





import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Dict     ,							__lowercase:		str     ,							__lowercase:		int = False     ,							__lowercase:		int = 1     ,							__lowercase:		List[Any] = 1     ,							__lowercase:		Optional[Any] = 1_00_00     ,							):



  '''simple docstring'''






  assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"

  __lowerCAmelCase			=				embedding_dim // 2
  __lowerCAmelCase			=				-math.log(UpperCAmelCase_) * torch.arange(
      start=0     ,							end=UpperCAmelCase_     ,							dtype=torch.floataa     ,							device=timesteps.device)
  __lowerCAmelCase			=				exponent / (half_dim - downscale_freq_shift)

  __lowerCAmelCase			=				torch.exp(UpperCAmelCase_)
  __lowerCAmelCase			=				timesteps[:, None].float() * emb[None, :]

  # scale embeddings
  __lowerCAmelCase			=				scale * emb

  # concat sine and cosine embeddings
  __lowerCAmelCase			=				torch.cat([torch.sin(UpperCAmelCase_), torch.cos(UpperCAmelCase_)]     ,							dim=-1)

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

  # zero pad
  if embedding_dim % 2 == 1:
    __lowerCAmelCase			=				torch.nn.functional.pad(UpperCAmelCase_     ,							(0, 1, 0, 0))
  return emb


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		List[str]     ,							__lowercase:		Union[str, Any]     ,							__lowercase:		Any=False     ,							__lowercase:		List[Any]=0):



  '''simple docstring'''






  __lowerCAmelCase			=				np.arange(UpperCAmelCase_     ,							dtype=np.floataa)
  __lowerCAmelCase			=				np.arange(UpperCAmelCase_     ,							dtype=np.floataa)
  __lowerCAmelCase			=				np.meshgrid(UpperCAmelCase_     ,							UpperCAmelCase_)  # here w goes first
  __lowerCAmelCase			=				np.stack(UpperCAmelCase_     ,							axis=0)

  __lowerCAmelCase			=				grid.reshape([2, 1, grid_size, grid_size])
  __lowerCAmelCase			=				get_ad_sincos_pos_embed_from_grid(UpperCAmelCase_     ,							UpperCAmelCase_)
  if cls_token and extra_tokens > 0:
    __lowerCAmelCase			=				np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed]     ,							axis=0)
  return pos_embed


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		List[Any]     ,							__lowercase:		Union[str, Any]):



  '''simple docstring'''






  if embed_dim % 2 != 0:
    raise ValueError('''embed_dim must be divisible by 2''')

  # use half of dimensions to encode grid_h
  __lowerCAmelCase			=				get_ad_sincos_pos_embed_from_grid(embed_dim // 2     ,							grid[0])  # (H*W, D/2)
  __lowerCAmelCase			=				get_ad_sincos_pos_embed_from_grid(embed_dim // 2     ,							grid[1])  # (H*W, D/2)

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


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Dict     ,							__lowercase:		Dict):



  '''simple docstring'''






  if embed_dim % 2 != 0:
    raise ValueError('''embed_dim must be divisible by 2''')

  __lowerCAmelCase			=				np.arange(embed_dim // 2     ,							dtype=np.floataa)
  omega /= embed_dim / 2.0
  __lowerCAmelCase			=				1.0 / 1_00_00**omega  # (D/2,)

  __lowerCAmelCase			=				pos.reshape(-1)  # (M,)
  __lowerCAmelCase			=				np.einsum('''m,d->md'''     ,							UpperCAmelCase_     ,							UpperCAmelCase_)  # (M, D/2), outer product

  __lowerCAmelCase			=				np.sin(UpperCAmelCase_)  # (M, D/2)
  __lowerCAmelCase			=				np.cos(UpperCAmelCase_)  # (M, D/2)

  __lowerCAmelCase			=				np.concatenate([emb_sin, emb_cos]     ,							axis=1)  # (M, D)
  return emb

class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		List[str]			,						snake_case						:		Dict=224			,						snake_case						:		Dict=224			,						snake_case						:		Optional[int]=16			,						snake_case						:		Optional[int]=3			,						snake_case						:		int=768			,						snake_case						:		Tuple=False			,						snake_case						:		Optional[Any]=True			,						snake_case						:		Any=True			,						):

