"""simple docstring"""





from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor


class      UpperCAmelCase_	:


      def __init__(   self		,			a		,			a=1_3		,			a=3_0		,			a=2		,			a=3		,			a=True		,			a=True		,			a=3_2		,			a=2		,			a=4		,			a=3_7		,			a="gelu"		,			a=0.1		,			a=0.1		,			a=1_0		,			a=0.02		,			a=3		,			a=0.6		,			a=None		,			)       ->					Any:
             lowercase__			:		List[str]       =	parent
             lowercase__			:		List[Any]       =	batch_size
             lowercase__			:		List[Any]       =	image_size
             lowercase__			:		List[Any]       =	patch_size
             lowercase__			:		List[Any]       =	num_channels
             lowercase__			:		List[str]       =	is_training
             lowercase__			:		int       =	use_labels
             lowercase__			:		Optional[Any]       =	hidden_size
             lowercase__			:		Union[str, Any]       =	num_hidden_layers
             lowercase__			:		Tuple       =	num_attention_heads
             lowercase__			:		Any       =	intermediate_size
             lowercase__			:		str       =	hidden_act
             lowercase__			:		Any       =	hidden_dropout_prob
             lowercase__			:		Optional[int]       =	attention_probs_dropout_prob
             lowercase__			:		int       =	type_sequence_label_size
             lowercase__			:		List[str]       =	initializer_range
             lowercase__			:		Optional[Any]       =	mask_ratio
             lowercase__			:		Union[str, Any]       =	scope

             # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
             # (we add 1 for the [CLS] token)
             lowercase__			:		List[Any]       =	(image_size // patch_size) ** 2
             lowercase__			:		int       =	int(math.ceil((1 - mask_ratio) * (num_patches + 1)							)							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Dict       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             lowercase__			:		Dict       =	None
             if self.use_labels:
                    lowercase__			:		int       =	ids_tensor([self.batch_size]		,			self.type_sequence_label_size							)

             lowercase__			:		str       =	self.get_config()

             return config, pixel_values, labels


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             return ViTMAEConfig(
                 image_size=self.image_size		,			patch_size=self.patch_size		,			num_channels=self.num_channels		,			hidden_size=self.hidden_size		,			num_hidden_layers=self.num_hidden_layers		,			num_attention_heads=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			decoder_hidden_size=self.hidden_size		,			decoder_num_hidden_layers=self.num_hidden_layers		,			decoder_num_attention_heads=self.num_attention_heads		,			decoder_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		,			is_decoder=a		,			initializer_range=self.initializer_range		,			mask_ratio=self.mask_ratio		,			)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					Optional[Any]:
             lowercase__			:		Optional[int]       =	TFViTMAEModel(config=a							)
             lowercase__			:		Any       =	model(a		,			training=a							)
             self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					List[Any]:
             lowercase__			:		Tuple       =	TFViTMAEForPreTraining(a							)
             lowercase__			:		Any       =	model(a		,			training=a							)
             # expected sequence length = num_patches
             lowercase__			:		Optional[Any]       =	(self.image_size // self.patch_size) ** 2
             lowercase__			:		Tuple       =	self.patch_size**2 * self.num_channels
             self.parent.assertEqual(result.logits.shape		,			(self.batch_size, num_patches, expected_num_channels)							)

             # test greyscale images
             lowercase__			:		Union[str, Any]       =	1
             lowercase__			:		Optional[int]       =	TFViTMAEForPreTraining(a							)

             lowercase__			:		Any       =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
             lowercase__			:		Optional[int]       =	model(a		,			training=a							)
             lowercase__			:		Any       =	self.patch_size**2
             self.parent.assertEqual(result.logits.shape		,			(self.batch_size, num_patches, expected_num_channels)							)


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__			:		List[str]       =	self.prepare_config_and_inputs()
             ((lowercase__)    ,      (lowercase__)    ,      (lowercase__))			:		str       =	config_and_inputs
             lowercase__			:		Union[str, Any]       =	{'pixel_values': pixel_values}
             return config, inputs_dict





@require_tf
class      UpperCAmelCase_	(    _a ,							_a ,							unittest.TestCase):
      lowerCamelCase__		:    List[Any]   =  (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
      lowerCamelCase__		:    List[str]   =  {"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

      lowerCamelCase__		:    List[str]   =  False
      lowerCamelCase__		:    List[Any]   =  False
      lowerCamelCase__		:    Optional[int]   =  False
      lowerCamelCase__		:    Optional[int]   =  False


      def 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		Dict       =	TFViTMAEModelTester(self							)
             lowercase__			:		Any       =	ConfigTester(self		,			config_class=a		,			has_text_modality=a		,			hidden_size=3_7							)


      def 		_UpperCAmelCase       (   self							)       ->					str:
             self.config_tester.run_common_tests()


