
"""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	(   __SCREAMING_SNAKE_CASE    ):
					lowercase		     =		42


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ):





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

						'''simple docstring'''




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

						__UpperCamelCase       						= in_channels

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

						# 3. Define transformers blocks
						__UpperCamelCase       						= 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   )
						    ]   )

						__UpperCamelCase       						= nn.Linear(__lowerCAmelCase			,  __lowerCAmelCase   )







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

						'''simple docstring'''




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

						__UpperCamelCase       						= hidden_states

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

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

						__UpperCamelCase       						= self.proj_in(__lowerCAmelCase   )

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

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

						__UpperCamelCase       						= hidden_states + residual

						if not return_dict:
							return (output,)

						return TransformerTemporalModelOutput(sample=__lowerCAmelCase   )





"""simple docstring"""

from __future__ import annotations

import os
from typing import Any

import requests

UpperCamelCase			:       Union[str, Any]									=				"https://api.github.com"

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCamelCase			:       Union[str, Any]									=				BASE_URL + "/user"

# https://github.com/settings/tokens
UpperCamelCase			:       Optional[int]									=				os.environ.get("USER_TOKEN", "")






def 							A		(       snake_case   :str	)   ->			dict[Any, Any]:
 __UpperCamelCase       						= {
     'Authorization': f'token {auth_token}',
     'Accept': 'application/vnd.github.v3+json',
 }
 return requests.get(snake_case       ,      headers=snake_case	).json()


if __name__ == "__main__":  # pragma: no cover
      if USER_TOKEN:
            for key, value in fetch_github_info(USER_TOKEN).items():
                  print(f'''{key}: {value}''')
      else:
            raise ValueError("'USER_TOKEN' field cannot be empty.")



