




'''simple docstring'''





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin

@dataclass
class        _a    (						A__							):
     SCREAMING_SNAKE_CASE_							:   torch.FloatTensor







class        _a    (						A__ , A__							):






     @register_to_config
     def __init__(			self ,_SCREAMING_SNAKE_CASE = 16 ,_SCREAMING_SNAKE_CASE = 88 ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = 1 ,_SCREAMING_SNAKE_CASE = 0.0 ,_SCREAMING_SNAKE_CASE = 32 ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = "geglu" ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = True ,)							->      str:
          super().__init__()
          _snake_case							=				num_attention_heads
          _snake_case							=				attention_head_dim
          _snake_case							=				num_attention_heads * attention_head_dim

          _snake_case							=				in_channels

          _snake_case							=				torch.nn.GroupNorm(num_groups=lowerCamelCase__ ,num_channels=lowerCamelCase__ ,eps=1e-6 ,affine=lowerCamelCase__   )
          _snake_case							=				nn.Linear(lowerCamelCase__ ,lowerCamelCase__   )

          # 3. Define transformers blocks
          _snake_case							=				nn.ModuleList(
              [
                  BasicTransformerBlock(
                      lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,dropout=lowerCamelCase__ ,cross_attention_dim=lowerCamelCase__ ,activation_fn=lowerCamelCase__ ,attention_bias=lowerCamelCase__ ,double_self_attention=lowerCamelCase__ ,norm_elementwise_affine=lowerCamelCase__ ,)
                  for d in range(lowerCamelCase__   )
              ]   )

          _snake_case							=				nn.Linear(lowerCamelCase__ ,lowerCamelCase__   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE = True ,)							->      Dict:
          _snake_case							, _snake_case							, _snake_case							, _snake_case							=				hidden_states.shape
          _snake_case							=				batch_frames // num_frames

          _snake_case							=				hidden_states

          _snake_case							=				hidden_states[None, :].reshape(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__   )
          _snake_case							=				hidden_states.permute(0 ,2 ,1 ,3 ,4   )

          _snake_case							=				self.norm(lowerCamelCase__   )
          _snake_case							=				hidden_states.permute(0 ,3 ,4 ,2 ,1   ).reshape(batch_size * height * width ,lowerCamelCase__ ,lowerCamelCase__   )

          _snake_case							=				self.proj_in(lowerCamelCase__   )

          # 2. Blocks
          for block in self.transformer_blocks:
               _snake_case							=				block(
                   lowerCamelCase__ ,encoder_hidden_states=lowerCamelCase__ ,timestep=lowerCamelCase__ ,cross_attention_kwargs=lowerCamelCase__ ,class_labels=lowerCamelCase__ ,)

          # 3. Output
          _snake_case							=				self.proj_out(lowerCamelCase__   )
          _snake_case							=				(
              hidden_states[None, None, :]
              .reshape(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__   )
              .permute(0 ,3 ,4 ,1 ,2   )
              .contiguous()
          )
          _snake_case							=				hidden_states.reshape(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__   )

          _snake_case							=				hidden_states + residual

          if not return_dict:
               return (output,)

          return TransformerTemporalModelOutput(sample=lowerCamelCase__   )








'''simple docstring'''





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Any       =						["""image_processor""", """tokenizer"""]
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						"""BridgeTowerImageProcessor"""
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Tuple:
          super().__init__(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def __call__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = 0 ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      BatchEncoding:
          _snake_case							=				self.tokenizer(
              text=_SCREAMING_SNAKE_CASE ,add_special_tokens=_SCREAMING_SNAKE_CASE ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=_SCREAMING_SNAKE_CASE ,stride=_SCREAMING_SNAKE_CASE ,pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,return_token_type_ids=_SCREAMING_SNAKE_CASE ,return_attention_mask=_SCREAMING_SNAKE_CASE ,return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,return_offsets_mapping=_SCREAMING_SNAKE_CASE ,return_length=_SCREAMING_SNAKE_CASE ,verbose=_SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)
          # add pixel_values + pixel_mask
          _snake_case							=				self.image_processor(
              _SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,do_normalize=_SCREAMING_SNAKE_CASE ,do_center_crop=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )
          encoding.update(_SCREAMING_SNAKE_CASE   )

          return encoding






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     @property
     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				self.tokenizer.model_input_names
          _snake_case							=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )


