

import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation

def      a__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     List[str] = False							,					UpperCAmelCase				:     Dict = 1							,					UpperCAmelCase				:     str = 1							,					UpperCAmelCase				:     Union[str, Any] = 10_000							,					)    ->		str:
  assert len(timesteps.shape     ) == 1, "Timesteps should be a 1d-array"

  UpperCAmelCase			:				Union[str, Any]										=					embedding_dim // 2
  UpperCAmelCase			:				str										=					-math.log(lowerCamelCase_     ) * torch.arange(
      start=0							,					end=lowerCamelCase_							,					dtype=torch.floataa							,					device=timesteps.device     )
  UpperCAmelCase			:				Any										=					exponent / (half_dim - downscale_freq_shift)

  UpperCAmelCase			:				Optional[int]										=					torch.exp(lowerCamelCase_     )
  UpperCAmelCase			:				Tuple										=					timesteps[:, None].float() * emb[None, :]

  # scale embeddings
  UpperCAmelCase			:				List[str]										=					scale * emb

  # concat sine and cosine embeddings
  UpperCAmelCase			:				int										=					torch.cat([torch.sin(lowerCamelCase_     ), torch.cos(lowerCamelCase_     )]							,					dim=-1     )

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

  # zero pad
  if embedding_dim % 2 == 1:
    UpperCAmelCase			:				List[Any]										=					torch.nn.functional.pad(lowerCamelCase_							,					(0, 1, 0, 0)     )
  return emb

def      a__	(						UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     Any							,					UpperCAmelCase				:     List[Any]=False							,					UpperCAmelCase				:     Dict=0     )    ->		Union[str, Any]:
  UpperCAmelCase			:				Tuple										=					np.arange(lowerCamelCase_							,					dtype=np.floataa     )
  UpperCAmelCase			:				Any										=					np.arange(lowerCamelCase_							,					dtype=np.floataa     )
  UpperCAmelCase			:				int										=					np.meshgrid(lowerCamelCase_							,					lowerCamelCase_     )  # here w goes first
  UpperCAmelCase			:				str										=					np.stack(lowerCamelCase_							,					axis=0     )

  UpperCAmelCase			:				Optional[Any]										=					grid.reshape([2, 1, grid_size, grid_size]     )
  UpperCAmelCase			:				int										=					get_ad_sincos_pos_embed_from_grid(lowerCamelCase_							,					lowerCamelCase_     )
  if cls_token and extra_tokens > 0:
    UpperCAmelCase			:				Dict										=					np.concatenate([np.zeros([extra_tokens, embed_dim]     ), pos_embed]							,					axis=0     )
  return pos_embed

def      a__	(						UpperCAmelCase				:     Union[str, Any]							,					UpperCAmelCase				:     int     )    ->		Optional[Any]:
  if embed_dim % 2 != 0:
    raise ValueError('''embed_dim must be divisible by 2'''     )

  # use half of dimensions to encode grid_h
  UpperCAmelCase			:				Union[str, Any]										=					get_ad_sincos_pos_embed_from_grid(embed_dim // 2							,					grid[0]     )  # (H*W, D/2)
  UpperCAmelCase			:				Optional[int]										=					get_ad_sincos_pos_embed_from_grid(embed_dim // 2							,					grid[1]     )  # (H*W, D/2)

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

def      a__	(						UpperCAmelCase				:     int							,					UpperCAmelCase				:     List[str]     )    ->		Any:
  if embed_dim % 2 != 0:
    raise ValueError('''embed_dim must be divisible by 2'''     )

  UpperCAmelCase			:				Optional[Any]										=					np.arange(embed_dim // 2							,					dtype=np.floataa     )
  omega /= embed_dim / 2.0
  UpperCAmelCase			:				List[str]										=					1.0 / 10_000**omega  # (D/2,)

  UpperCAmelCase			:				List[str]										=					pos.reshape(-1     )  # (M,)
  UpperCAmelCase			:				Tuple										=					np.einsum('''m,d->md'''							,					lowerCamelCase_							,					lowerCamelCase_     )  # (M, D/2), outer product

  UpperCAmelCase			:				str										=					np.sin(lowerCamelCase_     )  # (M, D/2)
  UpperCAmelCase			:				List[str]										=					np.cos(lowerCamelCase_     )  # (M, D/2)

