
import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor

__SCREAMING_SNAKE_CASE      : List[Any]       =  random.Random()
def      UpperCAmelCase__				(							__magic_name__    :  Optional[Any]				,	__magic_name__    :  Any=1.0				,	__magic_name__    :  str=None				,	__magic_name__    :  Optional[int]=None	):


					'''simple docstring'''

					if rng is None:
										lowerCAmelCase		:       List[str]							=  global_rng

					lowerCAmelCase		:       List[str]							=  []
					for batch_idx in range(shape[0]	):
										values.append([]	)
										for _ in range(shape[1]	):
															values[-1].append(rng.random() * scale	)

					return values


@require_torch
@require_torchaudio
class 		__magic_name__ (    unittest.TestCase     ):





		def __init__(				self     :						Dict    ,	lowerCamelCase__     :						Any    ,	lowerCamelCase__     :						List[str]=7    ,	lowerCamelCase__     :						Dict=4_0_0    ,	lowerCamelCase__     :						Optional[Any]=2_0_0_0    ,	lowerCamelCase__     :						Tuple=2_4    ,	lowerCamelCase__     :						Optional[Any]=2_4    ,	lowerCamelCase__     :						Any=0.0    ,	lowerCamelCase__     :						List[Any]=1_6_0_0_0    ,	lowerCamelCase__     :						List[str]=True    ,	lowerCamelCase__     :						str=True    ,	):
							lowerCAmelCase		:       List[Any]							=  parent
							lowerCAmelCase		:       str							=  batch_size
							lowerCAmelCase		:       Any							=  min_seq_length
							lowerCAmelCase		:       List[Any]							=  max_seq_length
							lowerCAmelCase		:       List[str]							=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							lowerCAmelCase		:       str							=  feature_size
							lowerCAmelCase		:       str							=  num_mel_bins
							lowerCAmelCase		:       int							=  padding_value
							lowerCAmelCase		:       Optional[Any]							=  sampling_rate
							lowerCAmelCase		:       List[Any]							=  return_attention_mask
							lowerCAmelCase		:       Union[str, Any]							=  do_normalize





		def    _A				(				self     :						List[Any]					):
							return {
							    "feature_size": self.feature_size,
							    "num_mel_bins": self.num_mel_bins,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							    "do_normalize": self.do_normalize,
							}





		def    _A				(				self     :						Optional[Any]    ,	lowerCamelCase__     :						Dict=False    ,	lowerCamelCase__     :						Optional[int]=False					):

							def _flatten(lowerCamelCase__     :						Optional[Any]					):
												return list(itertools.chain(*lowerCamelCase__					)					)

							if equal_length:
												lowerCAmelCase		:       List[Any]							=  [floats_list((self.max_seq_length, self.feature_size)					) for _ in range(self.batch_size					)]
							else:
												# make sure that inputs increase in size
												lowerCAmelCase		:       Optional[Any]							=  [
												    floats_list((x, self.feature_size)					)
												    for x in range(self.min_seq_length    ,	self.max_seq_length    ,	self.seq_length_diff					)
												]
							if numpify:
												lowerCAmelCase		:       Dict							=  [np.asarray(lowerCamelCase__					) for x in speech_inputs]
							return speech_inputs



@require_torch
@require_torchaudio
class 		__magic_name__ (    _snake_case,					unittest.TestCase     ):
		_lowerCAmelCase		=						SpeechaTextFeatureExtractor if is_speech_available() else None





		def    _A				(				self     :						Any					):
							lowerCAmelCase		:       Optional[Any]							=  SpeechaTextFeatureExtractionTester(self					)





		def    _A				(				self     :						int    ,	lowerCamelCase__     :						Tuple					):
							self.assertTrue(np.all(np.mean(lowerCamelCase__    ,	axis=0					) < 1E-3					)					)
							self.assertTrue(np.all(np.abs(np.var(lowerCamelCase__    ,	axis=0					) - 1					) < 1E-3					)					)





		def    _A				(				self     :						List[Any]					):
							lowerCAmelCase		:       Union[str, Any]							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							# create three inputs of length 800, 1000, and 1200
							lowerCAmelCase		:       Tuple							=  [floats_list((1, x)					)[0] for x in range(8_0_0    ,	1_4_0_0    ,	2_0_0					)]
							lowerCAmelCase		:       Union[str, Any]							=  [np.asarray(lowerCamelCase__					) for speech_input in speech_inputs]

