


'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						=logging.get_logger(__name__)

__A						={
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}


class 		_snake_case       (  a__						):
					lowerCAmelCase   :List[Any]				=       """biogpt"""




					def __init__(   self				,				_lowerCamelCase=4_2384				,				_lowerCamelCase=1024				,				_lowerCamelCase=24				,				_lowerCamelCase=16				,				_lowerCamelCase=4096				,				_lowerCamelCase="gelu"				,				_lowerCamelCase=0.1				,				_lowerCamelCase=0.1				,				_lowerCamelCase=1024				,				_lowerCamelCase=0.02				,				_lowerCamelCase=1e-1_2				,				_lowerCamelCase=True				,				_lowerCamelCase=True				,				_lowerCamelCase=0.0				,				_lowerCamelCase=0.0				,				_lowerCamelCase=1				,				_lowerCamelCase=0				,				_lowerCamelCase=2				,				**_lowerCamelCase				,				):
								UpperCAmelCase__	:       List[Any]   =  vocab_size
								UpperCAmelCase__	:       str   =  max_position_embeddings
								UpperCAmelCase__	:       List[str]   =  hidden_size
								UpperCAmelCase__	:       Any   =  num_hidden_layers
								UpperCAmelCase__	:       int   =  num_attention_heads
								UpperCAmelCase__	:       Optional[int]   =  intermediate_size
								UpperCAmelCase__	:       Union[str, Any]   =  hidden_act
								UpperCAmelCase__	:       Any   =  hidden_dropout_prob
								UpperCAmelCase__	:       Union[str, Any]   =  attention_probs_dropout_prob
								UpperCAmelCase__	:       Union[str, Any]   =  initializer_range
								UpperCAmelCase__	:       Optional[Any]   =  layer_norm_eps
								UpperCAmelCase__	:       int   =  scale_embedding
								UpperCAmelCase__	:       List[str]   =  use_cache
								UpperCAmelCase__	:       int   =  layerdrop
								UpperCAmelCase__	:       Union[str, Any]   =  activation_dropout
								super().__init__(pad_token_id=__UpperCAmelCase				,				bos_token_id=__UpperCAmelCase				,				eos_token_id=__UpperCAmelCase				,				**__UpperCAmelCase)






"""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  (	a			):
       """simple docstring"""




       __magic_name__	:torch.FloatTensor






class    _lowerCAmelCase  (	a    ,  a			):
       """simple docstring"""




       @register_to_config
       def __init__(       self       ,					__UpperCAmelCase = 1_6       ,					__UpperCAmelCase = 8_8       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 1       ,					__UpperCAmelCase = 0.0       ,					__UpperCAmelCase = 3_2       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = "geglu"       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

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

         lowerCAmelCase__     :Any											=				in_channels

         lowerCAmelCase__     :str											=				torch.nn.GroupNorm(num_groups=__UpperCAmelCase       ,					num_channels=__UpperCAmelCase       ,					eps=1E-6       ,					affine=__UpperCAmelCase )
         lowerCAmelCase__     :int											=				nn.Linear(__UpperCAmelCase       ,					__UpperCAmelCase )

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

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






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=1       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :int											=				hidden_states.shape
         lowerCAmelCase__     :Tuple											=				batch_frames // num_frames

         lowerCAmelCase__     :str											=				hidden_states

         lowerCAmelCase__     :Union[str, Any]											=				hidden_states[None, :].reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
         lowerCAmelCase__     :str											=				hidden_states.permute(0       ,					2       ,					1       ,					3       ,					4 )

         lowerCAmelCase__     :Optional[int]											=				self.norm(__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				hidden_states.permute(0       ,					3       ,					4       ,					2       ,					1 ).reshape(batch_size * height * width       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				self.proj_in(__UpperCAmelCase )

         # 2. Blocks
         for block in self.transformer_blocks:
           lowerCAmelCase__     :Optional[int]											=				block(
               __UpperCAmelCase       ,					encoder_hidden_states=__UpperCAmelCase       ,					timestep=__UpperCAmelCase       ,					cross_attention_kwargs=__UpperCAmelCase       ,					class_labels=__UpperCAmelCase       ,					)

         # 3. Output
         lowerCAmelCase__     :Any											=				self.proj_out(__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				(
             hidden_states[None, None, :]
             .reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
             .permute(0       ,					3       ,					4       ,					1       ,					2 )
             .contiguous()
         )
         lowerCAmelCase__     :Optional[Any]											=				hidden_states.reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				hidden_states + residual

         if not return_dict:
           return (output,)

         return TransformerTemporalModelOutput(sample=__UpperCAmelCase )