  UpperCAmelCase			:				List[Any]										=					np.concatenate([emb_sin, emb_cos]							,					axis=1     )  # (M, D)
  return emb





class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	List[Any],						__A				:	Any=2_2_4,						__A				:	Optional[Any]=2_2_4,						__A				:	Any=1_6,						__A				:	Union[str, Any]=3,						__A				:	Optional[Any]=7_6_8,						__A				:	Dict=False,						__A				:	int=True,						__A				:	Optional[int]=True,						):
     super().__init__()

     UpperCAmelCase			:				str										=					(height // patch_size) * (width // patch_size)
     UpperCAmelCase			:				Tuple										=					flatten
     UpperCAmelCase			:				Any										=					layer_norm

     UpperCAmelCase			:				Optional[int]										=					nn.Convad(
         __A,						__A,						kernel_size=(patch_size, patch_size),						stride=__A,						bias=__A		)
     if layer_norm:
       UpperCAmelCase			:				Optional[int]										=					nn.LayerNorm(__A,						elementwise_affine=__A,						eps=1E-6		)
     else:
       UpperCAmelCase			:				Dict										=					None

     UpperCAmelCase			:				List[Any]										=					get_ad_sincos_pos_embed(__A,						int(num_patches**0.5		)		)
     self.register_buffer('''pos_embed''',						torch.from_numpy(__A		).float().unsqueeze(0		),						persistent=__A		)





   def 			__magic_name__       (				self				:	Tuple,						__A				:	Tuple		):
     UpperCAmelCase			:				Optional[int]										=					self.proj(__A		)
     if self.flatten:
       UpperCAmelCase			:				Optional[int]										=					latent.flatten(2		).transpose(1,						2		)  # BCHW -> BNC
     if self.layer_norm:
       UpperCAmelCase			:				Dict										=					self.norm(__A		)
     return latent + self.pos_embed






class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	Union[str, Any],						__A				:	int,						__A				:	int,						__A				:	str = "silu",						__A				:	int = None,						__A				:	Optional[str] = None,						__A				:	Optional[int]=None,						):
     super().__init__()

     UpperCAmelCase			:				Optional[int]										=					nn.Linear(__A,						__A		)

     if cond_proj_dim is not None:
       UpperCAmelCase			:				str										=					nn.Linear(__A,						__A,						bias=__A		)
     else:
       UpperCAmelCase			:				Union[str, Any]										=					None

     UpperCAmelCase			:				List[str]										=					get_activation(__A		)

     if out_dim is not None:
       UpperCAmelCase			:				Optional[Any]										=					out_dim
     else:
       UpperCAmelCase			:				int										=					time_embed_dim
     UpperCAmelCase			:				Optional[int]										=					nn.Linear(__A,						__A		)

     if post_act_fn is None:
       UpperCAmelCase			:				Union[str, Any]										=					None
     else:
       UpperCAmelCase			:				Any										=					get_activation(__A		)





   def 			__magic_name__       (				self				:	List[str],						__A				:	str,						__A				:	Tuple=None		):

     if condition is not None:
       UpperCAmelCase			:				Any										=					sample + self.cond_proj(__A		)
     UpperCAmelCase			:				Tuple										=					self.linear_a(__A		)

     if self.act is not None:
       UpperCAmelCase			:				Optional[Any]										=					self.act(__A		)

     UpperCAmelCase			:				int										=					self.linear_a(__A		)

     if self.post_act is not None:
       UpperCAmelCase			:				List[Any]										=					self.post_act(__A		)
     return sample






class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	List[str],						__A				:	int,						__A				:	bool,						__A				:	float		):
     super().__init__()
     UpperCAmelCase			:				str										=					num_channels
     UpperCAmelCase			:				Tuple										=					flip_sin_to_cos
     UpperCAmelCase			:				Optional[Any]										=					downscale_freq_shift





   def 			__magic_name__       (				self				:	Tuple,						__A				:	Optional[int]		):
     UpperCAmelCase			:				List[Any]										=					get_timestep_embedding(
         __A,						self.num_channels,						flip_sin_to_cos=self.flip_sin_to_cos,						downscale_freq_shift=self.downscale_freq_shift,						)
     return t_emb






class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	str,						__A				:	int = 2_5_6,						__A				:	float = 1.0,						__A				:	Optional[int]=True,						__A				:	Any=True,						__A				:	Optional[Any]=False		):
     super().__init__()
     UpperCAmelCase			:				Optional[int]										=					nn.Parameter(torch.randn(__A		) * scale,						requires_grad=__A		)
     UpperCAmelCase			:				List[str]										=					log
     UpperCAmelCase			:				Union[str, Any]										=					flip_sin_to_cos

     if set_W_to_weight:
       # to delete later
       UpperCAmelCase			:				int										=					nn.Parameter(torch.randn(__A		) * scale,						requires_grad=__A		)

