




import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MPNetForMaskedLM,
     MPNetForMultipleChoice,
     MPNetForQuestionAnswering,
     MPNetForSequenceClassification,
     MPNetForTokenClassification,
     MPNetModel,
 )







class     A:



 '''simple docstring'''


 def __init__(							self   :    Dict		,				A_   :    int		,				A_   :    str=13		,				A_   :    Dict=7		,				A_   :    Tuple=True		,				A_   :    str=True		,				A_   :    Tuple=False		,				A_   :    int=True		,				A_   :    Any=99		,				A_   :    Any=64		,				A_   :    Optional[int]=5		,				A_   :    int=4		,				A_   :    int=64		,				A_   :    Tuple="gelu"		,				A_   :    Any=0.1		,				A_   :    Any=0.1		,				A_   :    Optional[Any]=512		,				A_   :    Union[str, Any]=16		,				A_   :    Dict=2		,				A_   :    Any=0.02		,				A_   :    Union[str, Any]=3		,				A_   :    Optional[int]=4		,				A_   :    Tuple=None		,				)  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_							=       parent
       lowerCamelCase_							=       batch_size
       lowerCamelCase_							=       seq_length
       lowerCamelCase_							=       is_training
       lowerCamelCase_							=       use_input_mask
       lowerCamelCase_							=       use_token_type_ids
       lowerCamelCase_							=       use_labels
       lowerCamelCase_							=       vocab_size
       lowerCamelCase_							=       hidden_size
       lowerCamelCase_							=       num_hidden_layers
       lowerCamelCase_							=       num_attention_heads
       lowerCamelCase_							=       intermediate_size
       lowerCamelCase_							=       hidden_act
       lowerCamelCase_							=       hidden_dropout_prob
       lowerCamelCase_							=       attention_probs_dropout_prob
       lowerCamelCase_							=       max_position_embeddings
       lowerCamelCase_							=       type_vocab_size
       lowerCamelCase_							=       type_sequence_label_size
       lowerCamelCase_							=       initializer_range
       lowerCamelCase_							=       num_labels
       lowerCamelCase_							=       num_choices
       lowerCamelCase_							=       scope

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

       """simple docstring"""






       return MPNetConfig.from_pretrained('microsoft/mpnet-base'    )

 def 							a__					(							self   :    List[str]    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size    )

       lowerCamelCase_							=       None
       if self.use_input_mask:
             lowerCamelCase_							=       random_attention_mask([self.batch_size, self.seq_length]    )

       lowerCamelCase_							=       None
       lowerCamelCase_							=       None
       lowerCamelCase_							=       None
       if self.use_labels:
             lowerCamelCase_							=       ids_tensor([self.batch_size]		,				self.type_sequence_label_size    )
             lowerCamelCase_							=       ids_tensor([self.batch_size, self.seq_length]		,				self.num_labels    )
             lowerCamelCase_							=       ids_tensor([self.batch_size]		,				self.num_choices    )

       lowerCamelCase_							=       self.get_config()
       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

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

       """simple docstring"""






       return MPNetConfig(
           vocab_size=self.vocab_size		,				hidden_size=self.hidden_size		,				num_hidden_layers=self.num_hidden_layers		,				num_attention_heads=self.num_attention_heads		,				intermediate_size=self.intermediate_size		,				hidden_act=self.hidden_act		,				hidden_dropout_prob=self.hidden_dropout_prob		,				attention_probs_dropout_prob=self.attention_probs_dropout_prob		,				max_position_embeddings=self.max_position_embeddings		,				initializer_range=self.initializer_range		,				)

 def 							a__					(							self   :    int		,				A_   :    Any		,				A_   :    Union[str, Any]		,				A_   :    int		,				A_   :    List[str]		,				A_   :    List[str]		,				A_   :    Union[str, Any]    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       MPNetModel(config=snake_case__    )
       model.to(snake_case__    )
       model.eval()
       lowerCamelCase_							=       model(snake_case__		,				snake_case__    )
       lowerCamelCase_							=       model(snake_case__    )
       self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)    )
       self.parent.assertEqual(result.pooler_output.shape		,				(self.batch_size, self.hidden_size)    )

