



import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


_lowercase:					Tuple      =						logging.get_logger(__name__)

_lowercase:					Any      =						OrderedDict(
    [
        ("align", "AlignProcessor"),
        ("altclip", "AltCLIPProcessor"),
        ("bark", "BarkProcessor"),
        ("blip", "BlipProcessor"),
        ("blip-2", "Blip2Processor"),
        ("bridgetower", "BridgeTowerProcessor"),
        ("chinese_clip", "ChineseCLIPProcessor"),
        ("clap", "ClapProcessor"),
        ("clip", "CLIPProcessor"),
        ("clipseg", "CLIPSegProcessor"),
        ("flava", "FlavaProcessor"),
        ("git", "GitProcessor"),
        ("groupvit", "CLIPProcessor"),
        ("hubert", "Wav2Vec2Processor"),
        ("instructblip", "InstructBlipProcessor"),
        ("layoutlmv2", "LayoutLMv2Processor"),
        ("layoutlmv3", "LayoutLMv3Processor"),
        ("markuplm", "MarkupLMProcessor"),
        ("mctct", "MCTCTProcessor"),
        ("mgp-str", "MgpstrProcessor"),
        ("oneformer", "OneFormerProcessor"),
        ("owlvit", "OwlViTProcessor"),
        ("pix2struct", "Pix2StructProcessor"),
        ("sam", "SamProcessor"),
        ("sew", "Wav2Vec2Processor"),
        ("sew-d", "Wav2Vec2Processor"),
        ("speech_to_text", "Speech2TextProcessor"),
        ("speech_to_text_2", "Speech2Text2Processor"),
        ("speecht5", "SpeechT5Processor"),
        ("trocr", "TrOCRProcessor"),
        ("tvlt", "TvltProcessor"),
        ("unispeech", "Wav2Vec2Processor"),
        ("unispeech-sat", "Wav2Vec2Processor"),
        ("vilt", "ViltProcessor"),
        ("vision-text-dual-encoder", "VisionTextDualEncoderProcessor"),
        ("wav2vec2", "Wav2Vec2Processor"),
        ("wav2vec2-conformer", "Wav2Vec2Processor"),
        ("wavlm", "Wav2Vec2Processor"),
        ("whisper", "WhisperProcessor"),
        ("xclip", "XCLIPProcessor"),
    ]
)

_lowercase:					str      =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)






def        a( A			:       str			)						->			Any:
					"""simple docstring"""





					for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
										if class_name in processors:
															a   	=  model_type_to_module_name(A			)

															a   	=  importlib.import_module(f'''.{module_name}'''					,	"transformers.models"			)
															try:
																				return getattr(A					,	A			)
															except AttributeError:
																				continue

					for processor in PROCESSOR_MAPPING._extra_content.values():
										if getattr(A					,	"__name__"					,	A			) == class_name:
															return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
					a   	=  importlib.import_module("transformers"			)
					if hasattr(A					,	A			):
										return getattr(A					,	A			)

					return None







class       _lowercase				:





	"""simple docstring"""




	def __init__(self						):
						"""simple docstring"""

						raise EnvironmentError(
						    "AutoProcessor is designed to be instantiated "
						    "using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."						)




	@classmethod
	@replace_list_option_in_docstrings(lowerCamelCase_						)
	def 	UpperCamelCase_			(cls						,			lowerCamelCase_						,			**lowerCamelCase_						):
						"""simple docstring"""

						a   	=  kwargs.pop("config"						,			lowerCamelCase_						)
						a   	=  kwargs.pop("trust_remote_code"						,			lowerCamelCase_						)
						a   	=  True

						a   	=  None
						a   	=  None

						# First, let's see if we have a preprocessor config.
						# Filter the kwargs for `get_file_from_repo`.
						a   	=  {
						    key: kwargs[key] for key in inspect.signature(lowerCamelCase_						).parameters.keys() if key in kwargs
						}
						# Let's start by checking whether the processor class is saved in an image processor
						a   	=  get_file_from_repo(
						    lowerCamelCase_						,			lowerCamelCase_						,			**lowerCamelCase_						)
						if preprocessor_config_file is not None:
											a	,							a   	=  ImageProcessingMixin.get_image_processor_dict(lowerCamelCase_						,			**lowerCamelCase_						)
											a   	=  config_dict.get("processor_class"						,			lowerCamelCase_						)
											if "AutoProcessor" in config_dict.get("auto_map"						,			{}						):
																a   	=  config_dict["auto_map"]["AutoProcessor"]

