






"""simple docstring"""


import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_UpperCamelCase				:    List[Any]     =						'src/diffusers'
_UpperCamelCase				:    Tuple     =						'docs/source/en'
_UpperCamelCase				:    str     =						'.'




def 			snake_case  (A_      :Tuple	,			A_      :int	,			A_      :List[str]      ):







  '''simple docstring'''

  with open(A_	,			'r'	,			encoding='utf-8'	,			newline='\n'      ) as f:
    a     :			str				=   f.readlines()
  # Find the start prompt.
  a     :			Optional[Any]				=   0
  while not lines[start_index].startswith(A_      ):
    start_index += 1
  start_index += 1

  a     :			List[str]				=   start_index
  while not lines[end_index].startswith(A_      ):
    end_index += 1
  end_index -= 1

  while len(lines[start_index]      ) <= 1:
    start_index += 1
  while len(lines[end_index]      ) <= 1:
    end_index -= 1
  end_index += 1
  return "".join(lines[start_index:end_index]      ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
_UpperCamelCase				:    int     =						'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
_UpperCamelCase				:    Optional[Any]     =						re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
_UpperCamelCase				:    Dict     =						re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_UpperCamelCase				:    List[Any]     =						re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
_UpperCamelCase				:    Optional[int]     =						importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
_UpperCamelCase				:    Optional[int]     =						spec.loader.load_module()




def 			snake_case  (A_      :Union[str, Any]      ):







  '''simple docstring'''

  a     :			List[Any]				=   re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'	,			A_      )
  return [m.group(0      ) for m in matches]




def 			snake_case  (A_      :List[Any]	,			A_      :Optional[int]      ):







  '''simple docstring'''

  a     :			List[Any]				=   2 if text == '✅' or text == '❌' else len(A_      )
  a     :			Optional[int]				=   (width - text_length) // 2
  a     :			Union[str, Any]				=   width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent




def 			snake_case  ():







  '''simple docstring'''

  a     :			Any				=   diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  a     :			List[Any]				=   {
      name: config_mapping_names[code]
      for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
      if code in config_mapping_names
  }
  a     :			Optional[int]				=   {name: config.replace('ConfigMixin'	,			''      ) for name, config in model_name_to_config.items()}

  # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
  a     :			List[str]				=   collections.defaultdict(A_      )
  a     :			List[Any]				=   collections.defaultdict(A_      )
  a     :			Optional[Any]				=   collections.defaultdict(A_      )
  a     :			Any				=   collections.defaultdict(A_      )
  a     :			Dict				=   collections.defaultdict(A_      )

  # Let's lookup through all diffusers object (once).
  for attr_name in dir(A_      ):
    a     :			Dict				=   None
    if attr_name.endswith('Tokenizer'      ):
      a     :			int				=   slow_tokenizers
      a     :			Any				=   attr_name[:-9]
    elif attr_name.endswith('TokenizerFast'      ):
      a     :			int				=   fast_tokenizers
      a     :			Dict				=   attr_name[:-1_3]
    elif _re_tf_models.match(A_      ) is not None:
      a     :			List[Any]				=   tf_models
      a     :			Union[str, Any]				=   _re_tf_models.match(A_      ).groups()[0]
    elif _re_flax_models.match(A_      ) is not None:
      a     :			Union[str, Any]				=   flax_models
      a     :			Union[str, Any]				=   _re_flax_models.match(A_      ).groups()[0]
    elif _re_pt_models.match(A_      ) is not None:
      a     :			str				=   pt_models
      a     :			List[str]				=   _re_pt_models.match(A_      ).groups()[0]

    if lookup_dict is not None:
      while len(A_      ) > 0:
        if attr_name in model_name_to_prefix.values():
          a     :			Optional[Any]				=   True
          break
        # Try again after removing the last word in the name
        a     :			List[str]				=   ''.join(camel_case_split(A_      )[:-1]      )

    # Let's build that table!
  a     :			Tuple				=   list(model_name_to_config.keys()      )
  model_names.sort(key=str.lower      )
  a     :			Optional[Any]				=   ['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
  # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
  a     :			List[Any]				=   [len(A_      ) + 2 for c in columns]
  a     :			Union[str, Any]				=   max([len(A_      ) for name in model_names]      ) + 2