      """simple docstring"""





      super().__init__()

      __lowerCAmelCase			=				(height // patch_size) * (width // patch_size)
      __lowerCAmelCase			=				flatten
      __lowerCAmelCase			=				layer_norm

      __lowerCAmelCase			=				nn.Convad(
          snake_case			,						snake_case			,						kernel_size=(patch_size, patch_size)			,						stride=snake_case			,						bias=snake_case							)
      if layer_norm:
        __lowerCAmelCase			=				nn.LayerNorm(snake_case			,						elementwise_affine=snake_case			,						eps=1e-6							)
      else:
        __lowerCAmelCase			=				None

      __lowerCAmelCase			=				get_ad_sincos_pos_embed(snake_case			,						int(num_patches**0.5							)							)
      self.register_buffer('''pos_embed'''			,						torch.from_numpy(snake_case							).float().unsqueeze(0							)			,						persistent=snake_case							)





    def 	A		(   self						:		Any			,						snake_case						:		Optional[Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				self.proj(snake_case							)
      if self.flatten:
        __lowerCAmelCase			=				latent.flatten(2							).transpose(1			,						2							)  # BCHW -> BNC
      if self.layer_norm:
        __lowerCAmelCase			=				self.norm(snake_case							)
      return latent + self.pos_embed


class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		Dict			,						snake_case						:		int			,						snake_case						:		int			,						snake_case						:		str = "silu"			,						snake_case						:		int = None			,						snake_case						:		Optional[str] = None			,						snake_case						:		Any=None			,						):

      """simple docstring"""





      super().__init__()

      __lowerCAmelCase			=				nn.Linear(snake_case			,						snake_case							)

      if cond_proj_dim is not None:
        __lowerCAmelCase			=				nn.Linear(snake_case			,						snake_case			,						bias=snake_case							)
      else:
        __lowerCAmelCase			=				None

      __lowerCAmelCase			=				get_activation(snake_case							)

      if out_dim is not None:
        __lowerCAmelCase			=				out_dim
      else:
        __lowerCAmelCase			=				time_embed_dim
      __lowerCAmelCase			=				nn.Linear(snake_case			,						snake_case							)

      if post_act_fn is None:
        __lowerCAmelCase			=				None
      else:
        __lowerCAmelCase			=				get_activation(snake_case							)





    def 	A		(   self						:		str			,						snake_case						:		Tuple			,						snake_case						:		Any=None							):

      """simple docstring"""





      if condition is not None:
        __lowerCAmelCase			=				sample + self.cond_proj(snake_case							)
      __lowerCAmelCase			=				self.linear_a(snake_case							)

      if self.act is not None:
        __lowerCAmelCase			=				self.act(snake_case							)

      __lowerCAmelCase			=				self.linear_a(snake_case							)

      if self.post_act is not None:
        __lowerCAmelCase			=				self.post_act(snake_case							)
      return sample


class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		Tuple			,						snake_case						:		int			,						snake_case						:		bool			,						snake_case						:		float							):

      """simple docstring"""





      super().__init__()
      __lowerCAmelCase			=				num_channels
      __lowerCAmelCase			=				flip_sin_to_cos
      __lowerCAmelCase			=				downscale_freq_shift





    def 	A		(   self						:		Union[str, Any]			,						snake_case						:		Tuple							):

      """simple docstring"""





      __lowerCAmelCase			=				get_timestep_embedding(
          snake_case			,						self.num_channels			,						flip_sin_to_cos=self.flip_sin_to_cos			,						downscale_freq_shift=self.downscale_freq_shift			,						)
      return t_emb


class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		List[str]			,						snake_case						:		int = 256			,						snake_case						:		float = 1.0			,						snake_case						:		List[str]=True			,						snake_case						:		str=True			,						snake_case						:		List[Any]=False							):

      """simple docstring"""





      super().__init__()
      __lowerCAmelCase			=				nn.Parameter(torch.randn(snake_case							) * scale			,						requires_grad=snake_case							)
      __lowerCAmelCase			=				log
      __lowerCAmelCase			=				flip_sin_to_cos

      if set_W_to_weight:
        # to delete later
        __lowerCAmelCase			=				nn.Parameter(torch.randn(snake_case							) * scale			,						requires_grad=snake_case							)