 def 							a__					(							self   :    List[Any]		,				A_   :    Dict		,				A_   :    Any		,				A_   :    List[str]		,				A_   :    Optional[int]		,				A_   :    Union[str, Any]		,				A_   :    str    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       MPNetForQuestionAnswering(config=snake_case__    )
       model.to(snake_case__    )
       model.eval()
       lowerCamelCase_							=       model(
           snake_case__		,				attention_mask=snake_case__		,				start_positions=snake_case__		,				end_positions=snake_case__		,				)
       self.parent.assertEqual(result.start_logits.shape		,				(self.batch_size, self.seq_length)    )
       self.parent.assertEqual(result.end_logits.shape		,				(self.batch_size, self.seq_length)    )

 def 							a__					(							self   :    Optional[int]		,				A_   :    Union[str, Any]		,				A_   :    Any		,				A_   :    List[Any]		,				A_   :    Tuple		,				A_   :    Any		,				A_   :    Optional[int]    )  ->      Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.num_labels
       lowerCamelCase_							=       MPNetForSequenceClassification(snake_case__    )
       model.to(snake_case__    )
       model.eval()
       lowerCamelCase_							=       model(snake_case__		,				attention_mask=snake_case__		,				labels=snake_case__    )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)    )

 def 							a__					(							self   :    List[str]		,				A_   :    Any		,				A_   :    Union[str, Any]		,				A_   :    int		,				A_   :    Dict		,				A_   :    List[Any]		,				A_   :    Any    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_							=       self.num_choices
       lowerCamelCase_							=       MPNetForMultipleChoice(config=snake_case__    )
       model.to(snake_case__    )
       model.eval()
       lowerCamelCase_							=       input_ids.unsqueeze(1    ).expand(-1		,				self.num_choices		,				-1    ).contiguous()
       lowerCamelCase_							=       input_mask.unsqueeze(1    ).expand(-1		,				self.num_choices		,				-1    ).contiguous()
       lowerCamelCase_							=       model(
           snake_case__		,				attention_mask=snake_case__		,				labels=snake_case__		,				)
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_choices)    )

 def 							a__					(							self   :    str		,				A_   :    List[Any]		,				A_   :    Dict		,				A_   :    Optional[int]		,				A_   :    List[str]		,				A_   :    Tuple		,				A_   :    Optional[Any]    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       self.num_labels
       lowerCamelCase_							=       MPNetForTokenClassification(config=snake_case__    )
       model.to(snake_case__    )
       model.eval()
       lowerCamelCase_							=       model(snake_case__		,				attention_mask=snake_case__		,				labels=snake_case__    )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.num_labels)    )





 def 							a__					(							self   :    Dict    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       self.prepare_config_and_inputs()
       (lowerCamelCase_)							=       config_and_inputs
       lowerCamelCase_							=       {"input_ids": input_ids, "attention_mask": input_mask}
       return config, inputs_dict







@require_torch
class     A(		UpperCamelCase				, UpperCamelCase				, unittest.TestCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		(
     (
         MPNetForMaskedLM,
         MPNetForMultipleChoice,
         MPNetForQuestionAnswering,
         MPNetForSequenceClassification,
         MPNetForTokenClassification,
         MPNetModel,
     )
     if is_torch_available()
     else ()
 )
 UpperCamelCase		  =		(
     {
         '''feature-extraction''': MPNetModel,
         '''fill-mask''': MPNetForMaskedLM,
         '''question-answering''': MPNetForQuestionAnswering,
         '''text-classification''': MPNetForSequenceClassification,
         '''token-classification''': MPNetForTokenClassification,
         '''zero-shot''': MPNetForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 UpperCamelCase		  =		False
 UpperCamelCase		  =		True

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

       """simple docstring"""






       lowerCamelCase_							=       MPNetModelTester(self    )
       lowerCamelCase_							=       ConfigTester(self		,				config_class=snake_case__		,				hidden_size=37    )

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

       """simple docstring"""






       self.config_tester.run_common_tests()

 def 							a__					(							self   :    Tuple    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_model(*snake_case__    )

 def 							a__					(							self   :    Dict    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_sequence_classification(*snake_case__    )

