


"""simple docstring"""



import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, 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
from datasets.utils.py_utils import Literal


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

__snake_case         =					["""names""", """prefix"""]
__snake_case         =					["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
__snake_case         =					["""encoding_errors""", """on_bad_lines"""]
__snake_case         =					["""date_format"""]







@dataclass
class _lowerCAmelCase       (   datasets.BuilderConfig			):
   __UpperCAmelCase				: str													=						","
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: Optional[Union[int, List[int], str]]													=						"infer"
   __UpperCAmelCase				: Optional[List[str]]													=						None
   __UpperCAmelCase				: Optional[List[str]]													=						None
   __UpperCAmelCase				: Optional[Union[int, str, List[int], List[str]]]													=						None
   __UpperCAmelCase				: Optional[Union[List[int], List[str]]]													=						None
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: Optional[Literal["c", "python", "pyarrow"]]													=						None
   __UpperCAmelCase				: Dict[Union[int, str], Callable[[Any], Any]]													=						None
   __UpperCAmelCase				: Optional[list]													=						None
   __UpperCAmelCase				: Optional[list]													=						None
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: Optional[Union[int, List[int]]]													=						None
   __UpperCAmelCase				: Optional[int]													=						None
   __UpperCAmelCase				: Optional[Union[str, List[str]]]													=						None
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: str													=						"."
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: str													=						'"'
   __UpperCAmelCase				: int													=						0
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: int													=						0
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: Optional[str]													=						None
   __UpperCAmelCase				: int													=						10000
   __UpperCAmelCase				: Optional[datasets.Features]													=						None
   __UpperCAmelCase				: Optional[str]													=						"strict"
   __UpperCAmelCase				: Literal["error", "warn", "skip"]													=						"error"
   __UpperCAmelCase				: Optional[str]													=						None







   def 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       if self.delimiter is not None:
           snake_case					:   Optional[Any]         =					self.delimiter
       if self.column_names is not None:
           snake_case					:   Dict         =					self.column_names







   @property
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					{
           "sep": self.sep,
           "header": self.header,
           "names": self.names,
           "index_col": self.index_col,
           "usecols": self.usecols,
           "prefix": self.prefix,
           "mangle_dupe_cols": self.mangle_dupe_cols,
           "engine": self.engine,
           "converters": self.converters,
           "true_values": self.true_values,
           "false_values": self.false_values,
           "skipinitialspace": self.skipinitialspace,
           "skiprows": self.skiprows,
           "nrows": self.nrows,
           "na_values": self.na_values,
           "keep_default_na": self.keep_default_na,
           "na_filter": self.na_filter,
           "verbose": self.verbose,
           "skip_blank_lines": self.skip_blank_lines,
           "thousands": self.thousands,
           "decimal": self.decimal,
           "lineterminator": self.lineterminator,
           "quotechar": self.quotechar,
           "quoting": self.quoting,
           "escapechar": self.escapechar,
           "comment": self.comment,
           "encoding": self.encoding,
           "dialect": self.dialect,
           "error_bad_lines": self.error_bad_lines,
           "warn_bad_lines": self.warn_bad_lines,
           "skipfooter": self.skipfooter,
           "doublequote": self.doublequote,
           "memory_map": self.memory_map,
           "float_precision": self.float_precision,
           "chunksize": self.chunksize,
           "encoding_errors": self.encoding_errors,
           "on_bad_lines": self.on_bad_lines,
           "date_format": self.date_format,
       }

       # some kwargs must not be passed if they don't have a default value
       # some others are deprecated and we can also not pass them if they are the default value
       for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
           if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()		,			UpperCamelCase__		):
               del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
       if not (datasets.config.PANDAS_VERSION.major >= 2):
           for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
               del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
       if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
           for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
               del pd_read_csv_kwargs[pd_read_csv_parameter]

       return pd_read_csv_kwargs







class _lowerCAmelCase       (   datasets.ArrowBasedBuilder			):
   __UpperCAmelCase				: Union[str, Any]													=						CsvConfig







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[int]:

       '''simple docstring'''




       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}'		)
       snake_case					:   Optional[int]         =					dl_manager.download_and_extract(self.config.data_files		)
       if isinstance(UpperCamelCase__		,			(str, list, tuple)		):
           snake_case					:   List[str]         =					data_files
           if isinstance(UpperCamelCase__		,			UpperCamelCase__		):
               snake_case					:   Dict         =					[files]
           snake_case					:   List[Any]         =					[dl_manager.iter_files(UpperCamelCase__		) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,			gen_kwargs={"files": files}		)]
       snake_case					:   Optional[int]         =					[]
       for split_name, files in data_files.items():
           if isinstance(UpperCamelCase__		,			UpperCamelCase__		):
               snake_case					:   Any         =					[files]
           snake_case					:   Optional[int]         =					[dl_manager.iter_files(UpperCamelCase__		) for file in files]
           splits.append(datasets.SplitGenerator(name=UpperCamelCase__		,			gen_kwargs={"files": files}		)		)
       return splits