        __lowerCAmelCase			=				self.W





    def 	A		(   self						:		Any			,						snake_case						:		List[Any]							):

      """simple docstring"""





      if self.log:
        __lowerCAmelCase			=				torch.log(snake_case							)

      __lowerCAmelCase			=				x[:, None] * self.weight[None, :] * 2 * np.pi

      if self.flip_sin_to_cos:
        __lowerCAmelCase			=				torch.cat([torch.cos(snake_case							), torch.sin(snake_case							)]			,						dim=-1							)
      else:
        __lowerCAmelCase			=				torch.cat([torch.sin(snake_case							), torch.cos(snake_case							)]			,						dim=-1							)
      return out


class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		List[Any]			,						snake_case						:		int			,						snake_case						:		int			,						snake_case						:		int			,						snake_case						:		int			,						):

      """simple docstring"""





      super().__init__()

      __lowerCAmelCase			=				height
      __lowerCAmelCase			=				width
      __lowerCAmelCase			=				num_embed
      __lowerCAmelCase			=				embed_dim

      __lowerCAmelCase			=				nn.Embedding(self.num_embed			,						snake_case							)
      __lowerCAmelCase			=				nn.Embedding(self.height			,						snake_case							)
      __lowerCAmelCase			=				nn.Embedding(self.width			,						snake_case							)





    def 	A		(   self						:		Optional[int]			,						snake_case						:		Optional[Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				self.emb(snake_case							)

      __lowerCAmelCase			=				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
      __lowerCAmelCase			=				height_emb.unsqueeze(2							)

      __lowerCAmelCase			=				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
      __lowerCAmelCase			=				width_emb.unsqueeze(1							)

      __lowerCAmelCase			=				height_emb + width_emb

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

      __lowerCAmelCase			=				emb + pos_emb[:, : emb.shape[1], :]

      return emb


class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		Tuple			,						snake_case						:		Optional[Any]			,						snake_case						:		Dict			,						snake_case						:		Tuple							):

      """simple docstring"""





      super().__init__()
      __lowerCAmelCase			=				dropout_prob > 0
      __lowerCAmelCase			=				nn.Embedding(num_classes + use_cfg_embedding			,						snake_case							)
      __lowerCAmelCase			=				num_classes
      __lowerCAmelCase			=				dropout_prob





    def 	A		(   self						:		Union[str, Any]			,						snake_case						:		List[Any]			,						snake_case						:		Tuple=None							):

      """simple docstring"""





      if force_drop_ids is None:
        __lowerCAmelCase			=				torch.rand(labels.shape[0]			,						device=labels.device							) < self.dropout_prob
      else:
        __lowerCAmelCase			=				torch.tensor(force_drop_ids == 1							)
      __lowerCAmelCase			=				torch.where(snake_case			,						self.num_classes			,						snake_case							)
      return labels





    def 	A		(   self						:		Optional[Any]			,						snake_case						:		torch.LongTensor			,						snake_case						:		Dict=None							):

      """simple docstring"""





      __lowerCAmelCase			=				self.dropout_prob > 0
      if (self.training and use_dropout) or (force_drop_ids is not None):
        __lowerCAmelCase			=				self.token_drop(snake_case			,						snake_case							)
      __lowerCAmelCase			=				self.embedding_table(snake_case							)
      return embeddings


class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		List[Any]			,						snake_case						:		int = 1_024			,						snake_case						:		int = 768			,						snake_case						:		int = 768			,						snake_case						:		int = 10			,						):

      """simple docstring"""





      super().__init__()

      __lowerCAmelCase			=				num_image_text_embeds
      __lowerCAmelCase			=				nn.Linear(snake_case			,						self.num_image_text_embeds * cross_attention_dim							)
      __lowerCAmelCase			=				nn.Linear(snake_case			,						snake_case							)





    def 	A		(   self						:		int			,						snake_case						:		torch.FloatTensor			,						snake_case						:		torch.FloatTensor							):

      """simple docstring"""





