





'''simple docstring'''






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def 				_lowerCamelCase(	UpperCamelCase__     :			Union[str, Any]     ,							UpperCamelCase__     :			Any		)		->	Optional[Any]:
    A							:							Union[str, Any]				=    UNetaDModel(**UpperCAmelCase_		)
    A							:							Union[str, Any]				=    checkpoint['all_modules.0.W'].data
    A							:							int				=    checkpoint['all_modules.0.W'].data
    A							:							List[Any]				=    checkpoint['all_modules.1.weight'].data
    A							:							Tuple				=    checkpoint['all_modules.1.bias'].data

    A							:							List[Any]				=    checkpoint['all_modules.2.weight'].data
    A							:							Dict				=    checkpoint['all_modules.2.bias'].data

    A							:							Union[str, Any]				=    checkpoint['all_modules.3.weight'].data
    A							:							str				=    checkpoint['all_modules.3.bias'].data

    A							:							Optional[Any]				=    checkpoint[list(checkpoint.keys()		)[-4]].data
    A							:							Optional[Any]				=    checkpoint[list(checkpoint.keys()		)[-3]].data
    A							:							List[Any]				=    checkpoint[list(checkpoint.keys()		)[-2]].data
    A							:							Optional[int]				=    checkpoint[list(checkpoint.keys()		)[-1]].data

    A							:							Any				=    4

    def set_attention_weights(UpperCamelCase__     :			List[Any]     ,							UpperCamelCase__     :			List[str]     ,							UpperCamelCase__     :			int		):
        A							:							List[str]				=    old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
        A							:							List[str]				=    old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
        A							:							Optional[Any]				=    old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

        A							:							Tuple				=    old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
        A							:							Any				=    old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
        A							:							Dict				=    old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

        A							:							str				=    old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
        A							:							List[Any]				=    old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

        A							:							Any				=    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
        A							:							Dict				=    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

    def set_resnet_weights(UpperCamelCase__     :			Optional[int]     ,							UpperCamelCase__     :			str     ,							UpperCamelCase__     :			List[str]		):
        A							:							Optional[int]				=    old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
        A							:							Dict				=    old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
        A							:							Optional[Any]				=    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
        A							:							Optional[Any]				=    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

        A							:							Optional[int]				=    old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
        A							:							Optional[int]				=    old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
        A							:							Dict				=    old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
        A							:							Tuple				=    old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

        A							:							Union[str, Any]				=    old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
        A							:							Dict				=    old_checkpoint[F'''all_modules.{index}.Dense_0.bias'''].data

        if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
            A							:							Any				=    old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
            A							:							Optional[int]				=    old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

    for i, block in enumerate(new_model_architecture.downsample_blocks		):
        A							:							Any				=    hasattr(UpperCAmelCase_     ,							'''attentions'''		)
        for j in range(len(block.resnets		)		):
            set_resnet_weights(block.resnets[j]     ,							UpperCAmelCase_     ,							UpperCAmelCase_		)
            module_index += 1
            if has_attentions:
                set_attention_weights(block.attentions[j]     ,							UpperCAmelCase_     ,							UpperCAmelCase_		)
                module_index += 1

        if hasattr(UpperCAmelCase_     ,							'''downsamplers'''		) and block.downsamplers is not None:
            set_resnet_weights(block.resnet_down     ,							UpperCAmelCase_     ,							UpperCAmelCase_		)
            module_index += 1
            A							:							Union[str, Any]				=    checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
            A							:							List[Any]				=    checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
            module_index += 1

    set_resnet_weights(new_model_architecture.mid_block.resnets[0]     ,							UpperCAmelCase_     ,							UpperCAmelCase_		)
    module_index += 1
    set_attention_weights(new_model_architecture.mid_block.attentions[0]     ,							UpperCAmelCase_     ,							UpperCAmelCase_		)
    module_index += 1
    set_resnet_weights(new_model_architecture.mid_block.resnets[1]     ,							UpperCAmelCase_     ,							UpperCAmelCase_		)
    module_index += 1

