

import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


_SCREAMING_SNAKE_CASE          = datasets.utils.logging.get_logger(__name__)


@dataclass
class     lowerCamelCase_   ( datasets.BuilderConfig):
 _lowerCAmelCase       		=  None
 _lowerCAmelCase       		=  "utf-8"
 _lowerCAmelCase       		=  None
 _lowerCAmelCase       		=  10 << 20  # 10MB
 _lowerCAmelCase       		=  False
 _lowerCAmelCase       		=  "line"



class     lowerCamelCase_   ( datasets.ArrowBasedBuilder):
 _lowerCAmelCase       		=  TextConfig




 def 						a					( self       ):
   return datasets.DatasetInfo(features=self.config.features       )




 def 						a					( self     ,     _UpperCAmelCase       ):
   if not self.config.data_files:
     raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"       )
   __UpperCAmelCase  :     Optional[int]		=    dl_manager.download_and_extract(self.config.data_files       )
   if isinstance(_UpperCAmelCase     ,     (str, list, tuple)       ):
     __UpperCAmelCase  :     Union[str, Any]		=    data_files
     if isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       ):
       __UpperCAmelCase  :     List[str]		=    [files]
     __UpperCAmelCase  :     Optional[int]		=    [dl_manager.iter_files(_UpperCAmelCase       ) for file in files]
     return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,     gen_kwargs={'files': files}       )]
   __UpperCAmelCase  :     Dict		=    []
   for split_name, files in data_files.items():
     if isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       ):
       __UpperCAmelCase  :     int		=    [files]
     __UpperCAmelCase  :     Dict		=    [dl_manager.iter_files(_UpperCAmelCase       ) for file in files]
     splits.append(datasets.SplitGenerator(name=_UpperCAmelCase     ,     gen_kwargs={'files': files}       )       )
   return splits




 def 						a					( self     ,     _UpperCAmelCase       ):
   if self.config.features is not None:
     __UpperCAmelCase  :     List[str]		=    self.config.features.arrow_schema
     if all(not require_storage_cast(_UpperCAmelCase       ) for feature in self.config.features.values()       ):
       # cheaper cast
       __UpperCAmelCase  :     str		=    pa_table.cast(_UpperCAmelCase       )
     else:
       # more expensive cast; allows str <-> int/float or str to Audio for example
       __UpperCAmelCase  :     Tuple		=    table_cast(_UpperCAmelCase     ,     _UpperCAmelCase       )
     return pa_table
   else:
     return pa_table.cast(pa.schema({'text': pa.string()}       )       )




 def 						a					( self     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Optional[Any]		=    list(self.config.features       ) if self.config.features is not None else ['text']
   for file_idx, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase       )       ):
     # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
     with open(_UpperCAmelCase     ,     encoding=self.config.encoding     ,     errors=self.config.errors       ) as f:
       if self.config.sample_by == "line":
         __UpperCAmelCase  :     Tuple		=    0
         while True:
           __UpperCAmelCase  :     Tuple		=    f.read(self.config.chunksize       )
           if not batch:
             break
           batch += f.readline()  # finish current line
           # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
           __UpperCAmelCase  :     List[str]		=    StringIO(_UpperCAmelCase       ).readlines()
           if not self.config.keep_linebreaks:
             __UpperCAmelCase  :     int		=    [line.rstrip('\n'       ) for line in batch]
           __UpperCAmelCase  :     Tuple		=    pa.Table.from_arrays([pa.array(_UpperCAmelCase       )]     ,     names=_UpperCAmelCase       )
           # Uncomment for debugging (will print the Arrow table size and elements)
           # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
           # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
           yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase       )
           batch_idx += 1
       elif self.config.sample_by == "paragraph":
         __UpperCAmelCase  :     List[str]		=    0
         __UpperCAmelCase  :     List[Any]		=    ''
         while True:
           __UpperCAmelCase  :     Any		=    f.read(self.config.chunksize       )
           if not new_batch:
             break
           batch += new_batch
           batch += f.readline()  # finish current line
           __UpperCAmelCase  :     Tuple		=    batch.split('\n\n'       )
           __UpperCAmelCase  :     Tuple		=    pa.Table.from_arrays(
               [pa.array([example for example in batch[:-1] if example]       )]     ,     names=_UpperCAmelCase       )
           # Uncomment for debugging (will print the Arrow table size and elements)
           # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
           # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
           yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase       )
           batch_idx += 1
           __UpperCAmelCase  :     int		=    batch[-1]
         if batch:
           __UpperCAmelCase  :     List[str]		=    pa.Table.from_arrays([pa.array([batch]       )]     ,     names=_UpperCAmelCase       )
           yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase       )
       elif self.config.sample_by == "document":
         __UpperCAmelCase  :     Optional[Any]		=    f.read()
         __UpperCAmelCase  :     Dict		=    pa.Table.from_arrays([pa.array([text]       )]     ,     names=_UpperCAmelCase       )
         yield file_idx, self._cast_table(_UpperCAmelCase       )








