


import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[int]  ,			__lowerCamelCase:		Optional[int]  ,			__lowerCamelCase:		List[Any] = False  ,			__lowerCamelCase:		Tuple = 1  ,			__lowerCamelCase:		Any = 1  ,			__lowerCamelCase:		Union[str, Any] = 1_0000  ,			):







	'''simple docstring'''



	assert len(timesteps.shape  ) == 1, "Timesteps should be a 1d-array"

	lowercase_			 = embedding_dim // 2
	lowercase_			 = -math.log(_UpperCAmelCase  ) * torch.arange(
	    start=0  ,			end=_UpperCAmelCase  ,			dtype=torch.floataa  ,			device=timesteps.device  )
	lowercase_			 = exponent / (half_dim - downscale_freq_shift)

	lowercase_			 = torch.exp(_UpperCAmelCase  )
	lowercase_			 = timesteps[:, None].float() * emb[None, :]

	# scale embeddings
	lowercase_			 = scale * emb

	# concat sine and cosine embeddings
	lowercase_			 = torch.cat([torch.sin(_UpperCAmelCase  ), torch.cos(_UpperCAmelCase  )]  ,			dim=-1  )

	# flip sine and cosine embeddings
	if flip_sin_to_cos:
		lowercase_			 = torch.cat([emb[:, half_dim:], emb[:, :half_dim]]  ,			dim=-1  )

	# zero pad
	if embedding_dim % 2 == 1:
		lowercase_			 = torch.nn.functional.pad(_UpperCAmelCase  ,			(0, 1, 0, 0)  )
	return emb




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ,			__lowerCamelCase:		int  ,			__lowerCamelCase:		Any=False  ,			__lowerCamelCase:		Union[str, Any]=0  ):







	'''simple docstring'''



	lowercase_			 = np.arange(_UpperCAmelCase  ,			dtype=np.floataa  )
	lowercase_			 = np.arange(_UpperCAmelCase  ,			dtype=np.floataa  )
	lowercase_			 = np.meshgrid(_UpperCAmelCase  ,			_UpperCAmelCase  )  # here w goes first
	lowercase_			 = np.stack(_UpperCAmelCase  ,			axis=0  )

	lowercase_			 = grid.reshape([2, 1, grid_size, grid_size]  )
	lowercase_			 = get_ad_sincos_pos_embed_from_grid(_UpperCAmelCase  ,			_UpperCAmelCase  )
	if cls_token and extra_tokens > 0:
		lowercase_			 = np.concatenate([np.zeros([extra_tokens, embed_dim]  ), pos_embed]  ,			axis=0  )
	return pos_embed




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		List[Any]  ):







	'''simple docstring'''



	if embed_dim % 2 != 0:
		raise ValueError("embed_dim must be divisible by 2"  )

	# use half of dimensions to encode grid_h
	lowercase_			 = get_ad_sincos_pos_embed_from_grid(embed_dim // 2  ,			grid[0]  )  # (H*W, D/2)
	lowercase_			 = get_ad_sincos_pos_embed_from_grid(embed_dim // 2  ,			grid[1]  )  # (H*W, D/2)

	lowercase_			 = np.concatenate([emb_h, emb_w]  ,			axis=1  )  # (H*W, D)
	return emb




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ,			__lowerCamelCase:		Any  ):







	'''simple docstring'''



	if embed_dim % 2 != 0:
		raise ValueError("embed_dim must be divisible by 2"  )

	lowercase_			 = np.arange(embed_dim // 2  ,			dtype=np.floataa  )
	omega /= embed_dim / 2.0
	lowercase_			 = 1.0 / 1_0000**omega  # (D/2,)

	lowercase_			 = pos.reshape(-1  )  # (M,)
	lowercase_			 = np.einsum("m,d->md"  ,			_UpperCAmelCase  ,			_UpperCAmelCase  )  # (M, D/2), outer product

	lowercase_			 = np.sin(_UpperCAmelCase  )  # (M, D/2)
	lowercase_			 = np.cos(_UpperCAmelCase  )  # (M, D/2)

	lowercase_			 = np.concatenate([emb_sin, emb_cos]  ,			axis=1  )  # (M, D)
	return emb







class     __lowerCamelCase     (  nn.Module     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase=224						,       UpperCAmelCase=224						,       UpperCAmelCase=16						,       UpperCAmelCase=3						,       UpperCAmelCase=768						,       UpperCAmelCase=False						,       UpperCAmelCase=True						,       UpperCAmelCase=True						,       )     ->       Optional[Any]:






