





"""simple docstring"""


import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY







@is_pipeline_test
class 	__a   (  unittest.TestCase		):
     SCREAMING_SNAKE_CASE__ :      List[str]       		=			MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     SCREAMING_SNAKE_CASE__ :      Optional[Any]       		=			TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



     def 						snake_case_			(       self					,     a__					,     a__					,     a__   ):
            if isinstance(model.config					,     a__   ):
                   _lowerCamelCase	,	_lowerCamelCase    	=					list(tokenizer.lang_code_to_id.keys()   )[:2]
                   _lowerCamelCase    	=					TranslationPipeline(model=a__					,     tokenizer=a__					,     src_lang=a__					,     tgt_lang=a__   )
            else:
                   _lowerCamelCase    	=					TranslationPipeline(model=a__					,     tokenizer=a__   )
            return translator, ["Some string", "Some other text"]



     def 						snake_case_			(       self					,     a__					,     a__   ):
            _lowerCamelCase    	=					translator('Some string'   )
            self.assertEqual(a__					,     [{'translation_text': ANY(a__   )}]   )

            _lowerCamelCase    	=					translator(['Some string']   )
            self.assertEqual(a__					,     [{'translation_text': ANY(a__   )}]   )

            _lowerCamelCase    	=					translator(['Some string', 'other string']   )
            self.assertEqual(a__					,     [{'translation_text': ANY(a__   )}, {'translation_text': ANY(a__   )}]   )



     @require_torch
     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					pipeline('translation_en_to_ro'					,     model='patrickvonplaten/t5-tiny-random'					,     framework='pt'   )
            _lowerCamelCase    	=					translator('This is a test string'					,     max_length=20   )
            self.assertEqual(
                a__					,     [
                    {
                        'translation_text': (
                            'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                            ' Beide Beide'
                        )
                    }
                ]					,     )



     @require_tf
     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					pipeline('translation_en_to_ro'					,     model='patrickvonplaten/t5-tiny-random'					,     framework='tf'   )
            _lowerCamelCase    	=					translator('This is a test string'					,     max_length=20   )
            self.assertEqual(
                a__					,     [
                    {
                        'translation_text': (
                            'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                            ' Beide Beide'
                        )
                    }
                ]					,     )



     @require_torch
     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					pipeline('translation_en_to_de'					,     model='patrickvonplaten/t5-tiny-random'					,     framework='pt'   )
            _lowerCamelCase    	=					translator('This is a test string'					,     max_length=20   )
            self.assertEqual(
                a__					,     [
                    {
                        'translation_text': (
                            'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                            ' urine urine urine urine urine urine urine'
                        )
                    }
                ]					,     )



     @require_tf
     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					pipeline('translation_en_to_de'					,     model='patrickvonplaten/t5-tiny-random'					,     framework='tf'   )
            _lowerCamelCase    	=					translator('This is a test string'					,     max_length=20   )
            self.assertEqual(
                a__					,     [
                    {
                        'translation_text': (
                            'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                            ' urine urine urine urine urine urine urine'
                        )
                    }
                ]					,     )







class 	__a   (  unittest.TestCase		):



     @require_torch
     @slow
     def 						snake_case_			(       self   ):
            # We don't provide a default for this pair
            with self.assertRaises(a__   ):
                   pipeline(task='translation_cn_to_ar'   )

            # but we do for this one
            _lowerCamelCase    	=					pipeline(task='translation_en_to_de'   )
            self.assertEqual(translator._preprocess_params['src_lang']					,     'en'   )
            self.assertEqual(translator._preprocess_params['tgt_lang']					,     'de'   )



     @require_torch
     @slow
     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'   )
            _lowerCamelCase    	=					MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'   )

            _lowerCamelCase    	=					pipeline(task='translation'					,     model=a__					,     tokenizer=a__   )
            # Missing src_lang, tgt_lang
            with self.assertRaises(a__   ):
                   translator('This is a test'   )