      __lowerCAmelCase			=				text_embeds.shape[0]

      # image
      __lowerCAmelCase			=				self.image_embeds(snake_case							)
      __lowerCAmelCase			=				image_text_embeds.reshape(snake_case			,						self.num_image_text_embeds			,						-1							)

      # text
      __lowerCAmelCase			=				self.text_proj(snake_case							)

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


class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		List[str]			,						snake_case						:		int = 768			,						snake_case						:		int = 768			,						snake_case						:		int = 32			,						):

      """simple docstring"""





      super().__init__()

      __lowerCAmelCase			=				num_image_text_embeds
      __lowerCAmelCase			=				nn.Linear(snake_case			,						self.num_image_text_embeds * cross_attention_dim							)
      __lowerCAmelCase			=				nn.LayerNorm(snake_case							)





    def 	A		(   self						:		int			,						snake_case						:		torch.FloatTensor							):

      """simple docstring"""





      __lowerCAmelCase			=				image_embeds.shape[0]

      # image
      __lowerCAmelCase			=				self.image_embeds(snake_case							)
      __lowerCAmelCase			=				image_embeds.reshape(snake_case			,						self.num_image_text_embeds			,						-1							)
      __lowerCAmelCase			=				self.norm(snake_case							)
      return image_embeds


class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		int			,						snake_case						:		str			,						snake_case						:		Optional[Any]			,						snake_case						:		Union[str, Any]=0.1							):

      """simple docstring"""





      super().__init__()

      __lowerCAmelCase			=				Timesteps(num_channels=256			,						flip_sin_to_cos=snake_case			,						downscale_freq_shift=1							)
      __lowerCAmelCase			=				TimestepEmbedding(in_channels=256			,						time_embed_dim=snake_case							)
      __lowerCAmelCase			=				LabelEmbedding(snake_case			,						snake_case			,						snake_case							)





    def 	A		(   self						:		Tuple			,						snake_case						:		Any			,						snake_case						:		Optional[Any]			,						snake_case						:		int=None							):

      """simple docstring"""





      __lowerCAmelCase			=				self.time_proj(snake_case							)
      __lowerCAmelCase			=				self.timestep_embedder(timesteps_proj.to(dtype=snake_case							)							)  # (N, D)

      __lowerCAmelCase			=				self.class_embedder(snake_case							)  # (N, D)

      __lowerCAmelCase			=				timesteps_emb + class_labels  # (N, D)

      return conditioning


class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		Union[str, Any]			,						snake_case						:		int			,						snake_case						:		int			,						snake_case						:		int = 64							):

      """simple docstring"""





      super().__init__()
      __lowerCAmelCase			=				nn.LayerNorm(snake_case							)
      __lowerCAmelCase			=				AttentionPooling(snake_case			,						snake_case							)
      __lowerCAmelCase			=				nn.Linear(snake_case			,						snake_case							)
      __lowerCAmelCase			=				nn.LayerNorm(snake_case							)





    def 	A		(   self						:		Optional[Any]			,						snake_case						:		List[Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				self.norma(snake_case							)
      __lowerCAmelCase			=				self.pool(snake_case							)
      __lowerCAmelCase			=				self.proj(snake_case							)
      __lowerCAmelCase			=				self.norma(snake_case							)
      return hidden_states


class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		Optional[Any]			,						snake_case						:		int = 768			,						snake_case						:		int = 768			,						snake_case						:		int = 1_536							):

      """simple docstring"""





      super().__init__()
      __lowerCAmelCase			=				nn.Linear(snake_case			,						snake_case							)
      __lowerCAmelCase			=				nn.LayerNorm(snake_case							)
      __lowerCAmelCase			=				nn.Linear(snake_case			,						snake_case							)





    def 	A		(   self						:		Optional[int]			,						snake_case						:		torch.FloatTensor			,						snake_case						:		torch.FloatTensor							):

      """simple docstring"""





      __lowerCAmelCase			=				self.text_proj(snake_case							)
      __lowerCAmelCase			=				self.text_norm(snake_case							)

      # image
      __lowerCAmelCase			=				self.image_proj(snake_case							)

      return time_image_embeds + time_text_embeds


class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		int			,						snake_case						:		int = 768			,						snake_case						:		int = 1_536							):

