


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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__UpperCAmelCase      :Dict			    =       logging.get_logger(__name__)

__UpperCAmelCase      :Optional[int]			    =       {
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class      a    (		A__	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			str							       =       "gpt_neo"
     SCREAMING_SNAKE_CASE	:			Tuple							       =       ["past_key_values"]
     SCREAMING_SNAKE_CASE	:			int							       =       {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

     def __init__(    self					:		Optional[int]							,			snake_case					:		Dict=5_0257							,			snake_case					:		Tuple=2048							,			snake_case					:		Any=2048							,			snake_case					:		Optional[Any]=24							,			snake_case					:		Optional[Any]=[[["global", "local"], 12]]							,			snake_case					:		Optional[Any]=16							,			snake_case					:		str=None							,			snake_case					:		List[str]=256							,			snake_case					:		Optional[Any]="gelu_new"							,			snake_case					:		Optional[int]=0.0							,			snake_case					:		str=0.0							,			snake_case					:		List[Any]=0.0							,			snake_case					:		str=0.1							,			snake_case					:		Union[str, Any]=1E-5							,			snake_case					:		Optional[int]=0.02							,			snake_case					:		str=True							,			snake_case					:		Tuple=5_0256							,			snake_case					:		Dict=5_0256							,			**snake_case					:		Tuple							,			)						->							List[str]:
          __UpperCAmelCase     :			Any  							=      vocab_size
          __UpperCAmelCase     :			str  							=      max_position_embeddings
          __UpperCAmelCase     :			int  							=      hidden_size
          __UpperCAmelCase     :			Optional[Any]  							=      num_layers
          __UpperCAmelCase     :			Optional[int]  							=      num_heads
          __UpperCAmelCase     :			List[str]  							=      intermediate_size
          __UpperCAmelCase     :			List[str]  							=      window_size
          __UpperCAmelCase     :			List[Any]  							=      activation_function
          __UpperCAmelCase     :			List[Any]  							=      resid_dropout
          __UpperCAmelCase     :			Optional[Any]  							=      embed_dropout
          __UpperCAmelCase     :			List[str]  							=      attention_dropout
          __UpperCAmelCase     :			List[str]  							=      classifier_dropout
          __UpperCAmelCase     :			str  							=      layer_norm_epsilon
          __UpperCAmelCase     :			Tuple  							=      initializer_range
          __UpperCAmelCase     :			Dict  							=      use_cache

          __UpperCAmelCase     :			Any  							=      bos_token_id
          __UpperCAmelCase     :			List[Any]  							=      eos_token_id

          __UpperCAmelCase     :			List[Any]  							=      attention_types
          __UpperCAmelCase     :			List[Any]  							=      self.expand_attention_types_params(__snake_case      )

          if len(self.attention_layers      ) != self.num_layers:
               raise ValueError(
                   '''Configuration for convolutional module is incorrect. '''
                   '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
                   f'but is `len(config.attention_layers) = {len(self.attention_layers      )}`, '
                   f'`config.num_layers = {self.num_layers}`. '
                   '''`config.attention_layers` is prepared using `config.attention_types`. '''
                   '''Please verify the value of `config.attention_types` argument.'''      )

          super().__init__(bos_token_id=__snake_case							,			eos_token_id=__snake_case							,			**__snake_case      )






     @staticmethod
     def 			lowerCamelCase__			(    snake_case					:		Any      )						->							Optional[int]:
          __UpperCAmelCase     :			Optional[Any]  							=      []
          for item in attention_types:
               for _ in range(item[1]      ):
                    attentions.extend(item[0]      )
          return attentions




def      _a     (						_lowercase :     Optional[int]      , _lowercase :     Tuple      , _lowercase :     List[Any]      , _lowercase :     int  ):





     '''simple docstring'''






     import torch

     __UpperCAmelCase     :			Any  							=      input.size()
     __UpperCAmelCase     :			int  							=      len(__lowerCAmelCase  )
     __UpperCAmelCase     :			Union[str, Any]  							=      shape[dimension]