							# Test feature size
							lowerCAmelCase		:       Optional[int]							=  feature_extractor(lowerCamelCase__    ,	padding=lowerCamelCase__    ,	return_tensors='''np'''					).input_features
							self.assertTrue(input_features.ndim == 3					)
							self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size					)

							# Test not batched input
							lowerCAmelCase		:       Any							=  feature_extractor(speech_inputs[0]    ,	return_tensors='''np'''					).input_features
							lowerCAmelCase		:       Tuple							=  feature_extractor(np_speech_inputs[0]    ,	return_tensors='''np'''					).input_features
							self.assertTrue(np.allclose(lowerCamelCase__    ,	lowerCamelCase__    ,	atol=1E-3					)					)

							# Test batched
							lowerCAmelCase		:       Any							=  feature_extractor(lowerCamelCase__    ,	return_tensors='''np'''					).input_features
							lowerCAmelCase		:       Any							=  feature_extractor(lowerCamelCase__    ,	return_tensors='''np'''					).input_features
							for enc_seq_a, enc_seq_a in zip(lowerCamelCase__    ,	lowerCamelCase__					):
												self.assertTrue(np.allclose(lowerCamelCase__    ,	lowerCamelCase__    ,	atol=1E-3					)					)

							# Test 2-D numpy arrays are batched.
							lowerCAmelCase		:       Optional[Any]							=  [floats_list((1, x)					)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
							lowerCAmelCase		:       str							=  np.asarray(lowerCamelCase__					)
							lowerCAmelCase		:       Union[str, Any]							=  feature_extractor(lowerCamelCase__    ,	return_tensors='''np'''					).input_features
							lowerCAmelCase		:       List[str]							=  feature_extractor(lowerCamelCase__    ,	return_tensors='''np'''					).input_features
							for enc_seq_a, enc_seq_a in zip(lowerCamelCase__    ,	lowerCamelCase__					):
												self.assertTrue(np.allclose(lowerCamelCase__    ,	lowerCamelCase__    ,	atol=1E-3					)					)





		def    _A				(				self     :						Optional[Any]					):
							lowerCAmelCase		:       Optional[int]							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							lowerCAmelCase		:       int							=  [floats_list((1, x)					)[0] for x in range(8_0_0    ,	1_4_0_0    ,	2_0_0					)]

							lowerCAmelCase		:       Optional[Any]							=  ['''longest''', '''max_length''', '''do_not_pad''']
							lowerCAmelCase		:       Dict							=  [None, 1_6, None]
							for max_length, padding in zip(lowerCamelCase__    ,	lowerCamelCase__					):
												lowerCAmelCase		:       Optional[Any]							=  feature_extractor(
												    lowerCamelCase__    ,	padding=lowerCamelCase__    ,	max_length=lowerCamelCase__    ,	return_attention_mask=lowerCamelCase__					)
												lowerCAmelCase		:       int							=  inputs.input_features
												lowerCAmelCase		:       List[Any]							=  inputs.attention_mask
												lowerCAmelCase		:       Optional[Any]							=  [np.sum(lowerCamelCase__					) for x in attention_mask]

												self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]					)
												self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]					)
												self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]					)





		def    _A				(				self     :						Tuple					):
							lowerCAmelCase		:       List[Any]							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							lowerCAmelCase		:       Dict							=  [floats_list((1, x)					)[0] for x in range(8_0_0    ,	1_4_0_0    ,	2_0_0					)]