      """simple docstring"""





      super().__init__()
      __lowerCAmelCase			=				nn.Linear(snake_case			,						snake_case							)
      __lowerCAmelCase			=				nn.LayerNorm(snake_case							)





    def 	A		(   self						:		Optional[Any]			,						snake_case						:		torch.FloatTensor							):

      """simple docstring"""





      __lowerCAmelCase			=				self.image_proj(snake_case							)
      __lowerCAmelCase			=				self.image_norm(snake_case							)
      return time_image_embeds


class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		Optional[int]			,						snake_case						:		int = 768			,						snake_case						:		int = 1_536							):

      """simple docstring"""





      super().__init__()
      __lowerCAmelCase			=				nn.Linear(snake_case			,						snake_case							)
      __lowerCAmelCase			=				nn.LayerNorm(snake_case							)
      __lowerCAmelCase			=				nn.Sequential(
          nn.Convad(3			,						16			,						3			,						padding=1							)			,						nn.SiLU()			,						nn.Convad(16			,						16			,						3			,						padding=1							)			,						nn.SiLU()			,						nn.Convad(16			,						32			,						3			,						padding=1			,						stride=2							)			,						nn.SiLU()			,						nn.Convad(32			,						32			,						3			,						padding=1							)			,						nn.SiLU()			,						nn.Convad(32			,						96			,						3			,						padding=1			,						stride=2							)			,						nn.SiLU()			,						nn.Convad(96			,						96			,						3			,						padding=1							)			,						nn.SiLU()			,						nn.Convad(96			,						256			,						3			,						padding=1			,						stride=2							)			,						nn.SiLU()			,						nn.Convad(256			,						4			,						3			,						padding=1							)			,						)





    def 	A		(   self						:		Optional[int]			,						snake_case						:		torch.FloatTensor			,						snake_case						:		torch.FloatTensor							):

      """simple docstring"""





      __lowerCAmelCase			=				self.image_proj(snake_case							)
      __lowerCAmelCase			=				self.image_norm(snake_case							)
      __lowerCAmelCase			=				self.input_hint_block(snake_case							)
      return time_image_embeds, hint


class   __snake_case				(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		Tuple			,						snake_case						:		int			,						snake_case						:		Tuple			,						snake_case						:		List[Any]=None							):

      """simple docstring"""





      super().__init__()
      __lowerCAmelCase			=				dtype
      __lowerCAmelCase			=				nn.Parameter(torch.randn(1			,						snake_case							) / embed_dim**0.5							)
      __lowerCAmelCase			=				nn.Linear(snake_case			,						snake_case			,						dtype=self.dtype							)
      __lowerCAmelCase			=				nn.Linear(snake_case			,						snake_case			,						dtype=self.dtype							)
      __lowerCAmelCase			=				nn.Linear(snake_case			,						snake_case			,						dtype=self.dtype							)
      __lowerCAmelCase			=				num_heads
      __lowerCAmelCase			=				embed_dim // self.num_heads





    def 	A		(   self						:		List[Any]			,						snake_case						:		Optional[int]							):

      """simple docstring"""





      __lowerCAmelCase			=				x.size()

      def shape(snake_case						:		Any							):
        # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
        __lowerCAmelCase			=				x.view(snake_case			,						-1			,						self.num_heads			,						self.dim_per_head							)
        # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
        __lowerCAmelCase			=				x.transpose(1			,						2							)
        # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
        __lowerCAmelCase			=				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)
        __lowerCAmelCase			=				x.transpose(1			,						2							)
        return x

      __lowerCAmelCase			=				x.mean(dim=1			,						keepdim=snake_case							) + self.positional_embedding.to(x.dtype							)
      __lowerCAmelCase			=				torch.cat([class_token, x]			,						dim=1							)  # (bs, length+1, width)

      # (bs*n_heads, class_token_length, dim_per_head)
      __lowerCAmelCase			=				shape(self.q_proj(snake_case							)							)
      # (bs*n_heads, length+class_token_length, dim_per_head)
      __lowerCAmelCase			=				shape(self.k_proj(snake_case							)							)
      __lowerCAmelCase			=				shape(self.v_proj(snake_case							)							)

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

      # (bs*n_heads, dim_per_head, class_token_length)
      __lowerCAmelCase			=				torch.einsum('''bts,bcs->bct'''			,						snake_case			,						snake_case							)

