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_  : Optional[Any]								=					logging.get_logger()






@dataclass
class 		_lowerCamelCase		:
    __a						=   42
    __a						=   field(default_factory=UpperCamelCase_     )
    __a						=   field(default_factory=UpperCamelCase_     )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Any:
     SCREAMING_SNAKE_CASE__: str=   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			,			lowerCAmelCase )							->       Union[str, Any]:
     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     UpperCamelCase_					(	self )							->       int:
     return list(filter(lambda lowerCAmelCase : len(list(x.state_dict().keys() ) ) > 0			,			self.traced ) )






@dataclass
class 		_lowerCamelCase		:
    __a						=   42
    __a						=   42
    __a						=   1
    __a						=   field(default_factory=UpperCamelCase_     )
    __a						=   field(default_factory=UpperCamelCase_     )
    __a						=   True
    def __call__(	self			,			lowerCAmelCase )							->       List[str]:
     SCREAMING_SNAKE_CASE__: Optional[int]=   Tracker(self.dest )(a_ ).parametrized
     SCREAMING_SNAKE_CASE__: Optional[Any]=   Tracker(self.src )(a_ ).parametrized

     SCREAMING_SNAKE_CASE__: int=   list(filter(lambda lowerCAmelCase : type(a_ ) not in self.src_skip			,			a_ ) )
     SCREAMING_SNAKE_CASE__: Optional[int]=   list(filter(lambda lowerCAmelCase : 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 		_lowerCamelCase		( nn.Module     ):
    def __init__(	self			,			lowerCAmelCase )							->       Tuple:
     super().__init__()

     SCREAMING_SNAKE_CASE__: Union[str, Any]=   []
     # - 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__: Any=   len(a_ ) + 1
      feature_blocks.append((f'res{block_index}', v) )

     SCREAMING_SNAKE_CASE__: List[str]=   nn.ModuleDict(a_ )



    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       str:
     return get_trunk_forward_outputs(
         a_			,			out_feat_keys=a_			,			feature_blocks=self._feature_blocks			,			)






class 		_lowerCamelCase		( UpperCamelCase_     ):
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       str:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   x.split('''-''' )
     return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )



    def __getitem__(	self			,			lowerCAmelCase )							->       List[Any]:
     # default to timm!
     if x not in self:
      SCREAMING_SNAKE_CASE__: Optional[Any]=   self.convert_name_to_timm(a_ )
      SCREAMING_SNAKE_CASE__: Any=   partial(lambda: (timm.create_model(a_			,			pretrained=a_ ).eval(), None) )

     else:
      SCREAMING_SNAKE_CASE__: str=   super().__getitem__(a_ )

     return val






class 		_lowerCamelCase		( UpperCamelCase_     ):
    def __getitem__(	self			,			lowerCAmelCase )							->       str:
     if "seer" in x and "in1k" not in x:
      SCREAMING_SNAKE_CASE__: str=   RegNetModel
     else:
      SCREAMING_SNAKE_CASE__: Optional[int]=   RegNetForImageClassification
     return val







def       A__   (						snake_case_			:		Union[str, Any]							,					snake_case_			:		str							,					snake_case_			:		Optional[int]   ):





 for from_key, to_key in keys:
  SCREAMING_SNAKE_CASE__: Optional[Any]=   from_state_dict[from_key].clone()
  print(F'Copied key={from_key} to={to_key}'   )
 return to_state_dict







def       A__   (						snake_case_			:		Optional[Any]							,					snake_case_			:		Optional[Any]							,					snake_case_			:		Any							,					snake_case_			:		str							,					snake_case_			:		Any							,					snake_case_			:		Optional[int] = True							,					):
 print(F'Converting {name}...'   )
 with torch.no_grad():
  SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Any=   from_model_func()
  SCREAMING_SNAKE_CASE__: str=   our_model_func(snake_case__   ).eval()
  SCREAMING_SNAKE_CASE__: Tuple=   ModuleTransfer(src=snake_case__							,					dest=snake_case__							,					raise_if_mismatch=snake_case__   )
  SCREAMING_SNAKE_CASE__: List[str]=   torch.randn((1, 3, 224, 224)   )
  module_transfer(snake_case__   )

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

 SCREAMING_SNAKE_CASE__: Any=   our_model(snake_case__							,					output_hidden_states=snake_case__   )
 SCREAMING_SNAKE_CASE__: Any=   (
     our_outputs.logits if isinstance(snake_case__							,					snake_case__   ) else our_outputs.last_hidden_state
 )

 SCREAMING_SNAKE_CASE__: Optional[Any]=   from_model(snake_case__   )
 SCREAMING_SNAKE_CASE__: Optional[int]=   from_output[-1] if type(snake_case__   ) 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__: Tuple=   our_outputs.hidden_states[-1]

 assert torch.allclose(snake_case__							,					snake_case__   ), "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=snake_case__							,					)

