






import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







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


         """simple docstring"""
         _UpperCAmelCase							=    """laion/clap-htsat-unfused"""
         _UpperCAmelCase							=    tempfile.mkdtemp()


  def  lowerCamelCase       (      self		:			List[str]       ,				**lowerCamelCase		:			List[str]							)						->	List[Any]:


         """simple docstring"""
         return RobertaTokenizer.from_pretrained(self.checkpoint       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				**lowerCamelCase		:			Any							)						->	Any:


         """simple docstring"""
         return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	str:


         """simple docstring"""
         shutil.rmtree(self.tmpdirname							)


  def  lowerCamelCase       (      self		:			Any							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_tokenizer()
         _UpperCAmelCase							=    self.get_feature_extractor()

         _UpperCAmelCase							=    ClapProcessor(tokenizer=lowerCamelCase       ,				feature_extractor=lowerCamelCase							)

         processor.save_pretrained(self.tmpdirname							)
         _UpperCAmelCase							=    ClapProcessor.from_pretrained(self.tmpdirname							)

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

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


  def  lowerCamelCase       (      self		:			str							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    ClapProcessor(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=lowerCamelCase       ,				padding_value=1.0							)

         _UpperCAmelCase							=    ClapProcessor.from_pretrained(
             self.tmpdirname       ,				bos_token="""(BOS)"""       ,				eos_token="""(EOS)"""       ,				do_normalize=lowerCamelCase       ,				padding_value=1.0							)

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

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


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


         """simple docstring"""
         _UpperCAmelCase							=    self.get_feature_extractor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    ClapProcessor(tokenizer=lowerCamelCase       ,				feature_extractor=lowerCamelCase							)

         _UpperCAmelCase							=    floats_list((3, 1000)							)

         _UpperCAmelCase							=    feature_extractor(lowerCamelCase       ,				return_tensors="""np"""							)
         _UpperCAmelCase							=    processor(audios=lowerCamelCase       ,				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  lowerCamelCase       (      self		:			List[Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_feature_extractor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    ClapProcessor(tokenizer=lowerCamelCase       ,				feature_extractor=lowerCamelCase							)

         _UpperCAmelCase							=    """This is a test string"""

         _UpperCAmelCase							=    processor(text=lowerCamelCase							)

         _UpperCAmelCase							=    tokenizer(lowerCamelCase							)

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


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


         """simple docstring"""
         _UpperCAmelCase							=    self.get_feature_extractor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    ClapProcessor(tokenizer=lowerCamelCase       ,				feature_extractor=lowerCamelCase							)

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

         _UpperCAmelCase							=    processor.batch_decode(lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.batch_decode(lowerCamelCase							)

         self.assertListEqual(lowerCamelCase       ,				lowerCamelCase							)





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


         """simple docstring"""
         _UpperCAmelCase							=    self.get_feature_extractor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    ClapProcessor(tokenizer=lowerCamelCase       ,				feature_extractor=lowerCamelCase							)

         self.assertListEqual(
             processor.model_input_names[2:]       ,				feature_extractor.model_input_names       ,				msg="""`processor` and `feature_extractor` model input names do not match"""       ,				)




import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)

os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)


def A    (							__UpperCamelCase       )    ->   Union[str, Any]:
			if hor == 128:
						A__	  =     ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						A__	  =     (32, 128, 256)
						A__	  =     ('UpResnetBlock1D', 'UpResnetBlock1D')

			elif hor == 32:
						A__	  =     ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						A__	  =     (32, 64, 128, 256)
						A__	  =     ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
			A__	  =     torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''       )
			A__	  =     model.state_dict()
			A__	  =     {
			    'down_block_types': down_block_types,
			    'block_out_channels': block_out_channels,
			    'up_block_types': up_block_types,
			    'layers_per_block': 1,
			    'use_timestep_embedding': True,
			    'out_block_type': 'OutConv1DBlock',
			    'norm_num_groups': 8,
			    'downsample_each_block': False,
			    'in_channels': 14,
			    'out_channels': 14,
			    'extra_in_channels': 0,
			    'time_embedding_type': 'positional',
			    'flip_sin_to_cos': False,
			    'freq_shift': 1,
			    'sample_size': 65_536,
			    'mid_block_type': 'MidResTemporalBlock1D',
			    'act_fn': 'mish',
			}
			A__	  =     UNetaDModel(**__UpperCamelCase       )
			print(f'''length of state dict: {len(state_dict.keys()       )}'''       )
			print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()       )}'''       )
			A__	  =     dict(zip(model.state_dict().keys()			,						hf_value_function.state_dict().keys()       )       )
			for k, v in mapping.items():
						A__	  =     state_dict.pop(__UpperCamelCase       )
			hf_value_function.load_state_dict(__UpperCamelCase       )

			torch.save(hf_value_function.state_dict()			,						f'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'''       )
			with open(f'''hub/hopper-medium-v2/unet/hor{hor}/config.json'''			,						'w'       ) as f:
						json.dump(__UpperCamelCase			,						__UpperCamelCase       )


def A    (							)    ->   List[str]:
			A__	  =     {
			    'in_channels': 14,
			    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
			    'up_block_types': (),
			    'out_block_type': 'ValueFunction',
			    'mid_block_type': 'ValueFunctionMidBlock1D',
			    'block_out_channels': (32, 64, 128, 256),
			    'layers_per_block': 1,
			    'downsample_each_block': True,
			    'sample_size': 65_536,
			    'out_channels': 14,
			    'extra_in_channels': 0,
			    'time_embedding_type': 'positional',
			    'use_timestep_embedding': True,
			    'flip_sin_to_cos': False,
			    'freq_shift': 1,
			    'norm_num_groups': 8,
			    'act_fn': 'mish',
			}

			A__	  =     torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'       )
			A__	  =     model
			A__	  =     UNetaDModel(**__UpperCamelCase       )
			print(f'''length of state dict: {len(state_dict.keys()       )}'''       )
			print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()       )}'''       )

			A__	  =     dict(zip(state_dict.keys()			,						hf_value_function.state_dict().keys()       )       )
			for k, v in mapping.items():
						A__	  =     state_dict.pop(__UpperCamelCase       )

			hf_value_function.load_state_dict(__UpperCamelCase       )

			torch.save(hf_value_function.state_dict()			,						'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'       )
			with open('hub/hopper-medium-v2/value_function/config.json'			,						'w'       ) as f:
						json.dump(__UpperCamelCase			,						__UpperCamelCase       )


if __name__ == "__main__":
							unet(3_2)
							# unet(128)
							value_function()




