




import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


_lowerCamelCase			:	Tuple							 =     logging.get_logger(__name__)

_lowerCamelCase			:	str							 =     {"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}

_lowerCamelCase			:	Union[str, Any]							 =     {
    """vocab_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
    },
    """emoji_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
    },
}

_lowerCamelCase			:	Tuple							 =     {
    """abeja/gpt-neox-japanese-2.7b""": 2048,
}







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Optional[int]:



       """simple docstring"""

       with open(lowercase_				,       '''r'''				,       encoding='''utf-8'''		) as f:
              A__   				=					json.loads(f.read()		)

       A__   				=					collections.OrderedDict()
       A__   				=					collections.OrderedDict()
       A__   				=					collections.OrderedDict()
       with open(lowercase_				,       '''r'''				,       encoding='''utf-8'''		) as f:
              A__   				=					f.readlines()
       A__   				=					[[t.rstrip('''\n'''		)] if (t == ''',''' or ''',''' not in t) else t.rstrip('''\n'''		).split(''','''		) for t in token]
       for idx, b in enumerate(lowercase_		):
              A__   				=					b
              A__   				=					idx
              for wd in b:
                     A__   				=					idx

       return vocab, raw_vocab, ids_to_tokens, emoji


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   VOCAB_FILES_NAMES
    UpperCAmelCase__													=   PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__													=   ['''input_ids''', '''attention_mask''']


    def __init__(    self   :			Dict      ,				UpperCAmelCase__   :			Tuple      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Optional[Any]="<|endoftext|>"      ,				UpperCAmelCase__   :			Optional[int]="<|endoftext|>"      ,				UpperCAmelCase__   :			Any="<|startoftext|>"      ,				UpperCAmelCase__   :			List[Any]="<|endoftext|>"      ,				UpperCAmelCase__   :			Dict=False      ,				**UpperCAmelCase__   :			Tuple      ,				)		->str:





           '''simple docstring'''

           super().__init__(
               unk_token=UpperCAmelCase__      ,				pad_token=UpperCAmelCase__      ,				bos_token=UpperCAmelCase__      ,				eos_token=UpperCAmelCase__      ,				do_clean_text=UpperCAmelCase__      ,				**UpperCAmelCase__      ,				)
           if not os.path.isfile(UpperCAmelCase__):
                  raise ValueError(
                      f"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
                      ''' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`''')
           if not os.path.isfile(UpperCAmelCase__):
                  raise ValueError(
                      f"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
                      ''' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`''')
           A__   				=					do_clean_text
           A__				,							A__				,							A__				,							A__   				=					load_vocab_and_emoji(UpperCAmelCase__      ,				UpperCAmelCase__)
           A__   				=					SubWordJapaneseTokenizer(
               vocab=self.vocab      ,				ids_to_tokens=self.ids_to_tokens      ,				emoji=self.emoji)


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->str:





           '''simple docstring'''

           return len(self.raw_vocab)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->int:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			str)		->Union[str, Any]:





           '''simple docstring'''

           return self.subword_tokenizer.tokenize(UpperCAmelCase__      ,				clean=self.do_clean_text)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			Union[str, Any])		->Optional[Any]:





           '''simple docstring'''

           return self.vocab.get(UpperCAmelCase__      ,				self.vocab.get(self.unk_token))


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			Optional[Any])		->str:





           '''simple docstring'''

           return self.subword_tokenizer.convert_id_to_token(UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			Optional[Any])		->str:





           '''simple docstring'''

           A__   				=					''''''.join(UpperCAmelCase__).strip()
           return out_string


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			"Conversation")		->List[int]:





           '''simple docstring'''

           A__   				=					[]
           for is_user, text in conversation.iter_texts():
                  input_ids.extend(self.encode(UpperCAmelCase__      ,				add_special_tokens=UpperCAmelCase__) + [self.eos_token_id])

           if len(UpperCAmelCase__) > self.model_max_length:
                  A__   				=					input_ids[-self.model_max_length :]
           return input_ids





    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Optional[str] = None)		->Tuple[str]:





           '''simple docstring'''