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

       """simple docstring"""






       lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_multiple_choice(*snake_case__    )

 def 							a__					(							self   :    Any    )  ->      int:

       """simple docstring"""






       lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_token_classification(*snake_case__    )





 def 							a__					(							self   :    Any    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_question_answering(*snake_case__    )







@require_torch
class     A(		unittest.TestCase  ):



 '''simple docstring'''


 @slow
 def 							a__					(							self   :    Dict    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       MPNetModel.from_pretrained('microsoft/mpnet-base'    )
       lowerCamelCase_							=       torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]    )
       lowerCamelCase_							=       model(snake_case__    )[0]
       lowerCamelCase_							=       torch.Size((1, 11, 768)    )
       self.assertEqual(output.shape		,				snake_case__    )
       lowerCamelCase_							=       torch.tensor(
           [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]    )
       # compare the actual values for a slice.
       self.assertTrue(torch.allclose(output[:, :3, :3]		,				snake_case__		,				atol=1E-4    )    )











"""simple docstring"""



import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


_lowerCAmelCase	:	Tuple				=      {
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 65_536,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 65_536,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 131_072,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : List[str]				)->		Optional[int]:






     '''simple docstring'''



     return torch.atana(snake_case			,					snake_case				) / math.pi * 2


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]				)->		List[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							torch.sin(t * math.pi / 2				) ** 2
     UpperCAmelCase__						:    Optional[int]							=							(1 - sigma**2) ** 0.5
     return alpha_sigma_to_t(snake_case			,					snake_case				)




class 			lowerCAmelCase__			(    __magic_name__   ):
   pass




class 			lowerCAmelCase__			(    nn.Module   ):
   def __init__(		self      :	Any							,			snake_case__      :	str							):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    Any							=							DiffusionAttnUnetaD(snake_case__							,			n_attn_layers=4							)
        UpperCAmelCase__						:    Any							=							deepcopy(self.diffusion							)
        UpperCAmelCase__						:    Optional[Any]							=							torch.quasirandom.SobolEngine(1							,			scramble=snake_case__							)




def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Any							=							MODELS_MAP[model_name]["url"]
     os.system(f'wget {url} ./'				)

     return f'./{model_name}.ckpt'


_lowerCAmelCase	:	List[Any]				=      {
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
_lowerCAmelCase	:	Optional[Any]				=      {
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
_lowerCAmelCase	:	List[str]				=      {
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
    """8""": """resnets.3""",
    """9""": """attentions.3""",
    """10""": """resnets.4""",
    """11""": """attentions.4""",
    """12""": """resnets.5""",
    """13""": """attentions.5""",
}
_lowerCAmelCase	:	List[str]				=      {
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

_lowerCAmelCase	:	Dict				=      {
    """skip""": """conv_skip""",
    """main.0""": """conv_1""",
    """main.1""": """group_norm_1""",
    """main.3""": """conv_2""",
    """main.4""": """group_norm_2""",
}

_lowerCAmelCase	:	List[Any]				=      {
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Tuple				)->		Dict:






     '''simple docstring'''



     if name.startswith("skip"				):
          return name.replace("skip"			,					RES_CONV_MAP["skip"]				)

     # name has to be of format main.{digit}
     if not name.startswith("main."				):
          raise ValueError(f'ResConvBlock error with {name}'				)

     return name.replace(name[:6]			,					RES_CONV_MAP[name[:6]]				)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]				)->		int:






     '''simple docstring'''



     for key, value in ATTN_MAP.items():
          if name.startswith(snake_case				) and not isinstance(snake_case			,					snake_case				):
               return name.replace(snake_case			,					snake_case				)
          elif name.startswith(snake_case				):
               return [name.replace(snake_case			,					snake_case				) for v in value]
     raise ValueError(f'Attn error with {name}'				)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]			,					snake_case       : Optional[Any]=13				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Any							=							input_string

     if string.split("."				)[0] == "timestep_embed":
          return string.replace("timestep_embed"			,					"time_proj"				)

