


'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


__UpperCAmelCase      :List[Any]			    =						logging.get_logger(__name__)



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		Optional[int]							,			*snake_case					:		Optional[Any]							,			**snake_case					:		Tuple      )						->							None:
          warnings.warn(
              '''The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
              ''' Please use VideoMAEImageProcessor instead.'''							,			_snake_case							,			)
          super().__init__(*_snake_case							,			**_snake_case      )



'''simple docstring'''



from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin



@dataclass
class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			torch.FloatTensor



class      a    (		_a     ,  _a	):


     """simple docstring"""






     @register_to_config
     def __init__(    self					:		str							,			snake_case					:		int = 32							,			snake_case					:		int = 64							,			snake_case					:		int = 20							,			snake_case					:		int = 768							,			snake_case					:		Tuple=77							,			snake_case					:		List[Any]=4							,			snake_case					:		float = 0.0							,			snake_case					:		str = "silu"							,			snake_case					:		Optional[str] = None							,			snake_case					:		Optional[str] = None							,			snake_case					:		Optional[str] = "linear"							,			snake_case					:		Optional[str] = "prd"							,			snake_case					:		Optional[int] = None							,			snake_case					:		Optional[int] = None							,			snake_case					:		Optional[int] = None							,			)						->							List[str]:
          super().__init__()
          __UpperCAmelCase     :			List[Any]  							=      num_attention_heads
          __UpperCAmelCase     :			List[str]  							=      attention_head_dim
          __UpperCAmelCase     :			int  							=      num_attention_heads * attention_head_dim
          __UpperCAmelCase     :			List[Any]  							=      additional_embeddings

          __UpperCAmelCase     :			Any  							=      time_embed_dim or inner_dim
          __UpperCAmelCase     :			Any  							=      embedding_proj_dim or embedding_dim
          __UpperCAmelCase     :			Union[str, Any]  							=      clip_embed_dim or embedding_dim

          __UpperCAmelCase     :			List[Any]  							=      Timesteps(snake_case							,			snake_case							,			0      )
          __UpperCAmelCase     :			Optional[Any]  							=      TimestepEmbedding(snake_case							,			snake_case							,			out_dim=snake_case							,			act_fn=snake_case      )

          __UpperCAmelCase     :			str  							=      nn.Linear(snake_case							,			snake_case      )

          if embedding_proj_norm_type is None:
               __UpperCAmelCase     :			str  							=      None
          elif embedding_proj_norm_type == "layer":
               __UpperCAmelCase     :			str  							=      nn.LayerNorm(snake_case      )
          else:
               raise ValueError(f'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'      )

          __UpperCAmelCase     :			List[Any]  							=      nn.Linear(snake_case							,			snake_case      )

          if encoder_hid_proj_type is None:
               __UpperCAmelCase     :			Union[str, Any]  							=      None
          elif encoder_hid_proj_type == "linear":
               __UpperCAmelCase     :			Any  							=      nn.Linear(snake_case							,			snake_case      )
          else:
               raise ValueError(f'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'      )

          __UpperCAmelCase     :			Dict  							=      nn.Parameter(torch.zeros(1							,			num_embeddings + additional_embeddings							,			snake_case      )      )

          if added_emb_type == "prd":
               __UpperCAmelCase     :			Any  							=      nn.Parameter(torch.zeros(1							,			1							,			snake_case      )      )
          elif added_emb_type is None:
               __UpperCAmelCase     :			List[Any]  							=      None
          else:
               raise ValueError(
                   f'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'      )

          __UpperCAmelCase     :			Optional[int]  							=      nn.ModuleList(
              [
                  BasicTransformerBlock(
                      snake_case							,			snake_case							,			snake_case							,			dropout=snake_case							,			activation_fn='''gelu'''							,			attention_bias=snake_case							,			)
                  for d in range(snake_case      )
              ]      )

          if norm_in_type == "layer":
               __UpperCAmelCase     :			Tuple  							=      nn.LayerNorm(snake_case      )
          elif norm_in_type is None:
               __UpperCAmelCase     :			List[Any]  							=      None
          else:
               raise ValueError(f'Unsupported norm_in_type: {norm_in_type}.'      )

