





'''simple docstring'''

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_					= logging.get_logger()


@dataclass
class 					a_     :

    '''simple docstring'''
    UpperCamelCase     = 42
    UpperCamelCase     = field(default_factory=snake_case_       )
    UpperCamelCase     = field(default_factory=snake_case_       )


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			len(list(m.modules()       )       ) == 1 or isinstance(A ,		nn.Convad       ) or isinstance(A ,		nn.BatchNormad       )
        if has_not_submodules:
            self.traced.append(A       )


    def __call__(		self ,		A       )		->   int:
        for m in self.module.modules():
            self.handles.append(m.register_forward_hook(self._forward_hook       )       )
        self.module(A       )
        [x.remove() for x in self.handles]
        return self





    @property
    def 			snake_case_(		self       )		->   List[Any]:
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda A       : len(list(x.state_dict().keys()       )       ) > 0 ,		self.traced       )       )




@dataclass
class 					a_     :

    '''simple docstring'''
    UpperCamelCase     = 42
    UpperCamelCase     = 42
    UpperCamelCase     = 1
    UpperCamelCase     = field(default_factory=snake_case_       )
    UpperCamelCase     = field(default_factory=snake_case_       )
    UpperCamelCase     = True


    def __call__(		self ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			Tracker(self.dest       )(A       ).parametrized
        _SCREAMING_SNAKE_CASE									=			Tracker(self.src       )(A       ).parametrized

        _SCREAMING_SNAKE_CASE									=			list(filter(lambda A       : type(A       ) not in self.src_skip ,		A       )       )
        _SCREAMING_SNAKE_CASE									=			list(filter(lambda A       : type(A       ) not in self.dest_skip ,		A       )       )

        if len(A       ) != len(A       ) and self.raise_if_mismatch:
            raise Exception(
                f'Numbers of operations are different. Source module has {len(A       )} operations while'
                f' destination module has {len(A       )}.'       )

        for dest_m, src_m in zip(A ,		A       ):
            dest_m.load_state_dict(src_m.state_dict()       )
            if self.verbose == 1:
                print(f'Transfered from={src_m} to={dest_m}'       )




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   List[str]:
        super().__init__()

        _SCREAMING_SNAKE_CASE									=			[]
        # - get the stem
        feature_blocks.append(("""conv1""", model.stem)       )
        # - get all the feature blocks
        for k, v in model.trunk_output.named_children():
            assert k.startswith("""block"""       ), f'Unexpected layer name {k}'
            _SCREAMING_SNAKE_CASE									=			len(A       ) + 1
            feature_blocks.append((f'res{block_index}', v)       )

        _SCREAMING_SNAKE_CASE									=			nn.ModuleDict(A       )





    def 			snake_case_(		self ,		A       )		->   Tuple:
        return get_trunk_forward_outputs(
            A ,		out_feat_keys=A ,		feature_blocks=self._feature_blocks ,		)




class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def 			snake_case_(		self ,		A       )		->   str:
        _SCREAMING_SNAKE_CASE									=			x.split("""-"""       )
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]       )





    def __getitem__(		self ,		A       )		->   Callable[[], Tuple[nn.Module, Dict]]:
        # default to timm!
        if x not in self:
            _SCREAMING_SNAKE_CASE									=			self.convert_name_to_timm(A       )
            _SCREAMING_SNAKE_CASE									=			partial(lambda: (timm.create_model(A ,		pretrained=A       ).eval(), None)       )

        else:
            _SCREAMING_SNAKE_CASE									=			super().__getitem__(A       )

        return val




class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __getitem__(		self ,		A       )		->   Callable[[], nn.Module]:
        if "seer" in x and "in1k" not in x:
            _SCREAMING_SNAKE_CASE									=			RegNetModel
        else:
            _SCREAMING_SNAKE_CASE									=			RegNetForImageClassification
        return val





def lowerCamelCase						(     __lowerCamelCase :    int					,       __lowerCamelCase :    int					,       __lowerCamelCase :    List[Tuple[str, str]]    )						->Tuple:
    for from_key, to_key in keys:
        _SCREAMING_SNAKE_CASE									=			from_state_dict[from_key].clone()
        print(F'Copied key={from_key} to={to_key}'    )
    return to_state_dict