     UpperCAmelCase__						:    Tuple							=							0
     if string.startswith("net.3."				):
          depth += 1
          UpperCAmelCase__						:    Dict							=							string[6:]
     elif string.startswith("net."				):
          UpperCAmelCase__						:    Tuple							=							string[4:]

     while string.startswith("main.7."				):
          depth += 1
          UpperCAmelCase__						:    Any							=							string[7:]

     if string.startswith("main."				):
          UpperCAmelCase__						:    Union[str, Any]							=							string[5:]

     # mid block
     if string[:2].isdigit():
          UpperCAmelCase__						:    Optional[Any]							=							string[:2]
          UpperCAmelCase__						:    int							=							string[2:]
     else:
          UpperCAmelCase__						:    Optional[int]							=							string[0]
          UpperCAmelCase__						:    Any							=							string[1:]

     if depth == max_depth:
          UpperCAmelCase__						:    Any							=							MID_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__						:    int							=							"mid_block"
     elif depth > 0 and int(snake_case				) < 7:
          UpperCAmelCase__						:    Optional[Any]							=							DOWN_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__						:    int							=							f'down_blocks.{depth}'
     elif depth > 0 and int(snake_case				) > 7:
          UpperCAmelCase__						:    Optional[int]							=							UP_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__						:    Tuple							=							f'up_blocks.{max_depth - depth - 1}'
     elif depth == 0:
          UpperCAmelCase__						:    Tuple							=							DEPTH_0_TO_LAYER[layer_num]
          UpperCAmelCase__						:    Optional[Any]							=							f'up_blocks.{max_depth - 1}' if int(snake_case				) > 3 else "down_blocks.0"

     if not string_left.startswith("."				):
          raise ValueError(f'Naming error with {input_string} and string_left: {string_left}.'				)

     UpperCAmelCase__						:    List[Any]							=							string_left[1:]

     if "resnets" in new_layer:
          UpperCAmelCase__						:    Optional[int]							=							convert_resconv_naming(snake_case				)
     elif "attentions" in new_layer:
          UpperCAmelCase__						:    Optional[int]							=							convert_attn_naming(snake_case				)
          UpperCAmelCase__						:    Any							=							new_string_left

     if not isinstance(snake_case			,					snake_case				):
          UpperCAmelCase__						:    Tuple							=							prefix + "." + new_layer + "." + string_left
     else:
          UpperCAmelCase__						:    str							=							[prefix + "." + new_layer + "." + s for s in string_left]
     return new_string


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[str]				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							{}
     for k, v in state_dict.items():
          if k.endswith("kernel"				):
               # up- and downsample layers, don't have trainable weights
               continue

          UpperCAmelCase__						:    Optional[int]							=							rename(snake_case				)

          # check if we need to transform from Conv => Linear for attention
          if isinstance(snake_case			,					snake_case				):
               UpperCAmelCase__						:    Optional[Any]							=							transform_conv_attns(snake_case			,					snake_case			,					snake_case				)
          else:
               UpperCAmelCase__						:    List[Any]							=							v

     return new_state_dict


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]			,					snake_case       : List[Any]			,					snake_case       : int				)->		Union[str, Any]:






     '''simple docstring'''



     if len(snake_case				) == 1:
          if len(v.shape				) == 3:
               # weight
               UpperCAmelCase__						:    Optional[int]							=							v[:, :, 0]
          else:
               # bias
               UpperCAmelCase__						:    Any							=							v
     else:
          # qkv matrices
          UpperCAmelCase__						:    Any							=							v.shape[0]
          UpperCAmelCase__						:    Optional[Any]							=							trippled_shape // 3
          for i in range(3				):
               if len(v.shape				) == 3:
                    UpperCAmelCase__						:    List[Any]							=							v[i * single_shape : (i + 1) * single_shape, :, 0]
               else:
                    UpperCAmelCase__						:    Any							=							v[i * single_shape : (i + 1) * single_shape]
     return new_state_dict


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    Tuple							=							torch.device("cuda" if torch.cuda.is_available() else "cpu"				)

     UpperCAmelCase__						:    List[str]							=							args.model_path.split("/"				)[-1].split("."				)[0]
     if not os.path.isfile(args.model_path				):
          assert (
              model_name == args.model_path
          ), f'Make sure to provide one of the official model names {MODELS_MAP.keys()}'
          UpperCAmelCase__						:    Optional[int]							=							download(snake_case				)

