





'''simple docstring'''

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 UNetMidBlockaD, get_down_block, get_up_block


@dataclass
class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			42




class  a_					(    snake_case_				,   snake_case_	):

    '''simple docstring'''


    @register_to_config
    def __init__(		self			,		A = None			,		A = 3			,		A = 3			,		A = False			,		A = "positional"			,		A = 0			,		A = True			,		A = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")			,		A = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")			,		A = (224, 448, 672, 896)			,		A = 2			,		A = 1			,		A = 1			,		A = "conv"			,		A = "conv"			,		A = "silu"			,		A = 8			,		A = 32			,		A = 1e-5			,		A = "default"			,		A = True			,		A = None			,		A = None			,		)		->       Dict:
        super().__init__()

        _SCREAMING_SNAKE_CASE     =      sample_size
        _SCREAMING_SNAKE_CASE     =      block_out_channels[0] * 4

        # Check inputs
        if len(A	) != len(A	):
            raise ValueError(
                f'Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'	)

        if len(A	) != len(A	):
            raise ValueError(
                f'Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'	)

        # input
        _SCREAMING_SNAKE_CASE     =      nn.Convad(A			,		block_out_channels[0]			,		kernel_size=3			,		padding=(1, 1)	)

        # time
        if time_embedding_type == "fourier":
            _SCREAMING_SNAKE_CASE     =      GaussianFourierProjection(embedding_size=block_out_channels[0]			,		scale=16	)
            _SCREAMING_SNAKE_CASE     =      2 * block_out_channels[0]
        elif time_embedding_type == "positional":
            _SCREAMING_SNAKE_CASE     =      Timesteps(block_out_channels[0]			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      block_out_channels[0]

        _SCREAMING_SNAKE_CASE     =      TimestepEmbedding(A			,		A	)

        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
            _SCREAMING_SNAKE_CASE     =      nn.Embedding(A			,		A	)
        elif class_embed_type == "timestep":
            _SCREAMING_SNAKE_CASE     =      TimestepEmbedding(A			,		A	)
        elif class_embed_type == "identity":
            _SCREAMING_SNAKE_CASE     =      nn.Identity(A			,		A	)
        else:
            _SCREAMING_SNAKE_CASE     =      None

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

        # down
        _SCREAMING_SNAKE_CASE     =      block_out_channels[0]
        for i, down_block_type in enumerate(A	):
            _SCREAMING_SNAKE_CASE     =      output_channel
            _SCREAMING_SNAKE_CASE     =      block_out_channels[i]
            _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=A			,		add_downsample=not is_final_block			,		resnet_eps=A			,		resnet_act_fn=A			,		resnet_groups=A			,		attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel			,		downsample_padding=A			,		resnet_time_scale_shift=A			,		downsample_type=A			,		)
            self.down_blocks.append(A	)

        # mid
        _SCREAMING_SNAKE_CASE     =      UNetMidBlockaD(
            in_channels=block_out_channels[-1]			,		temb_channels=A			,		resnet_eps=A			,		resnet_act_fn=A			,		output_scale_factor=A			,		resnet_time_scale_shift=A			,		attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]			,		resnet_groups=A			,		add_attention=A			,		)

        # up
        _SCREAMING_SNAKE_CASE     =      list(reversed(A	)	)
        _SCREAMING_SNAKE_CASE     =      reversed_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]
            _SCREAMING_SNAKE_CASE     =      reversed_block_out_channels[min(i + 1			,		len(A	) - 1	)]

            _SCREAMING_SNAKE_CASE     =      i == len(A	) - 1

            _SCREAMING_SNAKE_CASE     =      get_up_block(
                A			,		num_layers=layers_per_block + 1			,		in_channels=A			,		out_channels=A			,		prev_output_channel=A			,		temb_channels=A			,		add_upsample=not is_final_block			,		resnet_eps=A			,		resnet_act_fn=A			,		resnet_groups=A			,		attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel			,		resnet_time_scale_shift=A			,		upsample_type=A			,		)
            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     =      nn.GroupNorm(num_channels=block_out_channels[0]			,		num_groups=A			,		eps=A	)
        _SCREAMING_SNAKE_CASE     =      nn.SiLU()
        _SCREAMING_SNAKE_CASE     =      nn.Convad(block_out_channels[0]			,		A			,		kernel_size=3			,		padding=1	)





    def       snake_case_(		self			,		A			,		A			,		A = None			,		A = True			,		)		->       Union[UNetaDOutput, Tuple]:
        # 0. center input if necessary
        if self.config.center_input_sample:
            _SCREAMING_SNAKE_CASE     =      2 * sample - 1.0