      @unittest.skip(reason='ViTMAE does not use inputs_embeds'							)
      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             pass


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__    ,      lowercase__			:		Union[str, Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__			:		List[str]       =	model_class(a							)
                    self.assertIsInstance(model.get_input_embeddings()		,			(tf.keras.layers.Layer)							)
                    lowercase__			:		Optional[Any]       =	model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(a		,			tf.keras.layers.Layer							)							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__    ,      lowercase__			:		Union[str, Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__			:		Union[str, Any]       =	model_class(a							)
                    lowercase__			:		int       =	inspect.signature(model.call							)
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    lowercase__			:		Union[str, Any]       =	[*signature.parameters.keys()]

                    lowercase__			:		Tuple       =	['pixel_values']
                    self.assertListEqual(arg_names[:1]		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		List[str]       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		Tuple       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_pretraining(*a							)


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             # make the mask reproducible
             np.random.seed(2							)

             lowercase__    ,      lowercase__			:		Dict       =	self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__			:		Tuple       =	int((config.image_size // config.patch_size) ** 2							)
             lowercase__			:		Optional[int]       =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

             for model_class in self.all_model_classes:
                    lowercase__			:		Tuple       =	model_class(a							)
                    lowercase__			:		str       =	self._prepare_for_class(a		,			a							)

                    lowercase__			:		Any       =	model(a		,			noise=a							)

                    lowercase__			:		List[str]       =	copy.deepcopy(self._prepare_for_class(a		,			a							)							)
                    lowercase__			:		str       =	model(**a		,			noise=a							)
                    lowercase__			:		str       =	outputs_dict[0].numpy()
                    lowercase__			:		Dict       =	outputs_keywords[0].numpy()

                    self.assertLess(np.sum(np.abs(output_dict - output_keywords							)							)		,			1e-6							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             # make the mask reproducible
             np.random.seed(2							)

             lowercase__    ,      lowercase__			:		Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__			:		Dict       =	int((config.image_size // config.patch_size) ** 2							)
             lowercase__			:		Optional[Any]       =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

             def prepare_numpy_arrays(a							):
                    lowercase__			:		Dict       =	{}
                    for k, v in inputs_dict.items():
                           if tf.is_tensor(a							):
                                  lowercase__			:		List[Any]       =	v.numpy()
                           else:
                                  lowercase__			:		Union[str, Any]       =	np.array(a							)

                    return inputs_np_dict

             for model_class in self.all_model_classes:
                    lowercase__			:		Any       =	model_class(a							)

                    lowercase__			:		Optional[int]       =	self._prepare_for_class(a		,			a							)
                    lowercase__			:		Tuple       =	prepare_numpy_arrays(a							)

                    lowercase__			:		str       =	model(a		,			noise=a							)
                    lowercase__			:		Any       =	model(**a		,			noise=a							)
                    self.assert_outputs_same(a		,			a							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					List[str]:
             # make masks reproducible
             np.random.seed(2							)

             lowercase__			:		int       =	int((tf_model.config.image_size // tf_model.config.patch_size) ** 2							)
             lowercase__			:		str       =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
             lowercase__			:		Union[str, Any]       =	tf.constant(a							)

             # Add `noise` argument.
             # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
             lowercase__			:		Tuple       =	tf_noise

             super().check_pt_tf_models(a		,			a		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             # make mask reproducible
             np.random.seed(2							)

             lowercase__    ,      lowercase__			:		Dict       =	self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__			:		List[Any]       =	{
                 module_member
                 for model_class in self.all_model_classes
                 for module in (import_module(model_class.__module__							),)
                 for module_member_name in dir(a							)
                 if module_member_name.endswith('MainLayer'							)
                 # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
                 and module_member_name[: -len('MainLayer'							)] == model_class.__name__[: -len('Model'							)]
                 for module_member in (getattr(a		,			a							),)
                 if isinstance(a		,			a							)
                 and tf.keras.layers.Layer in module_member.__bases__
                 and getattr(a		,			'_keras_serializable'		,			a							)
             }

             lowercase__			:		Optional[Any]       =	int((config.image_size // config.patch_size) ** 2							)
             lowercase__			:		Optional[Any]       =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
             lowercase__			:		Union[str, Any]       =	tf.convert_to_tensor(a							)
             inputs_dict.update({'noise': noise}							)

             for main_layer_class in tf_main_layer_classes:
                    lowercase__			:		int       =	main_layer_class(a							)

                    lowercase__			:		Dict       =	{
                        name: tf.keras.Input(tensor.shape[1:]		,			dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
                    }

                    lowercase__			:		List[str]       =	tf.keras.Model(a		,			outputs=main_layer(a							)							)
                    lowercase__			:		List[Any]       =	model(a							)

