



'''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							:Optional[Any]				    =					{
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 1_3_1_0_7_2,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
}



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





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



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





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







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							(			nn.Module ):


	def __init__(self       ,      lowercase							):
				super().__init__()

				A_	:						List[Any]	=     DiffusionAttnUnetaD(lowercase       ,      n_attn_layers=4							)
				A_	:						int	=     deepcopy(self.diffusion							)
				A_	:						str	=     torch.quasirandom.SobolEngine(1       ,      scramble=lowercase							)






def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						List[str]	=     MODELS_MAP[model_name]["""url"""]
			os.system(f'wget {url} ./'    )

			return f'./{model_name}.ckpt'


lowerCamelCase							:int				    =					{
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
lowerCamelCase							:List[Any]				    =					{
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
lowerCamelCase							:Dict				    =					{
    '''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							:Union[str, Any]				    =					{
    '''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							:str				    =					{
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}



def 	a			( lowerCamelCase__    ):
			'''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 	a			( lowerCamelCase__    ):
			'''simple docstring'''





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



def 	a			( lowerCamelCase__						,      lowerCamelCase__=13    ):
			'''simple docstring'''





			A_	:						Tuple	=     input_string

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

			A_	:						str	=     0
			if string.startswith("""net.3."""    ):
						depth += 1
						A_	:						List[Any]	=     string[6:]
			elif string.startswith("""net."""    ):
						A_	:						Union[str, Any]	=     string[4:]

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

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

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

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

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

			A_	:						str	=     string_left[1:]

			if "resnets" in new_layer:
						A_	:						Dict	=     convert_resconv_naming(lowerCamelCase__    )
			elif "attentions" in new_layer:
						A_	:						List[Any]	=     convert_attn_naming(lowerCamelCase__    )
						A_	:						str	=     new_string_left

			if not isinstance(lowerCamelCase__						,      lowerCamelCase__    ):
						A_	:						Optional[int]	=     prefix + """.""" + new_layer + """.""" + string_left
			else:
						A_	:						Union[str, Any]	=     [prefix + """.""" + new_layer + """.""" + s for s in string_left]
			return new_string



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





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

						A_	:						Tuple	=     rename(lowerCamelCase__    )

						# check if we need to transform from Conv => Linear for attention
						if isinstance(lowerCamelCase__						,      lowerCamelCase__    ):
									A_	:						Optional[int]	=     transform_conv_attns(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )
						else:
									A_	:						Dict	=     v

			return new_state_dict



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





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



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""    )

			A_	:						Tuple	=     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()}'
						A_	:						Optional[int]	=     download(lowerCamelCase__    )

			A_	:						Tuple	=     MODELS_MAP[model_name]["""sample_rate"""]
			A_	:						Dict	=     MODELS_MAP[model_name]["""sample_size"""]

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

			A_	:						List[str]	=     UNetaDModel(sample_size=lowerCamelCase__						,      sample_rate=lowerCamelCase__    )
			A_	:						List[str]	=     diffusers_model.state_dict()

			A_	:						List[Any]	=     DiffusionUncond(lowerCamelCase__    )
			orig_model.load_state_dict(torch.load(args.model_path						,      map_location=lowerCamelCase__    )["""state_dict"""]    )
			A_	:						Dict	=     orig_model.diffusion_ema.eval()
			A_	:						int	=     orig_model.state_dict()
			A_	:						Union[str, Any]	=     rename_orig_weights(lowerCamelCase__    )

			A_	:						str	=     set(renamed_state_dict.keys()    ) - set(diffusers_state_dict.keys()    )
			A_	:						Union[str, Any]	=     set(diffusers_state_dict.keys()    ) - set(renamed_state_dict.keys()    )

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

						A_	:						List[Any]	=     value

			diffusers_model.load_state_dict(lowerCamelCase__    )

			A_	:						int	=     1_00
			A_	:						List[str]	=     33

			A_	:						str	=     IPNDMScheduler(num_train_timesteps=lowerCamelCase__    )

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

			A_	:						Optional[Any]	=     torch.linspace(1						,      0						,      steps + 1						,      device=lowerCamelCase__    )[:-1]
			A_	:						str	=     get_crash_schedule(lowerCamelCase__    )

			A_	:						str	=     DanceDiffusionPipeline(unet=lowerCamelCase__						,      scheduler=lowerCamelCase__    )

			A_	:						Tuple	=     torch.manual_seed(33    )
			A_	:						List[Any]	=     pipe(num_inference_steps=lowerCamelCase__						,      generator=lowerCamelCase__    ).audios

