




'''simple docstring'''







import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor



def 					lowerCamelCase__							(			):
     snake_case :			Optional[Any]					   = set()
     # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
     for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
          snake_case :			int					   = getattr(transformers.models.auto    ,      __lowercase    ,      __lowercase							)
          if module is None:
               continue
          # all mappings in a single auto modeling file
          snake_case :			List[str]					   = [
              x
              for x in dir(__lowercase							)
              if x.endswith("""_MAPPING_NAMES"""							)
              and (x.startswith("""MODEL_"""							) or x.startswith("""TF_MODEL_"""							) or x.startswith("""FLAX_MODEL_"""							))
          ]
          for name in mapping_names:
               snake_case :			int					   = getattr(__lowercase    ,      __lowercase							)
               if mapping is not None:
                    for v in mapping.values():
                         if isinstance(__lowercase    ,      (list, tuple)							):
                              model_names.update(__lowercase							)
                         elif isinstance(__lowercase    ,      __lowercase							):
                              model_names.add(__lowercase							)

     return sorted(__lowercase							)



def 					lowerCamelCase__							(			):
     # All model names defined in auto mappings
     snake_case :			Optional[int]					   = set(get_all_model_names()							)

     with open("""tests/utils/tiny_model_summary.json"""							) as fp:
          snake_case :			Union[str, Any]					   = json.load(__lowercase							)
     snake_case :			List[str]					   = set()
     for model_base_name in tiny_model_info:
          tiny_models_names.update(tiny_model_info[model_base_name]["""model_classes"""]							)

     # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
     snake_case :			Union[str, Any]					   = model_names.difference(__lowercase							)
     for model_name in copy.copy(__lowercase							):
          if not model_name.startswith("""TF"""							) and F'''TF{model_name}''' in not_on_hub:
               tiny_models_names.remove(__lowercase							)
          elif model_name.startswith("""TF"""							) and model_name[2:] in not_on_hub:
               tiny_models_names.remove(__lowercase							)

     return sorted(__lowercase							)



def 					lowerCamelCase__							(			__lowercase							):
     snake_case :			int					   = COMPOSITE_MODELS.values()

     # All tiny model base names on Hub
     snake_case :			List[Any]					   = get_all_model_names()
     snake_case :			Any					   = hf_api.list_models(
         filter=ModelFilter(
             author="""hf-internal-testing"""    ,      )							)
     snake_case :			List[str]					   = set()
     for x in models:
          snake_case :			List[str]					   = x.modelId
          snake_case :			Tuple					   = model.split("""/"""							)
          if not model.startswith("""tiny-random-"""							):
               continue
          snake_case :			Tuple					   = model.replace("""tiny-random-"""    ,      """"""							)
          if not model[0].isupper():
               continue
          if model not in model_names and model not in special_models:
               continue
          _models.add(__lowercase							)

     snake_case :			Tuple					   = sorted(_models							)
     # All tiny model names on Hub
     snake_case :			Union[str, Any]					   = {}
     for model in models:
          snake_case :			Any					   = F'''hf-internal-testing/tiny-random-{model}'''
          snake_case :			Dict					   = model.split("""-"""							)[0]
          try:
               snake_case :			Union[str, Any]					   = hf_api.repo_info(__lowercase							)
               snake_case :			int					   = {
                   """tokenizer_classes""": set(),
                   """processor_classes""": set(),
                   """model_classes""": set(),
                   """sha""": repo_info.sha,
               }
          except Exception:
               continue
          try:
               time.sleep(1							)
               snake_case :			str					   = AutoTokenizer.from_pretrained(__lowercase							)
               content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__							)
          except Exception:
               pass
          try:
               time.sleep(1							)
               snake_case :			Any					   = AutoTokenizer.from_pretrained(__lowercase    ,      use_fast=__lowercase							)
               content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__							)
          except Exception:
               pass
          try:
               time.sleep(1							)
               snake_case :			Union[str, Any]					   = AutoImageProcessor.from_pretrained(__lowercase							)
               content["processor_classes"].add(img_p.__class__.__name__							)
          except Exception:
               pass
          try:
               time.sleep(1							)
               snake_case :			Tuple					   = AutoFeatureExtractor.from_pretrained(__lowercase							)
               if not isinstance(__lowercase    ,      __lowercase							):
                    content["processor_classes"].add(feat_p.__class__.__name__							)
          except Exception:
               pass
          try:
               time.sleep(1							)
               snake_case :			List[Any]					   = getattr(__lowercase    ,      __lowercase							)
               snake_case :			str					   = model_class.from_pretrained(__lowercase							)
               content["model_classes"].add(m.__class__.__name__							)
          except Exception:
               pass
          try:
               time.sleep(1							)
               snake_case :			Dict					   = getattr(__lowercase    ,      F'''TF{model}'''							)
               snake_case :			int					   = model_class.from_pretrained(__lowercase							)
               content["model_classes"].add(m.__class__.__name__							)
          except Exception:
               pass

          snake_case :			Optional[int]					   = sorted(content["""tokenizer_classes"""]							)
          snake_case :			List[str]					   = sorted(content["""processor_classes"""]							)
          snake_case :			Dict					   = sorted(content["""model_classes"""]							)

          snake_case :			List[str]					   = content
          with open(os.path.join(__lowercase    ,      """hub_tiny_model_summary.json"""							)    ,      """w"""							) as fp:
               json.dump(__lowercase    ,      __lowercase    ,      ensure_ascii=__lowercase    ,      indent=4							)


if __name__ == "__main__":
      lowercase			:      Tuple			=					argparse.ArgumentParser()
      parser.add_argument("""--num_workers""", default=1, type=int, help="""The number of workers to run.""")
      lowercase			:      Union[str, Any]			=					parser.parse_args()

      # This has to be `spawn` to avoid hanging forever!
      multiprocessing.set_start_method("""spawn""")

      lowercase			:      Union[str, Any]			=					"""tiny_models"""
      lowercase			:      Any			=					True
      lowercase			:      int			=					None
      lowercase			:      int			=					get_tiny_model_names_from_repo()
      lowercase			:      Union[str, Any]			=					True
      lowercase			:      int			=					True
      lowercase			:      Tuple			=					"""hf-internal-testing"""

      create_tiny_models(
          output_path,
          all,
          model_types,
          models_to_skip,
          no_check,
          upload,
          organization,
          token=os.environ.get("""TOKEN""", None),
          num_workers=args.num_workers,
      )









'''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."



