import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
   from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

   from .test_feature_extraction_speechta import floats_list


lowerCamelCase__   = get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')




@require_torch
class  _UpperCAmelCase       (			unittest.TestCase   ):



 '''simple docstring'''




 def 						__UpperCAmelCase							(						self							:					str)		->     List[Any]:


   """simple docstring"""







   _UpperCamelCase				=  tempfile.mkdtemp()

   _UpperCamelCase				=  SpeechTaTokenizer(a__)
   tokenizer.save_pretrained(self.tmpdirname)

   _UpperCamelCase				=  {
       "feature_size": 1,
       "padding_value": 0.0,
       "sampling_rate": 16000,
       "do_normalize": False,
       "num_mel_bins": 80,
       "hop_length": 16,
       "win_length": 64,
       "win_function": "hann_window",
       "fmin": 80,
       "fmax": 7600,
       "mel_floor": 1e-1_0,
       "reduction_factor": 2,
       "return_attention_mask": True,
   }

   _UpperCamelCase				=  os.path.join(self.tmpdirname				,      a__)
   with open(self.feature_extraction_file				,      "w"				,      encoding="utf-8") as fp:
     fp.write(json.dumps(a__) + "\n")







 def 						__UpperCAmelCase							(						self							:					List[Any]				,      **lowercase_							:					Any)		->     Dict:


   """simple docstring"""







   return SpeechTaTokenizer.from_pretrained(self.tmpdirname				,      **a__)







 def 						__UpperCAmelCase							(						self							:					Union[str, Any]				,      **lowercase_							:					List[Any])		->     Optional[int]:


   """simple docstring"""







   return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname				,      **a__)







 def 						__UpperCAmelCase							(						self							:					Optional[int])		->     List[Any]:


   """simple docstring"""







   shutil.rmtree(self.tmpdirname)







 def 						__UpperCAmelCase							(						self							:					int)		->     str:


   """simple docstring"""







   _UpperCamelCase				=  self.get_tokenizer()
   _UpperCamelCase				=  self.get_feature_extractor()

   _UpperCamelCase				=  SpeechTaProcessor(tokenizer=a__				,      feature_extractor=a__)

   processor.save_pretrained(self.tmpdirname)
   _UpperCamelCase				=  SpeechTaProcessor.from_pretrained(self.tmpdirname)

   self.assertEqual(processor.tokenizer.get_vocab()				,      tokenizer.get_vocab())
   self.assertIsInstance(processor.tokenizer				,      a__)

   self.assertEqual(processor.feature_extractor.to_json_string()				,      feature_extractor.to_json_string())
   self.assertIsInstance(processor.feature_extractor				,      a__)







 def 						__UpperCAmelCase							(						self							:					int)		->     List[Any]:


   """simple docstring"""







   _UpperCamelCase				=  SpeechTaProcessor(tokenizer=self.get_tokenizer()				,      feature_extractor=self.get_feature_extractor())
   processor.save_pretrained(self.tmpdirname)

   _UpperCamelCase				=  self.get_tokenizer(bos_token="(BOS)"				,      eos_token="(EOS)")
   _UpperCamelCase				=  self.get_feature_extractor(do_normalize=a__				,      padding_value=1.0)

   _UpperCamelCase				=  SpeechTaProcessor.from_pretrained(
       self.tmpdirname				,      bos_token="(BOS)"				,      eos_token="(EOS)"				,      do_normalize=a__				,      padding_value=1.0)

   self.assertEqual(processor.tokenizer.get_vocab()				,      tokenizer_add_kwargs.get_vocab())
   self.assertIsInstance(processor.tokenizer				,      a__)

   self.assertEqual(processor.feature_extractor.to_json_string()				,      feature_extractor_add_kwargs.to_json_string())
   self.assertIsInstance(processor.feature_extractor				,      a__)







 def 						__UpperCAmelCase							(						self							:					Union[str, Any])		->     int:


   """simple docstring"""







   _UpperCamelCase				=  self.get_feature_extractor()
   _UpperCamelCase				=  self.get_tokenizer()

   _UpperCamelCase				=  SpeechTaProcessor(tokenizer=a__				,      feature_extractor=a__)

   _UpperCamelCase				=  floats_list((3, 1000))

   _UpperCamelCase				=  feature_extractor(audio=a__				,      return_tensors="np")
   _UpperCamelCase				=  processor(audio=a__				,      return_tensors="np")

   for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum()				,      input_processor[key].sum()				,      delta=1e-2)







 def 						__UpperCAmelCase							(						self							:					Optional[int])		->     int:


   """simple docstring"""







   _UpperCamelCase				=  self.get_feature_extractor()
   _UpperCamelCase				=  self.get_tokenizer()

   _UpperCamelCase				=  SpeechTaProcessor(tokenizer=a__				,      feature_extractor=a__)

   _UpperCamelCase				=  floats_list((3, 1000))

   _UpperCamelCase				=  feature_extractor(audio_target=a__				,      return_tensors="np")
   _UpperCamelCase				=  processor(audio_target=a__				,      return_tensors="np")

   for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum()				,      input_processor[key].sum()				,      delta=1e-2)







 def 						__UpperCAmelCase							(						self							:					int)		->     Tuple:


   """simple docstring"""







   _UpperCamelCase				=  self.get_feature_extractor()
   _UpperCamelCase				=  self.get_tokenizer()

   _UpperCamelCase				=  SpeechTaProcessor(tokenizer=a__				,      feature_extractor=a__)

   _UpperCamelCase				=  "This is a test string"

   _UpperCamelCase				=  processor(text=a__)
   _UpperCamelCase				=  tokenizer(a__)

   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]				,      encoded_processor[key])







 def 						__UpperCAmelCase							(						self							:					int)		->     Tuple:


   """simple docstring"""







   _UpperCamelCase				=  self.get_feature_extractor()
   _UpperCamelCase				=  self.get_tokenizer()

   _UpperCamelCase				=  SpeechTaProcessor(tokenizer=a__				,      feature_extractor=a__)

   _UpperCamelCase				=  "This is a test string"

   _UpperCamelCase				=  processor(text_target=a__)
   _UpperCamelCase				=  tokenizer(a__)

   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]				,      encoded_processor[key])







 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase				=  self.get_feature_extractor()
   _UpperCamelCase				=  self.get_tokenizer()

   _UpperCamelCase				=  SpeechTaProcessor(tokenizer=a__				,      feature_extractor=a__)

   _UpperCamelCase				=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   _UpperCamelCase				=  processor.batch_decode(a__)
   _UpperCamelCase				=  tokenizer.batch_decode(a__)

   self.assertListEqual(a__				,      a__)







 def 						__UpperCAmelCase							(						self							:					Tuple)		->     List[Any]:


   """simple docstring"""







   _UpperCamelCase				=  self.get_feature_extractor()
   _UpperCamelCase				=  self.get_tokenizer()

   _UpperCamelCase				=  SpeechTaProcessor(tokenizer=a__				,      feature_extractor=a__)

   self.assertListEqual(
       processor.model_input_names				,      feature_extractor.model_input_names				,      msg="`processor` and `feature_extractor` model input names do not match"				,      )











"""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)