        # 1. time
        _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	)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        _SCREAMING_SNAKE_CASE     =      timesteps * torch.ones(sample.shape[0]			,		dtype=timesteps.dtype			,		device=timesteps.device	)

        _SCREAMING_SNAKE_CASE     =      self.time_proj(A	)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        _SCREAMING_SNAKE_CASE     =      t_emb.to(dtype=self.dtype	)
        _SCREAMING_SNAKE_CASE     =      self.time_embedding(A	)

        if self.class_embedding is not None:
            if class_labels is None:
                raise ValueError("""class_labels should be provided when doing class conditioning"""	)

            if self.config.class_embed_type == "timestep":
                _SCREAMING_SNAKE_CASE     =      self.time_proj(A	)

            _SCREAMING_SNAKE_CASE     =      self.class_embedding(A	).to(dtype=self.dtype	)
            _SCREAMING_SNAKE_CASE     =      emb + class_emb

        # 2. pre-process
        _SCREAMING_SNAKE_CASE     =      sample
        _SCREAMING_SNAKE_CASE     =      self.conv_in(A	)

        # 3. down
        _SCREAMING_SNAKE_CASE     =      (sample,)
        for downsample_block in self.down_blocks:
            if hasattr(A			,		"""skip_conv"""	):
                _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      downsample_block(
                    hidden_states=A			,		temb=A			,		skip_sample=A	)
            else:
                _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      downsample_block(hidden_states=A			,		temb=A	)

            down_block_res_samples += res_samples

        # 4. mid
        _SCREAMING_SNAKE_CASE     =      self.mid_block(A			,		A	)

        # 5. up
        _SCREAMING_SNAKE_CASE     =      None
        for upsample_block in self.up_blocks:
            _SCREAMING_SNAKE_CASE     =      down_block_res_samples[-len(upsample_block.resnets	) :]
            _SCREAMING_SNAKE_CASE     =      down_block_res_samples[: -len(upsample_block.resnets	)]

            if hasattr(A			,		"""skip_conv"""	):
                _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      upsample_block(A			,		A			,		A			,		A	)
            else:
                _SCREAMING_SNAKE_CASE     =      upsample_block(A			,		A			,		A	)

        # 6. post-process
        _SCREAMING_SNAKE_CASE     =      self.conv_norm_out(A	)
        _SCREAMING_SNAKE_CASE     =      self.conv_act(A	)
        _SCREAMING_SNAKE_CASE     =      self.conv_out(A	)

        if skip_sample is not None:
            sample += skip_sample

        if self.config.time_embedding_type == "fourier":
            _SCREAMING_SNAKE_CASE     =      timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]	)))	)
            _SCREAMING_SNAKE_CASE     =      sample / timesteps

        if not return_dict:
            return (sample,)

        return UNetaDOutput(sample=A	)










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_         =			logging.get_logger(__name__)

lowercase_         =			{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			'''rembert'''


    def __init__(		self			,		A=25_0300			,		A=1152			,		A=32			,		A=18			,		A=256			,		A=1664			,		A=4608			,		A="gelu"			,		A=0.0			,		A=0.0			,		A=0.1			,		A=512			,		A=2			,		A=0.02			,		A=1e-12			,		A=True			,		A=0			,		A=312			,		A=313			,		**A			,		)		->       Dict:
        super().__init__(pad_token_id=A			,		bos_token_id=A			,		eos_token_id=A			,		**A	)

        _SCREAMING_SNAKE_CASE     =      vocab_size
        _SCREAMING_SNAKE_CASE     =      input_embedding_size
        _SCREAMING_SNAKE_CASE     =      output_embedding_size
        _SCREAMING_SNAKE_CASE     =      max_position_embeddings
        _SCREAMING_SNAKE_CASE     =      hidden_size
        _SCREAMING_SNAKE_CASE     =      num_hidden_layers
        _SCREAMING_SNAKE_CASE     =      num_attention_heads
        _SCREAMING_SNAKE_CASE     =      intermediate_size
        _SCREAMING_SNAKE_CASE     =      hidden_act
        _SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
        _SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE     =      classifier_dropout_prob
        _SCREAMING_SNAKE_CASE     =      initializer_range
        _SCREAMING_SNAKE_CASE     =      type_vocab_size
        _SCREAMING_SNAKE_CASE     =      layer_norm_eps
        _SCREAMING_SNAKE_CASE     =      use_cache
        _SCREAMING_SNAKE_CASE     =      False




class  a_					(    snake_case_	):

    '''simple docstring'''


    @property
    def       snake_case_(		self	)		->       Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]	)





    @property
    def       snake_case_(		self	)		->       float:
        return 1e-4