          __UpperCAmelCase     :			Dict  							=      nn.LayerNorm(snake_case      )

          __UpperCAmelCase     :			Any  							=      nn.Linear(snake_case							,			snake_case      )

          __UpperCAmelCase     :			Any  							=      torch.full(
              [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]							,			-10_000.0      )
          causal_attention_mask.triu_(1      )
          __UpperCAmelCase     :			str  							=      causal_attention_mask[None, ...]
          self.register_buffer('''causal_attention_mask'''							,			snake_case							,			persistent=snake_case      )

          __UpperCAmelCase     :			Tuple  							=      nn.Parameter(torch.zeros(1							,			snake_case      )      )
          __UpperCAmelCase     :			List[str]  							=      nn.Parameter(torch.zeros(1							,			snake_case      )      )

     @property
     # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
     def 			lowerCamelCase__			(    self					:		int      )						->							Dict[str, AttentionProcessor]:
          __UpperCAmelCase     :			Optional[Any]  							=      {}

          def fn_recursive_add_processors(snake_case					:		str							,			snake_case					:		torch.nn.Module							,			snake_case					:		Dict[str, AttentionProcessor]      ):
               if hasattr(snake_case							,			'''set_processor'''      ):
                    __UpperCAmelCase     :			Union[str, Any]  							=      module.processor

               for sub_name, child in module.named_children():
                    fn_recursive_add_processors(f'{name}.{sub_name}'							,			snake_case							,			snake_case      )

               return processors

          for name, module in self.named_children():
               fn_recursive_add_processors(snake_case							,			snake_case							,			snake_case      )

          return processors

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		Union[AttentionProcessor, Dict[str, AttentionProcessor]]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Union[str, Any]  							=      len(self.attn_processors.keys()      )

          if isinstance(snake_case							,			snake_case      ) and len(snake_case      ) != count:
               raise ValueError(
                   f'A dict of processors was passed, but the number of processors {len(snake_case      )} does not match the'
                   f' number of attention layers: {count}. Please make sure to pass {count} processor classes.'      )

          def fn_recursive_attn_processor(snake_case					:		str							,			snake_case					:		torch.nn.Module							,			snake_case					:		int      ):
               if hasattr(snake_case							,			'''set_processor'''      ):
                    if not isinstance(snake_case							,			snake_case      ):
                         module.set_processor(snake_case      )
                    else:
                         module.set_processor(processor.pop(f'{name}.processor'      )      )

               for sub_name, child in module.named_children():
                    fn_recursive_attn_processor(f'{name}.{sub_name}'							,			snake_case							,			snake_case      )

          for name, module in self.named_children():
               fn_recursive_attn_processor(snake_case							,			snake_case							,			snake_case      )

     def 			lowerCamelCase__			(    self					:		str      )						->							Tuple:
          self.set_attn_processor(AttnProcessor()      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		List[Any]							,			snake_case					:		Union[torch.Tensor, float, int]							,			snake_case					:		torch.FloatTensor							,			snake_case					:		Optional[torch.FloatTensor] = None							,			snake_case					:		Optional[torch.BoolTensor] = None							,			snake_case					:		bool = True							,			)						->							List[Any]:
          __UpperCAmelCase     :			Any  							=      hidden_states.shape[0]

          __UpperCAmelCase     :			Optional[int]  							=      timestep
          if not torch.is_tensor(snake_case      ):
               __UpperCAmelCase     :			str  							=      torch.tensor([timesteps]							,			dtype=torch.long							,			device=hidden_states.device      )
          elif torch.is_tensor(snake_case      ) and len(timesteps.shape      ) == 0:
               __UpperCAmelCase     :			Any  							=      timesteps[None].to(hidden_states.device      )

          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          __UpperCAmelCase     :			Optional[int]  							=      timesteps * torch.ones(snake_case							,			dtype=timesteps.dtype							,			device=timesteps.device      )