  SCREAMING_SNAKE_CASE__: int=   224 if '''seer''' not in name else 384
  # we can use the convnext one
  SCREAMING_SNAKE_CASE__: List[Any]=   AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''							,					size=snake_case__   )
  image_processor.push_to_hub(
      repo_path_or_name=save_directory / name							,					commit_message='''Add image processor'''							,					use_temp_dir=snake_case__							,					)

  print(F'Pushed {name}'   )







def       A__   (						snake_case_			:		Optional[int]							,					snake_case_			:		Tuple = None							,					snake_case_			:		int = True   ):
 SCREAMING_SNAKE_CASE__: str=   '''imagenet-1k-id2label.json'''
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   1_000
 SCREAMING_SNAKE_CASE__: Tuple=   (1, num_labels)

 SCREAMING_SNAKE_CASE__: Optional[int]=   '''huggingface/label-files'''
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   num_labels
 SCREAMING_SNAKE_CASE__: Optional[int]=   json.load(open(cached_download(hf_hub_url(snake_case__							,					snake_case__							,					repo_type='''dataset'''   )   )							,					'''r'''   )   )
 SCREAMING_SNAKE_CASE__: Dict=   {int(snake_case__   ): v for k, v in idalabel.items()}

 SCREAMING_SNAKE_CASE__: Dict=   idalabel
 SCREAMING_SNAKE_CASE__: Optional[Any]=   {v: k for k, v in idalabel.items()}

 SCREAMING_SNAKE_CASE__: Optional[int]=   partial(snake_case__							,					num_labels=snake_case__							,					idalabel=snake_case__							,					labelaid=snake_case__   )

 SCREAMING_SNAKE_CASE__: str=   {
     '''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, 1_008]							,					groups_width=48							,					layer_type='''x'''   ),
     '''regnet-x-040''': ImageNetPreTrainedConfig(
         depths=[2, 5, 14, 2]							,					hidden_sizes=[80, 240, 560, 1_360]							,					groups_width=40							,					layer_type='''x'''   ),
     '''regnet-x-064''': ImageNetPreTrainedConfig(
         depths=[2, 4, 10, 1]							,					hidden_sizes=[168, 392, 784, 1_624]							,					groups_width=56							,					layer_type='''x'''   ),
     '''regnet-x-080''': ImageNetPreTrainedConfig(
         depths=[2, 5, 15, 1]							,					hidden_sizes=[80, 240, 720, 1_920]							,					groups_width=120							,					layer_type='''x'''   ),
     '''regnet-x-120''': ImageNetPreTrainedConfig(
         depths=[2, 5, 11, 1]							,					hidden_sizes=[224, 448, 896, 2_240]							,					groups_width=112							,					layer_type='''x'''   ),
     '''regnet-x-160''': ImageNetPreTrainedConfig(
         depths=[2, 6, 13, 1]							,					hidden_sizes=[256, 512, 896, 2_048]							,					groups_width=128							,					layer_type='''x'''   ),
     '''regnet-x-320''': ImageNetPreTrainedConfig(
         depths=[2, 7, 13, 1]							,					hidden_sizes=[336, 672, 1_344, 2_520]							,					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, 1_512]							,					groups_width=24   ),
     '''regnet-y-040''': ImageNetPreTrainedConfig(
         depths=[2, 6, 12, 2]							,					hidden_sizes=[128, 192, 512, 1_088]							,					groups_width=64   ),
     '''regnet-y-064''': ImageNetPreTrainedConfig(
         depths=[2, 7, 14, 2]							,					hidden_sizes=[144, 288, 576, 1_296]							,					groups_width=72   ),
     '''regnet-y-080''': ImageNetPreTrainedConfig(
         depths=[2, 4, 10, 1]							,					hidden_sizes=[168, 448, 896, 2_016]							,					groups_width=56   ),
     '''regnet-y-120''': ImageNetPreTrainedConfig(
         depths=[2, 5, 11, 1]							,					hidden_sizes=[224, 448, 896, 2_240]							,					groups_width=112   ),
     '''regnet-y-160''': ImageNetPreTrainedConfig(
         depths=[2, 4, 11, 1]							,					hidden_sizes=[224, 448, 1_232, 3_024]							,					groups_width=112   ),
     '''regnet-y-320''': ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]							,					hidden_sizes=[232, 696, 1_392, 3_712]							,					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, 1_392, 3_712]							,					groups_width=232   ),
     '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]							,					hidden_sizes=[328, 984, 1_968, 4_920]							,					groups_width=328   ),
     '''regnet-y-1280-seer''': RegNetConfig(
         depths=[2, 7, 17, 1]							,					hidden_sizes=[528, 1_056, 2_904, 7_392]							,					groups_width=264   ),
     '''regnet-y-2560-seer''': RegNetConfig(
         depths=[3, 7, 16, 1]							,					hidden_sizes=[640, 1_696, 2_544, 5_088]							,					groups_width=640   ),
     '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
         depths=[2, 7, 17, 1]							,					hidden_sizes=[2_020, 4_040, 11_110, 28_280]							,					groups_width=1_010   ),
     # finetuned on imagenet
     '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]							,					hidden_sizes=[232, 696, 1_392, 3_712]							,					groups_width=232   ),
     '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]							,					hidden_sizes=[328, 984, 1_968, 4_920]							,					groups_width=328   ),
     '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
         depths=[2, 7, 17, 1]							,					hidden_sizes=[528, 1_056, 2_904, 7_392]							,					groups_width=264   ),
     '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
         depths=[3, 7, 16, 1]							,					hidden_sizes=[640, 1_696, 2_544, 5_088]							,					groups_width=640   ),
     '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
         depths=[2, 7, 17, 1]							,					hidden_sizes=[2_020, 4_040, 11_110, 28_280]							,					groups_width=1_010   ),
 }

