from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class  _snake_case		:







				"""simple docstring"""






				a			=						42  # [batch_size x 3]
				a			=						42  # [batch_size x 3]
				a			=						42  # [batch_size x 3]
				a			=						42  # [batch_size x 3]
				a			=						42
				a			=						42
				a			=						42
				a			=						42
				a			=						42





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
											assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
											assert len(self.x.shape) == len(self.y.shape) == len(self.z.shape) == len(self.origin.shape) == 2





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											return torch.from_numpy(np.array([self.width, self.height]      ,    dtype=np.floataa))





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											return torch.from_numpy(np.array([self.x_fov, self.y_fov]      ,    dtype=np.floataa))





				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.arange(self.height * self.width)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.stack(
											    [
											        pixel_indices % self.width,
											        torch.div(_A      ,    self.width      ,    rounding_mode="""trunc"""),
											    ]      ,    axis=1      ,    )
											return coords





				@property
				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.shape
											_SCREAMING_SNAKE_CASE		:						Dict			  =    int(np.prod(_A))

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.get_image_coords()
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    torch.broadcast_to(coords.unsqueeze(0)      ,    [batch_size * inner_batch_size, *coords.shape])
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.get_camera_rays(_A)

											_SCREAMING_SNAKE_CASE		:						Any			  =    rays.view(_A      ,    inner_batch_size * self.height * self.width      ,    2      ,    3)

											return rays





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							torch.Tensor):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    coords.shape
											assert n_coords == 2
											assert batch_size == self.origin.shape[0]

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    coords.view(_A      ,    -1      ,    2)

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.resolution()
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.fov()

											_SCREAMING_SNAKE_CASE		:						int			  =    (flat.float() / (res - 1)) * 2 - 1
											_SCREAMING_SNAKE_CASE		:						str			  =    fracs * torch.tan(fov / 2)

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    fracs.view(_A      ,    -1      ,    2)
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    (
											    self.z.view(_A      ,    1      ,    3)
											    + self.x.view(_A      ,    1      ,    3) * fracs[:, :, :1]
											    + self.y.view(_A      ,    1      ,    3) * fracs[:, :, 1:]
											)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    directions / directions.norm(dim=-1      ,    keepdim=_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    torch.stack(
											    [
											        torch.broadcast_to(self.origin.view(_A      ,    1      ,    3)      ,    [batch_size, directions.shape[1], 3]),
											        directions,
											    ]      ,    dim=2      ,    )
											return rays.view(_A      ,    *_A      ,    2      ,    3)





				def 		_lowerCAmelCase		(      self							:							Dict      ,    _A							:							int      ,    _A							:							int):



											"""simple docstring"""







											assert width * self.height == height * self.width, "The aspect ratio should not change."
											return DifferentiableProjectiveCamera(
											    origin=self.origin      ,    x=self.x      ,    y=self.y      ,    z=self.z      ,    width=_A      ,    height=_A      ,    x_fov=self.x_fov      ,    y_fov=self.y_fov      ,    )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    DifferentiableProjectiveCamera:
							_SCREAMING_SNAKE_CASE		:						str			  =    []
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    []
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    []
							for theta in np.linspace(0     ,	2 * np.pi     ,	num=20  ):
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    np.array([np.sin(SCREAMING_SNAKE_CASE_  ), np.cos(SCREAMING_SNAKE_CASE_  ), -0.5]  )
														z /= np.sqrt(np.sum(z**2  )  )
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    -z * 4
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    np.array([np.cos(SCREAMING_SNAKE_CASE_  ), -np.sin(SCREAMING_SNAKE_CASE_  ), 0.0]  )
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    np.cross(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_  )
														origins.append(SCREAMING_SNAKE_CASE_  )
														xs.append(SCREAMING_SNAKE_CASE_  )
														ys.append(SCREAMING_SNAKE_CASE_  )
														zs.append(SCREAMING_SNAKE_CASE_  )
							return DifferentiableProjectiveCamera(
							    origin=torch.from_numpy(np.stack(SCREAMING_SNAKE_CASE_     ,	axis=0  )  ).float()     ,	x=torch.from_numpy(np.stack(SCREAMING_SNAKE_CASE_     ,	axis=0  )  ).float()     ,	y=torch.from_numpy(np.stack(SCREAMING_SNAKE_CASE_     ,	axis=0  )  ).float()     ,	z=torch.from_numpy(np.stack(SCREAMING_SNAKE_CASE_     ,	axis=0  )  ).float()     ,	width=SCREAMING_SNAKE_CASE_     ,	height=SCREAMING_SNAKE_CASE_     ,	x_fov=0.7     ,	y_fov=0.7     ,	shape=(1, len(SCREAMING_SNAKE_CASE_  ))     ,	)