							lowerCAmelCase		:       int							=  ['''longest''', '''max_length''', '''do_not_pad''']
							lowerCAmelCase		:       List[str]							=  [None, 1_6, None]
							for max_length, padding in zip(lowerCamelCase__    ,	lowerCamelCase__					):
												lowerCAmelCase		:       Dict							=  feature_extractor(
												    lowerCamelCase__    ,	max_length=lowerCamelCase__    ,	padding=lowerCamelCase__    ,	return_tensors='''np'''    ,	return_attention_mask=lowerCamelCase__					)
												lowerCAmelCase		:       Union[str, Any]							=  inputs.input_features
												lowerCAmelCase		:       Tuple							=  inputs.attention_mask
												lowerCAmelCase		:       List[str]							=  [np.sum(lowerCamelCase__					) for x in attention_mask]

												self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]					)
												self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6					)
												self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]					)
												self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6					)
												self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]					)





		def    _A				(				self     :						Dict					):
							lowerCAmelCase		:       Any							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							lowerCAmelCase		:       str							=  [floats_list((1, x)					)[0] for x in range(8_0_0    ,	1_4_0_0    ,	2_0_0					)]
							lowerCAmelCase		:       List[str]							=  feature_extractor(
							    lowerCamelCase__    ,	padding='''max_length'''    ,	max_length=4    ,	truncation=lowerCamelCase__    ,	return_tensors='''np'''    ,	return_attention_mask=lowerCamelCase__    ,	)
							lowerCAmelCase		:       Union[str, Any]							=  inputs.input_features
							lowerCAmelCase		:       Union[str, Any]							=  inputs.attention_mask
							lowerCAmelCase		:       Optional[Any]							=  np.sum(attention_mask == 1    ,	axis=1					)

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]					)
							self._check_zero_mean_unit_variance(input_features[1]					)
							self._check_zero_mean_unit_variance(input_features[2]					)





		def    _A				(				self     :						List[str]					):
							lowerCAmelCase		:       Tuple							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							lowerCAmelCase		:       Dict							=  [floats_list((1, x)					)[0] for x in range(8_0_0    ,	1_4_0_0    ,	2_0_0					)]
							lowerCAmelCase		:       Dict							=  feature_extractor(
							    lowerCamelCase__    ,	padding='''longest'''    ,	max_length=4    ,	truncation=lowerCamelCase__    ,	return_tensors='''np'''    ,	return_attention_mask=lowerCamelCase__    ,	)
							lowerCAmelCase		:       Tuple							=  inputs.input_features
							lowerCAmelCase		:       Union[str, Any]							=  inputs.attention_mask
							lowerCAmelCase		:       Optional[int]							=  np.sum(attention_mask == 1    ,	axis=1					)

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]					)
							self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]					)
							self._check_zero_mean_unit_variance(input_features[2]					)

							# make sure that if max_length < longest -> then pad to max_length
							self.assertEqual(input_features.shape    ,	(3, 4, 2_4)					)

							lowerCAmelCase		:       List[str]							=  [floats_list((1, x)					)[0] for x in range(8_0_0    ,	1_4_0_0    ,	2_0_0					)]
							lowerCAmelCase		:       List[Any]							=  feature_extractor(
							    lowerCamelCase__    ,	padding='''longest'''    ,	max_length=1_6    ,	truncation=lowerCamelCase__    ,	return_tensors='''np'''    ,	return_attention_mask=lowerCamelCase__    ,	)
							lowerCAmelCase		:       Dict							=  inputs.input_features
							lowerCAmelCase		:       str							=  inputs.attention_mask
							lowerCAmelCase		:       str							=  np.sum(attention_mask == 1    ,	axis=1					)

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]					)
							self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]					)
							self._check_zero_mean_unit_variance(input_features[2]					)