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           lowercase__			:		Optional[Any]       =	os.path.join(a		,			'keras_model.h5'							)
                           model.save(a							)
                           lowercase__			:		Tuple       =	tf.keras.models.load_model(
                               a		,			custom_objects={main_layer_class.__name__: main_layer_class}							)
                           assert isinstance(a		,			tf.keras.Model							)
                           lowercase__			:		int       =	model(a							)
                           self.assert_outputs_same(a		,			a							)


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             # make mask reproducible
             np.random.seed(2							)

             lowercase__    ,      lowercase__			:		int       =	self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__			:		Union[str, Any]       =	int((config.image_size // config.patch_size) ** 2							)
             lowercase__			:		Optional[int]       =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

             for model_class in self.all_model_classes:
                    lowercase__			:		int       =	model_class(a							)
                    lowercase__			:		List[Any]       =	self._prepare_for_class(a		,			a							)
                    lowercase__			:		Union[str, Any]       =	model(a		,			noise=a							)

                    if model_class.__name__ == "TFViTMAEModel":
                           lowercase__			:		List[Any]       =	outputs.last_hidden_state.numpy()
                           lowercase__			:		Tuple       =	0
                    else:
                           lowercase__			:		Tuple       =	outputs.logits.numpy()
                           lowercase__			:		Optional[int]       =	0

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           model.save_pretrained(a		,			saved_model=a							)
                           lowercase__			:		Tuple       =	model_class.from_pretrained(a							)
                           lowercase__			:		List[str]       =	model(a		,			noise=a							)

                           if model_class.__name__ == "TFViTMAEModel":
                                  lowercase__			:		List[Any]       =	after_outputs['last_hidden_state'].numpy()
                                  lowercase__			:		List[Any]       =	0
                           else:
                                  lowercase__			:		int       =	after_outputs['logits'].numpy()
                                  lowercase__			:		Any       =	0

                           lowercase__			:		Dict       =	np.amax(np.abs(out_a - out_a							)							)
                           self.assertLessEqual(a		,			1e-5							)


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             # make mask reproducible
             np.random.seed(2							)

             lowercase__    ,      lowercase__			:		int       =	self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__			:		str       =	int((config.image_size // config.patch_size) ** 2							)
             lowercase__			:		Optional[Any]       =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

             for model_class in self.all_model_classes:
                    lowercase__			:		List[str]       =	model_class(a							)
                    lowercase__			:		Any       =	self._prepare_for_class(a		,			a							)

                    lowercase__			:		List[str]       =	model(a		,			noise=a							)
                    lowercase__			:		Optional[Any]       =	model.get_config()
                    # make sure that returned config is jsonifiable, which is required by keras
                    json.dumps(a							)
                    lowercase__			:		Dict       =	model_class.from_config(model.get_config()							)
                    # make sure it also accepts a normal config
                    lowercase__			:		Tuple       =	model_class.from_config(model.config							)
                    lowercase__			:		str       =	new_model(a							)  # Build model
                    new_model.set_weights(model.get_weights()							)
                    lowercase__			:		List[Any]       =	new_model(a		,			noise=a							)

                    self.assert_outputs_same(a		,			a							)


      @unittest.skip(
          reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'							)
      def 		_UpperCAmelCase       (   self							)       ->					int:
             pass


      @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'							)
      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             pass


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		List[str]       =	TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'							)
             self.assertIsNotNone(a							)





def 				a_     (      ):





       '''simple docstring'''

       lowercase__			:		Dict       =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
       return image






@require_tf
@require_vision
class      UpperCAmelCase_	(    unittest.TestCase):


      @cached_property
      def 		_UpperCAmelCase       (   self							)       ->					str:
             return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'							) if is_vision_available() else None


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             # make random mask reproducible across the PT and TF model
             np.random.seed(2							)

             lowercase__			:		List[str]       =	TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'							)

             lowercase__			:		int       =	self.default_image_processor
             lowercase__			:		Tuple       =	prepare_img()
             lowercase__			:		Dict       =	image_processor(images=a		,			return_tensors='tf'							)