     __UpperCAmelCase     :			Union[str, Any]  							=      torch.arange(0      , __lowerCAmelCase      , __lowerCAmelCase  )
     __UpperCAmelCase     :			Any  							=      torch.div(sizedim - size      , __lowerCAmelCase      , rounding_mode='''floor'''  ) + 1
     __UpperCAmelCase     :			List[Any]  							=      torch.arange(__lowerCAmelCase  ) + low_indices[:min_length][:, None]

     __UpperCAmelCase     :			Optional[int]  							=      [slice(__lowerCAmelCase  )] * rank
     __UpperCAmelCase     :			Any  							=      indices
     __UpperCAmelCase     :			Optional[int]  							=      input[s]

     __UpperCAmelCase     :			int  							=      list(range(0      , rank + 1  )  )
     perm.append(perm.pop(dimension + 1  )  )

     return sliced.permute(__lowerCAmelCase  )




def      _a     (						_lowercase :     List[str]      , _lowercase :     str  ):





     '''simple docstring'''






     import torch

     __UpperCAmelCase     :			List[Any]  							=      torch.arange(1      , __lowerCAmelCase  )
     __UpperCAmelCase     :			int  							=      torch.remainder(__lowerCAmelCase      , __lowerCAmelCase  )
     __UpperCAmelCase     :			int  							=      remainders == 0
     __UpperCAmelCase     :			Dict  							=      candidates[divisor_indices]
     __UpperCAmelCase     :			Any  							=      torch.max(__lowerCAmelCase  )
     return largest_divisor, torch.div(__lowerCAmelCase      , __lowerCAmelCase      , rounding_mode='''floor'''  )



class      a    (		A__	):


     """simple docstring"""






     @property
     def 			lowerCamelCase__			(    self					:		Dict      )						->							Mapping[str, Mapping[int, str]]:
          __UpperCAmelCase     :			int  							=      OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}      )
          if self.use_past:
               self.fill_with_past_key_values_(__snake_case							,			direction='''inputs'''      )
               __UpperCAmelCase     :			Any  							=      {0: '''batch''', 1: '''past_sequence + sequence'''}
          else:
               __UpperCAmelCase     :			List[Any]  							=      {0: '''batch''', 1: '''sequence'''}

          return common_inputs

     @property
     def 			lowerCamelCase__			(    self					:		Tuple      )						->							int:
          return self._config.num_heads

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		PreTrainedTokenizer							,			snake_case					:		int = -1							,			snake_case					:		int = -1							,			snake_case					:		bool = False							,			snake_case					:		Optional[TensorType] = None							,			)						->							Mapping[str, Any]:
          __UpperCAmelCase     :			Dict  							=      super(__snake_case							,			self      ).generate_dummy_inputs(
              __snake_case							,			batch_size=__snake_case							,			seq_length=__snake_case							,			is_pair=__snake_case							,			framework=__snake_case      )

          # We need to order the input in the way they appears in the forward()
          __UpperCAmelCase     :			Tuple  							=      OrderedDict({'''input_ids''': common_inputs['''input_ids''']}      )

          # Need to add the past_keys
          if self.use_past:
               if not is_torch_available():
                    raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''      )
               else:
                    import torch

                    __UpperCAmelCase							,       __UpperCAmelCase     :			int  							=      common_inputs['''input_ids'''].shape
                    # Not using the same length for past_key_values
                    __UpperCAmelCase     :			Dict  							=      seqlen + 2
                    __UpperCAmelCase     :			Dict  							=      (
                        batch,
                        self.num_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // self.num_attention_heads,
                    )
                    __UpperCAmelCase     :			str  							=      [
                        (torch.zeros(__snake_case      ), torch.zeros(__snake_case      )) for _ in range(self.num_layers      )
                    ]