							# make sure that if max_length < longest -> then pad to max_length
							self.assertEqual(input_features.shape    ,	(3, 6, 2_4)					)





		def    _A				(				self     :						str					):
							import torch

							lowerCAmelCase		:       Any							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							lowerCAmelCase		:       List[str]							=  np.random.rand(1_0_0    ,	3_2					).astype(np.floataa					)
							lowerCAmelCase		:       int							=  np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
												lowerCAmelCase		:       Any							=  feature_extractor.pad([{'''input_features''': inputs}]    ,	return_tensors='''np'''					)
												self.assertTrue(np_processed.input_features.dtype == np.floataa					)
												lowerCAmelCase		:       Dict							=  feature_extractor.pad([{'''input_features''': inputs}]    ,	return_tensors='''pt'''					)
												self.assertTrue(pt_processed.input_features.dtype == torch.floataa					)





		def    _A				(				self     :						Optional[int]    ,	lowerCamelCase__     :						Union[str, Any]					):
							from datasets import load_dataset

							lowerCAmelCase		:       Optional[int]							=  load_dataset('''hf-internal-testing/librispeech_asr_dummy'''    ,	'''clean'''    ,	split='''validation'''					)
							# automatic decoding with librispeech
							lowerCAmelCase		:       Dict							=  ds.sort('''id'''					).select(range(lowerCamelCase__					)					)[:num_samples]['''audio''']

							return [x["array"] for x in speech_samples]





		def    _A				(				self     :						List[Any]					):
							lowerCAmelCase		:       int							=  np.array([
							    -1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
							    -1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
							    -1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
							]					)
							# fmt: on

							lowerCAmelCase		:       List[str]							=  self._load_datasamples(1					)
							lowerCAmelCase		:       Any							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							lowerCAmelCase		:       Dict							=  feature_extractor(lowerCamelCase__    ,	return_tensors='''pt'''					).input_features
							self.assertEquals(input_features.shape    ,	(1, 5_8_4, 2_4)					)
							self.assertTrue(np.allclose(input_features[0, 0, :3_0]    ,	lowerCamelCase__    ,	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


a   	=    {
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 48000,
        "sample_size": 65536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 48000,
        "sample_size": 65536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 48000,
        "sample_size": 131072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
}

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

   '''simple docstring'''







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

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

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							torch.sin(t * math.pi / 2   ) ** 2
   __SCREAMING_SNAKE_CASE   				=							(1 - sigma**2) ** 0.5
   return alpha_sigma_to_t(__UpperCAmelCase				,						__UpperCAmelCase   )







class __a			( _snake_case      ):
   pass







class __a			( nn.Module      ):





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

      '''simple docstring'''

      super().__init__()

      __SCREAMING_SNAKE_CASE   				=							DiffusionAttnUnetaD(lowerCamelCase					,n_attn_layers=4     )
      __SCREAMING_SNAKE_CASE   				=							deepcopy(self.diffusion     )
      __SCREAMING_SNAKE_CASE   				=							torch.quasirandom.SobolEngine(1					,scramble=lowerCamelCase     )





def     __magic_name__							(				__UpperCAmelCase   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							MODELS_MAP[model_name]["""url"""]
   os.system(f"""wget {url} ./"""   )

   return f"""./{model_name}.ckpt"""


a   	=    {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
a   	=    {
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
a   	=    {
    "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",
}
a   	=    {
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

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

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

def     __magic_name__							(				__UpperCAmelCase   )    ->       int:

   '''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     __magic_name__							(				__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=13   )    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							input_string

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

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

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

   if string.startswith("""main."""   ):
      __SCREAMING_SNAKE_CASE   				=							string[5:]

   # mid block
   if string[:2].isdigit():
      __SCREAMING_SNAKE_CASE   				=							string[:2]
      __SCREAMING_SNAKE_CASE   				=							string[2:]
   else:
      __SCREAMING_SNAKE_CASE   				=							string[0]
      __SCREAMING_SNAKE_CASE   				=							string[1:]

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

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

   __SCREAMING_SNAKE_CASE   				=							string_left[1:]

   if "resnets" in new_layer:
      __SCREAMING_SNAKE_CASE   				=							convert_resconv_naming(__UpperCAmelCase   )
   elif "attentions" in new_layer:
      __SCREAMING_SNAKE_CASE   				=							convert_attn_naming(__UpperCAmelCase   )
      __SCREAMING_SNAKE_CASE   				=							new_string_left

   if not isinstance(__UpperCAmelCase				,						__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							prefix + """.""" + new_layer + """.""" + string_left
   else:
      __SCREAMING_SNAKE_CASE   				=							[prefix + """.""" + new_layer + """.""" + s for s in string_left]
   return new_string

