

import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase = False					,			_UpperCamelCase = 1					,			_UpperCamelCase = 1					,			_UpperCamelCase = 10_000					,			)				->      Optional[int]:



  """simple docstring"""






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

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

  _snake_case		=				torch.exp(lowercase__	)
  _snake_case		=				timesteps[:, None].float() * emb[None, :]

  # scale embeddings
  _snake_case		=				scale * emb

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

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

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



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase=False					,			_UpperCamelCase=0	)				->      str:



  """simple docstring"""






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

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



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      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
  _snake_case		=				get_ad_sincos_pos_embed_from_grid(embed_dim // 2					,			grid[0]	)  # (H*W, D/2)
  _snake_case		=				get_ad_sincos_pos_embed_from_grid(embed_dim // 2					,			grid[1]	)  # (H*W, D/2)

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



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      Union[str, Any]:



  """simple docstring"""






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

  _snake_case		=				np.arange(embed_dim // 2					,			dtype=np.floataa	)
  omega /= embed_dim / 2.0
  _snake_case		=				1.0 / 10_000**omega  # (D/2,)

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

  _snake_case		=				np.sin(lowercase__	)  # (M, D/2)
  _snake_case		=				np.cos(lowercase__	)  # (M, D/2)

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



class     lowercase_       (    nn.Module	):



      def __init__(     self :							Dict     ,   A__ :							Optional[int]=224     ,   A__ :							Dict=224     ,   A__ :							Union[str, Any]=16     ,   A__ :							List[str]=3     ,   A__ :							List[str]=768     ,   A__ :							List[str]=False     ,   A__ :							int=True     ,   A__ :							Optional[int]=True     ,   )       ->	str:
        super().__init__()

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

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

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







      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							Union[str, Any]						)       ->	int:
        _snake_case		=				self.proj(__A						)
        if self.flatten:
          _snake_case		=				latent.flatten(2						).transpose(1     ,   2						)  # BCHW -> BNC
        if self.layer_norm:
          _snake_case		=				self.norm(__A						)
        return latent + self.pos_embed



class     lowercase_       (    nn.Module	):



      def __init__(     self :							Dict     ,   A__ :							int     ,   A__ :							Any     ,   A__ :							Dict = "silu"     ,   A__ :							Tuple = None     ,   A__ :							Optional[Any] = None     ,   A__ :							Dict=None     ,   )       ->	List[str]:
        super().__init__()

        _snake_case		=				nn.Linear(__A     ,   __A						)

        if cond_proj_dim is not None:
          _snake_case		=				nn.Linear(__A     ,   __A     ,   bias=__A						)
        else:
          _snake_case		=				None

        _snake_case		=				get_activation(__A						)

        if out_dim is not None:
          _snake_case		=				out_dim
        else:
          _snake_case		=				time_embed_dim
        _snake_case		=				nn.Linear(__A     ,   __A						)

        if post_act_fn is None:
          _snake_case		=				None
        else:
          _snake_case		=				get_activation(__A						)







      def 		UpperCamelCase_						(     self :							int     ,   A__ :							List[Any]     ,   A__ :							Optional[Any]=None						)       ->	Optional[Any]:
        if condition is not None:
          _snake_case		=				sample + self.cond_proj(__A						)
        _snake_case		=				self.linear_a(__A						)

        if self.act is not None:
          _snake_case		=				self.act(__A						)

        _snake_case		=				self.linear_a(__A						)

        if self.post_act is not None:
          _snake_case		=				self.post_act(__A						)
        return sample



class     lowercase_       (    nn.Module	):



      def __init__(     self :							Optional[int]     ,   A__ :							List[str]     ,   A__ :							List[Any]     ,   A__ :							Optional[Any]						)       ->	str:
        super().__init__()
        _snake_case		=				num_channels
        _snake_case		=				flip_sin_to_cos
        _snake_case		=				downscale_freq_shift







      def 		UpperCamelCase_						(     self :							Any     ,   A__ :							Optional[int]						)       ->	List[Any]:
        _snake_case		=				get_timestep_embedding(
            __A     ,   self.num_channels     ,   flip_sin_to_cos=self.flip_sin_to_cos     ,   downscale_freq_shift=self.downscale_freq_shift     ,   )
        return t_emb



class     lowercase_       (    nn.Module	):



      def __init__(     self :							int     ,   A__ :							Union[str, Any] = 256     ,   A__ :							List[Any] = 1.0     ,   A__ :							str=True     ,   A__ :							List[str]=True     ,   A__ :							Optional[Any]=False						)       ->	List[Any]:
        super().__init__()
        _snake_case		=				nn.Parameter(torch.randn(__A						) * scale     ,   requires_grad=__A						)
        _snake_case		=				log
        _snake_case		=				flip_sin_to_cos

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

          _snake_case		=				self.W







      def 		UpperCamelCase_						(     self :							Tuple     ,   A__ :							List[Any]						)       ->	List[Any]:
        if self.log:
          _snake_case		=				torch.log(__A						)