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	pa.Table:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   List[Any]         =					self.config.features.arrow_schema if self.config.features else None
       # dtype allows reading an int column as str
       snake_case					:   Tuple         =					(
           {
               name: dtype.to_pandas_dtype() if not require_storage_cast(UpperCamelCase__		) else object
               for name, dtype, feature in zip(schema.names		,			schema.types		,			self.config.features.values()		)
           }
           if schema is not None
           else None
       )
       for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__		)		):
           snake_case					:   Optional[Any]         =					pd.read_csv(UpperCamelCase__		,			iterator=UpperCamelCase__		,			dtype=UpperCamelCase__		,			**self.config.pd_read_csv_kwargs		)
           try:
               for batch_idx, df in enumerate(UpperCamelCase__		):
                   snake_case					:   List[str]         =					pa.Table.from_pandas(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__		)
           except ValueError as e:
               logger.error(F'Failed to read file \'{file}\' with error {type(UpperCamelCase__		)}: {e}'		)
               raise









"""simple docstring"""



import argparse
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_dummies.py
__snake_case         =					"""src/diffusers"""

# Matches is_xxx_available()
__snake_case         =					re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
__snake_case         =					re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


__snake_case         =					"""
{0} = None
"""

__snake_case         =					"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


__snake_case         =					"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""







def 						__lowerCAmelCase		(		lowercase						:						Tuple				)			->		str:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					_re_backend.findall(lowercase				)
    if len(lowercase				) == 0:
        return None

    return "_and_".join(lowercase				)







def 						__lowerCAmelCase		(		)			->		Tuple:


    """simple docstring"""





    with open(os.path.join(lowercase		,							"__init__.py"				)		,							"r"		,							encoding="utf-8"		,							newline="\n"				) as f:
        snake_case					:   Tuple         =					f.readlines()

    # Get to the point we do the actual imports for type checking
    snake_case					:   Tuple         =					0
    snake_case					:   Optional[Any]         =					{}
    # Go through the end of the file
    while line_index < len(lowercase				):
        # If the line contains is_backend_available, we grab all objects associated with the `else` block
        snake_case					:   List[Any]         =					find_backend(lines[line_index]				)
        if backend is not None:
            while not lines[line_index].startswith("else:"				):
                line_index += 1
            line_index += 1
            snake_case					:   str         =					[]
            # Until we unindent, add backend objects to the list
            while line_index < len(lowercase				) and len(lines[line_index]				) > 1:
                snake_case					:   List[str]         =					lines[line_index]
                snake_case					:   Any         =					_re_single_line_import.search(lowercase				)
                if single_line_import_search is not None:
                    objects.extend(single_line_import_search.groups()[0].split(", "				)				)
                elif line.startswith(" " * 8				):
                    objects.append(line[8:-2]				)
                line_index += 1

            if len(lowercase				) > 0:
                snake_case					:   Any         =					objects
        else:
            line_index += 1

    return backend_specific_objects







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]		,							lowercase						:						Tuple				)			->		int:


    """simple docstring"""





    if name.isupper():
        return DUMMY_CONSTANT.format(lowercase				)
    elif name.islower():
        return DUMMY_FUNCTION.format(lowercase		,							lowercase				)
    else:
        return DUMMY_CLASS.format(lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]=None				)			->		Optional[int]:


    """simple docstring"""





    if backend_specific_objects is None:
        snake_case					:   List[str]         =					read_init()
    # For special correspondence backend to module name as used in the function requires_modulename
    snake_case					:   Dict         =					{}

    for backend, objects in backend_specific_objects.items():
        snake_case					:   Union[str, Any]         =					"[" + ", ".join(F'"{b}"' for b in backend.split("_and_"				)				) + "]"
        snake_case					:   Union[str, Any]         =					"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
        dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
        dummy_file += "\n".join([create_dummy_object(lowercase		,							lowercase				) for o in objects]				)
        snake_case					:   Optional[Any]         =					dummy_file

    return dummy_files







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]=False				)			->		Any:


    """simple docstring"""





    snake_case					:   str         =					create_dummy_files()
    # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
    snake_case					:   Dict         =					{"torch": "pt"}

    # Locate actual dummy modules and read their content.
    snake_case					:   Optional[Any]         =					os.path.join(lowercase		,							"utils"				)
    snake_case					:   Union[str, Any]         =					{
        backend: os.path.join(lowercase		,							F'dummy_{short_names.get(lowercase		,							lowercase				)}_objects.py'				)
        for backend in dummy_files.keys()
    }

    snake_case					:   Optional[Any]         =					{}
    for backend, file_path in dummy_file_paths.items():
        if os.path.isfile(lowercase				):
            with open(lowercase		,							"r"		,							encoding="utf-8"		,							newline="\n"				) as f:
                snake_case					:   int         =					f.read()
        else:
            snake_case					:   Optional[Any]         =					""

    for backend in dummy_files.keys():
        if dummy_files[backend] != actual_dummies[backend]:
            if overwrite:
                print(
                    F'Updating diffusers.utils.dummy_{short_names.get(lowercase		,							lowercase				)}_objects.py as the main '
                    "__init__ has new objects."				)
                with open(dummy_file_paths[backend]		,							"w"		,							encoding="utf-8"		,							newline="\n"				) as f:
                    f.write(dummy_files[backend]				)
            else:
                raise ValueError(
                    "The main __init__ has objects that are not present in "
                    F'diffusers.utils.dummy_{short_names.get(lowercase		,							lowercase				)}_objects.py. Run `make fix-copies` '
                    "to fix this."				)


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

  check_dummies(args.fix_and_overwrite)





