



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	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   torch.FloatTensor


class    _A	(     __UpperCAmelCase				,__UpperCAmelCase   ):



       @register_to_config
       def __init__(		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					int = 16					,		__SCREAMING_SNAKE_CASE							:					int = 88					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					int = 1					,		__SCREAMING_SNAKE_CASE							:					float = 0.0					,		__SCREAMING_SNAKE_CASE							:					int = 32					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					str = "geglu"					,		__SCREAMING_SNAKE_CASE							:					bool = True					,		__SCREAMING_SNAKE_CASE							:					bool = True					,		):


           '''simple docstring'''

           super().__init__()
           __a							=    num_attention_heads
           __a							=    attention_head_dim
           __a							=    num_attention_heads * attention_head_dim

           __a							=    in_channels

           __a							=    torch.nn.GroupNorm(num_groups=__SCREAMING_SNAKE_CASE					,		num_channels=__SCREAMING_SNAKE_CASE					,		eps=1E-6					,		affine=__SCREAMING_SNAKE_CASE)
           __a							=    nn.Linear(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

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

           __a							=    nn.Linear(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					List[Any]=None					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=None					,		__SCREAMING_SNAKE_CASE							:					str=1					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					bool = True					,		):


           '''simple docstring'''

           __a	,       __a	,       __a	,       __a							=    hidden_states.shape
           __a							=    batch_frames // num_frames

           __a							=    hidden_states

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

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

           __a							=    self.proj_in(__SCREAMING_SNAKE_CASE)

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

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

           __a							=    hidden_states + residual

           if not return_dict:
               return (output,)

           return TransformerTemporalModelOutput(sample=__SCREAMING_SNAKE_CASE)










def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):

    def get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    ) -> str:
        __a							=    []
        __a							=    min(len(_stra    )       ,			len(_stra    )    ) // 2
        for i, l in enumerate(_stra    ):
            __a							=    int(max(0       ,			i - limit    )    )
            __a							=    int(min(i + limit + 1       ,			len(_stra    )    )    )
            if l in _stra[left:right]:
                matched.append(_UpperCAmelCase    )
                __a							=    f'{_stra[0:_stra.index(_UpperCAmelCase    )]} {_stra[_stra.index(_UpperCAmelCase    ) + 1:]}'

        return "".join(_UpperCAmelCase    )

    # matching characters
    __a							=    get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    len(_UpperCAmelCase    )

    # transposition
    __a							=    (
        len([(ca, ca) for ca, ca in zip(_UpperCAmelCase       ,			_UpperCAmelCase    ) if ca != ca]    ) // 2
    )

    if not match_count:
        __a							=    0.0
    else:
        __a							=    (
            1
            / 3
            * (
                match_count / len(_UpperCAmelCase    )
                + match_count / len(_UpperCAmelCase    )
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    __a							=    0
    for ca, ca in zip(stra[:4]       ,			stra[:4]    ):
        if ca == ca:
            prefix_len += 1
        else:
            break

    return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(jaro_winkler('''hello''', '''world'''))