             # prepare a noise vector that will be also used for testing the TF model
             # (this way we can ensure that the PT and TF models operate on the same inputs)
             lowercase__			:		Optional[Any]       =	ViTMAEConfig()
             lowercase__			:		Optional[int]       =	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
             lowercase__			:		Optional[Any]       =	np.random.uniform(size=(1, num_patches)							)

             # forward pass
             lowercase__			:		List[Any]       =	model(**a		,			noise=a							)

             # verify the logits
             lowercase__			:		str       =	tf.convert_to_tensor([1, 1_9_6, 7_6_8]							)
             self.assertEqual(outputs.logits.shape		,			a							)

             lowercase__			:		List[str]       =	tf.convert_to_tensor(
                 [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]							)

             tf.debugging.assert_near(outputs.logits[0, :3, :3]		,			a		,			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


__UpperCamelCase						=      {
    '''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     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						Optional[Any]:
						return torch.atana(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) / math.pi * 2


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						int:
						SCREAMING_SNAKE_CASE													=					torch.sin(t * math.pi / 2  ) ** 2
						SCREAMING_SNAKE_CASE													=					(1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		pass





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__				)     ->							int:
								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     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					MODELS_MAP[model_name]['url']
						os.system(F'wget {url} ./'  )

						return F'./{model_name}.ckpt'


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

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

__UpperCamelCase						=      {
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						List[Any]:
						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     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						Dict:
						for key, value in ATTN_MAP.items():
												if name.startswith(SCREAMING_SNAKE_CASE_  ) and not isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ):
																		return name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												elif name.startswith(SCREAMING_SNAKE_CASE_  ):
																		return [name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) for v in value]
						raise ValueError(F'Attn error with {name}'  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Tuple=13  )   ->						Union[str, Any]:
						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(SCREAMING_SNAKE_CASE_  ) < 7:
												SCREAMING_SNAKE_CASE													=					DOWN_NUM_TO_LAYER[layer_num]
												SCREAMING_SNAKE_CASE													=					F'down_blocks.{depth}'
						elif depth > 0 and int(SCREAMING_SNAKE_CASE_  ) > 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(SCREAMING_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}.'  )

						SCREAMING_SNAKE_CASE													=					string_left[1:]

						if "resnets" in new_layer:
												SCREAMING_SNAKE_CASE													=					convert_resconv_naming(SCREAMING_SNAKE_CASE_  )
						elif "attentions" in new_layer:
												SCREAMING_SNAKE_CASE													=					convert_attn_naming(SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE													=					new_string_left

						if not isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ):
												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     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						Dict:
						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(SCREAMING_SNAKE_CASE_  )

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

						return new_state_dict


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Optional[int] ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						str:
						if len(SCREAMING_SNAKE_CASE_  ) == 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     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						List[Any]:
						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(SCREAMING_SNAKE_CASE_  )

						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=SCREAMING_SNAKE_CASE_ ,			sample_rate=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					diffusers_model.state_dict()

						SCREAMING_SNAKE_CASE													=					DiffusionUncond(SCREAMING_SNAKE_CASE_  )
						orig_model.load_state_dict(torch.load(args.model_path ,			map_location=SCREAMING_SNAKE_CASE_  )['state_dict']  )
						SCREAMING_SNAKE_CASE													=					orig_model.diffusion_ema.eval()
						SCREAMING_SNAKE_CASE													=					orig_model.state_dict()
						SCREAMING_SNAKE_CASE													=					rename_orig_weights(SCREAMING_SNAKE_CASE_  )

						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(SCREAMING_SNAKE_CASE_  ) == 0, F'Problem with {renamed_minus_diffusers}'
						assert all(k.endswith('kernel'  ) for k in list(SCREAMING_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":
																		SCREAMING_SNAKE_CASE													=					value.squeeze()

												SCREAMING_SNAKE_CASE													=					value

						diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					1_00
						SCREAMING_SNAKE_CASE													=					33

						SCREAMING_SNAKE_CASE													=					IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_  )

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

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

						SCREAMING_SNAKE_CASE													=					DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_ ,			scheduler=SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					torch.manual_seed(33  )
						SCREAMING_SNAKE_CASE													=					pipe(num_inference_steps=SCREAMING_SNAKE_CASE_ ,			generator=SCREAMING_SNAKE_CASE_  ).audios

						SCREAMING_SNAKE_CASE													=					sampling.iplms_sample(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			{}  )
						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' ,			SCREAMING_SNAKE_CASE_  )
						print('Diff max' ,			SCREAMING_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__":
	__UpperCamelCase						=      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.''')
	__UpperCamelCase						=      parser.parse_args()

	main(args)