    for i, block in enumerate(new_model_architecture.up_blocks		):
        A							:							Tuple				=    hasattr(UpperCAmelCase_     ,							'''attentions'''		)
        for j in range(len(block.resnets		)		):
            set_resnet_weights(block.resnets[j]     ,							UpperCAmelCase_     ,							UpperCAmelCase_		)
            module_index += 1
        if has_attentions:
            set_attention_weights(
                block.attentions[0]     ,							UpperCAmelCase_     ,							UpperCAmelCase_		)  # why can there only be a single attention layer for up?
            module_index += 1

        if hasattr(UpperCAmelCase_     ,							'''resnet_up'''		) and block.resnet_up is not None:
            A							:							Dict				=    checkpoint[F'''all_modules.{module_index}.weight'''].data
            A							:							Dict				=    checkpoint[F'''all_modules.{module_index}.bias'''].data
            module_index += 1
            A							:							Optional[int]				=    checkpoint[F'''all_modules.{module_index}.weight'''].data
            A							:							Optional[Any]				=    checkpoint[F'''all_modules.{module_index}.bias'''].data
            module_index += 1
            set_resnet_weights(block.resnet_up     ,							UpperCAmelCase_     ,							UpperCAmelCase_		)
            module_index += 1

    A							:							Dict				=    checkpoint[F'''all_modules.{module_index}.weight'''].data
    A							:							str				=    checkpoint[F'''all_modules.{module_index}.bias'''].data
    module_index += 1
    A							:							Dict				=    checkpoint[F'''all_modules.{module_index}.weight'''].data
    A							:							Tuple				=    checkpoint[F'''all_modules.{module_index}.bias'''].data

    return new_model_architecture.state_dict()


if __name__ == "__main__":
     snake_case_     			=						argparse.ArgumentParser()

     parser.add_argument(
         """--checkpoint_path""",
         default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
         type=str,
         required=False,
         help="""Path to the checkpoint to convert.""",
     )

     parser.add_argument(
         """--config_file""",
         default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
         type=str,
         required=False,
         help="""The config json file corresponding to the architecture.""",
     )

     parser.add_argument(
         """--dump_path""",
         default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
         type=str,
         required=False,
         help="""Path to the output model.""",
     )

     snake_case_     			=						parser.parse_args()

     snake_case_     			=						torch.load(args.checkpoint_path, map_location="""cpu""")

     with open(args.config_file) as f:
          snake_case_     			=						json.loads(f.read())

     snake_case_     			=						convert_ncsnpp_checkpoint(
         checkpoint,
         config,
     )

     if "sde" in config:
          del config["sde"]

     snake_case_     			=						UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          snake_case_     			=						ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

          snake_case_     			=						ScoreSdeVePipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)



'''simple docstring'''

import sacrebleu as scb
from packaging import version

import datasets


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

lowercase__            =     "\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"

lowercase__            =     "\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    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).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    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`).\n    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:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    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`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"




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






     '''simple docstring'''



     def   UpperCAmelCase_			(						self					:	Tuple			)		->      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   UpperCAmelCase_			(						self					:	Union[str, Any]	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	int	,     lowercase_					:	List[Any]="exp"	,     lowercase_					:	Optional[int]=None	,     lowercase_					:	List[str]=False	,     lowercase_					:	Tuple=False	,     lowercase_					:	int=None	,     lowercase_					:	str=False	,     )		->      Dict:
      UpperCAmelCase			:      Union[str, Any]		   =					len(references[0]			)
      if any(len(lowercase_			) != references_per_prediction for refs in references			):
       raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
      UpperCAmelCase			:      Optional[Any]		   =					[[refs[i] for refs in references] for i in range(lowercase_			)]
      UpperCAmelCase			:      List[Any]		   =					scb.corpus_bleu(
          lowercase_	,     lowercase_	,     smooth_method=lowercase_	,     smooth_value=lowercase_	,     force=lowercase_	,     lowercase=lowercase_	,     use_effective_order=lowercase_	,     **({'tokenize': tokenize} if tokenize else {})	,     )
      UpperCAmelCase			:      Union[str, Any]		   =					{
          '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