								'''simple docstring'''






								super().__init__()

								lowercase_			 = (height // patch_size) * (width // patch_size)
								lowercase_			 = flatten
								lowercase_			 = layer_norm

								lowercase_			 = nn.Convad(
								    snake_case_						,       snake_case_						,       kernel_size=(patch_size, patch_size)						,       stride=snake_case_						,       bias=snake_case_  )
								if layer_norm:
									lowercase_			 = nn.LayerNorm(snake_case_						,       elementwise_affine=snake_case_						,       eps=1e-6  )
								else:
									lowercase_			 = None

								lowercase_			 = get_ad_sincos_pos_embed(snake_case_						,       int(num_patches**0.5  )  )
								self.register_buffer("pos_embed"						,       torch.from_numpy(snake_case_  ).float().unsqueeze(0  )						,       persistent=snake_case_  )




							def 				A__      (  self						,       UpperCAmelCase  )     ->       Any:






								'''simple docstring'''






								lowercase_			 = self.proj(snake_case_  )
								if self.flatten:
									lowercase_			 = latent.flatten(2  ).transpose(1						,       2  )  # BCHW -> BNC
								if self.layer_norm:
									lowercase_			 = self.norm(snake_case_  )
								return latent + self.pos_embed







class     __lowerCamelCase     (  nn.Module     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase = "silu"						,       UpperCAmelCase = None						,       UpperCAmelCase = None						,       UpperCAmelCase=None						,       )     ->       Optional[Any]:






								'''simple docstring'''






								super().__init__()

								lowercase_			 = nn.Linear(snake_case_						,       snake_case_  )

								if cond_proj_dim is not None:
									lowercase_			 = nn.Linear(snake_case_						,       snake_case_						,       bias=snake_case_  )
								else:
									lowercase_			 = None

								lowercase_			 = get_activation(snake_case_  )

								if out_dim is not None:
									lowercase_			 = out_dim
								else:
									lowercase_			 = time_embed_dim
								lowercase_			 = nn.Linear(snake_case_						,       snake_case_  )

								if post_act_fn is None:
									lowercase_			 = None
								else:
									lowercase_			 = get_activation(snake_case_  )




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase=None  )     ->       int:






								'''simple docstring'''






								if condition is not None:
									lowercase_			 = sample + self.cond_proj(snake_case_  )
								lowercase_			 = self.linear_a(snake_case_  )

								if self.act is not None:
									lowercase_			 = self.act(snake_case_  )

								lowercase_			 = self.linear_a(snake_case_  )

								if self.post_act is not None:
									lowercase_			 = self.post_act(snake_case_  )
								return sample







class     __lowerCamelCase     (  nn.Module     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       Any:






								'''simple docstring'''






								super().__init__()
								lowercase_			 = num_channels
								lowercase_			 = flip_sin_to_cos
								lowercase_			 = downscale_freq_shift




							def 				A__      (  self						,       UpperCAmelCase  )     ->       Optional[Any]:






								'''simple docstring'''






								lowercase_			 = get_timestep_embedding(
								    snake_case_						,       self.num_channels						,       flip_sin_to_cos=self.flip_sin_to_cos						,       downscale_freq_shift=self.downscale_freq_shift						,       )
								return t_emb







class     __lowerCamelCase     (  nn.Module     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase = 256						,       UpperCAmelCase = 1.0						,       UpperCAmelCase=True						,       UpperCAmelCase=True						,       UpperCAmelCase=False  )     ->       Any:






