






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class 		_snake_case  (  A__					):
   _lowercase		:    torch.FloatTensor



class 		_snake_case  (  A__   ,    A__					):






   @register_to_config
   def __init__(       self		,					a = 6_5536		,					a = None		,					a = 2		,					a = 2		,					a = 0		,					a = "fourier"		,					a = True		,					a = False		,					a = 0.0		,					a = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,					a = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,					a = "UNetMidBlock1D"		,					a = None		,					a = (32, 32, 64)		,					a = None		,					a = 8		,					a = 1		,					a = False		,					)      ->       Any:
      super().__init__()
      SCREAMING_SNAKE_CASE						= sample_size

      # time
      if time_embedding_type == "fourier":
         SCREAMING_SNAKE_CASE						= GaussianFourierProjection(
             embedding_size=8		,					set_W_to_weight=a		,					log=a		,					flip_sin_to_cos=a)
         SCREAMING_SNAKE_CASE						= 2 * block_out_channels[0]
      elif time_embedding_type == "positional":
         SCREAMING_SNAKE_CASE						= Timesteps(
             block_out_channels[0]		,					flip_sin_to_cos=a		,					downscale_freq_shift=a)
         SCREAMING_SNAKE_CASE						= block_out_channels[0]

      if use_timestep_embedding:
         SCREAMING_SNAKE_CASE						= block_out_channels[0] * 4
         SCREAMING_SNAKE_CASE						= TimestepEmbedding(
             in_channels=a		,					time_embed_dim=a		,					act_fn=a		,					out_dim=block_out_channels[0]		,					)

      SCREAMING_SNAKE_CASE						= nn.ModuleList([])
      SCREAMING_SNAKE_CASE						= None
      SCREAMING_SNAKE_CASE						= nn.ModuleList([])
      SCREAMING_SNAKE_CASE						= None

      # down
      SCREAMING_SNAKE_CASE						= in_channels
      for i, down_block_type in enumerate(a):
         SCREAMING_SNAKE_CASE						= output_channel
         SCREAMING_SNAKE_CASE						= block_out_channels[i]

         if i == 0:
            input_channel += extra_in_channels

         SCREAMING_SNAKE_CASE						= i == len(a) - 1

         SCREAMING_SNAKE_CASE						= get_down_block(
             a		,					num_layers=a		,					in_channels=a		,					out_channels=a		,					temb_channels=block_out_channels[0]		,					add_downsample=not is_final_block or downsample_each_block		,					)
         self.down_blocks.append(a)

      # mid
      SCREAMING_SNAKE_CASE						= get_mid_block(
          a		,					in_channels=block_out_channels[-1]		,					mid_channels=block_out_channels[-1]		,					out_channels=block_out_channels[-1]		,					embed_dim=block_out_channels[0]		,					num_layers=a		,					add_downsample=a		,					)

      # up
      SCREAMING_SNAKE_CASE						= list(reversed(a))
      SCREAMING_SNAKE_CASE						= reversed_block_out_channels[0]
      if out_block_type is None:
         SCREAMING_SNAKE_CASE						= out_channels
      else:
         SCREAMING_SNAKE_CASE						= block_out_channels[0]

      for i, up_block_type in enumerate(a):
         SCREAMING_SNAKE_CASE						= output_channel
         SCREAMING_SNAKE_CASE						= (
             reversed_block_out_channels[i + 1] if i < len(a) - 1 else final_upsample_channels
         )

         SCREAMING_SNAKE_CASE						= i == len(a) - 1

         SCREAMING_SNAKE_CASE						= get_up_block(
             a		,					num_layers=a		,					in_channels=a		,					out_channels=a		,					temb_channels=block_out_channels[0]		,					add_upsample=not is_final_block		,					)
         self.up_blocks.append(a)
         SCREAMING_SNAKE_CASE						= output_channel

      # out
      SCREAMING_SNAKE_CASE						= norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,					32)
      SCREAMING_SNAKE_CASE						= get_out_block(
          out_block_type=a		,					num_groups_out=a		,					embed_dim=block_out_channels[0]		,					out_channels=a		,					act_fn=a		,					fc_dim=block_out_channels[-1] // 4		,					)







   def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = True		,					)      ->       Union[UNetaDOutput, Tuple]:
      SCREAMING_SNAKE_CASE						= timestep
      if not torch.is_tensor(a):
         SCREAMING_SNAKE_CASE						= torch.tensor([timesteps]		,					dtype=torch.long		,					device=sample.device)
      elif torch.is_tensor(a) and len(timesteps.shape) == 0:
         SCREAMING_SNAKE_CASE						= timesteps[None].to(sample.device)

      SCREAMING_SNAKE_CASE						= self.time_proj(a)
      if self.config.use_timestep_embedding:
         SCREAMING_SNAKE_CASE						= self.time_mlp(a)
      else:
         SCREAMING_SNAKE_CASE						= timestep_embed[..., None]
         SCREAMING_SNAKE_CASE						= timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
         SCREAMING_SNAKE_CASE						= timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

      # 2. down
      SCREAMING_SNAKE_CASE						= ()
      for downsample_block in self.down_blocks:
         SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= downsample_block(hidden_states=a		,					temb=a)
         down_block_res_samples += res_samples

      # 3. mid
      if self.mid_block:
         SCREAMING_SNAKE_CASE						= self.mid_block(a		,					a)

      # 4. up
      for i, upsample_block in enumerate(self.up_blocks):
         SCREAMING_SNAKE_CASE						= down_block_res_samples[-1:]
         SCREAMING_SNAKE_CASE						= down_block_res_samples[:-1]
         SCREAMING_SNAKE_CASE						= upsample_block(a		,					res_hidden_states_tuple=a		,					temb=a)

      # 5. post-process
      if self.out_block:
         SCREAMING_SNAKE_CASE						= self.out_block(a		,					a)

      if not return_dict:
         return (sample,)

      return UNetaDOutput(sample=a)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_		:						List[Any]             =    {
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Dict             =    [
				    'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BigBirdPegasusForCausalLM',
				    'BigBirdPegasusForConditionalGeneration',
				    'BigBirdPegasusForQuestionAnswering',
				    'BigBirdPegasusForSequenceClassification',
				    'BigBirdPegasusModel',
				    'BigBirdPegasusPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_bigbird_pegasus import (
				    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BigBirdPegasusConfig,
				    BigBirdPegasusOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bigbird_pegasus import (
								    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BigBirdPegasusForCausalLM,
								    BigBirdPegasusForConditionalGeneration,
								    BigBirdPegasusForQuestionAnswering,
								    BigBirdPegasusForSequenceClassification,
								    BigBirdPegasusModel,
								    BigBirdPegasusPreTrainedModel,
								)


else:
				import sys

				a_		:						Tuple             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