       UpperCAmelCase			:				List[Any]										=					self.W





   def 			__magic_name__       (				self				:	Optional[int],						__A				:	Union[str, Any]		):

     if self.log:
       UpperCAmelCase			:				Optional[int]										=					torch.log(__A		)

     UpperCAmelCase			:				List[str]										=					x[:, None] * self.weight[None, :] * 2 * np.pi

     if self.flip_sin_to_cos:
       UpperCAmelCase			:				Optional[Any]										=					torch.cat([torch.cos(__A		), torch.sin(__A		)],						dim=-1		)
     else:
       UpperCAmelCase			:				Union[str, Any]										=					torch.cat([torch.sin(__A		), torch.cos(__A		)],						dim=-1		)
     return out






class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	List[Any],						__A				:	int,						__A				:	int,						__A				:	int,						__A				:	int,						):
     super().__init__()

     UpperCAmelCase			:				List[str]										=					height
     UpperCAmelCase			:				Optional[Any]										=					width
     UpperCAmelCase			:				Optional[Any]										=					num_embed
     UpperCAmelCase			:				Union[str, Any]										=					embed_dim

     UpperCAmelCase			:				Any										=					nn.Embedding(self.num_embed,						__A		)
     UpperCAmelCase			:				int										=					nn.Embedding(self.height,						__A		)
     UpperCAmelCase			:				Dict										=					nn.Embedding(self.width,						__A		)





   def 			__magic_name__       (				self				:	int,						__A				:	List[str]		):
     UpperCAmelCase			:				str										=					self.emb(__A		)

     UpperCAmelCase			:				Union[str, Any]										=					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
     UpperCAmelCase			:				str										=					height_emb.unsqueeze(2		)

     UpperCAmelCase			:				Dict										=					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
     UpperCAmelCase			:				List[str]										=					width_emb.unsqueeze(1		)

     UpperCAmelCase			:				Any										=					height_emb + width_emb

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

     UpperCAmelCase			:				List[str]										=					emb + pos_emb[:, : emb.shape[1], :]

     return emb






class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	Tuple,						__A				:	Dict,						__A				:	Union[str, Any],						__A				:	Tuple		):
     super().__init__()
     UpperCAmelCase			:				int										=					dropout_prob > 0
     UpperCAmelCase			:				str										=					nn.Embedding(num_classes + use_cfg_embedding,						__A		)
     UpperCAmelCase			:				int										=					num_classes
     UpperCAmelCase			:				Optional[Any]										=					dropout_prob



   def 			__magic_name__       (				self				:	Optional[int],						__A				:	Optional[Any],						__A				:	Any=None		):

     if force_drop_ids is None:
       UpperCAmelCase			:				str										=					torch.rand(labels.shape[0],						device=labels.device		) < self.dropout_prob
     else:
       UpperCAmelCase			:				List[str]										=					torch.tensor(force_drop_ids == 1		)
     UpperCAmelCase			:				Any										=					torch.where(__A,						self.num_classes,						__A		)
     return labels





   def 			__magic_name__       (				self				:	Optional[int],						__A				:	torch.LongTensor,						__A				:	Union[str, Any]=None		):
     UpperCAmelCase			:				Dict										=					self.dropout_prob > 0
     if (self.training and use_dropout) or (force_drop_ids is not None):
       UpperCAmelCase			:				List[str]										=					self.token_drop(__A,						__A		)
     UpperCAmelCase			:				Optional[int]										=					self.embedding_table(__A		)
     return embeddings






class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	int,						__A				:	int = 1_0_2_4,						__A				:	int = 7_6_8,						__A				:	int = 7_6_8,						__A				:	int = 1_0,						):
     super().__init__()