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Callable[[], nn.Module]					,       __lowerCamelCase :    Callable[[], nn.Module]					,       __lowerCamelCase :    RegNetConfig					,       __lowerCamelCase :    Path					,       __lowerCamelCase :    bool = True					,       )						->Dict:
    print(F'Converting {name}...'    )
    with torch.no_grad():
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			from_model_func()
        _SCREAMING_SNAKE_CASE									=			our_model_func(__lowerCamelCase    ).eval()
        _SCREAMING_SNAKE_CASE									=			ModuleTransfer(src=__lowerCamelCase					,       dest=__lowerCamelCase					,       raise_if_mismatch=__lowerCamelCase    )
        _SCREAMING_SNAKE_CASE									=			torch.randn((1, 3, 224, 224)    )
        module_transfer(__lowerCamelCase    )

    if from_state_dict is not None:
        _SCREAMING_SNAKE_CASE									=			[]
        # for seer - in1k finetuned we have to manually copy the head
        if "seer" in name and "in1k" in name:
            _SCREAMING_SNAKE_CASE									=			[("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
        _SCREAMING_SNAKE_CASE									=			manually_copy_vissl_head(__lowerCamelCase					,       our_model.state_dict()					,       __lowerCamelCase    )
        our_model.load_state_dict(__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			our_model(__lowerCamelCase					,       output_hidden_states=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			(
        our_outputs.logits if isinstance(__lowerCamelCase					,       __lowerCamelCase    ) else our_outputs.last_hidden_state
    )

    _SCREAMING_SNAKE_CASE									=			from_model(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			from_output[-1] if type(__lowerCamelCase    ) is list else from_output

    # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
    if "seer" in name and "in1k" in name:
        _SCREAMING_SNAKE_CASE									=			our_outputs.hidden_states[-1]

    assert torch.allclose(__lowerCamelCase					,       __lowerCamelCase    ), "The model logits don't match the original one."

    if push_to_hub:
        our_model.push_to_hub(
            repo_path_or_name=save_directory / name					,       commit_message="""Add model"""					,       use_temp_dir=__lowerCamelCase					,       )

        _SCREAMING_SNAKE_CASE									=			224 if """seer""" not in name else 384
        # we can use the convnext one
        _SCREAMING_SNAKE_CASE									=			AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""					,       size=__lowerCamelCase    )
        image_processor.push_to_hub(
            repo_path_or_name=save_directory / name					,       commit_message="""Add image processor"""					,       use_temp_dir=__lowerCamelCase					,       )

        print(F'Pushed {name}'    )





def lowerCamelCase						(     __lowerCamelCase :    Path					,       __lowerCamelCase :    str = None					,       __lowerCamelCase :    bool = True    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			"""imagenet-1k-id2label.json"""
    _SCREAMING_SNAKE_CASE									=			1000
    _SCREAMING_SNAKE_CASE									=			(1, num_labels)

    _SCREAMING_SNAKE_CASE									=			"""huggingface/label-files"""
    _SCREAMING_SNAKE_CASE									=			num_labels
    _SCREAMING_SNAKE_CASE									=			json.load(open(cached_download(hf_hub_url(__lowerCamelCase					,       __lowerCamelCase					,       repo_type="""dataset"""    )    )					,       """r"""    )    )
    _SCREAMING_SNAKE_CASE									=			{int(__lowerCamelCase    ): v for k, v in idalabel.items()}

    _SCREAMING_SNAKE_CASE									=			idalabel
    _SCREAMING_SNAKE_CASE									=			{v: k for k, v in idalabel.items()}

