
import math

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


__A							=logging.get_logger(__name__)

__A							={
    '''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					'data2vec-audio'



	def __init__(       self		,    lowercase=32		,    lowercase=768		,    lowercase=12		,    lowercase=12		,    lowercase=3072		,    lowercase="gelu"		,    lowercase=0.1		,    lowercase=0.1		,    lowercase=0.1		,    lowercase=0.0		,    lowercase=0.1		,    lowercase=0.1		,    lowercase=0.0_2		,    lowercase=1e-5		,    lowercase="gelu"		,    lowercase=(512, 512, 512, 512, 512, 512, 512)		,    lowercase=(5, 2, 2, 2, 2, 2, 2)		,    lowercase=(10, 3, 3, 3, 3, 2, 2)		,    lowercase=False		,    lowercase=16		,    lowercase=19		,    lowercase=5		,    lowercase=0.0_5		,    lowercase=10		,    lowercase=2		,    lowercase=0.0		,    lowercase=10		,    lowercase=0		,    lowercase="sum"		,    lowercase=False		,    lowercase=False		,    lowercase=256		,    lowercase=(512, 512, 512, 512, 1500)		,    lowercase=(5, 3, 3, 1, 1)		,    lowercase=(1, 2, 3, 1, 1)		,    lowercase=512		,    lowercase=0		,    lowercase=1		,    lowercase=2		,    lowercase=False		,    lowercase=3		,    lowercase=2		,    lowercase=3		,    lowercase=None		,    **lowercase		,    )							->			Dict:
					super().__init__(**lowercase		,    pad_token_id=lowercase		,    bos_token_id=lowercase		,    eos_token_id=lowercase					)
					lowerCamelCase_         =     hidden_size
					lowerCamelCase_         =     feat_extract_activation
					lowerCamelCase_         =     list(lowercase					)
					lowerCamelCase_         =     list(lowercase					)
					lowerCamelCase_         =     list(lowercase					)
					lowerCamelCase_         =     conv_bias
					lowerCamelCase_         =     num_conv_pos_embeddings
					lowerCamelCase_         =     num_conv_pos_embedding_groups
					lowerCamelCase_         =     conv_pos_kernel_size
					lowerCamelCase_         =     len(self.conv_dim					)
					lowerCamelCase_         =     num_hidden_layers
					lowerCamelCase_         =     intermediate_size
					lowerCamelCase_         =     hidden_act
					lowerCamelCase_         =     num_attention_heads
					lowerCamelCase_         =     hidden_dropout
					lowerCamelCase_         =     attention_dropout
					lowerCamelCase_         =     activation_dropout
					lowerCamelCase_         =     feat_proj_dropout
					lowerCamelCase_         =     final_dropout
					lowerCamelCase_         =     layerdrop
					lowerCamelCase_         =     layer_norm_eps
					lowerCamelCase_         =     initializer_range
					lowerCamelCase_         =     vocab_size
					lowerCamelCase_         =     use_weighted_layer_sum

					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)`, but is `len(config.conv_dim) ="
									    f' {len(self.conv_dim					)}`, `len(config.conv_stride) = {len(self.conv_stride					)}`,'
									    f' `len(config.conv_kernel) = {len(self.conv_kernel					)}`.'					)

					# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
					lowerCamelCase_         =     mask_time_prob
					lowerCamelCase_         =     mask_time_length
					lowerCamelCase_         =     mask_time_min_masks
					lowerCamelCase_         =     mask_feature_prob
					lowerCamelCase_         =     mask_feature_length
					lowerCamelCase_         =     mask_feature_min_masks

					# ctc loss
					lowerCamelCase_         =     ctc_loss_reduction
					lowerCamelCase_         =     ctc_zero_infinity

					# adapter
					lowerCamelCase_         =     add_adapter
					lowerCamelCase_         =     adapter_kernel_size
					lowerCamelCase_         =     adapter_stride
					lowerCamelCase_         =     num_adapter_layers
					lowerCamelCase_         =     output_hidden_size or hidden_size

					# SequenceClassification-specific parameter. Feel free to ignore for other classes.
					lowerCamelCase_         =     classifier_proj_size

