def 			UpperCamelCase_      (			self       ):
      return dict(self.encoder							,		**self.added_tokens_encoder       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      if token in self.cache:
        return self.cache[token]
      lowercase							    =       tuple(_lowerCamelCase       )
      lowercase							    =       tuple(list(word[:-1]       ) + [word[-1] + '</w>']       )
      lowercase							    =       get_pairs(_lowerCamelCase       )

      if not pairs:
        return token

      while True:
        lowercase							    =       min(_lowerCamelCase							,		key=lambda _lowerCamelCase       : self.bpe_ranks.get(_lowerCamelCase							,		float('inf'       )       )       )
        if bigram not in self.bpe_ranks:
          break
        lowercase    ,     lowercase							    =       bigram
        lowercase							    =       []
        lowercase							    =       0
        while i < len(_lowerCamelCase       ):
          try:
            lowercase							    =       word.index(_lowerCamelCase							,		_lowerCamelCase       )
          except ValueError:
            new_word.extend(word[i:]       )
            break
          else:
            new_word.extend(word[i:j]       )
            lowercase							    =       j

          if word[i] == first and i < len(_lowerCamelCase       ) - 1 and word[i + 1] == second:
            new_word.append(first + second       )
            i += 2
          else:
            new_word.append(word[i]       )
            i += 1
        lowercase							    =       tuple(_lowerCamelCase       )
        lowercase							    =       new_word
        if len(_lowerCamelCase       ) == 1:
          break
        else:
          lowercase							    =       get_pairs(_lowerCamelCase       )
      lowercase							    =       '@@ '.join(_lowerCamelCase       )
      lowercase							    =       word[:-4]
      lowercase							    =       word
      return word







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       []

      lowercase							    =       re.findall(R'\S+\n?'							,		_lowerCamelCase       )

      for token in words:
        split_tokens.extend(list(self.bpe(_lowerCamelCase       ).split(' '       )       )       )
      return split_tokens







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      return self.encoder.get(_lowerCamelCase							,		self.encoder.get(self.unk_token       )       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      return self.decoder.get(_lowerCamelCase							,		self.unk_token       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       ' '.join(_lowerCamelCase       ).replace('@@ '							,		''       ).strip()
      return out_string







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase = None       ):
      if not os.path.isdir(_lowerCamelCase       ):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'       )
        return
      lowercase							    =       os.path.join(
          _lowerCamelCase							,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
      lowercase							    =       os.path.join(
          _lowerCamelCase							,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']       )

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

      lowercase							    =       0
      with open(_lowerCamelCase							,		'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 _lowerCamelCase       : 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!'       )
            lowercase							    =       token_index
          writer.write(' '.join(_lowerCamelCase       ) + '\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)










"""simple docstring"""



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class        a     :
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     int
    UpperCAmelCase_ :     int
    UpperCAmelCase_ :     float
    UpperCAmelCase_ :     float
    UpperCAmelCase_ :     Tuple[int]







    def 			UpperCamelCase_      (			self       ):
      assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
      assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
      assert len(self.x.shape       ) == len(self.y.shape       ) == len(self.z.shape       ) == len(self.origin.shape       ) == 2







    def 			UpperCamelCase_      (			self       ):
      return torch.from_numpy(np.array([self.width, self.height]							,		dtype=np.floataa       )       )







    def 			UpperCamelCase_      (			self       ):
      return torch.from_numpy(np.array([self.x_fov, self.y_fov]							,		dtype=np.floataa       )       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       torch.arange(self.height * self.width       )
      lowercase							    =       torch.stack(
          [
              pixel_indices % self.width,
              torch.div(_lowerCamelCase							,		self.width							,		rounding_mode='trunc'       ),
          ]							,		axis=1							,		)
      return coords







    @property
    def 			UpperCamelCase_      (			self       ):
      lowercase    ,     *lowercase							    =       self.shape
      lowercase							    =       int(np.prod(_lowerCamelCase       )       )

      lowercase							    =       self.get_image_coords()
      lowercase							    =       torch.broadcast_to(coords.unsqueeze(0       )							,		[batch_size * inner_batch_size, *coords.shape]       )
      lowercase							    =       self.get_camera_rays(_lowerCamelCase       )

      lowercase							    =       rays.view(_lowerCamelCase							,		inner_batch_size * self.height * self.width							,		2							,		3       )

      return rays







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase    ,     *lowercase    ,     lowercase							    =       coords.shape
      assert n_coords == 2
      assert batch_size == self.origin.shape[0]

      lowercase							    =       coords.view(_lowerCamelCase							,		-1							,		2       )

      lowercase							    =       self.resolution()
      lowercase							    =       self.fov()

      lowercase							    =       (flat.float() / (res - 1)) * 2 - 1
      lowercase							    =       fracs * torch.tan(fov / 2       )

      lowercase							    =       fracs.view(_lowerCamelCase							,		-1							,		2       )
      lowercase							    =       (
          self.z.view(_lowerCamelCase							,		1							,		3       )
          + self.x.view(_lowerCamelCase							,		1							,		3       ) * fracs[:, :, :1]
          + self.y.view(_lowerCamelCase							,		1							,		3       ) * fracs[:, :, 1:]
      )
      lowercase							    =       directions / directions.norm(dim=-1							,		keepdim=_lowerCamelCase       )
      lowercase							    =       torch.stack(
          [
              torch.broadcast_to(self.origin.view(_lowerCamelCase							,		1							,		3       )							,		[batch_size, directions.shape[1], 3]       ),
              directions,
          ]							,		dim=2							,		)
      return rays.view(_lowerCamelCase							,		*_lowerCamelCase							,		2							,		3       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      assert width * self.height == height * self.width, "The aspect ratio should not change."
      return DifferentiableProjectiveCamera(
          origin=self.origin							,		x=self.x							,		y=self.y							,		z=self.z							,		width=_lowerCamelCase							,		height=_lowerCamelCase							,		x_fov=self.x_fov							,		y_fov=self.y_fov							,		)


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int    ):





  '''simple docstring'''





  lowercase							    =       []
  lowercase							    =       []
  lowercase							    =       []
  lowercase							    =       []
  for theta in np.linspace(0		,					2 * np.pi		,					num=20    ):
    lowercase							    =       np.array([np.sin(__snake_case    ), np.cos(__snake_case    ), -0.5]    )
    z /= np.sqrt(np.sum(z**2    )    )
    lowercase							    =       -z * 4
    lowercase							    =       np.array([np.cos(__snake_case    ), -np.sin(__snake_case    ), 0.0]    )
    lowercase							    =       np.cross(__snake_case		,					__snake_case    )
    origins.append(__snake_case    )
    xs.append(__snake_case    )
    ys.append(__snake_case    )
    zs.append(__snake_case    )
  return DifferentiableProjectiveCamera(
      origin=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					x=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					y=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					z=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					width=__snake_case		,					height=__snake_case		,					x_fov=0.7		,					y_fov=0.7		,					shape=(1, len(__snake_case    ))		,					)