           A__   				=					0
           if os.path.isdir(UpperCAmelCase__):
                  A__   				=					os.path.join(
                      UpperCAmelCase__      ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
                  A__   				=					os.path.join(
                      UpperCAmelCase__      ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''emoji_file'''])
           else:
                  A__   				=					(
                      (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''vocab_file''']
                  )
                  A__   				=					(
                      (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''emoji_file''']
                  )
           with open(UpperCAmelCase__      ,				'''w'''      ,				encoding='''utf-8''') as writer:
                  for token_index, token in self.ids_to_tokens.items():
                         if index != token_index:
                                logger.warning(
                                    f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                                    ''' Please check that the vocabulary is not corrupted!''')
                                A__   				=					token_index
                         writer.write(''','''.join(UpperCAmelCase__) + '''\n''')
                         index += 1
           with open(UpperCAmelCase__      ,				'''w'''      ,				encoding='''utf-8''') as writer:
                  json.dump(self.emoji      ,				UpperCAmelCase__)
           return vocab_file, emoji_file


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    def __init__(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Optional[Any])		->List[str]:





           '''simple docstring'''

           A__   				=					vocab  # same as swe
           A__   				=					ids_to_tokens  # same as bpe
           A__   				=					emoji
           A__   				=					np.max([len(UpperCAmelCase__) for w in self.vocab.keys()])
           A__   				=					re.compile(R'''(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)''')
           A__   				=					re.compile(R'''[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*''')
           A__   				=					re.compile(R'''[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}''')
           A__   				=					re.compile(
               R'''([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*''')
           A__   				=					re.compile(
               R'''(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*''')
           A__   				=					re.compile(
               R'''((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*''')
           A__   				=					'''─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'''
           A__   				=					'''▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'''
           A__   				=					str.maketrans({k: '''<BLOCK>''' for k in keisen + blocks})


    def __len__(    self   :			Union[str, Any])		->List[Any]:





           '''simple docstring'''

           return len(self.ids_to_tokens)


    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			List[Any])		->Dict:





           '''simple docstring'''

           A__   				=					self.content_repattera.sub('''<URL>'''      ,				UpperCAmelCase__)
           A__   				=					self.content_repattera.sub('''<EMAIL>'''      ,				UpperCAmelCase__)
           A__   				=					self.content_repattera.sub('''<TEL>'''      ,				UpperCAmelCase__)
           A__   				=					self.content_repattera.sub('''<DATE>'''      ,				UpperCAmelCase__)
           A__   				=					self.content_repattera.sub('''<DATE>'''      ,				UpperCAmelCase__)
           A__   				=					self.content_repattera.sub('''<PRICE>'''      ,				UpperCAmelCase__)
           A__   				=					content.translate(self.content_transa)
           while "<BLOCK><BLOCK>" in content:
                  A__   				=					content.replace('''<BLOCK><BLOCK>'''      ,				'''<BLOCK>''')
           return content


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			str=False)		->List[Any]:





           '''simple docstring'''

           A__   				=					text.replace(''' '''      ,				'''<SP>''')
           A__   				=					text.replace('''　'''      ,				'''<SP>''')
           A__   				=					text.replace('''\r\n'''      ,				'''<BR>''')
           A__   				=					text.replace('''\n'''      ,				'''<BR>''')
           A__   				=					text.replace('''\r'''      ,				'''<BR>''')
           A__   				=					text.replace('''\t'''      ,				'''<TAB>''')
           A__   				=					text.replace('''—'''      ,				'''ー''')
           A__   				=					text.replace('''−'''      ,				'''ー''')
           for k, v in self.emoji["emoji"].items():
                  if k in text:
                         A__   				=					text.replace(UpperCAmelCase__      ,				UpperCAmelCase__)
           if clean:
                  A__   				=					self.clean_text(UpperCAmelCase__)

           def check_simbol(UpperCAmelCase__   :			str):
                  A__   				=					x.encode()
                  if len(UpperCAmelCase__) == 1 and len(UpperCAmelCase__) == 2:
                         A__   				=					(int(e[0]) << 8) + int(e[1])
                         if (
                             (c >= 0xc2_a1 and c <= 0xc2_bf)
                             or (c >= 0xc7_80 and c <= 0xc7_83)
                             or (c >= 0xca_b9 and c <= 0xcb_bf)
                             or (c >= 0xcc_80 and c <= 0xcd_a2)
                         ):
                                return True
                  return False

           def checkuae(UpperCAmelCase__   :			Union[str, Any]):
                  A__   				=					x.encode()
                  if len(UpperCAmelCase__) == 1 and len(UpperCAmelCase__) == 3:
                         A__   				=					(int(e[0]) << 16) + (int(e[1]) << 8) + int(e[2])
                         if c >= 0xe2_80_80 and c <= 0xe2_b0_7f:
                                return True
                  return False