      # (bs, length+1, width)
      __lowerCAmelCase			=				a.reshape(snake_case			,						-1			,						1							).transpose(1			,						2							)

      return a[:, 0, :]  # cls_token







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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


__lowercase													=			logging.get_logger(__name__)

__lowercase													=			{
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}

class   __snake_case				(			_a   ):






    '''simple docstring'''
    _lowerCAmelCase     =	"""blenderbot"""
    _lowerCAmelCase     =	["""past_key_values"""]
    _lowerCAmelCase     =	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





    def __init__(   self						:		Any			,						snake_case						:		Union[str, Any]=8_008			,						snake_case						:		List[str]=128			,						snake_case						:		Tuple=2			,						snake_case						:		int=10_240			,						snake_case						:		Optional[int]=32			,						snake_case						:		Dict=24			,						snake_case						:		List[Any]=10_240			,						snake_case						:		Optional[int]=32			,						snake_case						:		List[str]=0.0			,						snake_case						:		Optional[int]=0.0			,						snake_case						:		Optional[Any]=True			,						snake_case						:		int=True			,						snake_case						:		List[str]="gelu"			,						snake_case						:		Any=2_560			,						snake_case						:		Optional[Any]=0.1			,						snake_case						:		Union[str, Any]=0.0			,						snake_case						:		List[Any]=0.0			,						snake_case						:		Dict=0.02			,						snake_case						:		int=1			,						snake_case						:		Dict=False			,						snake_case						:		List[str]=0			,						snake_case						:		Optional[Any]=1			,						snake_case						:		List[Any]=2			,						snake_case						:		List[str]=3			,						snake_case						:		List[str]=2			,						**snake_case						:		int			,						):

      """simple docstring"""





      __lowerCAmelCase			=				vocab_size
      __lowerCAmelCase			=				max_position_embeddings
      __lowerCAmelCase			=				d_model
      __lowerCAmelCase			=				encoder_ffn_dim
      __lowerCAmelCase			=				encoder_layers
      __lowerCAmelCase			=				encoder_attention_heads
      __lowerCAmelCase			=				decoder_ffn_dim
      __lowerCAmelCase			=				decoder_layers
      __lowerCAmelCase			=				decoder_attention_heads
      __lowerCAmelCase			=				dropout
      __lowerCAmelCase			=				attention_dropout
      __lowerCAmelCase			=				activation_dropout
      __lowerCAmelCase			=				activation_function
      __lowerCAmelCase			=				init_std
      __lowerCAmelCase			=				encoder_layerdrop
      __lowerCAmelCase			=				decoder_layerdrop
      __lowerCAmelCase			=				use_cache
      __lowerCAmelCase			=				encoder_layers
      __lowerCAmelCase			=				scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=snake_case			,						bos_token_id=snake_case			,						eos_token_id=snake_case			,						is_encoder_decoder=snake_case			,						decoder_start_token_id=snake_case			,						encoder_no_repeat_ngram_size=snake_case			,						forced_eos_token_id=snake_case			,						**snake_case			,						)


class   __snake_case				(			_a   ):






    '''simple docstring'''





    @property
    def 	A		(   self						:		int							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
            ]							)
        if self.use_past:
          __lowerCAmelCase			=				{0: '''batch'''}
          __lowerCAmelCase			=				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
        else:
          __lowerCAmelCase			=				{0: '''batch''', 1: '''decoder_sequence'''}
          __lowerCAmelCase			=				{0: '''batch''', 1: '''decoder_sequence'''}
        if self.use_past:
          self.fill_with_past_key_values_(snake_case			,						direction='''inputs'''							)
      elif self.task == "causal-lm":
        __lowerCAmelCase			=				OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
            ]							)
        if self.use_past:
          __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
          for i in range(snake_case							):
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
      else:
        __lowerCAmelCase			=				OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
            ]							)

      return common_inputs





    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				super().outputs
      else:
        __lowerCAmelCase			=				super(snake_case			,						self							).outputs
        if self.use_past:
          __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
          for i in range(snake_case							):
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
      return common_outputs