    _SCREAMING_SNAKE_CASE									=			partial(__lowerCamelCase					,       num_labels=__lowerCamelCase					,       idalabel=__lowerCamelCase					,       labelaid=__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			{
        """regnet-x-002""": ImageNetPreTrainedConfig(
            depths=[1, 1, 4, 7]					,       hidden_sizes=[24, 56, 152, 368]					,       groups_width=8					,       layer_type="""x"""    ),
        """regnet-x-004""": ImageNetPreTrainedConfig(
            depths=[1, 2, 7, 12]					,       hidden_sizes=[32, 64, 160, 384]					,       groups_width=16					,       layer_type="""x"""    ),
        """regnet-x-006""": ImageNetPreTrainedConfig(
            depths=[1, 3, 5, 7]					,       hidden_sizes=[48, 96, 240, 528]					,       groups_width=24					,       layer_type="""x"""    ),
        """regnet-x-008""": ImageNetPreTrainedConfig(
            depths=[1, 3, 7, 5]					,       hidden_sizes=[64, 128, 288, 672]					,       groups_width=16					,       layer_type="""x"""    ),
        """regnet-x-016""": ImageNetPreTrainedConfig(
            depths=[2, 4, 10, 2]					,       hidden_sizes=[72, 168, 408, 912]					,       groups_width=24					,       layer_type="""x"""    ),
        """regnet-x-032""": ImageNetPreTrainedConfig(
            depths=[2, 6, 15, 2]					,       hidden_sizes=[96, 192, 432, 1008]					,       groups_width=48					,       layer_type="""x"""    ),
        """regnet-x-040""": ImageNetPreTrainedConfig(
            depths=[2, 5, 14, 2]					,       hidden_sizes=[80, 240, 560, 1360]					,       groups_width=40					,       layer_type="""x"""    ),
        """regnet-x-064""": ImageNetPreTrainedConfig(
            depths=[2, 4, 10, 1]					,       hidden_sizes=[168, 392, 784, 1624]					,       groups_width=56					,       layer_type="""x"""    ),
        """regnet-x-080""": ImageNetPreTrainedConfig(
            depths=[2, 5, 15, 1]					,       hidden_sizes=[80, 240, 720, 1920]					,       groups_width=120					,       layer_type="""x"""    ),
        """regnet-x-120""": ImageNetPreTrainedConfig(
            depths=[2, 5, 11, 1]					,       hidden_sizes=[224, 448, 896, 2240]					,       groups_width=112					,       layer_type="""x"""    ),
        """regnet-x-160""": ImageNetPreTrainedConfig(
            depths=[2, 6, 13, 1]					,       hidden_sizes=[256, 512, 896, 2048]					,       groups_width=128					,       layer_type="""x"""    ),
        """regnet-x-320""": ImageNetPreTrainedConfig(
            depths=[2, 7, 13, 1]					,       hidden_sizes=[336, 672, 1344, 2520]					,       groups_width=168					,       layer_type="""x"""    ),
        # y variant
        """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]					,       hidden_sizes=[24, 56, 152, 368]					,       groups_width=8    ),
        """regnet-y-004""": ImageNetPreTrainedConfig(
            depths=[1, 3, 6, 6]					,       hidden_sizes=[48, 104, 208, 440]					,       groups_width=8    ),
        """regnet-y-006""": ImageNetPreTrainedConfig(
            depths=[1, 3, 7, 4]					,       hidden_sizes=[48, 112, 256, 608]					,       groups_width=16    ),
        """regnet-y-008""": ImageNetPreTrainedConfig(
            depths=[1, 3, 8, 2]					,       hidden_sizes=[64, 128, 320, 768]					,       groups_width=16    ),
        """regnet-y-016""": ImageNetPreTrainedConfig(
            depths=[2, 6, 17, 2]					,       hidden_sizes=[48, 120, 336, 888]					,       groups_width=24    ),
        """regnet-y-032""": ImageNetPreTrainedConfig(
            depths=[2, 5, 13, 1]					,       hidden_sizes=[72, 216, 576, 1512]					,       groups_width=24    ),
        """regnet-y-040""": ImageNetPreTrainedConfig(
            depths=[2, 6, 12, 2]					,       hidden_sizes=[128, 192, 512, 1088]					,       groups_width=64    ),
        """regnet-y-064""": ImageNetPreTrainedConfig(
            depths=[2, 7, 14, 2]					,       hidden_sizes=[144, 288, 576, 1296]					,       groups_width=72    ),
        """regnet-y-080""": ImageNetPreTrainedConfig(
            depths=[2, 4, 10, 1]					,       hidden_sizes=[168, 448, 896, 2016]					,       groups_width=56    ),
        """regnet-y-120""": ImageNetPreTrainedConfig(
            depths=[2, 5, 11, 1]					,       hidden_sizes=[224, 448, 896, 2240]					,       groups_width=112    ),
        """regnet-y-160""": ImageNetPreTrainedConfig(
            depths=[2, 4, 11, 1]					,       hidden_sizes=[224, 448, 1232, 3024]					,       groups_width=112    ),
        """regnet-y-320""": ImageNetPreTrainedConfig(
            depths=[2, 5, 12, 1]					,       hidden_sizes=[232, 696, 1392, 3712]					,       groups_width=232    ),
        # models created by SEER -> https://arxiv.org/abs/2202.08360
        """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]					,       hidden_sizes=[232, 696, 1392, 3712]					,       groups_width=232    ),
        """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]					,       hidden_sizes=[328, 984, 1968, 4920]					,       groups_width=328    ),
        """regnet-y-1280-seer""": RegNetConfig(
            depths=[2, 7, 17, 1]					,       hidden_sizes=[528, 1056, 2904, 7392]					,       groups_width=264    ),
        """regnet-y-2560-seer""": RegNetConfig(
            depths=[3, 7, 16, 1]					,       hidden_sizes=[640, 1696, 2544, 5088]					,       groups_width=640    ),
        """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]					,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]					,       groups_width=1010    ),
        # finetuned on imagenet
        """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
            depths=[2, 5, 12, 1]					,       hidden_sizes=[232, 696, 1392, 3712]					,       groups_width=232    ),
        """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
            depths=[2, 5, 12, 1]					,       hidden_sizes=[328, 984, 1968, 4920]					,       groups_width=328    ),
        """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]					,       hidden_sizes=[528, 1056, 2904, 7392]					,       groups_width=264    ),
        """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
            depths=[3, 7, 16, 1]					,       hidden_sizes=[640, 1696, 2544, 5088]					,       groups_width=640    ),
        """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]					,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]					,       groups_width=1010    ),
    }

