


"""simple docstring"""
from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)






@flax.struct.dataclass
class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: jnp.ndarray
    __lowercase							: jnp.ndarray






class      _UpperCAmelCase				(					nn.Module):
    __lowercase							: int
    __lowercase							: Tuple[int]  =					(1_6, 3_2, 9_6, 2_5_6)
    __lowercase							: jnp.dtype  =					jnp.floataa
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Any						=   nn.Conv(
          self.block_out_channels[0]						,    kernel_size=(3, 3)						,    padding=((1, 1), (1, 1))						,    dtype=self.dtype						,    )

      _snake_case     :						Any						=   []
      for i in range(len(self.block_out_channels     ) - 1     ):
        _snake_case     :						Dict						=   self.block_out_channels[i]
        _snake_case     :						int						=   self.block_out_channels[i + 1]
        _snake_case     :						str						=   nn.Conv(
            snake_case_						,    kernel_size=(3, 3)						,    padding=((1, 1), (1, 1))						,    dtype=self.dtype						,    )
        blocks.append(snake_case_     )
        _snake_case     :						Any						=   nn.Conv(
            snake_case_						,    kernel_size=(3, 3)						,    strides=(2, 2)						,    padding=((1, 1), (1, 1))						,    dtype=self.dtype						,    )
        blocks.append(snake_case_     )
      _snake_case     :						Any						=   blocks

      _snake_case     :						Dict						=   nn.Conv(
          self.conditioning_embedding_channels						,    kernel_size=(3, 3)						,    padding=((1, 1), (1, 1))						,    kernel_init=nn.initializers.zeros_init()						,    bias_init=nn.initializers.zeros_init()						,    dtype=self.dtype						,    )


    def __call__(    self						,    snake_case_     ):
      _snake_case     :						List[Any]						=   self.conv_in(snake_case_     )
      _snake_case     :						List[Any]						=   nn.silu(snake_case_     )

      for block in self.blocks:
        _snake_case     :						Tuple						=   block(snake_case_     )
        _snake_case     :						str						=   nn.silu(snake_case_     )

      _snake_case     :						Dict						=   self.conv_out(snake_case_     )

      return embedding






@flax_register_to_config
class      _UpperCAmelCase				(					nn.Module		,					_snake_case		,					_snake_case):
    __lowercase							: int  =					3_2
    __lowercase							: int  =					4
    __lowercase							: Tuple[str]  =					(
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "DownBlock2D",
    )
    __lowercase							: Union[bool, Tuple[bool]]  =					False
    __lowercase							: Tuple[int]  =					(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
    __lowercase							: int  =					2
    __lowercase							: Union[int, Tuple[int]]  =					8
    __lowercase							: Optional[Union[int, Tuple[int]]]  =					None
    __lowercase							: int  =					1_2_8_0
    __lowercase							: float  =					0.0
    __lowercase							: bool  =					False
    __lowercase							: jnp.dtype  =					jnp.floataa
    __lowercase							: bool  =					True
    __lowercase							: int  =					0
    __lowercase							: str  =					"rgb"
    __lowercase							: Tuple[int]  =					(1_6, 3_2, 9_6, 2_5_6)
    def 					lowerCamelCase__	(    self						,    snake_case_     ):
      # init input tensors
      _snake_case     :						int						=   (1, self.in_channels, self.sample_size, self.sample_size)
      _snake_case     :						Dict						=   jnp.zeros(snake_case_						,    dtype=jnp.floataa     )
      _snake_case     :						Any						=   jnp.ones((1,)						,    dtype=jnp.intaa     )
      _snake_case     :						Any						=   jnp.zeros((1, 1, self.cross_attention_dim)						,    dtype=jnp.floataa     )
      _snake_case     :						int						=   (1, 3, self.sample_size * 8, self.sample_size * 8)
      _snake_case     :						Dict						=   jnp.zeros(snake_case_						,    dtype=jnp.floataa     )

      _snake_case     :						List[Any]						=   jax.random.split(snake_case_     )
      _snake_case     :						Any						=   {"params": params_rng, "dropout": dropout_rng}

      return self.init(snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_     )["params"]
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						int						=   self.block_out_channels
      _snake_case     :						Tuple						=   block_out_channels[0] * 4

      # If `num_attention_heads` is not defined (which is the case for most models)
      # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
      # The reason for this behavior is to correct for incorrectly named variables that were introduced
      # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
      # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
      # which is why we correct for the naming here.
      _snake_case     :						int						=   self.num_attention_heads or self.attention_head_dim