					# XVector-specific parameters. Feel free to ignore for other classes.
					lowerCamelCase_         =     list(lowercase					)
					lowerCamelCase_         =     list(lowercase					)
					lowerCamelCase_         =     list(lowercase					)
					lowerCamelCase_         =     xvector_output_dim



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					return math.prod(self.conv_stride					)




from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class        _SCREAMING_SNAKE_CASE	:
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					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        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					return torch.from_numpy(np.array([self.width, self.height]		,    dtype=np.floataa					)					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					return torch.from_numpy(np.array([self.x_fov, self.y_fov]		,    dtype=np.floataa					)					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			torch.Tensor:
					lowerCamelCase_         =     torch.arange(self.height * self.width					)
					lowerCamelCase_         =     torch.stack(
					    [
					        pixel_indices % self.width,
					        torch.div(lowercase		,    self.width		,    rounding_mode="trunc"					),
					    ]		,    axis=1		,    )
					return coords



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					lowerCamelCase_      ,       *lowerCamelCase_         =     self.shape
					lowerCamelCase_         =     int(np.prod(lowercase					)					)

					lowerCamelCase_         =     self.get_image_coords()
					lowerCamelCase_         =     torch.broadcast_to(coords.unsqueeze(0					)		,    [batch_size * inner_batch_size, *coords.shape]					)
					lowerCamelCase_         =     self.get_camera_rays(lowercase					)

					lowerCamelCase_         =     rays.view(lowercase		,    inner_batch_size * self.height * self.width		,    2		,    3					)

					return rays



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			torch.Tensor:
					lowerCamelCase_      ,       *lowerCamelCase_      ,       lowerCamelCase_         =     coords.shape
					assert n_coords == 2
					assert batch_size == self.origin.shape[0]

					lowerCamelCase_         =     coords.view(lowercase		,    -1		,    2					)

					lowerCamelCase_         =     self.resolution()
					lowerCamelCase_         =     self.fov()

					lowerCamelCase_         =     (flat.float() / (res - 1)) * 2 - 1
					lowerCamelCase_         =     fracs * torch.tan(fov / 2					)

					lowerCamelCase_         =     fracs.view(lowercase		,    -1		,    2					)
					lowerCamelCase_         =     (
					    self.z.view(lowercase		,    1		,    3					)
					    + self.x.view(lowercase		,    1		,    3					) * fracs[:, :, :1]
					    + self.y.view(lowercase		,    1		,    3					) * fracs[:, :, 1:]
					)
					lowerCamelCase_         =     directions / directions.norm(dim=-1		,    keepdim=lowercase					)
					lowerCamelCase_         =     torch.stack(
					    [
					        torch.broadcast_to(self.origin.view(lowercase		,    1		,    3					)		,    [batch_size, directions.shape[1], 3]					),
					        directions,
					    ]		,    dim=2		,    )
					return rays.view(lowercase		,    *lowercase		,    2		,    3					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase					)							->			"DifferentiableProjectiveCamera":
					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=lowercase		,    height=lowercase		,    x_fov=self.x_fov		,    y_fov=self.y_fov		,    )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				for theta in np.linspace(0      ,					2 * np.pi      ,					num=2_0       ):
								lowerCamelCase_         =     np.array([np.sin(lowerCamelCase__       ), np.cos(lowerCamelCase__       ), -0.5]       )
								z /= np.sqrt(np.sum(z**2       )       )
								lowerCamelCase_         =     -z * 4
								lowerCamelCase_         =     np.array([np.cos(lowerCamelCase__       ), -np.sin(lowerCamelCase__       ), 0.0]       )
								lowerCamelCase_         =     np.cross(lowerCamelCase__      ,					lowerCamelCase__       )
								origins.append(lowerCamelCase__       )
								xs.append(lowerCamelCase__       )
								ys.append(lowerCamelCase__       )
								zs.append(lowerCamelCase__       )
				return DifferentiableProjectiveCamera(
				    origin=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					x=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					y=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					z=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					width=lowerCamelCase__      ,					height=lowerCamelCase__      ,					x_fov=0.7      ,					y_fov=0.7      ,					shape=(1, len(lowerCamelCase__       ))      ,					)