			A_	:						int	=     sampling.iplms_sample(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      {}    )
			A_	:						Optional[Any]	=     generated.clamp(-1						,      1    )

			A_	:						int	=     (generated - audio).abs().sum()
			A_	:						Tuple	=     (generated - audio).abs().max()

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

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

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

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


if __name__ == "__main__":
						lowerCamelCase							:Optional[Any]				    =					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							:Dict				    =					parser.parse_args()

						main(args)




'''simple docstring'''




import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax.numpy as jnp

						from transformers.models.distilbert.modeling_flax_distilbert import (
						    FlaxDistilBertForMaskedLM,
						    FlaxDistilBertForMultipleChoice,
						    FlaxDistilBertForQuestionAnswering,
						    FlaxDistilBertForSequenceClassification,
						    FlaxDistilBertForTokenClassification,
						    FlaxDistilBertModel,
						)







class 	_lowerCAmelCase							(			unittest.TestCase ):


	def __init__(self       ,      lowercase       ,      lowercase=13       ,      lowercase=7       ,      lowercase=True       ,      lowercase=True       ,      lowercase=True       ,      lowercase=True       ,      lowercase=99       ,      lowercase=32       ,      lowercase=5       ,      lowercase=4       ,      lowercase=37       ,      lowercase="gelu"       ,      lowercase=0.1       ,      lowercase=0.1       ,      lowercase=512       ,      lowercase=16       ,      lowercase=2       ,      lowercase=0.02       ,      lowercase=4       ,      ):
				A_	:						List[Any]	=     parent
				A_	:						str	=     batch_size
				A_	:						List[Any]	=     seq_length
				A_	:						Dict	=     is_training
				A_	:						List[Any]	=     use_attention_mask
				A_	:						Any	=     use_token_type_ids
				A_	:						Optional[int]	=     use_labels
				A_	:						Tuple	=     vocab_size
				A_	:						List[str]	=     hidden_size
				A_	:						List[str]	=     num_hidden_layers
				A_	:						Optional[Any]	=     num_attention_heads
				A_	:						int	=     intermediate_size
				A_	:						Optional[Any]	=     hidden_act
				A_	:						List[Any]	=     hidden_dropout_prob
				A_	:						Optional[Any]	=     attention_probs_dropout_prob
				A_	:						Any	=     max_position_embeddings
				A_	:						Union[str, Any]	=     type_vocab_size
				A_	:						int	=     type_sequence_label_size
				A_	:						Any	=     initializer_range
				A_	:						List[str]	=     num_choices


	def     _a	(self							):
				A_	:						List[str]	=     ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)

				A_	:						Any	=     None
				if self.use_attention_mask:
							A_	:						Any	=     random_attention_mask([self.batch_size, self.seq_length]							)

				A_	:						Union[str, Any]	=     DistilBertConfig(
				    vocab_size=self.vocab_size       ,      dim=self.hidden_size       ,      n_layers=self.num_hidden_layers       ,      n_heads=self.num_attention_heads       ,      hidden_dim=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      dropout=self.hidden_dropout_prob       ,      attention_dropout=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      initializer_range=self.initializer_range       ,      tie_weights_=lowercase       ,      )

				return config, input_ids, attention_mask


	def     _a	(self							):
				A_	:						List[str]	=     self.prepare_config_and_inputs()
				A_,    A_,    A_	:						str	=     config_and_inputs
				A_	:						Any	=     {"""input_ids""": input_ids, """attention_mask""": attention_mask}
				return config, inputs_dict







@require_flax
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)


	def     _a	(self							):
				A_	:						Tuple	=     FlaxDistilBertModelTester(self							)


	@slow
	def     _a	(self							):
				for model_class_name in self.all_model_classes:
							A_	:						Union[str, Any]	=     model_class_name.from_pretrained("""distilbert-base-uncased"""							)
							A_	:						Any	=     model(np.ones((1, 1)							)							)
							self.assertIsNotNone(lowercase							)







@require_flax
class 	_lowerCAmelCase							(			unittest.TestCase ):


	@slow
	def     _a	(self							):
				A_	:						List[str]	=     FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased"""							)
				A_	:						Optional[Any]	=     np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]							)
				A_	:						int	=     np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
				A_	:						Optional[int]	=     model(lowercase       ,      attention_mask=lowercase							)[0]
				A_	:						Optional[Any]	=     (1, 11, 768)
				self.assertEqual(output.shape       ,      lowercase							)
				A_	:						Union[str, Any]	=     np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]							)

				self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]       ,      lowercase       ,      atol=1E-4							)							)