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

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



class     lowercase_       (    nn.Module	):



      def __init__(     self :							int     ,   A__ :							Any     ,   A__ :							Union[str, Any]     ,   A__ :							str     ,   A__ :							Optional[int]     ,   )       ->	List[str]:
        super().__init__()

        _snake_case		=				height
        _snake_case		=				width
        _snake_case		=				num_embed
        _snake_case		=				embed_dim

        _snake_case		=				nn.Embedding(self.num_embed     ,   __A						)
        _snake_case		=				nn.Embedding(self.height     ,   __A						)
        _snake_case		=				nn.Embedding(self.width     ,   __A						)







      def 		UpperCamelCase_						(     self :							List[str]     ,   A__ :							Optional[Any]						)       ->	Tuple:
        _snake_case		=				self.emb(__A						)

        _snake_case		=				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
        _snake_case		=				height_emb.unsqueeze(2						)

        _snake_case		=				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
        _snake_case		=				width_emb.unsqueeze(1						)

        _snake_case		=				height_emb + width_emb

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

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

        return emb



class     lowercase_       (    nn.Module	):



      def __init__(     self :							Any     ,   A__ :							Optional[int]     ,   A__ :							Optional[int]     ,   A__ :							str						)       ->	List[str]:
        super().__init__()
        _snake_case		=				dropout_prob > 0
        _snake_case		=				nn.Embedding(num_classes + use_cfg_embedding     ,   __A						)
        _snake_case		=				num_classes
        _snake_case		=				dropout_prob



      def 		UpperCamelCase_						(     self :							int     ,   A__ :							str     ,   A__ :							Optional[int]=None						)       ->	Optional[int]:
        if force_drop_ids is None:
          _snake_case		=				torch.rand(labels.shape[0]     ,   device=labels.device						) < self.dropout_prob
        else:
          _snake_case		=				torch.tensor(force_drop_ids == 1						)
        _snake_case		=				torch.where(__A     ,   self.num_classes     ,   __A						)
        return labels







      def 		UpperCamelCase_						(     self :							List[str]     ,   A__ :							Optional[int]     ,   A__ :							Any=None						)       ->	List[str]:
        _snake_case		=				self.dropout_prob > 0
        if (self.training and use_dropout) or (force_drop_ids is not None):
          _snake_case		=				self.token_drop(__A     ,   __A						)
        _snake_case		=				self.embedding_table(__A						)
        return embeddings



class     lowercase_       (    nn.Module	):



      def __init__(     self :							Tuple     ,   A__ :							Tuple = 1024     ,   A__ :							Any = 768     ,   A__ :							str = 768     ,   A__ :							Union[str, Any] = 10     ,   )       ->	Dict:
        super().__init__()

        _snake_case		=				num_image_text_embeds
        _snake_case		=				nn.Linear(__A     ,   self.num_image_text_embeds * cross_attention_dim						)
        _snake_case		=				nn.Linear(__A     ,   __A						)







      def 		UpperCamelCase_						(     self :							int     ,   A__ :							str     ,   A__ :							int						)       ->	List[Any]:
        _snake_case		=				text_embeds.shape[0]

        # image
        _snake_case		=				self.image_embeds(__A						)
        _snake_case		=				image_text_embeds.reshape(__A     ,   self.num_image_text_embeds     ,   -1						)

        # text
        _snake_case		=				self.text_proj(__A						)

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



class     lowercase_       (    nn.Module	):



      def __init__(     self :							List[str]     ,   A__ :							str = 768     ,   A__ :							Optional[int] = 768     ,   A__ :							Optional[int] = 32     ,   )       ->	Dict:
        super().__init__()

        _snake_case		=				num_image_text_embeds
        _snake_case		=				nn.Linear(__A     ,   self.num_image_text_embeds * cross_attention_dim						)
        _snake_case		=				nn.LayerNorm(__A						)







      def 		UpperCamelCase_						(     self :							str     ,   A__ :							str						)       ->	Optional[int]:
        _snake_case		=				image_embeds.shape[0]

        # image
        _snake_case		=				self.image_embeds(__A						)
        _snake_case		=				image_embeds.reshape(__A     ,   self.num_image_text_embeds     ,   -1						)
        _snake_case		=				self.norm(__A						)
        return image_embeds



class     lowercase_       (    nn.Module	):



      def __init__(     self :							Tuple     ,   A__ :							int     ,   A__ :							str     ,   A__ :							int=0.1						)       ->	Dict:
        super().__init__()

        _snake_case		=				Timesteps(num_channels=256     ,   flip_sin_to_cos=__A     ,   downscale_freq_shift=1						)
        _snake_case		=				TimestepEmbedding(in_channels=256     ,   time_embed_dim=__A						)
        _snake_case		=				LabelEmbedding(__A     ,   __A     ,   __A						)