            _lowerCamelCase    	=					translator('This is a test'					,     src_lang='en_XX'					,     tgt_lang='ar_AR'   )
            self.assertEqual(a__					,     [{'translation_text': 'هذا إختبار'}]   )

            _lowerCamelCase    	=					translator('This is a test'					,     src_lang='en_XX'					,     tgt_lang='hi_IN'   )
            self.assertEqual(a__					,     [{'translation_text': 'यह एक परीक्षण है'}]   )

            # src_lang, tgt_lang can be defined at pipeline call time
            _lowerCamelCase    	=					pipeline(task='translation'					,     model=a__					,     tokenizer=a__					,     src_lang='en_XX'					,     tgt_lang='ar_AR'   )
            _lowerCamelCase    	=					translator('This is a test'   )
            self.assertEqual(a__					,     [{'translation_text': 'هذا إختبار'}]   )



     @require_torch
     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					'patrickvonplaten/t5-tiny-random'
            _lowerCamelCase    	=					pipeline(task='translation_cn_to_ar'					,     model=a__   )
            self.assertEqual(translator._preprocess_params['src_lang']					,     'cn'   )
            self.assertEqual(translator._preprocess_params['tgt_lang']					,     'ar'   )



     @require_torch
     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					'patrickvonplaten/t5-tiny-random'
            with pytest.warns(a__					,     match=r'.*translation_en_to_de.*'   ):
                   _lowerCamelCase    	=					pipeline(task='translation'					,     model=a__   )
            self.assertEqual(translator.task					,     'translation_en_to_de'   )
            self.assertEqual(translator._preprocess_params['src_lang']					,     'en'   )
            self.assertEqual(translator._preprocess_params['tgt_lang']					,     'de'   )



     @require_torch
     def 						snake_case_			(       self   ):
            with self.assertRaises(a__   ):
                   pipeline(task='translation'   )













"""simple docstring"""


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Tuple						,  snake_case   :					List[Any]		)->		str:
       _lowerCamelCase    	=					UNetaDModel(**snake_case		)
       _lowerCamelCase    	=					checkpoint['all_modules.0.W'].data
       _lowerCamelCase    	=					checkpoint['all_modules.0.W'].data
       _lowerCamelCase    	=					checkpoint['all_modules.1.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.1.bias'].data

       _lowerCamelCase    	=					checkpoint['all_modules.2.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.2.bias'].data

       _lowerCamelCase    	=					checkpoint['all_modules.3.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.3.bias'].data

       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-4]].data
       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-3]].data
       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-2]].data
       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-1]].data

       _lowerCamelCase    	=					4

       def set_attention_weights(snake_case   :					List[Any]						,  snake_case   :					Any						,  snake_case   :					List[Any]		):
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_0.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_1.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_2.W'].data.T

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_0.b'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_1.b'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_2.b'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_3.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_3.b'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

       def set_resnet_weights(snake_case   :					Tuple						,  snake_case   :					Optional[int]						,  snake_case   :					Tuple		):
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_0.bias'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_1.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_1.bias'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_1.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_1.bias'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Dense_0.weight'].data
              _lowerCamelCase    	=					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:
                     _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_2.weight'].data
                     _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_2.bias'].data

       for i, block in enumerate(new_model_architecture.downsample_blocks		):
              _lowerCamelCase    	=					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
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.Conv_0.weight'].data
                     _lowerCamelCase    	=					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		):
              _lowerCamelCase    	=					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:
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
                     module_index += 1
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up						,  snake_case						,  snake_case		)
                     module_index += 1

       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
       module_index += 1
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
       _lowerCamelCase    	=					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_     :      int								=parser.parse_args()

      A_     :      Dict								=torch.load(args.checkpoint_path, map_location="""cpu""")

      with open(args.config_file) as f:
            A_     :      List[str]								=json.loads(f.read())

      A_     :      str								=convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )

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

      A_     :      str								=UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

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

            A_     :      Optional[Any]								=ScoreSdeVePipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
            model.save_pretrained(args.dump_path)