     UpperCAmelCase__						:    List[str]							=							MODELS_MAP[model_name]["sample_rate"]
     UpperCAmelCase__						:    Dict							=							MODELS_MAP[model_name]["sample_size"]

     UpperCAmelCase__						:    Optional[int]							=							Object()
     UpperCAmelCase__						:    int							=							sample_size
     UpperCAmelCase__						:    List[Any]							=							sample_rate
     UpperCAmelCase__						:    int							=							0

     UpperCAmelCase__						:    int							=							UNetaDModel(sample_size=snake_case			,					sample_rate=snake_case				)
     UpperCAmelCase__						:    int							=							diffusers_model.state_dict()

     UpperCAmelCase__						:    Optional[Any]							=							DiffusionUncond(snake_case				)
     orig_model.load_state_dict(torch.load(args.model_path			,					map_location=snake_case				)["state_dict"]				)
     UpperCAmelCase__						:    List[str]							=							orig_model.diffusion_ema.eval()
     UpperCAmelCase__						:    Optional[int]							=							orig_model.state_dict()
     UpperCAmelCase__						:    Any							=							rename_orig_weights(snake_case				)

     UpperCAmelCase__						:    List[Any]							=							set(renamed_state_dict.keys()				) - set(diffusers_state_dict.keys()				)
     UpperCAmelCase__						:    Optional[int]							=							set(diffusers_state_dict.keys()				) - set(renamed_state_dict.keys()				)

     assert len(snake_case				) == 0, f'Problem with {renamed_minus_diffusers}'
     assert all(k.endswith("kernel"				) for k in list(snake_case				)				), f'Problem with {diffusers_minus_renamed}'

     for key, value in renamed_state_dict.items():
          assert (
              diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
          ), f'Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'
          if key == "time_proj.weight":
               UpperCAmelCase__						:    Union[str, Any]							=							value.squeeze()

          UpperCAmelCase__						:    List[str]							=							value

     diffusers_model.load_state_dict(snake_case				)

     UpperCAmelCase__						:    Any							=							100
     UpperCAmelCase__						:    str							=							33

     UpperCAmelCase__						:    Optional[int]							=							IPNDMScheduler(num_train_timesteps=snake_case				)

     UpperCAmelCase__						:    Any							=							torch.manual_seed(snake_case				)
     UpperCAmelCase__						:    List[str]							=							torch.randn([1, 2, config.sample_size]			,					generator=snake_case				).to(snake_case				)

     UpperCAmelCase__						:    Dict							=							torch.linspace(1			,					0			,					steps + 1			,					device=snake_case				)[:-1]
     UpperCAmelCase__						:    List[str]							=							get_crash_schedule(snake_case				)

     UpperCAmelCase__						:    List[str]							=							DanceDiffusionPipeline(unet=snake_case			,					scheduler=snake_case				)

     UpperCAmelCase__						:    Optional[Any]							=							torch.manual_seed(33				)
     UpperCAmelCase__						:    int							=							pipe(num_inference_steps=snake_case			,					generator=snake_case				).audios

     UpperCAmelCase__						:    Optional[Any]							=							sampling.iplms_sample(snake_case			,					snake_case			,					snake_case			,					{}				)
     UpperCAmelCase__						:    Tuple							=							generated.clamp(-1			,					1				)

     UpperCAmelCase__						:    Optional[int]							=							(generated - audio).abs().sum()
     UpperCAmelCase__						:    List[Any]							=							(generated - audio).abs().max()

     if args.save:
          pipe.save_pretrained(args.checkpoint_path				)

     print("Diff sum"			,					snake_case				)
     print("Diff max"			,					snake_case				)

     assert diff_max < 1E-3, f'Diff max: {diff_max} is too much :-/'

     print(f'Conversion for {model_name} successful!'				)


if __name__ == "__main__":
 _lowerCAmelCase	:	Dict				=      argparse.ArgumentParser()

 parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
 parser.add_argument(
     """--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
 )
 parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
 _lowerCAmelCase	:	Tuple				=      parser.parse_args()

 main(args)


