




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
						from ... import FeatureExtractionMixin, TensorType

SCREAMING_SNAKE_CASE:     str									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="imagegpt"
						lowerCAmelCase__			      =["past_key_values"]
						lowerCAmelCase__			      ={
						    "hidden_size": "n_embd",
						    "max_position_embeddings": "n_positions",
						    "num_attention_heads": "n_head",
						    "num_hidden_layers": "n_layer",
						}



						def __init__(			self	:   Union[str, Any]					,							snake_case__	:   Dict=5_12 + 1					,							snake_case__	:   List[Any]=32 * 32					,							snake_case__	:   str=5_12					,							snake_case__	:   Tuple=24					,							snake_case__	:   List[Any]=8					,							snake_case__	:   Optional[Any]=None					,							snake_case__	:   List[Any]="quick_gelu"					,							snake_case__	:   List[str]=0.1					,							snake_case__	:   str=0.1					,							snake_case__	:   Optional[int]=0.1					,							snake_case__	:   Any=1e-5					,							snake_case__	:   Optional[Any]=0.02					,							snake_case__	:   Union[str, Any]=True					,							snake_case__	:   Optional[int]=True					,							snake_case__	:   Tuple=False					,							snake_case__	:   str=False					,							snake_case__	:   str=False					,							**snake_case__	:   Dict					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							vocab_size
								SCREAMING_SNAKE_CASE_					      =							n_positions
								SCREAMING_SNAKE_CASE_					      =							n_embd
								SCREAMING_SNAKE_CASE_					      =							n_layer
								SCREAMING_SNAKE_CASE_					      =							n_head
								SCREAMING_SNAKE_CASE_					      =							n_inner
								SCREAMING_SNAKE_CASE_					      =							activation_function
								SCREAMING_SNAKE_CASE_					      =							resid_pdrop
								SCREAMING_SNAKE_CASE_					      =							embd_pdrop
								SCREAMING_SNAKE_CASE_					      =							attn_pdrop
								SCREAMING_SNAKE_CASE_					      =							layer_norm_epsilon
								SCREAMING_SNAKE_CASE_					      =							initializer_range
								SCREAMING_SNAKE_CASE_					      =							scale_attn_weights
								SCREAMING_SNAKE_CASE_					      =							use_cache
								SCREAMING_SNAKE_CASE_					      =							scale_attn_by_inverse_layer_idx
								SCREAMING_SNAKE_CASE_					      =							reorder_and_upcast_attn
								SCREAMING_SNAKE_CASE_					      =							tie_word_embeddings

								super().__init__(tie_word_embeddings=snake_case__					,							**snake_case__  )



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						@property
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								return OrderedDict(
								    [
								        ('input_ids', {0: 'batch', 1: 'sequence'}),
								    ]  )






						def 	__a					(			self	:   Union[str, Any]					,							snake_case__	:   "FeatureExtractionMixin"					,							snake_case__	:   int = 1					,							snake_case__	:   int = -1					,							snake_case__	:   bool = False					,							snake_case__	:   Optional["TensorType"] = None					,							snake_case__	:   int = 3					,							snake_case__	:   int = 32					,							snake_case__	:   int = 32					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self._generate_dummy_images(snake_case__					,							snake_case__					,							snake_case__					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							dict(preprocessor(images=snake_case__					,							return_tensors=snake_case__  )  )

								return inputs





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class    lowercase_		:
						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 	__a					(			self	:   Optional[Any]  ):

								"""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 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							torch.arange(self.height * self.width  )
								SCREAMING_SNAKE_CASE_					      =							torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(snake_case__					,							self.width					,							rounding_mode='trunc'  ),
								    ]					,							axis=1					,							)
								return coords



						@property
						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_					      =							self.shape
								SCREAMING_SNAKE_CASE_					      =							int(np.prod(snake_case__  )  )

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

								SCREAMING_SNAKE_CASE_					      =							rays.view(snake_case__					,							inner_batch_size * self.height * self.width					,							2					,							3  )

								return rays



						def 	__a					(			self	:   Optional[Any]					,							snake_case__	:   torch.Tensor  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							coords.shape
								assert n_coords == 2
								assert batch_size == self.origin.shape[0]

								SCREAMING_SNAKE_CASE_					      =							coords.view(snake_case__					,							-1					,							2  )

								SCREAMING_SNAKE_CASE_					      =							self.resolution()
								SCREAMING_SNAKE_CASE_					      =							self.fov()

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

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






						def 	__a					(			self	:   Optional[int]					,							snake_case__	:   int					,							snake_case__	:   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=snake_case__					,							height=snake_case__					,							x_fov=self.x_fov					,							y_fov=self.y_fov					,							)


def 					_a							(					lowerCAmelCase )->	DifferentiableProjectiveCamera:
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		for theta in np.linspace(0    ,						2 * np.pi    ,						num=20 ):
				SCREAMING_SNAKE_CASE_					      =							np.array([np.sin(lowerCAmelCase ), np.cos(lowerCAmelCase ), -0.5] )
				z /= np.sqrt(np.sum(z**2 ) )
				SCREAMING_SNAKE_CASE_					      =							-z * 4
				SCREAMING_SNAKE_CASE_					      =							np.array([np.cos(lowerCAmelCase ), -np.sin(lowerCAmelCase ), 0.0] )
				SCREAMING_SNAKE_CASE_					      =							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 ))    ,						)