        # If not found, let's check whether the processor class is saved in a feature extractor config
						if preprocessor_config_file is not None and processor_class is None:
											a	,							a   	=  FeatureExtractionMixin.get_feature_extractor_dict(lowerCamelCase_						,			**lowerCamelCase_						)
											a   	=  config_dict.get("processor_class"						,			lowerCamelCase_						)
											if "AutoProcessor" in config_dict.get("auto_map"						,			{}						):
																a   	=  config_dict["auto_map"]["AutoProcessor"]

						if processor_class is None:
											# Next, let's check whether the processor class is saved in a tokenizer
											a   	=  get_file_from_repo(
											    lowerCamelCase_						,			lowerCamelCase_						,			**lowerCamelCase_						)
											if tokenizer_config_file is not None:
																with open(lowerCamelCase_						,			encoding="utf-8"						) as reader:
																					a   	=  json.load(lowerCamelCase_						)

																a   	=  config_dict.get("processor_class"						,			lowerCamelCase_						)
																if "AutoProcessor" in config_dict.get("auto_map"						,			{}						):
																					a   	=  config_dict["auto_map"]["AutoProcessor"]

						if processor_class is None:
											# Otherwise, load config, if it can be loaded.
											if not isinstance(lowerCamelCase_						,			lowerCamelCase_						):
																a   	=  AutoConfig.from_pretrained(
																    lowerCamelCase_						,			trust_remote_code=lowerCamelCase_						,			**lowerCamelCase_						)

											# And check if the config contains the processor class.
											a   	=  getattr(lowerCamelCase_						,			"processor_class"						,			lowerCamelCase_						)
											if hasattr(lowerCamelCase_						,			"auto_map"						) and "AutoProcessor" in config.auto_map:
																a   	=  config.auto_map["AutoProcessor"]

						if processor_class is not None:
											a   	=  processor_class_from_name(lowerCamelCase_						)

						a   	=  processor_auto_map is not None
						a   	=  processor_class is not None or type(lowerCamelCase_						) in PROCESSOR_MAPPING
						a   	=  resolve_trust_remote_code(
						    lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						)

						if has_remote_code and trust_remote_code:
											a   	=  get_class_from_dynamic_module(
											    lowerCamelCase_						,			lowerCamelCase_						,			**lowerCamelCase_						)
											a   	=  kwargs.pop("code_revision"						,			lowerCamelCase_						)
											if os.path.isdir(lowerCamelCase_						):
																processor_class.register_for_auto_class()
											return processor_class.from_pretrained(
											    lowerCamelCase_						,			trust_remote_code=lowerCamelCase_						,			**lowerCamelCase_						)
						elif processor_class is not None:
											return processor_class.from_pretrained(
											    lowerCamelCase_						,			trust_remote_code=lowerCamelCase_						,			**lowerCamelCase_						)
						# Last try: we use the PROCESSOR_MAPPING.
						elif type(lowerCamelCase_						) in PROCESSOR_MAPPING:
											return PROCESSOR_MAPPING[type(lowerCamelCase_						)].from_pretrained(lowerCamelCase_						,			**lowerCamelCase_						)

						# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
						# tokenizer.
						try:
											return AutoTokenizer.from_pretrained(
											    lowerCamelCase_						,			trust_remote_code=lowerCamelCase_						,			**lowerCamelCase_						)
						except Exception:
											try:
																return AutoImageProcessor.from_pretrained(
																    lowerCamelCase_						,			trust_remote_code=lowerCamelCase_						,			**lowerCamelCase_						)
											except Exception:
																pass

											try:
																return AutoFeatureExtractor.from_pretrained(
																    lowerCamelCase_						,			trust_remote_code=lowerCamelCase_						,			**lowerCamelCase_						)
											except Exception:
																pass

						raise ValueError(
						    F'''Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '''
						    "tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains"
						    "the files of at least one of those processing classes."						)




	@staticmethod
	def 	UpperCamelCase_			(lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

						PROCESSOR_MAPPING.register(lowerCamelCase_						,			lowerCamelCase_						)





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


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



@dataclass
class       _lowercase				(  datasets.BuilderConfig      ):





	"""simple docstring"""
	__A		      =				None
	__A		      =				None
	__A		      =				None
	__A		      =				True
	__A		      =				None
	__A		      =				None
	__A		      =				None
	__A		      =				10000
	__A		      =				None




	def 	UpperCamelCase_			(self						):
						"""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 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						,			):
						"""simple docstring"""

						a   	=  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   	=  config_kwargs["sql"]
						if not isinstance(lowerCamelCase_						,			lowerCamelCase_						):
											if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
																import sqlalchemy

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

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




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  {
						    "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       _lowercase				(  datasets.ArrowBasedBuilder      ):





	"""simple docstring"""
	__A		      =				SqlConfig




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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