      # input
      _snake_case     :						Optional[Any]						=   nn.Conv(
          block_out_channels[0]						,    kernel_size=(3, 3)						,    strides=(1, 1)						,    padding=((1, 1), (1, 1))						,    dtype=self.dtype						,    )

      # time
      _snake_case     :						Tuple						=   FlaxTimesteps(
          block_out_channels[0]						,    flip_sin_to_cos=self.flip_sin_to_cos						,    freq_shift=self.config.freq_shift     )
      _snake_case     :						Tuple						=   FlaxTimestepEmbedding(snake_case_						,    dtype=self.dtype     )

      _snake_case     :						Any						=   FlaxControlNetConditioningEmbedding(
          conditioning_embedding_channels=block_out_channels[0]						,    block_out_channels=self.conditioning_embedding_out_channels						,    )

      _snake_case     :						int						=   self.only_cross_attention
      if isinstance(snake_case_						,    snake_case_     ):
        _snake_case     :						Optional[int]						=   (only_cross_attention,) * len(self.down_block_types     )

      if isinstance(snake_case_						,    snake_case_     ):
        _snake_case     :						Optional[int]						=   (num_attention_heads,) * len(self.down_block_types     )

      # down
      _snake_case     :						Optional[Any]						=   []
      _snake_case     :						Optional[int]						=   []

      _snake_case     :						List[Any]						=   block_out_channels[0]

      _snake_case     :						Union[str, Any]						=   nn.Conv(
          snake_case_						,    kernel_size=(1, 1)						,    padding="VALID"						,    kernel_init=nn.initializers.zeros_init()						,    bias_init=nn.initializers.zeros_init()						,    dtype=self.dtype						,    )
      controlnet_down_blocks.append(snake_case_     )

      for i, down_block_type in enumerate(self.down_block_types     ):
        _snake_case     :						Optional[Any]						=   output_channel
        _snake_case     :						Dict						=   block_out_channels[i]
        _snake_case     :						int						=   i == len(snake_case_     ) - 1

        if down_block_type == "CrossAttnDownBlock2D":
          _snake_case     :						Tuple						=   FlaxCrossAttnDownBlockaD(
              in_channels=snake_case_						,    out_channels=snake_case_						,    dropout=self.dropout						,    num_layers=self.layers_per_block						,    num_attention_heads=num_attention_heads[i]						,    add_downsample=not is_final_block						,    use_linear_projection=self.use_linear_projection						,    only_cross_attention=only_cross_attention[i]						,    dtype=self.dtype						,    )
        else:
          _snake_case     :						Optional[Any]						=   FlaxDownBlockaD(
              in_channels=snake_case_						,    out_channels=snake_case_						,    dropout=self.dropout						,    num_layers=self.layers_per_block						,    add_downsample=not is_final_block						,    dtype=self.dtype						,    )

        down_blocks.append(snake_case_     )

        for _ in range(self.layers_per_block     ):
          _snake_case     :						Tuple						=   nn.Conv(
              snake_case_						,    kernel_size=(1, 1)						,    padding="VALID"						,    kernel_init=nn.initializers.zeros_init()						,    bias_init=nn.initializers.zeros_init()						,    dtype=self.dtype						,    )
          controlnet_down_blocks.append(snake_case_     )

        if not is_final_block:
          _snake_case     :						Dict						=   nn.Conv(
              snake_case_						,    kernel_size=(1, 1)						,    padding="VALID"						,    kernel_init=nn.initializers.zeros_init()						,    bias_init=nn.initializers.zeros_init()						,    dtype=self.dtype						,    )
          controlnet_down_blocks.append(snake_case_     )

      _snake_case     :						int						=   down_blocks
      _snake_case     :						Dict						=   controlnet_down_blocks

      # mid
      _snake_case     :						Union[str, Any]						=   block_out_channels[-1]
      _snake_case     :						int						=   FlaxUNetMidBlockaDCrossAttn(
          in_channels=snake_case_						,    dropout=self.dropout						,    num_attention_heads=num_attention_heads[-1]						,    use_linear_projection=self.use_linear_projection						,    dtype=self.dtype						,    )

      _snake_case     :						Union[str, Any]						=   nn.Conv(
          snake_case_						,    kernel_size=(1, 1)						,    padding="VALID"						,    kernel_init=nn.initializers.zeros_init()						,    bias_init=nn.initializers.zeros_init()						,    dtype=self.dtype						,    )


    def __call__(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_ = 1.0						,    snake_case_ = True						,    snake_case_ = False						,    ):
      _snake_case     :						Union[str, Any]						=   self.controlnet_conditioning_channel_order
      if channel_order == "bgr":
        _snake_case     :						List[str]						=   jnp.flip(snake_case_						,    axis=1     )