     UpperCAmelCase			:				int										=					num_image_text_embeds
     UpperCAmelCase			:				Optional[Any]										=					nn.Linear(__A,						self.num_image_text_embeds * cross_attention_dim		)
     UpperCAmelCase			:				str										=					nn.Linear(__A,						__A		)





   def 			__magic_name__       (				self				:	Any,						__A				:	torch.FloatTensor,						__A				:	torch.FloatTensor		):
     UpperCAmelCase			:				List[Any]										=					text_embeds.shape[0]

     # image
     UpperCAmelCase			:				Optional[int]										=					self.image_embeds(__A		)
     UpperCAmelCase			:				Dict										=					image_text_embeds.reshape(__A,						self.num_image_text_embeds,						-1		)

     # text
     UpperCAmelCase			:				Optional[int]										=					self.text_proj(__A		)

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






class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	Dict,						__A				:	int = 7_6_8,						__A				:	int = 7_6_8,						__A				:	int = 3_2,						):
     super().__init__()

     UpperCAmelCase			:				Dict										=					num_image_text_embeds
     UpperCAmelCase			:				Tuple										=					nn.Linear(__A,						self.num_image_text_embeds * cross_attention_dim		)
     UpperCAmelCase			:				Dict										=					nn.LayerNorm(__A		)





   def 			__magic_name__       (				self				:	Optional[int],						__A				:	torch.FloatTensor		):
     UpperCAmelCase			:				Tuple										=					image_embeds.shape[0]

     # image
     UpperCAmelCase			:				Optional[Any]										=					self.image_embeds(__A		)
     UpperCAmelCase			:				List[Any]										=					image_embeds.reshape(__A,						self.num_image_text_embeds,						-1		)
     UpperCAmelCase			:				Optional[Any]										=					self.norm(__A		)
     return image_embeds






class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	Tuple,						__A				:	int,						__A				:	Any,						__A				:	Dict=0.1		):
     super().__init__()

     UpperCAmelCase			:				str										=					Timesteps(num_channels=2_5_6,						flip_sin_to_cos=__A,						downscale_freq_shift=1		)
     UpperCAmelCase			:				Dict										=					TimestepEmbedding(in_channels=2_5_6,						time_embed_dim=__A		)
     UpperCAmelCase			:				str										=					LabelEmbedding(__A,						__A,						__A		)





   def 			__magic_name__       (				self				:	List[Any],						__A				:	str,						__A				:	Union[str, Any],						__A				:	str=None		):
     UpperCAmelCase			:				Optional[Any]										=					self.time_proj(__A		)
     UpperCAmelCase			:				Tuple										=					self.timestep_embedder(timesteps_proj.to(dtype=__A		)		)  # (N, D)

     UpperCAmelCase			:				Any										=					self.class_embedder(__A		)  # (N, D)

     UpperCAmelCase			:				int										=					timesteps_emb + class_labels  # (N, D)

     return conditioning






class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	str,						__A				:	int,						__A				:	int,						__A				:	int = 6_4		):
     super().__init__()
     UpperCAmelCase			:				Tuple										=					nn.LayerNorm(__A		)
     UpperCAmelCase			:				str										=					AttentionPooling(__A,						__A		)
     UpperCAmelCase			:				str										=					nn.Linear(__A,						__A		)
     UpperCAmelCase			:				List[str]										=					nn.LayerNorm(__A		)





   def 			__magic_name__       (				self				:	Tuple,						__A				:	List[Any]		):
     UpperCAmelCase			:				List[str]										=					self.norma(__A		)
     UpperCAmelCase			:				str										=					self.pool(__A		)
     UpperCAmelCase			:				Optional[int]										=					self.proj(__A		)
     UpperCAmelCase			:				List[str]										=					self.norma(__A		)
     return hidden_states






class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	int,						__A				:	int = 7_6_8,						__A				:	int = 7_6_8,						__A				:	int = 1_5_3_6		):
     super().__init__()
     UpperCAmelCase			:				Tuple										=					nn.Linear(__A,						__A		)
     UpperCAmelCase			:				Tuple										=					nn.LayerNorm(__A		)
     UpperCAmelCase			:				Optional[Any]										=					nn.Linear(__A,						__A		)





   def 			__magic_name__       (				self				:	Optional[int],						__A				:	torch.FloatTensor,						__A				:	torch.FloatTensor		):
     UpperCAmelCase			:				Tuple										=					self.text_proj(__A		)
     UpperCAmelCase			:				int										=					self.text_norm(__A		)