  # Build the table per se
  a     :			Tuple				=   '|' + '|'.join([_center_text(A_	,			A_      ) for c, w in zip(A_	,			A_      )]      ) + '|\n'
  # Use ":-----:" format to center-aligned table cell texts
  table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]      ) + "|\n"

  a     :			Tuple				=   {True: '✅', False: '❌'}
  for name in model_names:
    a     :			List[str]				=   model_name_to_prefix[name]
    a     :			Any				=   [
        name,
        check[slow_tokenizers[prefix]],
        check[fast_tokenizers[prefix]],
        check[pt_models[prefix]],
        check[tf_models[prefix]],
        check[flax_models[prefix]],
    ]
    table += "|" + "|".join([_center_text(A_	,			A_      ) for l, w in zip(A_	,			A_      )]      ) + "|\n"
  return table




def 			snake_case  (A_      :List[str]=False      ):







  '''simple docstring'''

  a,     a,     a,     a     :			Any				=   _find_text_in_file(
      filename=os.path.join(A_	,			'index.mdx'      )	,			start_prompt='<!--This table is updated automatically from the auto modules'	,			end_prompt='<!-- End table-->'	,			)
  a     :			Any				=   get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(A_	,			'index.mdx'      )	,			'w'	,			encoding='utf-8'	,			newline='\n'      ) as f:
        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]      )
    else:
      raise ValueError(
          'The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'      )


if __name__ == "__main__":
   _UpperCamelCase				:    Tuple     =						argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   _UpperCamelCase				:    int     =						parser.parse_args()

   check_model_table(args.fix_and_overwrite)












"""simple docstring"""


import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

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


if TYPE_CHECKING:
   import sqlitea

   import sqlalchemy


_UpperCamelCase				:    Optional[Any]     =						datasets.utils.logging.get_logger(__name__)






@dataclass
class 						snake_case  (  datasets.BuilderConfig       ):
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     True
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     1_0000
    __magic_name__         =     None





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      if self.sql is None:
        raise ValueError('sql must be specified'			)
      if self.con is None:
        raise ValueError('con must be specified'			)





    def 				lowerCamelCase__      (							self	:					int				,		A	:					dict				,		A	:					Optional[datasets.Features] = None				,		):


      '''simple docstring'''




      a     :			str				=   config_kwargs.copy()
      # We need to stringify the Selectable object to make its hash deterministic

      # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
      a     :			List[str]				=   config_kwargs['sql']
      if not isinstance(A				,		A			):
        if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
          import sqlalchemy

          if isinstance(A				,		sqlalchemy.sql.Selectable			):
            a     :			Union[str, Any]				=   sqlalchemy.create_engine(config_kwargs['con'].split('://'			)[0] + '://'			)
            a     :			List[Any]				=   str(sql.compile(dialect=engine.dialect			)			)
            a     :			str				=   sql_str
          else:
            raise TypeError(
                F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(A			)}: {sql}'''			)
        else:
          raise TypeError(
              F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(A			)}: {sql}'''			)
      a     :			List[str]				=   config_kwargs['con']
      if not isinstance(A				,		A			):
        a     :			Optional[Any]				=   id(A			)
        logger.info(
            F'''SQL connection \'con\' of type {type(A			)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''			)

      return super().create_config_id(A				,		custom_features=A			)





    @property
    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			int				=   {
          'index_col': self.index_col,
          'columns': self.columns,
          'params': self.params,
          'coerce_float': self.coerce_float,
          'parse_dates': self.parse_dates,
      }
      return pd_read_sql_kwargs






class 						snake_case  (  datasets.ArrowBasedBuilder       ):
    __magic_name__         =     SqlConfig





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      return datasets.DatasetInfo(features=self.config.features			)





    def 				lowerCamelCase__      (							self	:					Dict				,		A	:					Optional[int]			):


      '''simple docstring'''




      return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,		gen_kwargs={}			)]





    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		A	:					pa.Table			):


      '''simple docstring'''




      if self.config.features is not None:
        a     :			List[Any]				=   self.config.features.arrow_schema
        if all(not require_storage_cast(A			) for feature in self.config.features.values()			):
          # cheaper cast
          a     :			Dict				=   pa.Table.from_arrays([pa_table[field.name] for field in schema]				,		schema=A			)
        else:
          # more expensive cast; allows str <-> int/float or str to Audio for example
          a     :			Union[str, Any]				=   table_cast(A				,		A			)
      return pa_table





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a     :			List[Any]				=   self.config.chunksize
      a     :			Union[str, Any]				=   pd.read_sql(
          self.config.sql				,		self.config.con				,		chunksize=A				,		**self.config.pd_read_sql_kwargs			)
      a     :			Optional[Any]				=   [sql_reader] if chunksize is None else sql_reader
      for chunk_idx, df in enumerate(A			):
        a     :			List[Any]				=   pa.Table.from_pandas(A			)
        yield chunk_idx, self._cast_table(A			)