    _SCREAMING_SNAKE_CASE									=			NameToOurModelFuncMap()
    _SCREAMING_SNAKE_CASE									=			NameToFromModelFuncMap()
    # add seer weights logic

    def load_using_classy_vision(__lowerCamelCase :    str					,       __lowerCamelCase :    Callable[[], nn.Module]    ) -> Tuple[nn.Module, Dict]:
        _SCREAMING_SNAKE_CASE									=			torch.hub.load_state_dict_from_url(__lowerCamelCase					,       model_dir=str(__lowerCamelCase    )					,       map_location="""cpu"""    )
        _SCREAMING_SNAKE_CASE									=			model_func()
        # check if we have a head, if yes add it
        _SCREAMING_SNAKE_CASE									=			files["""classy_state_dict"""]["""base_model"""]["""model"""]
        _SCREAMING_SNAKE_CASE									=			model_state_dict["""trunk"""]
        model.load_state_dict(__lowerCamelCase    )
        return model.eval(), model_state_dict["heads"]

    # pretrained
    _SCREAMING_SNAKE_CASE									=			partial(
        __lowerCamelCase					,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""					,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )					,       )

    _SCREAMING_SNAKE_CASE									=			partial(
        __lowerCamelCase					,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""					,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )					,       )

    _SCREAMING_SNAKE_CASE									=			partial(
        __lowerCamelCase					,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""					,       lambda: FakeRegNetVisslWrapper(RegNetYaaagf()    )					,       )

    _SCREAMING_SNAKE_CASE									=			partial(
        __lowerCamelCase					,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""					,       lambda: FakeRegNetVisslWrapper(
            RegNet(RegNetParams(depth=27					,       group_width=1010					,       w_a=1744					,       w_a=620.83					,       w_m=2.52    )    )    )					,       )

    # IN1K finetuned
    _SCREAMING_SNAKE_CASE									=			partial(
        __lowerCamelCase					,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""					,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )					,       )

    _SCREAMING_SNAKE_CASE									=			partial(
        __lowerCamelCase					,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""					,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )					,       )

    _SCREAMING_SNAKE_CASE									=			partial(
        __lowerCamelCase					,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""					,       lambda: FakeRegNetVisslWrapper(RegNetYaaagf()    )					,       )

