
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      __A(    __lowerCamelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      42






class      __A(    __lowerCamelCase    ,				__lowerCamelCase			):

  """simple docstring"""







  @register_to_config
  def __init__(self	,      SCREAMING_SNAKE_CASE_ = 6_55_36	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = 2	,      SCREAMING_SNAKE_CASE_ = 2	,      SCREAMING_SNAKE_CASE_ = 0	,      SCREAMING_SNAKE_CASE_ = "fourier"	,      SCREAMING_SNAKE_CASE_ = True	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = 0.0	,      SCREAMING_SNAKE_CASE_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")	,      SCREAMING_SNAKE_CASE_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")	,      SCREAMING_SNAKE_CASE_ = "UNetMidBlock1D"	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = (32, 32, 64)	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = 8	,      SCREAMING_SNAKE_CASE_ = 1	,      SCREAMING_SNAKE_CASE_ = False	,      ):
       super().__init__()
       UpperCamelCase__				=    sample_size

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

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

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

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

            if i == 0:
                 input_channel += extra_in_channels

            UpperCamelCase__				=    i == len(SCREAMING_SNAKE_CASE_						) - 1

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

       # mid
       UpperCamelCase__				=    get_mid_block(
           SCREAMING_SNAKE_CASE_	,      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=SCREAMING_SNAKE_CASE_	,      add_downsample=SCREAMING_SNAKE_CASE_	,      )

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

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

            UpperCamelCase__				=    i == len(SCREAMING_SNAKE_CASE_						) - 1

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

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







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = True	,      ):
       UpperCamelCase__				=    timestep
       if not torch.is_tensor(SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    torch.tensor([timesteps]	,      dtype=torch.long	,      device=sample.device						)
       elif torch.is_tensor(SCREAMING_SNAKE_CASE_						) and len(timesteps.shape						) == 0:
            UpperCamelCase__				=    timesteps[None].to(sample.device						)

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

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

       # 3. mid
       if self.mid_block:
            UpperCamelCase__				=    self.mid_block(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

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

       # 5. post-process
       if self.out_block:
            UpperCamelCase__				=    self.out_block(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       if not return_dict:
            return (sample,)

       return UNetaDOutput(sample=SCREAMING_SNAKE_CASE_						)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_						     =					{
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					[
        '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''GitForCausalLM''',
        '''GitModel''',
        '''GitPreTrainedModel''',
        '''GitVisionModel''',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

    lowerCamelCase_						     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





