

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_   (      _a						):


    '''simple docstring'''






    a__     							=       42






class 					A_   (      _a ,     _a						):


    '''simple docstring'''






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

        __UpperCAmelCase									=			sample_size
        __UpperCAmelCase									=			block_out_channels[0] * 4

        # Check inputs
        if len(lowercase__  ) != len(lowercase__  ):
            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(lowercase__  ) != len(lowercase__  ):
            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
        __UpperCAmelCase									=			nn.Convad(lowercase__     ,      block_out_channels[0]     ,      kernel_size=3     ,      padding=(1, 1)  )

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

        __UpperCAmelCase									=			TimestepEmbedding(lowercase__     ,      lowercase__  )

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

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

        # down
        __UpperCAmelCase									=			block_out_channels[0]
        for i, down_block_type in enumerate(lowercase__  ):
            __UpperCAmelCase									=			output_channel
            __UpperCAmelCase									=			block_out_channels[i]
            __UpperCAmelCase									=			i == len(lowercase__  ) - 1

            __UpperCAmelCase									=			get_down_block(
                lowercase__     ,      num_layers=lowercase__     ,      in_channels=lowercase__     ,      out_channels=lowercase__     ,      temb_channels=lowercase__     ,      add_downsample=not is_final_block     ,      resnet_eps=lowercase__     ,      resnet_act_fn=lowercase__     ,      resnet_groups=lowercase__     ,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel     ,      downsample_padding=lowercase__     ,      resnet_time_scale_shift=lowercase__     ,      downsample_type=lowercase__     ,      )
            self.down_blocks.append(lowercase__  )

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

        # up
        __UpperCAmelCase									=			list(reversed(lowercase__  )  )
        __UpperCAmelCase									=			reversed_block_out_channels[0]
        for i, up_block_type in enumerate(lowercase__  ):
            __UpperCAmelCase									=			output_channel
            __UpperCAmelCase									=			reversed_block_out_channels[i]
            __UpperCAmelCase									=			reversed_block_out_channels[min(i + 1     ,      len(lowercase__  ) - 1  )]

            __UpperCAmelCase									=			i == len(lowercase__  ) - 1

            __UpperCAmelCase									=			get_up_block(
                lowercase__     ,      num_layers=layers_per_block + 1     ,      in_channels=lowercase__     ,      out_channels=lowercase__     ,      prev_output_channel=lowercase__     ,      temb_channels=lowercase__     ,      add_upsample=not is_final_block     ,      resnet_eps=lowercase__     ,      resnet_act_fn=lowercase__     ,      resnet_groups=lowercase__     ,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel     ,      resnet_time_scale_shift=lowercase__     ,      upsample_type=lowercase__     ,      )
            self.up_blocks.append(lowercase__  )
            __UpperCAmelCase									=			output_channel

        # out
        __UpperCAmelCase									=			norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     ,      32  )
        __UpperCAmelCase									=			nn.GroupNorm(num_channels=block_out_channels[0]     ,      num_groups=lowercase__     ,      eps=lowercase__  )
        __UpperCAmelCase									=			nn.SiLU()
        __UpperCAmelCase									=			nn.Convad(block_out_channels[0]     ,      lowercase__     ,      kernel_size=3     ,      padding=1  )






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

        # 1. time
        __UpperCAmelCase									=			timestep
        if not torch.is_tensor(lowercase__  ):
            __UpperCAmelCase									=			torch.tensor([timesteps]     ,      dtype=torch.long     ,      device=sample.device  )
        elif torch.is_tensor(lowercase__  ) and len(timesteps.shape  ) == 0:
            __UpperCAmelCase									=			timesteps[None].to(sample.device  )

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

        __UpperCAmelCase									=			self.time_proj(lowercase__  )

        # 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.
        __UpperCAmelCase									=			t_emb.to(dtype=self.dtype  )
        __UpperCAmelCase									=			self.time_embedding(lowercase__  )

        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":
                __UpperCAmelCase									=			self.time_proj(lowercase__  )

            __UpperCAmelCase									=			self.class_embedding(lowercase__  ).to(dtype=self.dtype  )
            __UpperCAmelCase									=			emb + class_emb

        # 2. pre-process
        __UpperCAmelCase									=			sample
        __UpperCAmelCase									=			self.conv_in(lowercase__  )

