





'''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_					(    lowerCAmelCase__	):

    '''simple docstring'''
    UpperCamelCase			   =			42




class  a_					(    lowerCAmelCase__				,   lowerCAmelCase__	):

    '''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			,		)		->       List[Any]:
        super().__init__()

        _SCREAMING_SNAKE_CASE     =      sample_size
        _SCREAMING_SNAKE_CASE     =      block_out_channels[0] * 4

        # Check inputs
        if len(UpperCamelCase__	) != len(UpperCamelCase__	):
            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(UpperCamelCase__	) != len(UpperCamelCase__	):
            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(UpperCamelCase__			,		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]			,		UpperCamelCase__			,		UpperCamelCase__	)
            _SCREAMING_SNAKE_CASE     =      block_out_channels[0]

        _SCREAMING_SNAKE_CASE     =      TimestepEmbedding(UpperCamelCase__			,		UpperCamelCase__	)

        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
            _SCREAMING_SNAKE_CASE     =      nn.Embedding(UpperCamelCase__			,		UpperCamelCase__	)
        elif class_embed_type == "timestep":
            _SCREAMING_SNAKE_CASE     =      TimestepEmbedding(UpperCamelCase__			,		UpperCamelCase__	)
        elif class_embed_type == "identity":
            _SCREAMING_SNAKE_CASE     =      nn.Identity(UpperCamelCase__			,		UpperCamelCase__	)
        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(UpperCamelCase__	):
            _SCREAMING_SNAKE_CASE     =      output_channel
            _SCREAMING_SNAKE_CASE     =      block_out_channels[i]
            _SCREAMING_SNAKE_CASE     =      i == len(UpperCamelCase__	) - 1

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

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

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

            _SCREAMING_SNAKE_CASE     =      i == len(UpperCamelCase__	) - 1

            _SCREAMING_SNAKE_CASE     =      get_up_block(
                UpperCamelCase__			,		num_layers=layers_per_block + 1			,		in_channels=UpperCamelCase__			,		out_channels=UpperCamelCase__			,		prev_output_channel=UpperCamelCase__			,		temb_channels=UpperCamelCase__			,		add_upsample=not is_final_block			,		resnet_eps=UpperCamelCase__			,		resnet_act_fn=UpperCamelCase__			,		resnet_groups=UpperCamelCase__			,		attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel			,		resnet_time_scale_shift=UpperCamelCase__			,		upsample_type=UpperCamelCase__			,		)
            self.up_blocks.append(UpperCamelCase__	)
            _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=UpperCamelCase__			,		eps=UpperCamelCase__	)
        _SCREAMING_SNAKE_CASE     =      nn.SiLU()
        _SCREAMING_SNAKE_CASE     =      nn.Convad(block_out_channels[0]			,		UpperCamelCase__			,		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(UpperCamelCase__	):
            _SCREAMING_SNAKE_CASE     =      torch.tensor([timesteps]			,		dtype=torch.long			,		device=sample.device	)
        elif torch.is_tensor(UpperCamelCase__	) 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(UpperCamelCase__	)

        # 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(UpperCamelCase__	)

        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(UpperCamelCase__	)

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

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

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

            down_block_res_samples += res_samples

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

        # 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(UpperCamelCase__			,		"""skip_conv"""	):
                _SCREAMING_SNAKE_CASE     =      upsample_block(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__	)
            else:
                _SCREAMING_SNAKE_CASE     =      upsample_block(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__	)

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

        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=UpperCamelCase__	)










'''simple docstring'''

import sacrebleu as scb
from packaging import version

import datasets


lowercase_         =			"""\
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

lowercase_         =			"""\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

lowercase_         =			"""
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,   _KWARGS_DESCRIPTION	)
class  a_					(    datasets.Metric	):

    '''simple docstring'''


    def       snake_case_(		self	)		->       Union[str, Any]:
        if version.parse(scb.__version__	) < version.parse("""1.4.12"""	):
            raise ImportWarning(
                """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
                """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""	)
        return datasets.MetricInfo(
            description=_DESCRIPTION			,		citation=_CITATION			,		homepage="""https://github.com/mjpost/sacreBLEU"""			,		inputs_description=_KWARGS_DESCRIPTION			,		features=datasets.Features(
                {
                    """predictions""": datasets.Value("""string"""			,		id="""sequence"""	),
                    """references""": datasets.Sequence(datasets.Value("""string"""			,		id="""sequence"""	)			,		id="""references"""	),
                }	)			,		codebase_urls=["""https://github.com/mjpost/sacreBLEU"""]			,		reference_urls=[
                """https://github.com/mjpost/sacreBLEU""",
                """https://en.wikipedia.org/wiki/BLEU""",
                """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
            ]			,		)





    def       snake_case_(		self			,		A			,		A			,		A="exp"			,		A=None			,		A=False			,		A=False			,		A=None			,		A=False			,		)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      len(references[0]	)
        if any(len(A	) != references_per_prediction for refs in references	):
            raise ValueError("""Sacrebleu requires the same number of references for each prediction"""	)
        _SCREAMING_SNAKE_CASE     =      [[refs[i] for refs in references] for i in range(A	)]
        _SCREAMING_SNAKE_CASE     =      scb.corpus_bleu(
            A			,		A			,		smooth_method=A			,		smooth_value=A			,		force=A			,		lowercase=A			,		use_effective_order=A			,		**({"""tokenize""": tokenize} if tokenize else {})			,		)
        _SCREAMING_SNAKE_CASE     =      {
            """score""": output.score,
            """counts""": output.counts,
            """totals""": output.totals,
            """precisions""": output.precisions,
            """bp""": output.bp,
            """sys_len""": output.sys_len,
            """ref_len""": output.ref_len,
        }
        return output_dict