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    _snake_case		()       ->		Optional[int]:
  __UpperCAmelCase  :     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"]:
    __UpperCAmelCase  :     str		=    getattr(transformers.models.auto						,					snake_case_						,					snake_case_     )
    if module is None:
      continue
    # all mappings in a single auto modeling file
    __UpperCAmelCase  :     Dict		=    [
        x
        for x in dir(snake_case_     )
        if x.endswith('_MAPPING_NAMES'     )
        and (x.startswith('MODEL_'     ) or x.startswith('TF_MODEL_'     ) or x.startswith('FLAX_MODEL_'     ))
    ]
    for name in mapping_names:
      __UpperCAmelCase  :     Optional[int]		=    getattr(snake_case_						,					snake_case_     )
      if mapping is not None:
        for v in mapping.values():
          if isinstance(snake_case_						,					(list, tuple)     ):
            model_names.update(snake_case_     )
          elif isinstance(snake_case_						,					snake_case_     ):
            model_names.add(snake_case_     )

  return sorted(snake_case_     )





def    _snake_case		()       ->		Any:
  # All model names defined in auto mappings
  __UpperCAmelCase  :     int		=    set(get_all_model_names()     )

  with open('tests/utils/tiny_model_summary.json'     ) as fp:
    __UpperCAmelCase  :     Tuple		=    json.load(snake_case_     )
  __UpperCAmelCase  :     int		=    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.
  __UpperCAmelCase  :     Tuple		=    model_names.difference(snake_case_     )
  for model_name in copy.copy(snake_case_     ):
    if not model_name.startswith('TF'     ) and F"TF{model_name}" in not_on_hub:
      tiny_models_names.remove(snake_case_     )
    elif model_name.startswith('TF'     ) and model_name[2:] in not_on_hub:
      tiny_models_names.remove(snake_case_     )

  return sorted(snake_case_     )





def    _snake_case		(snake_case_				:    List[str]     )       ->		Tuple:
  __UpperCAmelCase  :     Dict		=    COMPOSITE_MODELS.values()

  # All tiny model base names on Hub
  __UpperCAmelCase  :     Optional[int]		=    get_all_model_names()
  __UpperCAmelCase  :     List[Any]		=    hf_api.list_models(
      filter=ModelFilter(
          author='hf-internal-testing'						,					)     )
  __UpperCAmelCase  :     Dict		=    set()
  for x in models:
    __UpperCAmelCase  :     Union[str, Any]		=    x.modelId
    __UpperCAmelCase, __UpperCAmelCase  :     List[str]		=    model.split('/'     )
    if not model.startswith('tiny-random-'     ):
      continue
    __UpperCAmelCase  :     Optional[Any]		=    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(snake_case_     )

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

    __UpperCAmelCase  :     Optional[int]		=    sorted(content['tokenizer_classes']     )
    __UpperCAmelCase  :     List[str]		=    sorted(content['processor_classes']     )
    __UpperCAmelCase  :     Tuple		=    sorted(content['model_classes']     )

    __UpperCAmelCase  :     str		=    content
    with open(os.path.join(snake_case_						,					'hub_tiny_model_summary.json'     )						,					'w'     ) as fp:
      json.dump(snake_case_						,					snake_case_						,					ensure_ascii=snake_case_						,					indent=4     )


if __name__ == "__main__":
      _SCREAMING_SNAKE_CASE          = argparse.ArgumentParser()
      parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
      _SCREAMING_SNAKE_CASE          = parser.parse_args()

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

      _SCREAMING_SNAKE_CASE          = '''tiny_models'''
      _SCREAMING_SNAKE_CASE          = True
      _SCREAMING_SNAKE_CASE          = None
      _SCREAMING_SNAKE_CASE          = get_tiny_model_names_from_repo()
      _SCREAMING_SNAKE_CASE          = True
      _SCREAMING_SNAKE_CASE          = True
      _SCREAMING_SNAKE_CASE          = '''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,
      )