 SCREAMING_SNAKE_CASE__: Optional[Any]=   NameToOurModelFuncMap()
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   NameToFromModelFuncMap()
 # add seer weights logic

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

 # pretrained
 SCREAMING_SNAKE_CASE__: Tuple=   partial(
     snake_case__							,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''							,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )							,					)

 SCREAMING_SNAKE_CASE__: int=   partial(
     snake_case__							,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''							,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )							,					)

 SCREAMING_SNAKE_CASE__: Any=   partial(
     snake_case__							,					'''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__: List[Any]=   partial(
     snake_case__							,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''							,					lambda: FakeRegNetVisslWrapper(
         RegNet(RegNetParams(depth=27							,					group_width=1_010							,					w_a=1_744							,					w_a=6_20.83							,					w_m=2.52   )   )   )							,					)

 # IN1K finetuned
 SCREAMING_SNAKE_CASE__: List[str]=   partial(
     snake_case__							,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''							,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )							,					)

 SCREAMING_SNAKE_CASE__: Optional[int]=   partial(
     snake_case__							,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''							,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )							,					)

 SCREAMING_SNAKE_CASE__: str=   partial(
     snake_case__							,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''							,					lambda: FakeRegNetVisslWrapper(RegNetYaaagf()   )							,					)

 SCREAMING_SNAKE_CASE__: List[str]=   partial(
     snake_case__							,					'''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=1_010							,					w_a=1_744							,					w_a=6_20.83							,					w_m=2.52   )   )   )							,					)

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


if __name__ == "__main__":
      lowercase_  : Any								=					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_  : int								=					parser.parse_args()

      lowercase_  : Path								=					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


_lowerCAmelCase	:   Any    =   datasets.utils.logging.get_logger(__name__)

_lowerCAmelCase	:   Any    =   ["names", "prefix"]
_lowerCAmelCase	:   str    =   ["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
_lowerCAmelCase	:   List[str]    =   ["encoding_errors", "on_bad_lines"]
_lowerCAmelCase	:   int    =   ["date_format"]


@dataclass
class 	__snake_case   ( datasets.BuilderConfig ):
		SCREAMING_SNAKE_CASE__								=	","
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	"infer"
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	"."
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	'"'
		SCREAMING_SNAKE_CASE__								=	0
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	0
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	10000
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	"strict"
		SCREAMING_SNAKE_CASE__								=	"error"
		SCREAMING_SNAKE_CASE__								=	None
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.delimiter is not None:
										lowerCAmelCase__		  =  self.delimiter
						if self.column_names is not None:
										lowerCAmelCase__		  =  self.column_names



		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  {
						    '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 	__snake_case   ( datasets.ArrowBasedBuilder ):
		SCREAMING_SNAKE_CASE__								=	CsvConfig
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return datasets.DatasetInfo(features=self.config.features						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""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}'						)
						lowerCAmelCase__		  =  dl_manager.download_and_extract(self.config.data_files						)
						if isinstance(a_    ,(str, list, tuple)						):
										lowerCAmelCase__		  =  data_files
										if isinstance(a_    ,a_						):
														lowerCAmelCase__		  =  [files]
										lowerCAmelCase__		  =  [dl_manager.iter_files(a_						) for file in files]
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,gen_kwargs={'files': files}						)]
						lowerCAmelCase__		  =  []
						for split_name, files in data_files.items():
										if isinstance(a_    ,a_						):
														lowerCAmelCase__		  =  [files]
										lowerCAmelCase__		  =  [dl_manager.iter_files(a_						) for file in files]
										splits.append(datasets.SplitGenerator(name=a_    ,gen_kwargs={'files': files}						)						)
						return splits
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						if self.config.features is not None:
										lowerCAmelCase__		  =  self.config.features.arrow_schema
										if all(not require_storage_cast(a_						) for feature in self.config.features.values()						):
														# cheaper cast
														lowerCAmelCase__		  =  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
														lowerCAmelCase__		  =  table_cast(a_    ,a_						)
						return pa_table



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.config.features.arrow_schema if self.config.features else None
						# dtype allows reading an int column as str
						lowerCAmelCase__		  =  (
						    {
						        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_						)						):
										lowerCAmelCase__		  =  pd.read_csv(a_    ,iterator=a_    ,dtype=a_    ,**self.config.pd_read_csv_kwargs						)
										try:
														for batch_idx, df in enumerate(a_						):
																		lowerCAmelCase__		  =  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