          __UpperCAmelCase     :			Union[str, Any]  							=      common_inputs['''attention_mask''']
          if self.use_past:
               __UpperCAmelCase     :			str  							=      ordered_inputs['''attention_mask'''].dtype
               __UpperCAmelCase     :			Tuple  							=      torch.cat(
                   [ordered_inputs['''attention_mask'''], torch.ones(__snake_case							,			__snake_case							,			dtype=__snake_case      )]							,			dim=1      )

          return ordered_inputs






     @property
     def 			lowerCamelCase__			(    self					:		Dict      )						->							int:
          return 13



'''simple docstring'''



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class      a    :


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			torch.Tensor  # [batch_size x 3]
     SCREAMING_SNAKE_CASE	:			torch.Tensor  # [batch_size x 3]
     SCREAMING_SNAKE_CASE	:			torch.Tensor  # [batch_size x 3]
     SCREAMING_SNAKE_CASE	:			torch.Tensor  # [batch_size x 3]
     SCREAMING_SNAKE_CASE	:			int
     SCREAMING_SNAKE_CASE	:			int
     SCREAMING_SNAKE_CASE	:			float
     SCREAMING_SNAKE_CASE	:			float
     SCREAMING_SNAKE_CASE	:			Tuple[int]

     def 			lowerCamelCase__			(    self					:		Any      )						->							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 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							str:
          return torch.from_numpy(np.array([self.width, self.height]							,			dtype=np.floataa      )      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							Optional[Any]:
          return torch.from_numpy(np.array([self.x_fov, self.y_fov]							,			dtype=np.floataa      )      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							torch.Tensor:
          __UpperCAmelCase     :			Dict  							=      torch.arange(self.height * self.width      )
          __UpperCAmelCase     :			Dict  							=      torch.stack(
              [
                  pixel_indices % self.width,
                  torch.div(snake_case							,			self.width							,			rounding_mode='''trunc'''      ),
              ]							,			axis=1							,			)
          return coords

     @property
     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          __UpperCAmelCase							,       *__UpperCAmelCase     :			str  							=      self.shape
          __UpperCAmelCase     :			Dict  							=      int(np.prod(snake_case      )      )

          __UpperCAmelCase     :			Tuple  							=      self.get_image_coords()
          __UpperCAmelCase     :			List[Any]  							=      torch.broadcast_to(coords.unsqueeze(0      )							,			[batch_size * inner_batch_size, *coords.shape]      )
          __UpperCAmelCase     :			Any  							=      self.get_camera_rays(snake_case      )

          __UpperCAmelCase     :			List[str]  							=      rays.view(snake_case							,			inner_batch_size * self.height * self.width							,			2							,			3      )

          return rays

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		torch.Tensor      )						->							torch.Tensor:
          __UpperCAmelCase							,       *__UpperCAmelCase							,       __UpperCAmelCase     :			List[str]  							=      coords.shape
          assert n_coords == 2
          assert batch_size == self.origin.shape[0]

          __UpperCAmelCase     :			List[str]  							=      coords.view(snake_case							,			-1							,			2      )

          __UpperCAmelCase     :			Optional[Any]  							=      self.resolution()
          __UpperCAmelCase     :			Tuple  							=      self.fov()

          __UpperCAmelCase     :			Optional[int]  							=      (flat.float() / (res - 1)) * 2 - 1
          __UpperCAmelCase     :			Union[str, Any]  							=      fracs * torch.tan(fov / 2      )

          __UpperCAmelCase     :			str  							=      fracs.view(snake_case							,			-1							,			2      )
          __UpperCAmelCase     :			Any  							=      (
              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:]
          )
          __UpperCAmelCase     :			Union[str, Any]  							=      directions / directions.norm(dim=-1							,			keepdim=snake_case      )
          __UpperCAmelCase     :			Union[str, Any]  							=      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 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		int							,			snake_case					:		int      )						->							"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=snake_case							,			height=snake_case							,			x_fov=self.x_fov							,			y_fov=self.y_fov							,			)




def      _a     (						_lowercase :     int  ):





     '''simple docstring'''






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