           A__   				=					0
           A__   				=					[]
           while pos < len(UpperCAmelCase__):
                  A__   				=					min(len(UpperCAmelCase__)      ,				pos + self.maxlen + 1) if text[pos] == '''<''' else pos + 3
                  A__   				=					[]  # (token_id, token, pos)
                  for e in range(UpperCAmelCase__      ,				UpperCAmelCase__      ,				-1):
                         A__   				=					text[pos:e]
                         if wd in self.vocab:
                                if wd[0] == "<" and len(UpperCAmelCase__) > 2:
                                       A__   				=					[(self.vocab[wd], wd, e)]
                                       break
                                else:
                                       candidates.append((self.vocab[wd], wd, e))
                  if len(UpperCAmelCase__) > 0:
                         # the smallest token_id is adopted
                         A__				,							A__				,							A__   				=					sorted(UpperCAmelCase__      ,				key=lambda UpperCAmelCase__: x[0])[0]
                         result.append(UpperCAmelCase__)
                         A__   				=					e
                  else:
                         A__   				=					pos + 1
                         A__   				=					text[pos:end]
                         if check_simbol(UpperCAmelCase__):
                                result.append('''<KIGOU>''')
                         elif checkuae(UpperCAmelCase__):
                                result.append('''<U2000U2BFF>''')
                         else:
                                for i in wd.encode('''utf-8'''):
                                       result.append('''<|byte%d|>''' % i)
                         A__   				=					end
           return result





    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Dict="\n")		->Dict:





           '''simple docstring'''

           A__   				=					[]
           A__   				=					[]
           A__   				=					self.ids_to_tokens[index][0]
           if word[:6] == "<|byte" and word[-2:] == "|>":
                  byte_tokens.append(int(word[6:-2]))
           else:
                  if len(UpperCAmelCase__) > 0:
                         words.append(bytearray(UpperCAmelCase__).decode('''utf-8'''      ,				errors='''replace'''))
                         A__   				=					[]
                  if word[:7] == "<|emoji" and word[-2:] == "|>":
                         words.append(self.emoji['''emoji_inv'''][word])
                  elif word == "<SP>":
                         words.append(''' ''')
                  elif word == "<BR>":
                         words.append(UpperCAmelCase__)
                  elif word == "<TAB>":
                         words.append('''\t''')
                  elif word == "<BLOCK>":
                         words.append('''▀''')
                  elif word == "<KIGOU>":
                         words.append('''ǀ''')
                  elif word == "<U2000U2BFF>":
                         words.append('''‖''')
                  else:
                         words.append(UpperCAmelCase__)
           if len(UpperCAmelCase__) > 0:
                  words.append(bytearray(UpperCAmelCase__).decode('''utf-8'''      ,				errors='''replace'''))
           A__   				=					''''''.join(UpperCAmelCase__)
           return text








import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
_lowerCamelCase			:	List[str]							 =     (720, 1280)  # Height, Width
_lowerCamelCase			:	Optional[Any]							 =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowerCamelCase			:	List[Any]							 =     1 / 100
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	Union[str, Any]							 =     250







def SCREAMING_SNAKE_CASE       ( )     -> None:



       """simple docstring"""

       A__				,							A__   				=					get_dataset(lowercase_				,       lowercase_		)
       for index in range(lowercase_		):
              A__   				=					random.sample(range(len(lowercase_		)		)				,       4		)
              A__				,							A__				,							A__   				=					update_image_and_anno(
                  lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       filter_scale=lowercase_				,       )