     # image
     UpperCAmelCase			:				str										=					self.image_proj(__A		)

     return time_image_embeds + time_text_embeds






class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	List[str],						__A				:	int = 7_6_8,						__A				:	int = 1_5_3_6		):
     super().__init__()
     UpperCAmelCase			:				int										=					nn.Linear(__A,						__A		)
     UpperCAmelCase			:				int										=					nn.LayerNorm(__A		)





   def 			__magic_name__       (				self				:	str,						__A				:	torch.FloatTensor		):
     UpperCAmelCase			:				Dict										=					self.image_proj(__A		)
     UpperCAmelCase			:				int										=					self.image_norm(__A		)
     return time_image_embeds






class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	List[str],						__A				:	int = 7_6_8,						__A				:	int = 1_5_3_6		):
     super().__init__()
     UpperCAmelCase			:				Tuple										=					nn.Linear(__A,						__A		)
     UpperCAmelCase			:				int										=					nn.LayerNorm(__A		)
     UpperCAmelCase			:				Any										=					nn.Sequential(
         nn.Convad(3,						1_6,						3,						padding=1		),						nn.SiLU(),						nn.Convad(1_6,						1_6,						3,						padding=1		),						nn.SiLU(),						nn.Convad(1_6,						3_2,						3,						padding=1,						stride=2		),						nn.SiLU(),						nn.Convad(3_2,						3_2,						3,						padding=1		),						nn.SiLU(),						nn.Convad(3_2,						9_6,						3,						padding=1,						stride=2		),						nn.SiLU(),						nn.Convad(9_6,						9_6,						3,						padding=1		),						nn.SiLU(),						nn.Convad(9_6,						2_5_6,						3,						padding=1,						stride=2		),						nn.SiLU(),						nn.Convad(2_5_6,						4,						3,						padding=1		),						)





   def 			__magic_name__       (				self				:	Any,						__A				:	torch.FloatTensor,						__A				:	torch.FloatTensor		):
     UpperCAmelCase			:				Any										=					self.image_proj(__A		)
     UpperCAmelCase			:				str										=					self.image_norm(__A		)
     UpperCAmelCase			:				Optional[Any]										=					self.input_hint_block(__A		)
     return time_image_embeds, hint






class    __UpperCAmelCase	(			nn.Module		):



   def __init__(				self				:	Optional[int],						__A				:	Optional[int],						__A				:	Optional[Any],						__A				:	List[Any]=None		):
     super().__init__()
     UpperCAmelCase			:				Union[str, Any]										=					dtype
     UpperCAmelCase			:				int										=					nn.Parameter(torch.randn(1,						__A		) / embed_dim**0.5		)
     UpperCAmelCase			:				List[str]										=					nn.Linear(__A,						__A,						dtype=self.dtype		)
     UpperCAmelCase			:				Optional[Any]										=					nn.Linear(__A,						__A,						dtype=self.dtype		)
     UpperCAmelCase			:				Tuple										=					nn.Linear(__A,						__A,						dtype=self.dtype		)
     UpperCAmelCase			:				Optional[Any]										=					num_heads
     UpperCAmelCase			:				Optional[int]										=					embed_dim // self.num_heads





   def 			__magic_name__       (				self				:	str,						__A				:	List[str]		):
     UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				Union[str, Any]										=					x.size()

     def shape(__A				:	Optional[Any]		):
       # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
       UpperCAmelCase			:				Optional[Any]										=					x.view(__A,						-1,						self.num_heads,						self.dim_per_head		)
       # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
       UpperCAmelCase			:				Optional[Any]										=					x.transpose(1,						2		)
       # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
       UpperCAmelCase			:				Tuple										=					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)
       UpperCAmelCase			:				Tuple										=					x.transpose(1,						2		)
       return x

     UpperCAmelCase			:				Optional[int]										=					x.mean(dim=1,						keepdim=__A		) + self.positional_embedding.to(x.dtype		)
     UpperCAmelCase			:				int										=					torch.cat([class_token, x],						dim=1		)  # (bs, length+1, width)

