
"""simple docstring"""




import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





     def __init__(   self			,      __UpperCamelCase			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      __UpperCamelCase = False			,      __UpperCamelCase = False			,      __UpperCamelCase = None			,      **__UpperCamelCase			,      )->       str:
          super().__init__(
              __UpperCamelCase			,      split=__UpperCamelCase			,      features=__UpperCamelCase			,      cache_dir=__UpperCamelCase			,      keep_in_memory=__UpperCamelCase			,      streaming=__UpperCamelCase			,      num_proc=__UpperCamelCase			,      **__UpperCamelCase			,      )
          UpperCAmelCase__				:			List[Any]							=    path_or_paths if isinstance(__UpperCamelCase			,      __UpperCamelCase	) else {self.split: path_or_paths}
          UpperCAmelCase__				:			Optional[int]							=    Csv(
              cache_dir=__UpperCamelCase			,      data_files=__UpperCamelCase			,      features=__UpperCamelCase			,      **__UpperCamelCase			,      )



     def 			lowerCAmelCase__							(   self	)->       Optional[int]:
          # Build iterable dataset
          if self.streaming:
               UpperCAmelCase__				:			str							=    self.builder.as_streaming_dataset(split=self.split	)
          # Build regular (map-style) dataset
          else:
               UpperCAmelCase__				:			Dict							=    None
               UpperCAmelCase__				:			str							=    None
               UpperCAmelCase__				:			Optional[int]							=    None
               UpperCAmelCase__				:			int							=    None

               self.builder.download_and_prepare(
                   download_config=__UpperCamelCase			,      download_mode=__UpperCamelCase			,      verification_mode=__UpperCamelCase			,      base_path=__UpperCamelCase			,      num_proc=self.num_proc			,      )
               UpperCAmelCase__				:			Union[str, Any]							=    self.builder.as_dataset(
                   split=self.split			,      verification_mode=__UpperCamelCase			,      in_memory=self.keep_in_memory	)
          return dataset







class 	_lowercase    :


     '''simple docstring'''





     def __init__(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      **__UpperCamelCase			,      )->       Any:
          if num_proc is not None and num_proc <= 0:
               raise ValueError(F"num_proc {num_proc} must be an integer > 0."	)

          UpperCAmelCase__				:			Optional[int]							=    dataset
          UpperCAmelCase__				:			Tuple							=    path_or_buf
          UpperCAmelCase__				:			Optional[int]							=    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          UpperCAmelCase__				:			Union[str, Any]							=    num_proc
          UpperCAmelCase__				:			Tuple							=    "utf-8"
          UpperCAmelCase__				:			str							=    to_csv_kwargs



     def 			lowerCAmelCase__							(   self	)->       int:
          UpperCAmelCase__				:			int							=    self.to_csv_kwargs.pop("path_or_buf"			,      __UpperCamelCase	)
          UpperCAmelCase__				:			int							=    self.to_csv_kwargs.pop("header"			,      __UpperCamelCase	)
          UpperCAmelCase__				:			Any							=    self.to_csv_kwargs.pop("index"			,      __UpperCamelCase	)

          if isinstance(self.path_or_buf			,      (str, bytes, os.PathLike)	):
               with open(self.path_or_buf			,      "wb+"	) as buffer:
                    UpperCAmelCase__				:			Dict							=    self._write(file_obj=__UpperCamelCase			,      header=__UpperCamelCase			,      index=__UpperCamelCase			,      **self.to_csv_kwargs	)
          else:
               UpperCAmelCase__				:			Dict							=    self._write(file_obj=self.path_or_buf			,      header=__UpperCamelCase			,      index=__UpperCamelCase			,      **self.to_csv_kwargs	)
          return written



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       List[Any]:
          UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			Any							=    args

          UpperCAmelCase__				:			Tuple							=    query_table(
              table=self.dataset.data			,      key=slice(__UpperCamelCase			,      offset + self.batch_size	)			,      indices=self.dataset._indices			,      )
          UpperCAmelCase__				:			Union[str, Any]							=    batch.to_pandas().to_csv(
              path_or_buf=__UpperCamelCase			,      header=header if (offset == 0) else False			,      index=__UpperCamelCase			,      **__UpperCamelCase	)
          return csv_str.encode(self.encoding	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      **__UpperCamelCase	)->       int:
          UpperCAmelCase__				:			List[Any]							=    0

          if self.num_proc is None or self.num_proc == 1:
               for offset in logging.tqdm(
                   range(0			,      len(self.dataset	)			,      self.batch_size	)			,      unit="ba"			,      disable=not logging.is_progress_bar_enabled()			,      desc="Creating CSV from Arrow format"			,      ):
                    UpperCAmelCase__				:			Union[str, Any]							=    self._batch_csv((offset, header, index, to_csv_kwargs)	)
                    written += file_obj.write(__UpperCamelCase	)

          else:
               UpperCAmelCase__				,						UpperCAmelCase__				:			Dict							=    len(self.dataset	), self.batch_size
               with multiprocessing.Pool(self.num_proc	) as pool:
                    for csv_str in logging.tqdm(
                        pool.imap(
                            self._batch_csv			,      [(offset, header, index, to_csv_kwargs) for offset in range(0			,      __UpperCamelCase			,      __UpperCamelCase	)]			,      )			,      total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,      unit="ba"			,      disable=not logging.is_progress_bar_enabled()			,      desc="Creating CSV from Arrow format"			,      ):
                         written += file_obj.write(__UpperCamelCase	)

          return written





"""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
A__   :						List[str] 							=     """src/diffusers"""
A__   :						List[str] 							=     """docs/source/en"""
A__   :						List[Any] 							=     """."""