								'''simple docstring'''






								super().__init__()
								lowercase_			 = nn.Parameter(torch.randn(snake_case_  ) * scale						,       requires_grad=snake_case_  )
								lowercase_			 = log
								lowercase_			 = flip_sin_to_cos

								if set_W_to_weight:
									# to delete later
									lowercase_			 = nn.Parameter(torch.randn(snake_case_  ) * scale						,       requires_grad=snake_case_  )

									lowercase_			 = self.W




							def 				A__      (  self						,       UpperCAmelCase  )     ->       Union[str, Any]:






								'''simple docstring'''






								if self.log:
									lowercase_			 = torch.log(snake_case_  )

								lowercase_			 = x[:, None] * self.weight[None, :] * 2 * np.pi

								if self.flip_sin_to_cos:
									lowercase_			 = torch.cat([torch.cos(snake_case_  ), torch.sin(snake_case_  )]						,       dim=-1  )
								else:
									lowercase_			 = torch.cat([torch.sin(snake_case_  ), torch.cos(snake_case_  )]						,       dim=-1  )
								return out







class     __lowerCamelCase     (  nn.Module     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase						,       )     ->       Tuple:






								'''simple docstring'''






								super().__init__()

								lowercase_			 = height
								lowercase_			 = width
								lowercase_			 = num_embed
								lowercase_			 = embed_dim

								lowercase_			 = nn.Embedding(self.num_embed						,       snake_case_  )
								lowercase_			 = nn.Embedding(self.height						,       snake_case_  )
								lowercase_			 = nn.Embedding(self.width						,       snake_case_  )




							def 				A__      (  self						,       UpperCAmelCase  )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = self.emb(snake_case_  )

								lowercase_			 = self.height_emb(torch.arange(self.height						,       device=index.device  ).view(1						,       self.height  )  )

								# 1 x H x D -> 1 x H x 1 x D
								lowercase_			 = height_emb.unsqueeze(2  )

								lowercase_			 = self.width_emb(torch.arange(self.width						,       device=index.device  ).view(1						,       self.width  )  )

								# 1 x W x D -> 1 x 1 x W x D
								lowercase_			 = width_emb.unsqueeze(1  )

								lowercase_			 = height_emb + width_emb

								# 1 x H x W x D -> 1 x L xD
								lowercase_			 = pos_emb.view(1						,       self.height * self.width						,       -1  )

								lowercase_			 = emb + pos_emb[:, : emb.shape[1], :]

								return emb







class     __lowerCamelCase     (  nn.Module     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       Tuple:






								'''simple docstring'''






								super().__init__()
								lowercase_			 = dropout_prob > 0
								lowercase_			 = nn.Embedding(num_classes + use_cfg_embedding						,       snake_case_  )
								lowercase_			 = num_classes
								lowercase_			 = dropout_prob




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase=None  )     ->       str:






								'''simple docstring'''






								if force_drop_ids is None:
									lowercase_			 = torch.rand(labels.shape[0]						,       device=labels.device  ) < self.dropout_prob
								else:
									lowercase_			 = torch.tensor(force_drop_ids == 1  )
								lowercase_			 = torch.where(snake_case_						,       self.num_classes						,       snake_case_  )
								return labels




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase=None  )     ->       Any:






								'''simple docstring'''






								lowercase_			 = self.dropout_prob > 0
								if (self.training and use_dropout) or (force_drop_ids is not None):
									lowercase_			 = self.token_drop(snake_case_						,       snake_case_  )
								lowercase_			 = self.embedding_table(snake_case_  )
								return embeddings







class     __lowerCamelCase     (  nn.Module     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase = 1024						,       UpperCAmelCase = 768						,       UpperCAmelCase = 768						,       UpperCAmelCase = 10						,       )     ->       Union[str, Any]:






								'''simple docstring'''






								super().__init__()

								lowercase_			 = num_image_text_embeds
								lowercase_			 = nn.Linear(snake_case_						,       self.num_image_text_embeds * cross_attention_dim  )
								lowercase_			 = nn.Linear(snake_case_						,       snake_case_  )




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       List[Any]:






								'''simple docstring'''






								lowercase_			 = text_embeds.shape[0]

								# image
								lowercase_			 = self.image_embeds(snake_case_  )
								lowercase_			 = image_text_embeds.reshape(snake_case_						,       self.num_image_text_embeds						,       -1  )