    def 	A		(   self						:		int			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)
      # Generate decoder inputs
      __lowerCAmelCase			=				seq_length if not self.use_past else 1
      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)
      __lowerCAmelCase			=				{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      __lowerCAmelCase			=				dict(**snake_case			,						**snake_case							)

      if self.use_past:
        if not is_torch_available():
          raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
        else:
          import torch
        __lowerCAmelCase	,						__lowerCAmelCase			=				common_inputs['''input_ids'''].shape
        __lowerCAmelCase			=				common_inputs['''decoder_input_ids'''].shape[1]
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_attention_heads
        __lowerCAmelCase			=				(
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        __lowerCAmelCase			=				decoder_seq_length
        __lowerCAmelCase			=				(
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )
        __lowerCAmelCase			=				torch.cat(
            [common_inputs['''decoder_attention_mask'''], torch.ones(snake_case			,						snake_case							)]			,						dim=1							)
        __lowerCAmelCase			=				[]
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers

        for _ in range(snake_case							):
          common_inputs["past_key_values"].append(
              (
                  torch.zeros(snake_case							),
                  torch.zeros(snake_case							),
                  torch.zeros(snake_case							),
                  torch.zeros(snake_case							),
              )							)
      return common_inputs





    def 	A		(   self						:		str			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)

      if self.use_past:
        if not is_torch_available():
          raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
        else:
          import torch
        __lowerCAmelCase	,						__lowerCAmelCase			=				common_inputs['''input_ids'''].shape
        __lowerCAmelCase			=				seqlen
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_attention_heads
        __lowerCAmelCase			=				(
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        __lowerCAmelCase			=				common_inputs['''attention_mask'''].dtype
        __lowerCAmelCase			=				torch.cat(
            [common_inputs['''attention_mask'''], torch.ones(snake_case			,						snake_case			,						dtype=snake_case							)]			,						dim=1							)
        __lowerCAmelCase			=				[
            (torch.zeros(snake_case							), torch.zeros(snake_case							)) for _ in range(snake_case							)
        ]
      return common_inputs





    def 	A		(   self						:		Optional[int]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				compute_effective_axis_dimension(
          snake_case			,						fixed_dimension=OnnxConfig.default_fixed_batch			,						num_token_to_add=0							)

      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      __lowerCAmelCase			=				tokenizer.num_special_tokens_to_add(snake_case							)
      __lowerCAmelCase			=				compute_effective_axis_dimension(
          snake_case			,						fixed_dimension=OnnxConfig.default_fixed_sequence			,						num_token_to_add=snake_case							)

      # Generate dummy inputs according to compute batch and sequence
      __lowerCAmelCase			=				[''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
      __lowerCAmelCase			=				dict(tokenizer(snake_case			,						return_tensors=snake_case							)							)
      return common_inputs





    def 	A		(   self						:		str			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)

      elif self.task == "causal-lm":
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_causal_lm(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)
      else:
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)

      return common_inputs





    def 	A		(   self						:		Optional[Any]			,						snake_case						:		int			,						snake_case						:		str			,						snake_case						:		str			,						snake_case						:		List[str]							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				super()._flatten_past_key_values_(snake_case			,						snake_case			,						snake_case			,						snake_case							)
      else:
        __lowerCAmelCase			=				super(snake_case			,						self							)._flatten_past_key_values_(
            snake_case			,						snake_case			,						snake_case			,						snake_case							)





    def 	A		(   self						:		List[Any]			,						snake_case						:		Mapping[str, Mapping[int, str]]			,						snake_case						:		str							):

      """simple docstring"""





      if direction not in ["inputs", "outputs"]:
        raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''							)

      __lowerCAmelCase			=				'''past_key_values''' if direction == '''inputs''' else '''present'''
      __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers

      __lowerCAmelCase			=				'''past_encoder_sequence'''
      __lowerCAmelCase			=				'''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

      for i in range(snake_case							):
        __lowerCAmelCase			=				{0: '''batch''', 2: decoder_sequence}
        __lowerCAmelCase			=				{0: '''batch''', 2: decoder_sequence}
        __lowerCAmelCase			=				{0: '''batch''', 2: encoder_sequence}
        __lowerCAmelCase			=				{0: '''batch''', 2: encoder_sequence}
