

"""simple docstring"""




import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


__A     :    Dict    =		logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
__A     :    Optional[Any]    =		direct_transformers_import(Path(__file__).parent)


__A     :    int    =		{
    'AutoTokenizer': 'PreTrainedTokenizerBase',
    'AutoFeatureExtractor': 'FeatureExtractionMixin',
    'AutoImageProcessor': 'ImageProcessingMixin',
}





class 			__UpperCamelCase (   _A						):
		SCREAMING_SNAKE_CASE								=    ["feature_extractor", "tokenizer"]
		# Names need to be attr_class for attr in attributes
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    None


		def __init__(self :		Optional[Any]     ,     *__SCREAMING_SNAKE_CASE :		Union[str, Any]     ,     **__SCREAMING_SNAKE_CASE :		List[str]):
							# Sanitize args and kwargs
							for key in kwargs:
												if key not in self.attributes:
																	raise TypeError(F"""Unexpected keyword argument {key}.""")
							for arg, attribute_name in zip(__SCREAMING_SNAKE_CASE     ,     self.attributes):
												if attribute_name in kwargs:
																	raise TypeError(F"""Got multiple values for argument {attribute_name}.""")
												else:
																	A			=					arg

							if len(__SCREAMING_SNAKE_CASE) != len(self.attributes):
												raise ValueError(
												    F"""This processor requires {len(self.attributes)} arguments: {", ".join(self.attributes)}. Got """
												    F"""{len(__SCREAMING_SNAKE_CASE)} arguments instead.""")

							# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
							for attribute_name, arg in kwargs.items():
												A			=					getattr(self     ,     F"""{attribute_name}_class""")
												# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
												A			=					AUTO_TO_BASE_CLASS_MAPPING.get(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
												if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
																	A			=					tuple(getattr(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE) for n in class_name if n is not None)
												else:
																	A			=					getattr(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

												if not isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
																	raise ValueError(
																	    F"""Received a {type(__SCREAMING_SNAKE_CASE).__name__} for argument {attribute_name}, but a {class_name} was expected.""")

												setattr(self     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)


		def __repr__(self :		int):
							A			=					[F"""- {name}: {repr(getattr(self     ,     __SCREAMING_SNAKE_CASE))}""" for name in self.attributes]
							A			=					"\n".join(__SCREAMING_SNAKE_CASE)
							return F"""{self.__class__.__name__}:\n{attributes_repr}"""


		def 					SCREAMING_SNAKE_CASE__   (self :		str     ,     __SCREAMING_SNAKE_CASE :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     **__SCREAMING_SNAKE_CASE :		Any):
							os.makedirs(__SCREAMING_SNAKE_CASE     ,     exist_ok=__SCREAMING_SNAKE_CASE)

							if push_to_hub:
												A			=					kwargs.pop("commit_message"     ,     __SCREAMING_SNAKE_CASE)
												A			=					kwargs.pop("repo_id"     ,     save_directory.split(os.path.sep)[-1])
												A			=					self._create_repo(__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)
												A			=					self._get_files_timestamps(__SCREAMING_SNAKE_CASE)
							# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
							# loaded from the Hub.
							if self._auto_class is not None:
												A			=					[getattr(self     ,     __SCREAMING_SNAKE_CASE) for attribute_name in self.attributes]
												A			=					[(a.init_kwargs if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE) else a) for a in attrs]
												custom_object_save(self     ,     __SCREAMING_SNAKE_CASE     ,     config=__SCREAMING_SNAKE_CASE)

							for attribute_name in self.attributes:
												A			=					getattr(self     ,     __SCREAMING_SNAKE_CASE)
												# Include the processor class in the attribute config so this processor can then be reloaded with the
												# `AutoProcessor` API.
												if hasattr(__SCREAMING_SNAKE_CASE     ,     "_set_processor_class"):
																	attribute._set_processor_class(self.__class__.__name__)
												attribute.save_pretrained(__SCREAMING_SNAKE_CASE)

							if self._auto_class is not None:
												# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
												for attribute_name in self.attributes:
																	A			=					getattr(self     ,     __SCREAMING_SNAKE_CASE)
																	if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
																						del attribute.init_kwargs["auto_map"]

							if push_to_hub:
												self._upload_modified_files(
												    __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     commit_message=__SCREAMING_SNAKE_CASE     ,     token=kwargs.get("use_auth_token")     ,     )