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              A__   				=					random_chars(32		)
              A__   				=					path.split(os.sep		)[-1].rsplit('''.'''				,       1		)[0]
              A__   				=					f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
              cva.imwrite(f"""{file_root}.jpg"""				,       lowercase_				,       [cva.IMWRITE_JPEG_QUALITY, 85]		)
              print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""		)
              A__   				=					[]
              for anno in new_annos:
                     A__   				=					anno[3] - anno[1]
                     A__   				=					anno[4] - anno[2]
                     A__   				=					anno[1] + width / 2
                     A__   				=					anno[2] + height / 2
                     A__   				=					f"""{anno[0]} {x_center} {y_center} {width} {height}"""
                     annos_list.append(lowercase_		)
              with open(f"""{file_root}.txt"""				,       '''w'''		) as outfile:
                     outfile.write('''\n'''.join(line for line in annos_list		)		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> tuple[list, list]:



       """simple docstring"""

       A__   				=					[]
       A__   				=					[]
       for label_file in glob.glob(os.path.join(lowercase_				,       '''*.txt'''		)		):
              A__   				=					label_file.split(os.sep		)[-1].rsplit('''.'''				,       1		)[0]
              with open(lowercase_		) as in_file:
                     A__   				=					in_file.readlines()
              A__   				=					os.path.join(lowercase_				,       f"""{label_name}.jpg"""		)

              A__   				=					[]
              for obj_list in obj_lists:
                     A__   				=					obj_list.rstrip('''\n'''		).split(''' '''		)
                     A__   				=					float(obj[1]		) - float(obj[3]		) / 2
                     A__   				=					float(obj[2]		) - float(obj[4]		) / 2
                     A__   				=					float(obj[1]		) + float(obj[3]		) / 2
                     A__   				=					float(obj[2]		) + float(obj[4]		) / 2

                     boxes.append([int(obj[0]		), xmin, ymin, xmax, ymax]		)
              if not boxes:
                     continue
              img_paths.append(lowercase_		)
              labels.append(lowercase_		)
       return img_paths, labels







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_ = 0.0				,       )     -> tuple[list, list, str]:



       """simple docstring"""

       A__   				=					np.zeros([output_size[0], output_size[1], 3]				,       dtype=np.uinta		)
       A__   				=					scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       A__   				=					scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       A__   				=					int(scale_x * output_size[1]		)
       A__   				=					int(scale_y * output_size[0]		)

       A__   				=					[]
       A__   				=					[]
       for i, index in enumerate(lowercase_		):
              A__   				=					all_img_list[index]
              path_list.append(lowercase_		)
              A__   				=					all_annos[index]
              A__   				=					cva.imread(lowercase_		)
              if i == 0:  # top-left
                     A__   				=					cva.resize(lowercase_				,       (divid_point_x, divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					bbox[1] * scale_x
                            A__   				=					bbox[2] * scale_y
                            A__   				=					bbox[3] * scale_x
                            A__   				=					bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
              elif i == 1:  # top-right
                     A__   				=					cva.resize(lowercase_				,       (output_size[1] - divid_point_x, divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					scale_x + bbox[1] * (1 - scale_x)
                            A__   				=					bbox[2] * scale_y
                            A__   				=					scale_x + bbox[3] * (1 - scale_x)
                            A__   				=					bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
              elif i == 2:  # bottom-left
                     A__   				=					cva.resize(lowercase_				,       (divid_point_x, output_size[0] - divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					bbox[1] * scale_x
                            A__   				=					scale_y + bbox[2] * (1 - scale_y)
                            A__   				=					bbox[3] * scale_x
                            A__   				=					scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
              else:  # bottom-right
                     A__   				=					cva.resize(
                         lowercase_				,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					scale_x + bbox[1] * (1 - scale_x)
                            A__   				=					scale_y + bbox[2] * (1 - scale_y)
                            A__   				=					scale_x + bbox[3] * (1 - scale_x)
                            A__   				=					scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)

    # Remove bounding box small than scale of filter
       if filter_scale > 0:
              A__   				=					[
                  anno
                  for anno in new_anno
                  if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
              ]

       return output_img, new_anno, path_list[0]







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

       assert number_char > 1, "The number of character should greater than 1"
       A__   				=					ascii_lowercase + digits
       return "".join(random.choice(lowercase_		) for _ in range(lowercase_		)		)


if __name__ == "__main__":
  main()
  print("""DONE ✅""")