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

   '''simple docstring'''







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

      __SCREAMING_SNAKE_CASE   				=							rename(__UpperCAmelCase   )

      # check if we need to transform from Conv => Linear for attention
      if isinstance(__UpperCAmelCase				,						__UpperCAmelCase   ):
         __SCREAMING_SNAKE_CASE   				=							transform_conv_attns(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
      else:
         __SCREAMING_SNAKE_CASE   				=							v

   return new_state_dict

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[Any]:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""   )

   __SCREAMING_SNAKE_CASE   				=							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()}"""
      __SCREAMING_SNAKE_CASE   				=							download(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							MODELS_MAP[model_name]["""sample_rate"""]
   __SCREAMING_SNAKE_CASE   				=							MODELS_MAP[model_name]["""sample_size"""]

   __SCREAMING_SNAKE_CASE   				=							Object()
   __SCREAMING_SNAKE_CASE   				=							sample_size
   __SCREAMING_SNAKE_CASE   				=							sample_rate
   __SCREAMING_SNAKE_CASE   				=							0

   __SCREAMING_SNAKE_CASE   				=							UNetaDModel(sample_size=__UpperCAmelCase				,						sample_rate=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							diffusers_model.state_dict()

   __SCREAMING_SNAKE_CASE   				=							DiffusionUncond(__UpperCAmelCase   )
   orig_model.load_state_dict(torch.load(args.model_path				,						map_location=__UpperCAmelCase   )["""state_dict"""]   )
   __SCREAMING_SNAKE_CASE   				=							orig_model.diffusion_ema.eval()
   __SCREAMING_SNAKE_CASE   				=							orig_model.state_dict()
   __SCREAMING_SNAKE_CASE   				=							rename_orig_weights(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							set(renamed_state_dict.keys()   ) - set(diffusers_state_dict.keys()   )
   __SCREAMING_SNAKE_CASE   				=							set(diffusers_state_dict.keys()   ) - set(renamed_state_dict.keys()   )

   assert len(__UpperCAmelCase   ) == 0, f"""Problem with {renamed_minus_diffusers}"""
   assert all(k.endswith("""kernel"""   ) for k in list(__UpperCAmelCase   )   ), 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":
         __SCREAMING_SNAKE_CASE   				=							value.squeeze()

      __SCREAMING_SNAKE_CASE   				=							value

   diffusers_model.load_state_dict(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							100
   __SCREAMING_SNAKE_CASE   				=							33

   __SCREAMING_SNAKE_CASE   				=							IPNDMScheduler(num_train_timesteps=__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							torch.manual_seed(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							torch.randn([1, 2, config.sample_size]				,						generator=__UpperCAmelCase   ).to(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							torch.linspace(1				,						0				,						steps + 1				,						device=__UpperCAmelCase   )[:-1]
   __SCREAMING_SNAKE_CASE   				=							get_crash_schedule(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							DanceDiffusionPipeline(unet=__UpperCAmelCase				,						scheduler=__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							torch.manual_seed(33   )
   __SCREAMING_SNAKE_CASE   				=							pipe(num_inference_steps=__UpperCAmelCase				,						generator=__UpperCAmelCase   ).audios

   __SCREAMING_SNAKE_CASE   				=							sampling.iplms_sample(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						{}   )
   __SCREAMING_SNAKE_CASE   				=							generated.clamp(-1				,						1   )

   __SCREAMING_SNAKE_CASE   				=							(generated - audio).abs().sum()
   __SCREAMING_SNAKE_CASE   				=							(generated - audio).abs().max()

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

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

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

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


if __name__ == "__main__":
    a   	=    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.")
    a   	=    parser.parse_args()

    main(args)