		@classmethod
		def 					SCREAMING_SNAKE_CASE__   (cls :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		Union[str, os.PathLike]     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, os.PathLike]] = None     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, bool]] = None     ,     __SCREAMING_SNAKE_CASE :		str = "main"     ,     **__SCREAMING_SNAKE_CASE :		int     ,     ):
							A			=					cache_dir
							A			=					force_download
							A			=					local_files_only
							A			=					revision

							A			=					kwargs.pop("use_auth_token"     ,     __SCREAMING_SNAKE_CASE)
							if use_auth_token is not None:
												warnings.warn(
												    "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."     ,     __SCREAMING_SNAKE_CASE)
												if token is not None:
																	raise ValueError(
																	    "`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
												A			=					use_auth_token

							if token is not None:
												# change to `token` in a follow-up PR
												A			=					token

							A			=					cls._get_arguments_from_pretrained(__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)
							return cls(*__SCREAMING_SNAKE_CASE)


		@classmethod
		def 					SCREAMING_SNAKE_CASE__   (cls :		List[str]     ,     __SCREAMING_SNAKE_CASE :		Tuple="AutoProcessor"):
							if not isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
												A			=					auto_class.__name__

							import transformers.models.auto as auto_module

							if not hasattr(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
												raise ValueError(F"""{auto_class} is not a valid auto class.""")

							A			=					auto_class


		@classmethod
		def 					SCREAMING_SNAKE_CASE__   (cls :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		Tuple     ,     **__SCREAMING_SNAKE_CASE :		List[str]):
							A			=					[]
							for attribute_name in cls.attributes:
												A			=					getattr(cls     ,     F"""{attribute_name}_class""")
												if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
																	A			=					tuple(getattr(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE) if n is not None else None for n in class_name)
																	A			=					kwargs.get("use_fast"     ,     __SCREAMING_SNAKE_CASE)
																	if use_fast and classes[1] is not None:
																						A			=					classes[1]
																	else:
																						A			=					classes[0]
												else:
																	A			=					getattr(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

												args.append(attribute_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE))
							return args


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			=					getattr(self     ,     self.attributes[0])
							return getattr(__SCREAMING_SNAKE_CASE     ,     "model_input_names"     ,     __SCREAMING_SNAKE_CASE)

__A     :    Union[str, Any]    =		copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
	__A     :    List[str]    =		ProcessorMixin.push_to_hub.__doc__.format(
	    object='processor', object_class='AutoProcessor', object_files='processor files'
	)








"""simple docstring"""




import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil
@pytest.fixture
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					tmp_path / "text.txt"
					A			=					textwrap.dedent(
					    "\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        "      )
					with open(lowercase__			,							"w"			,							encoding="utf-8"      ) as f:
										f.write(lowercase__      )
					return str(lowercase__      )
@pytest.fixture
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					tmp_path / "text_with_image.txt"
					with open(lowercase__			,							"w"			,							encoding="utf-8"      ) as f:
										f.write(lowercase__      )
					return str(lowercase__      )
@pytest.mark.parametrize("keep_linebreaks"			,							[True, False]      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read().splitlines(keepends=lowercase__      )
					A			=					Text(keep_linebreaks=lowercase__			,							encoding="utf-8"      )
					A			=					text._generate_tables([[text_file]]      )
					A			=					pa.concat_tables([table for _, table in generator]      ).to_pydict()["text"]
					assert generated_content == expected_content
@require_pil
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read().splitlines()[0]
					A			=					Text(encoding="utf-8"			,							features=Features({"image": Image()}      )      )
					A			=					text._generate_tables([[text_file_with_image]]      )
					A			=					pa.concat_tables([table for _, table in generator]      )
					assert pa_table.schema.field("image"      ).type == Image()()
					A			=					pa_table.to_pydict()["image"]
					assert generated_content == [{"path": image_file, "bytes": None}]
@pytest.mark.parametrize("sample_by"			,							["line", "paragraph", "document"]      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read()
					if sample_by == "line":
										A			=					expected_content.splitlines()
					elif sample_by == "paragraph":
										A			=					expected_content.split("\n\n"      )
					elif sample_by == "document":
										A			=					[expected_content]
					A			=					Text(sample_by=lowercase__			,							encoding="utf-8"			,							chunksize=100      )
					A			=					text._generate_tables([[text_file]]      )
					A			=					pa.concat_tables([table for _, table in generator]      ).to_pydict()["text"]
					assert generated_content == expected_content





