'''simple docstring'''




from typing import TYPE_CHECKING

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


snake_case	=      {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case	=      ['DebertaTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case	=      [
     'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DebertaForMaskedLM',
     'DebertaForQuestionAnswering',
     'DebertaForSequenceClassification',
     'DebertaForTokenClassification',
     'DebertaModel',
     'DebertaPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case	=      [
     'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDebertaForMaskedLM',
     'TFDebertaForQuestionAnswering',
     'TFDebertaForSequenceClassification',
     'TFDebertaForTokenClassification',
     'TFDebertaModel',
     'TFDebertaPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

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









'''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     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		torch.FloatTensor


class     _lowerCamelCase       (     snake_case_     ,		snake_case_					):




 '''simple docstring'''


 @register_to_config
 def __init__(   self   ,   __lowercase = 16   ,   __lowercase = 88   ,   __lowercase = None   ,   __lowercase = None   ,   __lowercase = 1   ,   __lowercase = 0.0   ,   __lowercase = 32   ,   __lowercase = None   ,   __lowercase = False   ,   __lowercase = None   ,   __lowercase = "geglu"   ,   __lowercase = True   ,   __lowercase = True   ,   ):

  """simple docstring"""







  super().__init__()
  __A	:		Optional[int]	  =      num_attention_heads
  __A	:		Dict	  =      attention_head_dim
  __A	:		Optional[int]	  =      num_attention_heads * attention_head_dim

  __A	:		Optional[Any]	  =      in_channels

  __A	:		Optional[int]	  =      torch.nn.GroupNorm(num_groups=__lowercase   ,   num_channels=__lowercase   ,   eps=1E-6   ,   affine=__lowercase    )
  __A	:		Optional[Any]	  =      nn.Linear(__lowercase   ,   __lowercase    )

  # 3. Define transformers blocks
  __A	:		List[Any]	  =      nn.ModuleList(
      [
          BasicTransformerBlock(
              __lowercase   ,   __lowercase   ,   __lowercase   ,   dropout=__lowercase   ,   cross_attention_dim=__lowercase   ,   activation_fn=__lowercase   ,   attention_bias=__lowercase   ,   double_self_attention=__lowercase   ,   norm_elementwise_affine=__lowercase   ,   )
          for d in range(__lowercase    )
      ]    )

  __A	:		Union[str, Any]	  =      nn.Linear(__lowercase   ,   __lowercase    )







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=1   ,   __lowercase=None   ,   __lowercase = True   ,   ):

  """simple docstring"""







  __A  ,__A  ,__A  ,__A	:		int	  =      hidden_states.shape
  __A	:		Union[str, Any]	  =      batch_frames // num_frames

  __A	:		Union[str, Any]	  =      hidden_states

  __A	:		List[str]	  =      hidden_states[None, :].reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )
  __A	:		List[Any]	  =      hidden_states.permute(0   ,   2   ,   1   ,   3   ,   4    )

  __A	:		Dict	  =      self.norm(__lowercase    )
  __A	:		List[Any]	  =      hidden_states.permute(0   ,   3   ,   4   ,   2   ,   1    ).reshape(batch_size * height * width   ,   __lowercase   ,   __lowercase    )

  __A	:		Optional[Any]	  =      self.proj_in(__lowercase    )

  # 2. Blocks
  for block in self.transformer_blocks:
   __A	:		List[Any]	  =      block(
       __lowercase   ,   encoder_hidden_states=__lowercase   ,   timestep=__lowercase   ,   cross_attention_kwargs=__lowercase   ,   class_labels=__lowercase   ,   )

  # 3. Output
  __A	:		Optional[int]	  =      self.proj_out(__lowercase    )
  __A	:		int	  =      (
      hidden_states[None, None, :]
      .reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )
      .permute(0   ,   3   ,   4   ,   1   ,   2    )
      .contiguous()
  )
  __A	:		List[str]	  =      hidden_states.reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )

  __A	:		str	  =      hidden_states + residual

  if not return_dict:
   return (output,)

  return TransformerTemporalModelOutput(sample=__lowercase    )






