
"""simple docstring"""




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


_A      :	Dict       		=	-1 if torch_device == """cpu""" else 0

@is_pipeline_test
class 		a__						(				unittest.TestCase						):
   __lowerCAmelCase						=    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __lowerCAmelCase						=    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a    ):
          lowercase   :    List[Any]			=							SummarizationPipeline(model=_a     ,   tokenizer=_a    )
          return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]
   def       __magic_name__      (      self     ,   _a     ,   _a    ):
          lowercase   :    Dict			=							summarizer.model

          lowercase   :    str			=							summarizer("(CNN)The Palestinian Authority officially became"    )
          self.assertEqual(_a     ,   [{"summary_text": ANY(_a    )}]    )

          lowercase   :    Union[str, Any]			=							summarizer(
              "(CNN)The Palestinian Authority officially became "     ,   num_beams=2     ,   min_length=2     ,   max_length=5     ,   )
          self.assertEqual(_a     ,   [{"summary_text": ANY(_a    )}]    )

          # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
          lowercase   :    Any			=							[
              "SwitchTransformersConfig",
              "T5Config",
              "LongT5Config",
              "LEDConfig",
              "PegasusXConfig",
              "FSMTConfig",
              "M2M100Config",
              "ProphetNetConfig",  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
          ]
          if model.config.__class__.__name__ not in model_can_handle_longer_seq:
                 # Too long and exception is expected.
                 # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
                 # the embedding layer.
                 if not (
                     isinstance(_a     ,   _a    )
                     and get_gpu_count() > 0
                     and len(summarizer.model.trainable_weights    ) > 0
                 ):
                        with self.assertRaises(_a    ):
                               lowercase   :    List[Any]			=							summarizer("This " * 1_000    )
          lowercase   :    List[str]			=							summarizer("This " * 1_000     ,   truncation=TruncationStrategy.ONLY_FIRST    )
   @require_torch
   def       __magic_name__      (      self    ):
          lowercase   :    Tuple			=							pipeline(task="summarization"     ,   model="sshleifer/tiny-mbart"     ,   framework="pt"    )
          lowercase   :    str			=							summarizer("This is a small test"    )
          self.assertEqual(
              _a     ,   [
                  {
                      "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
                  }
              ]     ,   )
   @require_tf
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[int]			=							pipeline(task="summarization"     ,   model="sshleifer/tiny-mbart"     ,   framework="tf"    )
          lowercase   :    Optional[int]			=							summarizer("This is a small test"    )
          self.assertEqual(
              _a     ,   [
                  {
                      "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
                  }
              ]     ,   )

   @require_torch
   @slow
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							pipeline(task="summarization"     ,   device=_a    )
          lowercase   :    Tuple			=							(
              " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
              " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
              " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
              " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
              " jurisdiction over alleged crimes committed \"in the occupied Palestinian territory, including East"
              " Jerusalem, since June 13, 2014.\" Later that month, the ICC opened a preliminary examination into the"
              " situation in Palestinian territories, paving the way for possible war crimes investigations against"
              " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
              " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
              " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
              " move toward greater justice. \"As Palestine formally becomes a State Party to the Rome Statute today, the"
              " world is also a step closer to ending a long era of impunity and injustice,\" he said, according to an"
              " ICC news release. \"Indeed, today brings us closer to our shared goals of justice and peace.\" Judge"
              " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
              " Palestinians. \"As the Rome Statute today enters into force for the State of Palestine, Palestine"
              " acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
              " These are substantive commitments, which cannot be taken lightly,\" she said. Rights group Human Rights"
              " Watch welcomed the development. \"Governments seeking to penalize Palestine for joining the ICC should"
              " immediately end their pressure, and countries that support universal acceptance of the court's treaty"
              " should speak out to welcome its membership,\" said Balkees Jarrah, international justice counsel for the"
              " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
              " decision to join a treaty to which over 100 countries around the world are members.\" In January, when"
              " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
              " outrage, saying the court was overstepping its boundaries. The United States also said it \"strongly\""
              " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
              " state and therefore we do not believe that it is eligible to join the ICC,\" the State Department said in"
              " a statement. It urged the warring sides to resolve their differences through direct negotiations. \"We"
              " will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,\""
              " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
              " territories as \"Palestine.\" While a preliminary examination is not a formal investigation, it allows the"
              " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
              " Bensouda said her office would \"conduct its analysis in full independence and impartiality.\" The war"
              " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
              " will include alleged war crimes committed since June. The International Criminal Court was set up in"
              " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
              " and Faith Karimi contributed to this report."
          )
          lowercase   :    Any			=							(
              " The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives"
              " the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States"
              " opposed the Palestinians' efforts to join the court . Rights group Human Rights Watch welcomes the move,"
              " says governments seeking to penalize Palestine should end pressure ."
          )
          lowercase   :    Tuple			=							summarizer(_a    )
          self.assertEqual(result[0]["summary_text"]     ,   _a    )




"""simple docstring"""