        # 3. down
        __UpperCAmelCase									=			(sample,)
        for downsample_block in self.down_blocks:
            if hasattr(lowercase__     ,      '''skip_conv'''  ):
                __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			downsample_block(
                    hidden_states=lowercase__     ,      temb=lowercase__     ,      skip_sample=lowercase__  )
            else:
                __UpperCAmelCase					,     __UpperCAmelCase									=			downsample_block(hidden_states=lowercase__     ,      temb=lowercase__  )

            down_block_res_samples += res_samples

        # 4. mid
        __UpperCAmelCase									=			self.mid_block(lowercase__     ,      lowercase__  )

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

            if hasattr(lowercase__     ,      '''skip_conv'''  ):
                __UpperCAmelCase					,     __UpperCAmelCase									=			upsample_block(lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )
            else:
                __UpperCAmelCase									=			upsample_block(lowercase__     ,      lowercase__     ,      lowercase__  )

        # 6. post-process
        __UpperCAmelCase									=			self.conv_norm_out(lowercase__  )
        __UpperCAmelCase									=			self.conv_act(lowercase__  )
        __UpperCAmelCase									=			self.conv_out(lowercase__  )

        if skip_sample is not None:
            sample += skip_sample

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

        if not return_dict:
            return (sample,)

        return UNetaDOutput(sample=lowercase__  )







import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
A_		:			Any										=   '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'

# TODO: Add description of the dataset here
# You can copy an official description
A_		:			Optional[Any]										=   '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'

# TODO: Add a link to an official homepage for the dataset here
A_		:			Dict										=   ''

# TODO: Add the licence for the dataset here if you can find it
A_		:			Dict										=   ''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
A_		:			Union[str, Any]										=   {
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}
class 					A_   (      datasets.GeneratorBasedBuilder						):


    '''simple docstring'''






    a__     							=       datasets.Version("1.1.0"						)

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    a__     							=       [
        datasets.BuilderConfig(name="first_domain" ,     version=_a ,     description="This part of my dataset covers a first domain"						),
        datasets.BuilderConfig(name="second_domain" ,     version=_a ,     description="This part of my dataset covers a second domain"						),
    ]

    a__     							=       "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.


    def      lowerCAmelCase_						(self  )     ->      Tuple:
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            __UpperCAmelCase									=			datasets.Features(
                {
                    '''sentence''': datasets.Value('''string'''  ),
                    '''option1''': datasets.Value('''string'''  ),
                    '''answer''': datasets.Value('''string'''  )
                    # These are the features of your dataset like images, labels ...
                }  )
        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
            __UpperCAmelCase									=			datasets.Features(
                {
                    '''sentence''': datasets.Value('''string'''  ),
                    '''option2''': datasets.Value('''string'''  ),
                    '''second_domain_answer''': datasets.Value('''string'''  )
                    # These are the features of your dataset like images, labels ...
                }  )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION     ,      features=lowercase__     ,      homepage=_HOMEPAGE     ,      license=_LICENSE     ,      citation=_CITATION     ,      )


    def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        __UpperCAmelCase									=			_URLS[self.config.name]
        __UpperCAmelCase									=			dl_manager.download_and_extract(lowercase__  )
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN     ,      gen_kwargs={
                    '''filepath''': os.path.join(lowercase__     ,      '''train.jsonl'''  ),
                    '''split''': '''train''',
                }     ,      ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION     ,      gen_kwargs={
                    '''filepath''': os.path.join(lowercase__     ,      '''dev.jsonl'''  ),
                    '''split''': '''dev''',
                }     ,      ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST     ,      gen_kwargs={
                    '''filepath''': os.path.join(lowercase__     ,      '''test.jsonl'''  ),
                    '''split''': '''test'''
                }     ,      ),
        ]






    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(lowercase__     ,      encoding='''utf-8'''  ) as f:
            for key, row in enumerate(lowercase__  ):
                __UpperCAmelCase									=			json.loads(lowercase__  )
                if self.config.name == "first_domain":
                    # Yields examples as (key, example) tuples
                    yield key, {
                        "sentence": data["sentence"],
                        "option1": data["option1"],
                        "answer": "" if split == "test" else data["answer"],
                    }
                else:
                    yield key, {
                        "sentence": data["sentence"],
                        "option2": data["option2"],
                        "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                    }