    _SCREAMING_SNAKE_CASE									=			partial(
        __lowerCamelCase					,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""					,       lambda: FakeRegNetVisslWrapper(
            RegNet(RegNetParams(depth=27					,       group_width=1010					,       w_a=1744					,       w_a=620.83					,       w_m=2.52    )    )    )					,       )

    if model_name:
        convert_weight_and_push(
            __lowerCamelCase					,       names_to_from_model_map[model_name]					,       names_to_ours_model_map[model_name]					,       names_to_config[model_name]					,       __lowerCamelCase					,       __lowerCamelCase					,       )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(
                __lowerCamelCase					,       names_to_from_model_map[model_name]					,       names_to_ours_model_map[model_name]					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       )
    return config, expected_shape


if __name__ == "__main__":
      lowercase_					= argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default=None,
          type=str,
          help=(
              """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
              """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=Path,
          required=True,
          help="""Path to the output PyTorch model directory.""",
      )
      parser.add_argument(
          """--push_to_hub""",
          default=True,
          type=bool,
          required=False,
          help="""If True, push model and image processor to the hub.""",
      )

      lowercase_					= parser.parse_args()

      lowercase_					= args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






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       SCREAMING_SNAKE_CASE__					(datasets.BuilderConfig     ):
    __lowerCamelCase						:       str														=     ","
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[Union[int, List[int], str]]														=     "infer"
    __lowerCamelCase						:       Optional[List[str]]														=     None
    __lowerCamelCase						:       Optional[List[str]]														=     None
    __lowerCamelCase						:       Optional[Union[int, str, List[int], List[str]]]														=     None
    __lowerCamelCase						:       Optional[Union[List[int], List[str]]]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       Optional[Literal["c", "python", "pyarrow"]]														=     None
    __lowerCamelCase						:       Dict[Union[int, str], Callable[[Any], Any]]														=     None
    __lowerCamelCase						:       Optional[list]														=     None
    __lowerCamelCase						:       Optional[list]														=     None
    __lowerCamelCase						:       bool														=     False
    __lowerCamelCase						:       Optional[Union[int, List[int]]]														=     None
    __lowerCamelCase						:       Optional[int]														=     None
    __lowerCamelCase						:       Optional[Union[str, List[str]]]														=     None
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     False
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       str														=     "."
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       str														=     '"'
    __lowerCamelCase						:       int														=     0
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       int														=     0
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     False
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       int														=     1_0000
    __lowerCamelCase						:       Optional[datasets.Features]														=     None
    __lowerCamelCase						:       Optional[str]														=     "strict"
    __lowerCamelCase						:       Literal["error", "warn", "skip"]														=     "error"
    __lowerCamelCase						:       Optional[str]														=     None





    def       snake_case_				(      self):
         if self.delimiter is not None:
              lowercase__    :   List[Any]      =       self.delimiter
         if self.column_names is not None:
              lowercase__    :   Optional[int]      =       self.column_names





    @property
    def       snake_case_				(      self):
         lowercase__    :   Dict      =       {
             '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()						,   a):
                   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       SCREAMING_SNAKE_CASE__					(datasets.ArrowBasedBuilder     ):
    __lowerCamelCase						:       Optional[Any]														=     CsvConfig





    def       snake_case_				(      self):
         return datasets.DatasetInfo(features=self.config.features)





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





    def       snake_case_				(      self						,   a):
         if self.config.features is not None:
              lowercase__    :   Optional[int]      =       self.config.features.arrow_schema
              if all(not require_storage_cast(a) for feature in self.config.features.values()):
                   # cheaper cast
                   lowercase__    :   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
                   lowercase__    :   Optional[Any]      =       table_cast(a						,   a)
         return pa_table





    def       snake_case_				(      self						,   a):
         lowercase__    :   List[Any]      =       self.config.features.arrow_schema if self.config.features else None
         # dtype allows reading an int column as str
         lowercase__    :   Optional[int]      =       (
             {
                 name: dtype.to_pandas_dtype() if not require_storage_cast(a) 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(a)):
              lowercase__    :   int      =       pd.read_csv(a						,   iterator=a						,   dtype=a						,   **self.config.pd_read_csv_kwargs)
              try:
                   for batch_idx, df in enumerate(a):
                        lowercase__    :   List[str]      =       pa.Table.from_pandas(a)
                        # 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(a)
              except ValueError as e:
                   logger.error(f"""Failed to read file '{file}' with error {type(a)}: {e}""")
                   raise