      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							Tuple     ,   A__ :							Optional[Any]     ,   A__ :							Union[str, Any]=None						)       ->	int:
        _snake_case		=				self.time_proj(__A						)
        _snake_case		=				self.timestep_embedder(timesteps_proj.to(dtype=__A						)						)  # (N, D)

        _snake_case		=				self.class_embedder(__A						)  # (N, D)

        _snake_case		=				timesteps_emb + class_labels  # (N, D)

        return conditioning



class     lowercase_       (    nn.Module	):



      def __init__(     self :							List[str]     ,   A__ :							Union[str, Any]     ,   A__ :							Tuple     ,   A__ :							str = 64						)       ->	Optional[Any]:
        super().__init__()
        _snake_case		=				nn.LayerNorm(__A						)
        _snake_case		=				AttentionPooling(__A     ,   __A						)
        _snake_case		=				nn.Linear(__A     ,   __A						)
        _snake_case		=				nn.LayerNorm(__A						)







      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							Optional[int]						)       ->	Any:
        _snake_case		=				self.norma(__A						)
        _snake_case		=				self.pool(__A						)
        _snake_case		=				self.proj(__A						)
        _snake_case		=				self.norma(__A						)
        return hidden_states



class     lowercase_       (    nn.Module	):



      def __init__(     self :							Any     ,   A__ :							List[Any] = 768     ,   A__ :							Optional[int] = 768     ,   A__ :							str = 1536						)       ->	Optional[Any]:
        super().__init__()
        _snake_case		=				nn.Linear(__A     ,   __A						)
        _snake_case		=				nn.LayerNorm(__A						)
        _snake_case		=				nn.Linear(__A     ,   __A						)







      def 		UpperCamelCase_						(     self :							Optional[Any]     ,   A__ :							Union[str, Any]     ,   A__ :							Any						)       ->	Optional[int]:
        # text
        _snake_case		=				self.text_proj(__A						)
        _snake_case		=				self.text_norm(__A						)

        # image
        _snake_case		=				self.image_proj(__A						)

        return time_image_embeds + time_text_embeds



class     lowercase_       (    nn.Module	):



      def __init__(     self :							Dict     ,   A__ :							List[str] = 768     ,   A__ :							Optional[Any] = 1536						)       ->	str:
        super().__init__()
        _snake_case		=				nn.Linear(__A     ,   __A						)
        _snake_case		=				nn.LayerNorm(__A						)







      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							Tuple						)       ->	Tuple:
        # image
        _snake_case		=				self.image_proj(__A						)
        _snake_case		=				self.image_norm(__A						)
        return time_image_embeds



class     lowercase_       (    nn.Module	):



      def __init__(     self :							Tuple     ,   A__ :							str = 768     ,   A__ :							int = 1536						)       ->	str:
        super().__init__()
        _snake_case		=				nn.Linear(__A     ,   __A						)
        _snake_case		=				nn.LayerNorm(__A						)
        _snake_case		=				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 		UpperCamelCase_						(     self :							Any     ,   A__ :							Any     ,   A__ :							str						)       ->	List[Any]:
        # image
        _snake_case		=				self.image_proj(__A						)
        _snake_case		=				self.image_norm(__A						)
        _snake_case		=				self.input_hint_block(__A						)
        return time_image_embeds, hint



class     lowercase_       (    nn.Module	):



      def __init__(     self :							int     ,   A__ :							str     ,   A__ :							List[str]     ,   A__ :							Tuple=None						)       ->	Tuple:
        super().__init__()
        _snake_case		=				dtype
        _snake_case		=				nn.Parameter(torch.randn(1     ,   __A						) / embed_dim**0.5						)
        _snake_case		=				nn.Linear(__A     ,   __A     ,   dtype=self.dtype						)
        _snake_case		=				nn.Linear(__A     ,   __A     ,   dtype=self.dtype						)
        _snake_case		=				nn.Linear(__A     ,   __A     ,   dtype=self.dtype						)
        _snake_case		=				num_heads
        _snake_case		=				embed_dim // self.num_heads







      def 		UpperCamelCase_						(     self :							str     ,   A__ :							Tuple						)       ->	Optional[Any]:
        _snake_case		=				x.size()

        def shape(A__ :							Dict						):
          # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
          _snake_case		=				x.view(__A     ,   -1     ,   self.num_heads     ,   self.dim_per_head						)
          # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
          _snake_case		=				x.transpose(1     ,   2						)
          # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
          _snake_case		=				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)
          _snake_case		=				x.transpose(1     ,   2						)
          return x

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

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

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

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

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

        return a[:, 0, :]  # cls_token



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase 					=    {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['MarianTokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    [
				    'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MarianForCausalLM',
				    'MarianModel',
				    'MarianMTModel',
				    'MarianPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
				from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_marian import MarianTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_marian import (
								    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MarianForCausalLM,
								    MarianModel,
								    MarianMTModel,
								    MarianPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
				import sys

				__UpperCAmelCase 					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






