




'''simple docstring'''







import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase							):
     # Construct configuration depending on size
     snake_case :			Union[str, Any]					   = {"""small""": (512, 8, 24), """medium""": (1_024, 8, 36), """large""": (1_536, 16, 48)}
     snake_case :			Union[str, Any]					   = MODELS[model_size]  # set model hyperparameters
     snake_case :			str					   = ImageGPTConfig(n_embd=__lowercase    ,      n_layer=__lowercase    ,      n_head=__lowercase							)
     snake_case :			List[str]					   = ImageGPTForCausalLM(__lowercase							)

     # Load weights from numpy
     load_tf_weights_in_imagegpt(__lowercase    ,      __lowercase    ,      __lowercase							)

     # Save pytorch-model
     snake_case :			List[str]					   = pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
     snake_case :			Any					   = pytorch_dump_folder_path + """/""" + CONFIG_NAME
     print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''							)
     torch.save(model.state_dict()    ,      __lowercase							)
     print(F'''Save configuration file to {pytorch_config_dump_path}'''							)
     with open(__lowercase    ,      """w"""    ,      encoding="""utf-8"""							) as f:
          f.write(config.to_json_string()							)


if __name__ == "__main__":
      lowercase			:      int			=					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--imagegpt_checkpoint_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to the TensorFlow checkpoint path.""",
      )
      parser.add_argument(
          """--model_size""",
          default=None,
          type=str,
          required=True,
          help="""Size of the model (can be either 'small', 'medium' or 'large').""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      lowercase			:      Dict			=					parser.parse_args()
      convert_imagegpt_checkpoint_to_pytorch(
          args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
      )









'''simple docstring'''







from dataclasses import dataclass

lowercase			:      Tuple			=					"""0.1"""
lowercase			:      Optional[Any]			=					"""Lucia Harcekova"""







@dataclass
class 				_a   :





     '''simple docstring'''



     lowerCAmelCase_     :    int
     lowerCAmelCase_     :    int
     lowerCAmelCase_     :    str







     def __repr__(							self       )			->							str:
          return F'''({self.offset}, {self.length}, {self.indicator})'''







class 				_a   :





     '''simple docstring'''



     def __init__(							self       ,__a = 13       ,__a = 6       )			->							None:
          snake_case :			Dict					   = window_size
          snake_case :			Any					   = lookahead_buffer_size
          snake_case :			str					   = self.window_size - self.lookahead_buffer_size







     def  snake_case_			(							self       ,__a       )			->							list[Token]:
          snake_case :			Tuple					   = []
          snake_case :			Optional[int]					   = """"""

          # while there are still characters in text to compress
          while text:
               # find the next encoding phrase
               # - triplet with offset, length, indicator (the next encoding character)
               snake_case :			str					   = self._find_encoding_token(__a       ,__a       )

               # update the search buffer:
               # - add new characters from text into it
               # - check if size exceed the max search buffer size, if so, drop the
               #   oldest elements
               search_buffer += text[: token.length + 1]
               if len(__a       ) > self.search_buffer_size:
                    snake_case :			List[Any]					   = search_buffer[-self.search_buffer_size :]

               # update the text
               snake_case :			str					   = text[token.length + 1 :]

               # append the token to output
               output.append(__a       )

          return output







     def  snake_case_			(							self       ,__a       )			->							str:
          snake_case :			Union[str, Any]					   = """"""

          for token in tokens:
               for _ in range(token.length       ):
                    output += output[-token.offset]
               output += token.indicator

          return output







     def  snake_case_			(							self       ,__a       ,__a       )			->							Token:

          if not text:
               raise ValueError("""We need some text to work with."""       )

          # Initialise result parameters to default values
          snake_case   ,		snake_case :			int					   = 0, 0

          if not search_buffer:
               return Token(__a       ,__a       ,text[length]       )

          for i, character in enumerate(__a       ):
               snake_case :			Tuple					   = len(__a       ) - i
               if character == text[0]:
                    snake_case :			List[Any]					   = self._match_length_from_index(__a       ,__a       ,0       ,__a       )
                    # if the found length is bigger than the current or if it's equal,
                    # which means it's offset is smaller: update offset and length
                    if found_length >= length:
                         snake_case   ,		snake_case :			Optional[Any]					   = found_offset, found_length

          return Token(__a       ,__a       ,text[length]       )







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       )			->							int:
          if not text or text[text_index] != window[window_index]:
               return 0
          return 1 + self._match_length_from_index(
              __a       ,window + text[text_index]       ,text_index + 1       ,window_index + 1       )


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      # Initialize compressor class
      lowercase			:      int			=					LZaaCompressor(window_size=13, lookahead_buffer_size=6)

      # Example
      lowercase			:      int			=					"""cabracadabrarrarrad"""
      lowercase			:      str			=					lzaa_compressor.compress(TEXT)
      print(lzaa_compressor.compress("""ababcbababaa"""))
      lowercase			:      List[Any]			=					lzaa_compressor.decompress(compressed_text)
      assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."