								# text
								lowercase_			 = self.text_proj(snake_case_  )

								return torch.cat([image_text_embeds, text_embeds]						,       dim=1  )







class     __lowerCamelCase     (  nn.Module     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase = 768						,       UpperCAmelCase = 768						,       UpperCAmelCase = 32						,       )     ->       Union[str, Any]:






								'''simple docstring'''






								super().__init__()

								lowercase_			 = num_image_text_embeds
								lowercase_			 = nn.Linear(snake_case_						,       self.num_image_text_embeds * cross_attention_dim  )
								lowercase_			 = nn.LayerNorm(snake_case_  )




							def 				A__      (  self						,       UpperCAmelCase  )     ->       Union[str, Any]:






								'''simple docstring'''






								lowercase_			 = image_embeds.shape[0]

								# image
								lowercase_			 = self.image_embeds(snake_case_  )
								lowercase_			 = image_embeds.reshape(snake_case_						,       self.num_image_text_embeds						,       -1  )
								lowercase_			 = self.norm(snake_case_  )
								return image_embeds







class     __lowerCamelCase     (  nn.Module     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase=0.1  )     ->       Tuple:






								'''simple docstring'''






								super().__init__()

								lowercase_			 = Timesteps(num_channels=256						,       flip_sin_to_cos=snake_case_						,       downscale_freq_shift=1  )
								lowercase_			 = TimestepEmbedding(in_channels=256						,       time_embed_dim=snake_case_  )
								lowercase_			 = LabelEmbedding(snake_case_						,       snake_case_						,       snake_case_  )




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase=None  )     ->       Union[str, Any]:






								'''simple docstring'''






								lowercase_			 = self.time_proj(snake_case_  )
								lowercase_			 = self.timestep_embedder(timesteps_proj.to(dtype=snake_case_  )  )  # (N, D)

								lowercase_			 = self.class_embedder(snake_case_  )  # (N, D)

								lowercase_			 = timesteps_emb + class_labels  # (N, D)

								return conditioning







class     __lowerCamelCase     (  nn.Module     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase = 64  )     ->       List[Any]:






								'''simple docstring'''






								super().__init__()
								lowercase_			 = nn.LayerNorm(snake_case_  )
								lowercase_			 = AttentionPooling(snake_case_						,       snake_case_  )
								lowercase_			 = nn.Linear(snake_case_						,       snake_case_  )
								lowercase_			 = nn.LayerNorm(snake_case_  )




							def 				A__      (  self						,       UpperCAmelCase  )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = self.norma(snake_case_  )
								lowercase_			 = self.pool(snake_case_  )
								lowercase_			 = self.proj(snake_case_  )
								lowercase_			 = self.norma(snake_case_  )
								return hidden_states







class     __lowerCamelCase     (  nn.Module     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase = 768						,       UpperCAmelCase = 768						,       UpperCAmelCase = 1536  )     ->       Union[str, Any]:






								'''simple docstring'''






								super().__init__()
								lowercase_			 = nn.Linear(snake_case_						,       snake_case_  )
								lowercase_			 = nn.LayerNorm(snake_case_  )
								lowercase_			 = nn.Linear(snake_case_						,       snake_case_  )




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       Dict:






								'''simple docstring'''






								lowercase_			 = self.text_proj(snake_case_  )
								lowercase_			 = self.text_norm(snake_case_  )

								# image
								lowercase_			 = self.image_proj(snake_case_  )

								return time_image_embeds + time_text_embeds







class     __lowerCamelCase     (  nn.Module     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase = 768						,       UpperCAmelCase = 1536  )     ->       str:






								'''simple docstring'''






								super().__init__()
								lowercase_			 = nn.Linear(snake_case_						,       snake_case_  )
								lowercase_			 = nn.LayerNorm(snake_case_  )




							def 				A__      (  self						,       UpperCAmelCase  )     ->       Optional[int]:






								'''simple docstring'''






								lowercase_			 = self.image_proj(snake_case_  )
								lowercase_			 = self.image_norm(snake_case_  )
								return time_image_embeds







class     __lowerCamelCase     (  nn.Module     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase = 768						,       UpperCAmelCase = 1536  )     ->       Union[str, Any]:






								'''simple docstring'''






								super().__init__()
								lowercase_			 = nn.Linear(snake_case_						,       snake_case_  )
								lowercase_			 = nn.LayerNorm(snake_case_  )
								lowercase_			 = nn.Sequential(
								    nn.Convad(3						,       16						,       3						,       padding=1  )						,       nn.SiLU()						,       nn.Convad(16						,       16						,       3						,       padding=1  )						,       nn.SiLU()						,       nn.Convad(16						,       32						,       3						,       padding=1						,       stride=2  )						,       nn.SiLU()						,       nn.Convad(32						,       32						,       3						,       padding=1  )						,       nn.SiLU()						,       nn.Convad(32						,       96						,       3						,       padding=1						,       stride=2  )						,       nn.SiLU()						,       nn.Convad(96						,       96						,       3						,       padding=1  )						,       nn.SiLU()						,       nn.Convad(96						,       256						,       3						,       padding=1						,       stride=2  )						,       nn.SiLU()						,       nn.Convad(256						,       4						,       3						,       padding=1  )						,       )




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       Any:






								'''simple docstring'''






								lowercase_			 = self.image_proj(snake_case_  )
								lowercase_			 = self.image_norm(snake_case_  )
								lowercase_			 = self.input_hint_block(snake_case_  )
								return time_image_embeds, hint







class     __lowerCamelCase     (  nn.Module     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase=None  )     ->       Dict:






								'''simple docstring'''






								super().__init__()
								lowercase_			 = dtype
								lowercase_			 = nn.Parameter(torch.randn(1						,       snake_case_  ) / embed_dim**0.5  )
								lowercase_			 = nn.Linear(snake_case_						,       snake_case_						,       dtype=self.dtype  )
								lowercase_			 = nn.Linear(snake_case_						,       snake_case_						,       dtype=self.dtype  )
								lowercase_			 = nn.Linear(snake_case_						,       snake_case_						,       dtype=self.dtype  )
								lowercase_			 = num_heads
								lowercase_			 = embed_dim // self.num_heads




							def 				A__      (  self						,       UpperCAmelCase  )     ->       List[Any]:






								'''simple docstring'''






								lowercase_			 = x.size()

								def shape(UpperCAmelCase  ):
									# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
									lowercase_			 = x.view(snake_case_						,       -1						,       self.num_heads						,       self.dim_per_head  )
									# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
									lowercase_			 = x.transpose(1						,       2  )
									# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
									lowercase_			 = x.reshape(bs * self.num_heads						,       -1						,       self.dim_per_head  )
									# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
									lowercase_			 = x.transpose(1						,       2  )
									return x

								lowercase_			 = x.mean(dim=1						,       keepdim=snake_case_  ) + self.positional_embedding.to(x.dtype  )
								lowercase_			 = torch.cat([class_token, x]						,       dim=1  )  # (bs, length+1, width)

								# (bs*n_heads, class_token_length, dim_per_head)
								lowercase_			 = shape(self.q_proj(snake_case_  )  )
								# (bs*n_heads, length+class_token_length, dim_per_head)
								lowercase_			 = shape(self.k_proj(snake_case_  )  )
								lowercase_			 = shape(self.v_proj(snake_case_  )  )

								# (bs*n_heads, class_token_length, length+class_token_length):
								lowercase_			 = 1 / math.sqrt(math.sqrt(self.dim_per_head  )  )
								lowercase_			 = torch.einsum("bct,bcs->bts"						,       q * scale						,       k * scale  )  # More stable with f16 than dividing afterwards
								lowercase_			 = torch.softmax(weight.float()						,       dim=-1  ).type(weight.dtype  )

								# (bs*n_heads, dim_per_head, class_token_length)
								lowercase_			 = torch.einsum("bts,bcs->bct"						,       snake_case_						,       snake_case_  )