          __UpperCAmelCase     :			Tuple  							=      self.time_proj(snake_case      )

          # timesteps does not contain any weights and will always return f32 tensors
          # but time_embedding might be fp16, so we need to cast here.
          __UpperCAmelCase     :			int  							=      timesteps_projected.to(dtype=self.dtype      )
          __UpperCAmelCase     :			Optional[int]  							=      self.time_embedding(snake_case      )

          if self.embedding_proj_norm is not None:
               __UpperCAmelCase     :			Optional[Any]  							=      self.embedding_proj_norm(snake_case      )

          __UpperCAmelCase     :			str  							=      self.embedding_proj(snake_case      )
          if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
               __UpperCAmelCase     :			Dict  							=      self.encoder_hidden_states_proj(snake_case      )
          elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
               raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'''      )

          __UpperCAmelCase     :			Optional[int]  							=      self.proj_in(snake_case      )

          __UpperCAmelCase     :			Optional[Any]  							=      self.positional_embedding.to(hidden_states.dtype      )

          __UpperCAmelCase     :			Union[str, Any]  							=      []
          __UpperCAmelCase     :			Optional[Any]  							=      0

          if encoder_hidden_states is not None:
               additional_embeds.append(snake_case      )
               additional_embeddings_len += encoder_hidden_states.shape[1]

          if len(proj_embeddings.shape      ) == 2:
               __UpperCAmelCase     :			Optional[int]  							=      proj_embeddings[:, None, :]

          if len(hidden_states.shape      ) == 2:
               __UpperCAmelCase     :			Union[str, Any]  							=      hidden_states[:, None, :]

          __UpperCAmelCase     :			Union[str, Any]  							=      additional_embeds + [
              proj_embeddings,
              time_embeddings[:, None, :],
              hidden_states,
          ]

          if self.prd_embedding is not None:
               __UpperCAmelCase     :			Any  							=      self.prd_embedding.to(hidden_states.dtype      ).expand(snake_case							,			-1							,			-1      )
               additional_embeds.append(snake_case      )

          __UpperCAmelCase     :			Dict  							=      torch.cat(
              snake_case							,			dim=1							,			)

          # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
          __UpperCAmelCase     :			str  							=      additional_embeddings_len + proj_embeddings.shape[1] + 1
          if positional_embeddings.shape[1] < hidden_states.shape[1]:
               __UpperCAmelCase     :			Union[str, Any]  							=      F.pad(
                   snake_case							,			(
                       0,
                       0,
                       additional_embeddings_len,
                       self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
                   )							,			value=0.0							,			)

          __UpperCAmelCase     :			Optional[int]  							=      hidden_states + positional_embeddings

          if attention_mask is not None:
               __UpperCAmelCase     :			List[Any]  							=      (1 - attention_mask.to(hidden_states.dtype      )) * -10_000.0
               __UpperCAmelCase     :			str  							=      F.pad(snake_case							,			(0, self.additional_embeddings)							,			value=0.0      )
               __UpperCAmelCase     :			Optional[int]  							=      (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype      )
               __UpperCAmelCase     :			Optional[int]  							=      attention_mask.repeat_interleave(self.config.num_attention_heads							,			dim=0      )

          if self.norm_in is not None:
               __UpperCAmelCase     :			str  							=      self.norm_in(snake_case      )

          for block in self.transformer_blocks:
               __UpperCAmelCase     :			Optional[int]  							=      block(snake_case							,			attention_mask=snake_case      )

          __UpperCAmelCase     :			int  							=      self.norm_out(snake_case      )

          if self.prd_embedding is not None:
               __UpperCAmelCase     :			Optional[int]  							=      hidden_states[:, -1]
          else:
               __UpperCAmelCase     :			List[Any]  							=      hidden_states[:, additional_embeddings_len:]

          __UpperCAmelCase     :			Dict  							=      self.proj_to_clip_embeddings(snake_case      )

          if not return_dict:
               return (predicted_image_embedding,)

          return PriorTransformerOutput(predicted_image_embedding=snake_case      )






     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		List[str]      )						->							str:
          __UpperCAmelCase     :			Dict  							=      (prior_latents * self.clip_std) + self.clip_mean
          return prior_latents