def 				a__			(					lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							Any							,     lowerCAmelCase						:							List[Any]		):






     '''simple docstring'''
     with open(lowerCAmelCase							,     "r"							,     encoding="utf-8"							,     newline="\n"		) as f:
          UpperCAmelCase__				:			List[str]							=    f.readlines()
     # Find the start prompt.
     UpperCAmelCase__				:			Union[str, Any]							=    0
     while not lines[start_index].startswith(lowerCAmelCase		):
          start_index += 1
     start_index += 1

     UpperCAmelCase__				:			List[Any]							=    start_index
     while not lines[end_index].startswith(lowerCAmelCase		):
          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 |
A__   :						Tuple 							=     """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
A__   :						Any 							=     re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
A__   :						Tuple 							=     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.
A__   :						List[Any] 							=     re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
A__   :						List[Any] 							=     importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
A__   :						Dict 							=     spec.loader.load_module()



def 				a__			(					lowerCAmelCase						:							List[str]		):






     '''simple docstring'''
     UpperCAmelCase__				:			Dict							=    re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"							,     lowerCAmelCase		)
     return [m.group(0		) for m in matches]



def 				a__			(					lowerCAmelCase						:							Tuple							,     lowerCAmelCase						:							Union[str, Any]		):






     '''simple docstring'''
     UpperCAmelCase__				:			List[Any]							=    2 if text == "✅" or text == "❌" else len(lowerCAmelCase		)
     UpperCAmelCase__				:			Any							=    (width - text_length) // 2
     UpperCAmelCase__				:			List[Any]							=    width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     UpperCAmelCase__				:			Optional[int]							=    {
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     UpperCAmelCase__				:			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.
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			Optional[int]							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			Union[str, Any]							=    collections.defaultdict(lowerCAmelCase		)

     # Let's lookup through all diffusers object (once).
     for attr_name in dir(lowerCAmelCase		):
          UpperCAmelCase__				:			List[str]							=    None
          if attr_name.endswith("Tokenizer"		):
               UpperCAmelCase__				:			Union[str, Any]							=    slow_tokenizers
               UpperCAmelCase__				:			Optional[Any]							=    attr_name[:-9]
          elif attr_name.endswith("TokenizerFast"		):
               UpperCAmelCase__				:			List[str]							=    fast_tokenizers
               UpperCAmelCase__				:			Union[str, Any]							=    attr_name[:-13]
          elif _re_tf_models.match(lowerCAmelCase		) is not None:
               UpperCAmelCase__				:			Union[str, Any]							=    tf_models
               UpperCAmelCase__				:			Optional[int]							=    _re_tf_models.match(lowerCAmelCase		).groups()[0]
          elif _re_flax_models.match(lowerCAmelCase		) is not None:
               UpperCAmelCase__				:			List[Any]							=    flax_models
               UpperCAmelCase__				:			Any							=    _re_flax_models.match(lowerCAmelCase		).groups()[0]
          elif _re_pt_models.match(lowerCAmelCase		) is not None:
               UpperCAmelCase__				:			str							=    pt_models
               UpperCAmelCase__				:			List[str]							=    _re_pt_models.match(lowerCAmelCase		).groups()[0]

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

    # Let's build that table!
     UpperCAmelCase__				:			Tuple							=    list(model_name_to_config.keys()		)
     model_names.sort(key=str.lower		)
     UpperCAmelCase__				:			Dict							=    ["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).
     UpperCAmelCase__				:			List[str]							=    [len(lowerCAmelCase		) + 2 for c in columns]
     UpperCAmelCase__				:			Union[str, Any]							=    max([len(lowerCAmelCase		) for name in model_names]		) + 2

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

     UpperCAmelCase__				:			List[Any]							=    {True: "✅", False: "❌"}
     for name in model_names:
          UpperCAmelCase__				:			Optional[Any]							=    model_name_to_prefix[name]
          UpperCAmelCase__				:			List[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(lowerCAmelCase							,     lowerCAmelCase		) for l, w in zip(lowerCAmelCase							,     lowerCAmelCase		)]		) + "|\n"
     return table



def 				a__			(					lowerCAmelCase						:							Union[str, Any]=False		):






     '''simple docstring'''
     UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			int							=    _find_text_in_file(
         filename=os.path.join(lowerCAmelCase							,     "index.mdx"		)							,     start_prompt="<!--This table is updated automatically from the auto modules"							,     end_prompt="<!-- End table-->"							,     )
     UpperCAmelCase__				:			Optional[Any]							=    get_model_table_from_auto_modules()

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(lowerCAmelCase							,     "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__":
    A__   :						Union[str, Any] 							=     argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    A__   :						str 							=     parser.parse_args()

    check_model_table(args.fix_and_overwrite)