								# (bs, length+1, width)
								lowercase_			 = a.reshape(snake_case_						,       -1						,       1  ).transpose(1						,       2  )

								return a[:, 0, :]  # cls_token








"""simple docstring"""




import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = False   , snake_case_ = False   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        super().__init__(
            snake_case_   , split=snake_case_   , features=snake_case_   , cache_dir=snake_case_   , keep_in_memory=snake_case_   , streaming=snake_case_   , num_proc=snake_case_   , **snake_case_   , )
        A_    :			int									=    path_or_paths if isinstance(snake_case_   , snake_case_    ) else {self.split: path_or_paths}
        A_    :			List[str]									=    Csv(
            cache_dir=snake_case_   , data_files=snake_case_   , features=snake_case_   , **snake_case_   , )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if self.streaming:
          A_    :			List[str]									=    self.builder.as_streaming_dataset(split=self.split    )
        # Build regular (map-style) dataset
        else:
          A_    :			List[Any]									=    None
          A_    :			Optional[int]									=    None
          A_    :			Optional[int]									=    None
          A_    :			List[str]									=    None

          self.builder.download_and_prepare(
              download_config=snake_case_   , download_mode=snake_case_   , verification_mode=snake_case_   , base_path=snake_case_   , num_proc=self.num_proc   , )
          A_    :			List[Any]									=    self.builder.as_dataset(
              split=self.split   , verification_mode=snake_case_   , in_memory=self.keep_in_memory    )
        return dataset


class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_   , snake_case_ = None   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        if num_proc is not None and num_proc <= 0:
          raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""    )

        A_    :			Optional[int]									=    dataset
        A_    :			List[str]									=    path_or_buf
        A_    :			Optional[int]									=    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        A_    :			Optional[int]									=    num_proc
        A_    :			Optional[int]									=    'utf-8'
        A_    :			int									=    to_csv_kwargs

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    self.to_csv_kwargs.pop('path_or_buf'   , snake_case_    )
        A_    :			str									=    self.to_csv_kwargs.pop('header'   , snake_case_    )
        A_    :			Optional[Any]									=    self.to_csv_kwargs.pop('index'   , snake_case_    )

        if isinstance(self.path_or_buf   , (str, bytes, os.PathLike)    ):
          with open(self.path_or_buf   , 'wb+'    ) as buffer:
            A_    :			Optional[Any]									=    self._write(file_obj=snake_case_   , header=snake_case_   , index=snake_case_   , **self.to_csv_kwargs    )
        else:
          A_    :			int									=    self._write(file_obj=self.path_or_buf   , header=snake_case_   , index=snake_case_   , **self.to_csv_kwargs    )
        return written

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_		,			A_		,			A_		,			A_    :			Union[str, Any]									=    args

        A_    :			Dict									=    query_table(
            table=self.dataset.data   , key=slice(snake_case_   , offset + self.batch_size    )   , indices=self.dataset._indices   , )
        A_    :			List[str]									=    batch.to_pandas().to_csv(
            path_or_buf=snake_case_   , header=header if (offset == 0) else False   , index=snake_case_   , **snake_case_    )
        return csv_str.encode(self.encoding    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , **snake_case_    ):

        """simple docstring"""

        A_    :			Any									=    0

        if self.num_proc is None or self.num_proc == 1:
          for offset in logging.tqdm(
              range(0   , len(self.dataset    )   , self.batch_size    )   , unit='ba'   , disable=not logging.is_progress_bar_enabled()   , desc='Creating CSV from Arrow format'   , ):
            A_    :			Optional[int]									=    self._batch_csv((offset, header, index, to_csv_kwargs)    )
            written += file_obj.write(snake_case_    )

        else:
          A_		,			A_    :			Optional[int]									=    len(self.dataset    ), self.batch_size
          with multiprocessing.Pool(self.num_proc    ) as pool:
            for csv_str in logging.tqdm(
                pool.imap(
                    self._batch_csv   , [(offset, header, index, to_csv_kwargs) for offset in range(0   , snake_case_   , snake_case_    )]   , )   , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size   , unit='ba'   , disable=not logging.is_progress_bar_enabled()   , desc='Creating CSV from Arrow format'   , ):
              written += file_obj.write(snake_case_    )

        return written