"""simple docstring"""





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_     =	logging.get_logger(__name__)

lowerCAmelCase_     =	{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"sew"





				def __init__(      self							:							List[Any]      ,    _A							:							Tuple=3_2      ,    _A							:							str=7_6_8      ,    _A							:							Dict=1_2      ,    _A							:							Tuple=1_2      ,    _A							:							Optional[Any]=3_0_7_2      ,    _A							:							List[str]=2      ,    _A							:							Dict="gelu"      ,    _A							:							Union[str, Any]=0.1      ,    _A							:							Optional[int]=0.1      ,    _A							:							Optional[int]=0.1      ,    _A							:							Optional[int]=0.0      ,    _A							:							str=0.1      ,    _A							:							Tuple=0.1      ,    _A							:							Optional[int]=0.02      ,    _A							:							Dict=1e-5      ,    _A							:							str="group"      ,    _A							:							Tuple="gelu"      ,    _A							:							Union[str, Any]=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)      ,    _A							:							Optional[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)      ,    _A							:							Any=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)      ,    _A							:							Tuple=False      ,    _A							:							Tuple=1_2_8      ,    _A							:							int=1_6      ,    _A							:							Union[str, Any]=True      ,    _A							:							Optional[Any]=0.05      ,    _A							:							List[Any]=1_0      ,    _A							:							Union[str, Any]=2      ,    _A							:							Tuple=0.0      ,    _A							:							Union[str, Any]=1_0      ,    _A							:							Optional[int]=0      ,    _A							:							Union[str, Any]="mean"      ,    _A							:							Optional[int]=False      ,    _A							:							List[Any]=False      ,    _A							:							int=2_5_6      ,    _A							:							str=0      ,    _A							:							Optional[int]=1      ,    _A							:							List[Any]=2      ,    **_A							:							Dict      ,    ):



											"""simple docstring"""







											super().__init__(**_A      ,    pad_token_id=_A      ,    bos_token_id=_A      ,    eos_token_id=_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    feat_extract_norm
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    feat_extract_activation
											_SCREAMING_SNAKE_CASE		:						Dict			  =    list(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    list(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    list(_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    conv_bias
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    num_conv_pos_embeddings
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    num_conv_pos_embedding_groups
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    len(self.conv_dim)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    intermediate_size
											_SCREAMING_SNAKE_CASE		:						str			  =    squeeze_factor
											_SCREAMING_SNAKE_CASE		:						Dict			  =    hidden_act
											_SCREAMING_SNAKE_CASE		:						str			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						Dict			  =    hidden_dropout
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    attention_dropout
											_SCREAMING_SNAKE_CASE		:						int			  =    activation_dropout
											_SCREAMING_SNAKE_CASE		:						Any			  =    feat_proj_dropout
											_SCREAMING_SNAKE_CASE		:						str			  =    final_dropout
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    layerdrop
											_SCREAMING_SNAKE_CASE		:						Any			  =    layer_norm_eps
											_SCREAMING_SNAKE_CASE		:						int			  =    initializer_range
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    vocab_size

											if (
											    (len(self.conv_stride) != self.num_feat_extract_layers)
											    or (len(self.conv_kernel) != self.num_feat_extract_layers)
											    or (len(self.conv_dim) != self.num_feat_extract_layers)
											):
																		raise ValueError(
																		    """Configuration for convolutional layers is incorrect."""
																		    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
																		    f"""but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)"""
																		    f"""= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

											# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    apply_spec_augment
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mask_time_prob
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    mask_time_length
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mask_time_min_masks
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mask_feature_prob
											_SCREAMING_SNAKE_CASE		:						int			  =    mask_feature_length
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mask_feature_min_masks

											# ctc loss
											_SCREAMING_SNAKE_CASE		:						int			  =    ctc_loss_reduction
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    ctc_zero_infinity

											# sequence classification
											_SCREAMING_SNAKE_CASE		:						Dict			  =    use_weighted_layer_sum
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    classifier_proj_size





				@property
				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											return functools.reduce(operator.mul      ,    self.conv_stride      ,    1)