      # 1. time
      if not isinstance(snake_case_						,    jnp.ndarray     ):
        _snake_case     :						int						=   jnp.array([timesteps]						,    dtype=jnp.intaa     )
      elif isinstance(snake_case_						,    jnp.ndarray     ) and len(timesteps.shape     ) == 0:
        _snake_case     :						Optional[int]						=   timesteps.astype(dtype=jnp.floataa     )
        _snake_case     :						Union[str, Any]						=   jnp.expand_dims(snake_case_						,    0     )

      _snake_case     :						Any						=   self.time_proj(snake_case_     )
      _snake_case     :						Optional[int]						=   self.time_embedding(snake_case_     )

      # 2. pre-process
      _snake_case     :						Union[str, Any]						=   jnp.transpose(snake_case_						,    (0, 2, 3, 1)     )
      _snake_case     :						str						=   self.conv_in(snake_case_     )

      _snake_case     :						str						=   jnp.transpose(snake_case_						,    (0, 2, 3, 1)     )
      _snake_case     :						Dict						=   self.controlnet_cond_embedding(snake_case_     )
      sample += controlnet_cond

      # 3. down
      _snake_case     :						Any						=   (sample,)
      for down_block in self.down_blocks:
        if isinstance(snake_case_						,    snake_case_     ):
          _snake_case     :						List[Any]						=   down_block(snake_case_						,    snake_case_						,    snake_case_						,    deterministic=not train     )
        else:
          _snake_case     :						Dict						=   down_block(snake_case_						,    snake_case_						,    deterministic=not train     )
        down_block_res_samples += res_samples

      # 4. mid
      _snake_case     :						Any						=   self.mid_block(snake_case_						,    snake_case_						,    snake_case_						,    deterministic=not train     )

      # 5. contronet blocks
      _snake_case     :						Tuple						=   ()
      for down_block_res_sample, controlnet_block in zip(snake_case_						,    self.controlnet_down_blocks     ):
        _snake_case     :						Dict						=   controlnet_block(snake_case_     )
        controlnet_down_block_res_samples += (down_block_res_sample,)

      _snake_case     :						List[str]						=   controlnet_down_block_res_samples

      _snake_case     :						Tuple						=   self.controlnet_mid_block(snake_case_     )

      # 6. scaling
      _snake_case     :						int						=   [sample * conditioning_scale for sample in down_block_res_samples]
      mid_block_res_sample *= conditioning_scale

      if not return_dict:
        return (down_block_res_samples, mid_block_res_sample)

      return FlaxControlNetOutput(
          down_block_res_samples=snake_case_						,    mid_block_res_sample=snake_case_     )



"""simple docstring"""
import argparse
import json
import subprocess
def 				a__   (   a   :				Optional[Any]       ,     a   :				Optional[int]		):

  """simple docstring"""







  _snake_case     :						str						=   []

  _snake_case     :						Optional[Any]						=   (
      f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
      " https://api.github.com/repos/huggingface/transformers/actions/runners"
  )
  _snake_case     :						Dict						=   subprocess.run(a       ,     shell=a       ,     stdout=subprocess.PIPE		)
  _snake_case     :						Tuple						=   output.stdout.decode("utf-8"		)
  _snake_case     :						List[str]						=   json.loads(a		)

  _snake_case     :						Any						=   status["runners"]
  for runner in runners:
    if runner["name"] in target_runners:
      if runner["status"] == "offline":
        offline_runners.append(a		)

    # save the result so we can report them on Slack
  with open("offline_runners.txt"       ,     "w"		) as fp:
    fp.write(json.dumps(a		)		)

  if len(a		) > 0:
    _snake_case     :						Any						=   "\n".join([x["name"] for x in offline_runners]		)
    raise ValueError(f'The following runners are offline:\n{failed}'		)


if __name__ == "__main__":
    def 				a__   (   a   :				Optional[int]		):

      """simple docstring"""







      return values.split(","		)

    _a      :			Optional[int]					=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--target_runners""",
        default=None,
        type=list_str,
        required=True,
        help="""Comma-separated list of runners to check status.""",
    )

    parser.add_argument(
        """--token""", default=None, type=str, required=True, help="""A token that has actions:read permission."""
    )
    _a      :			List[str]					=							parser.parse_args()



    get_runner_status(args.target_runners, args.token)