     # (bs*n_heads, class_token_length, dim_per_head)
     UpperCAmelCase			:				Union[str, Any]										=					shape(self.q_proj(__A		)		)
     # (bs*n_heads, length+class_token_length, dim_per_head)
     UpperCAmelCase			:				Union[str, Any]										=					shape(self.k_proj(__A		)		)
     UpperCAmelCase			:				List[Any]										=					shape(self.v_proj(__A		)		)

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

     # (bs*n_heads, dim_per_head, class_token_length)
     UpperCAmelCase			:				Tuple										=					torch.einsum('''bts,bcs->bct''',						__A,						__A		)

     # (bs, length+1, width)
     UpperCAmelCase			:				int										=					a.reshape(__A,						-1,						1		).transpose(1,						2		)

     return a[:, 0, :]  # cls_token













"""simple docstring"""







# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
	import tensorflow as tf







class 							_SCREAMING_SNAKE_CASE    (	TensorFormatter[Mapping, 'tf.Tensor', Mapping]				):





							"""simple docstring"""





							def __init__( self:  Union[str, Any]					,		__A:  Union[str, Any]=None					,		**__A:  Union[str, Any]     ):

												'''simple docstring'''

												super().__init__(features=__A     )
												a__ 							=	tf_tensor_kwargs
												import tensorflow as tf  # noqa: import tf at initialization




							def 			lowercase		( self:  Optional[int]					,		__A:  List[Any]     ):

												'''simple docstring'''

												import tensorflow as tf

												if isinstance(__A					,		__A     ) and column:
																	if all(
																	    isinstance(__A					,		tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
																						return tf.stack(__A     )
																	elif all(
																	    isinstance(__A					,		(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
																	    for x in column     ):
																						# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																						return tf.ragged.stack(__A     )

												return column




							def 			lowercase		( self:  int					,		__A:  Optional[Any]     ):

												'''simple docstring'''

												import tensorflow as tf

												if value is None:
																	return value

												a__ 							=	{}

												if isinstance(__A					,		(np.number, np.ndarray)     ) and np.issubdtype(value.dtype					,		np.integer     ):
																	a__ 							=	{'''dtype''': tf.intaa}
												elif isinstance(__A					,		(np.number, np.ndarray)     ) and np.issubdtype(value.dtype					,		np.floating     ):
																	a__ 							=	{'''dtype''': tf.floataa}
												elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																	import PIL.Image

																	if isinstance(__A					,		PIL.Image.Image     ):
																						a__ 							=	np.asarray(__A     )

												return tf.convert_to_tensor(__A					,		**{**default_dtype, **self.tf_tensor_kwargs}     )




							def 			lowercase		( self:  List[Any]					,		__A:  str     ):

												'''simple docstring'''

												import tensorflow as tf

												# support for torch, tf, jax etc.
												if config.TORCH_AVAILABLE and "torch" in sys.modules:
																	import torch

																	if isinstance(__A					,		torch.Tensor     ):
																						return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
												if hasattr(__A					,		'''__array__'''     ) and not isinstance(__A					,		tf.Tensor     ):
																	a__ 							=	data_struct.__array__()
												# support for nested types like struct of list of struct
												if isinstance(__A					,		np.ndarray     ):
																	if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																						return self._consolidate([self.recursive_tensorize(__A     ) for substruct in data_struct]     )
												elif isinstance(__A					,		(list, tuple)     ):
																	return self._consolidate([self.recursive_tensorize(__A     ) for substruct in data_struct]     )
												return self._tensorize(__A     )




							def 			lowercase		( self:  int					,		__A:  dict     ):

												'''simple docstring'''

												return map_nested(self._recursive_tensorize					,		__A					,		map_list=__A     )




							def 			lowercase		( self:  Tuple					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_row(__A     )
												a__ 							=	self.python_features_decoder.decode_row(__A     )
												return self.recursive_tensorize(__A     )




							def 			lowercase		( self:  Dict					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_column(__A     )
												a__ 							=	self.python_features_decoder.decode_column(__A					,		pa_table.column_names[0]     )
												a__ 							=	self.recursive_tensorize(__A     )
												a__ 							=	self._consolidate(__A     )
												return column





							def 			lowercase		( self:  Tuple					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_batch(__A     )
												a__ 							=	self.python_features_decoder.decode_batch(__A     )
												a__ 							=	self.recursive_tensorize(__A     )
												for column_name in batch:
																	a__ 							=	self._consolidate(batch[column_name]     )
												return batch