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def   __magic_name__     (				__snake_case    :	str					,			__snake_case    :	Any  )			->    Union[str, Any]:
       lowercase   :    List[str]			=							UNetaDModel(**__snake_case  )
       lowercase   :    str			=							checkpoint["all_modules.0.W"].data
       lowercase   :    Union[str, Any]			=							checkpoint["all_modules.0.W"].data
       lowercase   :    Optional[Any]			=							checkpoint["all_modules.1.weight"].data
       lowercase   :    List[str]			=							checkpoint["all_modules.1.bias"].data

       lowercase   :    List[Any]			=							checkpoint["all_modules.2.weight"].data
       lowercase   :    Union[str, Any]			=							checkpoint["all_modules.2.bias"].data

       lowercase   :    str			=							checkpoint["all_modules.3.weight"].data
       lowercase   :    str			=							checkpoint["all_modules.3.bias"].data

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

       lowercase   :    Tuple			=							4

       def set_attention_weights(__snake_case    :	Optional[int]					,			__snake_case    :	List[str]					,			__snake_case    :	List[str]  ):
              lowercase   :    Union[str, Any]			=							old_checkpoint[f"""all_modules.{index}.NIN_0.W"""].data.T
              lowercase   :    Tuple			=							old_checkpoint[f"""all_modules.{index}.NIN_1.W"""].data.T
              lowercase   :    Optional[Any]			=							old_checkpoint[f"""all_modules.{index}.NIN_2.W"""].data.T

              lowercase   :    Union[str, Any]			=							old_checkpoint[f"""all_modules.{index}.NIN_0.b"""].data
              lowercase   :    int			=							old_checkpoint[f"""all_modules.{index}.NIN_1.b"""].data
              lowercase   :    List[Any]			=							old_checkpoint[f"""all_modules.{index}.NIN_2.b"""].data

              lowercase   :    List[str]			=							old_checkpoint[f"""all_modules.{index}.NIN_3.W"""].data.T
              lowercase   :    Optional[int]			=							old_checkpoint[f"""all_modules.{index}.NIN_3.b"""].data

              lowercase   :    Any			=							old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
              lowercase   :    Optional[int]			=							old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

       def set_resnet_weights(__snake_case    :	Tuple					,			__snake_case    :	Optional[int]					,			__snake_case    :	List[str]  ):
              lowercase   :    int			=							old_checkpoint[f"""all_modules.{index}.Conv_0.weight"""].data
              lowercase   :    Optional[Any]			=							old_checkpoint[f"""all_modules.{index}.Conv_0.bias"""].data
              lowercase   :    Tuple			=							old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
              lowercase   :    List[str]			=							old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

              lowercase   :    int			=							old_checkpoint[f"""all_modules.{index}.Conv_1.weight"""].data
              lowercase   :    Union[str, Any]			=							old_checkpoint[f"""all_modules.{index}.Conv_1.bias"""].data
              lowercase   :    Any			=							old_checkpoint[f"""all_modules.{index}.GroupNorm_1.weight"""].data
              lowercase   :    Optional[int]			=							old_checkpoint[f"""all_modules.{index}.GroupNorm_1.bias"""].data

              lowercase   :    Optional[Any]			=							old_checkpoint[f"""all_modules.{index}.Dense_0.weight"""].data
              lowercase   :    Any			=							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:
                     lowercase   :    str			=							old_checkpoint[f"""all_modules.{index}.Conv_2.weight"""].data
                     lowercase   :    Optional[Any]			=							old_checkpoint[f"""all_modules.{index}.Conv_2.bias"""].data

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

              if hasattr(__snake_case					,			"downsamplers"  ) and block.downsamplers is not None:
                     set_resnet_weights(block.resnet_down					,			__snake_case					,			__snake_case  )
                     module_index += 1
                     lowercase   :    Dict			=							checkpoint[f"""all_modules.{module_index}.Conv_0.weight"""].data
                     lowercase   :    Dict			=							checkpoint[f"""all_modules.{module_index}.Conv_0.bias"""].data
                     module_index += 1

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

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

              if hasattr(__snake_case					,			"resnet_up"  ) and block.resnet_up is not None:
                     lowercase   :    Any			=							checkpoint[f"""all_modules.{module_index}.weight"""].data
                     lowercase   :    Union[str, Any]			=							checkpoint[f"""all_modules.{module_index}.bias"""].data
                     module_index += 1
                     lowercase   :    Any			=							checkpoint[f"""all_modules.{module_index}.weight"""].data
                     lowercase   :    str			=							checkpoint[f"""all_modules.{module_index}.bias"""].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up					,			__snake_case					,			__snake_case  )
                     module_index += 1

       lowercase   :    str			=							checkpoint[f"""all_modules.{module_index}.weight"""].data
       lowercase   :    Optional[Any]			=							checkpoint[f"""all_modules.{module_index}.bias"""].data
       module_index += 1
       lowercase   :    Dict			=							checkpoint[f"""all_modules.{module_index}.weight"""].data
       lowercase   :    List[Any]			=							checkpoint[f"""all_modules.{module_index}.bias"""].data

       return new_model_architecture.state_dict()


if __name__ == "__main__":
   _A      :	Any       		=	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.""",
   )

   _A      :	Union[str, Any]       		=	parser.parse_args()

   _A      :	List[str]       		=	torch.load(args.checkpoint_path, map_location="""cpu""")

   with open(args.config_file) as f:
      _A      :	Optional[int]       		=	json.loads(f.read())

   _A      :	Any       		=	convert_ncsnpp_checkpoint(
       checkpoint,
       config,
   )

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

   _A      :	Any       		=	UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

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

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


