



"""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   :							List[str]										=   {
    "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   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    torch.sin(t * math.pi / 2				) ** 2
				_UpperCAmelCase					=    (1 - sigma**2) ** 0.5
				return alpha_sigma_to_t(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			pass



class 			_a (					nn.Module):
			"""simple docstring"""



			def __init__(		self     :    Union[str, Any]				,   __UpperCamelCase     :    str   )->Optional[Any]:
							super().__init__()

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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''simple docstring'''



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

				return f'./{model_name}.ckpt'


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

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

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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''simple docstring'''



				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   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						List[str]=13				):






				'''simple docstring'''



				_UpperCAmelCase					=    input_string

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

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

				while string.startswith('''main.7.'''				):
								depth += 1
								_UpperCAmelCase					=    string[7:]

				if string.startswith('''main.'''				):
								_UpperCAmelCase					=    string[5:]

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

				if depth == max_depth:
								_UpperCAmelCase					=    MID_NUM_TO_LAYER[layer_num]
								_UpperCAmelCase					=    '''mid_block'''
				elif depth > 0 and int(_SCREAMING_SNAKE_CASE				) < 7:
								_UpperCAmelCase					=    DOWN_NUM_TO_LAYER[layer_num]
								_UpperCAmelCase					=    f'down_blocks.{depth}'
				elif depth > 0 and int(_SCREAMING_SNAKE_CASE				) > 7:
								_UpperCAmelCase					=    UP_NUM_TO_LAYER[layer_num]
								_UpperCAmelCase					=    f'up_blocks.{max_depth - depth - 1}'
				elif depth == 0:
								_UpperCAmelCase					=    DEPTH_0_TO_LAYER[layer_num]
								_UpperCAmelCase					=    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}.'				)

				_UpperCAmelCase					=    string_left[1:]

				if "resnets" in new_layer:
								_UpperCAmelCase					=    convert_resconv_naming(_SCREAMING_SNAKE_CASE				)
				elif "attentions" in new_layer:
								_UpperCAmelCase					=    convert_attn_naming(_SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    new_string_left

				if not isinstance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
								_UpperCAmelCase					=    prefix + '''.''' + new_layer + '''.''' + string_left
				else:
								_UpperCAmelCase					=    [prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
				return new_string







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]				):






				'''simple docstring'''



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

								_UpperCAmelCase					=    rename(_SCREAMING_SNAKE_CASE				)

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

				return new_state_dict







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						Tuple				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''				)

				_UpperCAmelCase					=    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					=    download(_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    MODELS_MAP[model_name]['''sample_rate''']
				_UpperCAmelCase					=    MODELS_MAP[model_name]['''sample_size''']

				_UpperCAmelCase					=    Object()
				_UpperCAmelCase					=    sample_size
				_UpperCAmelCase					=    sample_rate
				_UpperCAmelCase					=    0

				_UpperCAmelCase					=    UNetaDModel(sample_size=_SCREAMING_SNAKE_CASE					,     sample_rate=_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    diffusers_model.state_dict()

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

				_UpperCAmelCase					=    set(renamed_state_dict.keys()				) - set(diffusers_state_dict.keys()				)
				_UpperCAmelCase					=    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":
												_UpperCAmelCase					=    value.squeeze()

								_UpperCAmelCase					=    value

				diffusers_model.load_state_dict(_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    100
				_UpperCAmelCase					=    33

				_UpperCAmelCase					=    IPNDMScheduler(num_train_timesteps=_SCREAMING_SNAKE_CASE				)

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

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

				_UpperCAmelCase					=    DanceDiffusionPipeline(unet=_SCREAMING_SNAKE_CASE					,     scheduler=_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    torch.manual_seed(33				)
				_UpperCAmelCase					=    pipe(num_inference_steps=_SCREAMING_SNAKE_CASE					,     generator=_SCREAMING_SNAKE_CASE				).audios

				_UpperCAmelCase					=    sampling.iplms_sample(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     {}				)
				_UpperCAmelCase					=    generated.clamp(-1					,     1				)

				_UpperCAmelCase					=    (generated - audio).abs().sum()
				_UpperCAmelCase					=    (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__":
					__A   :							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.")
					__A   :							str										=   parser.parse_args()

					main(args)








"""simple docstring"""


import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__A   :							str										=   [
    "cross_validation.py",
    "gradient_accumulation.py",
    "local_sgd.py",
    "multi_process_metrics.py",
    "memory.py",
    "automatic_gradient_accumulation.py",
    "fsdp_with_peak_mem_tracking.py",
    "deepspeed_with_config_support.py",
    "megatron_lm_gpt_pretraining.py",
]
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    bool				,   __UpperCamelCase     :    str = None				,   __UpperCamelCase     :    list = None   )->int:
							_UpperCAmelCase					=    None
							_UpperCAmelCase					=    os.path.abspath(os.path.join('''examples'''				,   '''by_feature'''   )   )
							_UpperCAmelCase					=    os.path.abspath('''examples'''   )
							for item in os.listdir(__UpperCamelCase   ):
											if item not in EXCLUDE_EXAMPLES:
															_UpperCAmelCase					=    os.path.join(__UpperCamelCase				,   __UpperCamelCase   )
															if os.path.isfile(__UpperCamelCase   ) and ".py" in item_path:
																			with self.subTest(
																			    tested_script=__UpperCamelCase				,   feature_script=__UpperCamelCase				,   tested_section='''main()''' if parser_only else '''training_function()'''				,   ):
																							_UpperCAmelCase					=    compare_against_test(
																							    os.path.join(__UpperCamelCase				,   __UpperCamelCase   )				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )
																							_UpperCAmelCase					=    '''\n'''.join(__UpperCamelCase   )
																							if special_strings is not None:
																											for string in special_strings:
																															_UpperCAmelCase					=    diff.replace(__UpperCamelCase				,   ''''''   )
																							self.assertEqual(__UpperCamelCase				,   ''''''   )



			def  lowercase__						(		self     :    Tuple   )->Any:
							self.one_complete_example('''complete_nlp_example.py'''				,   __UpperCamelCase   )
							self.one_complete_example('''complete_nlp_example.py'''				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[Any]   )->int:
							_UpperCAmelCase					=    os.path.abspath(os.path.join('''examples'''				,   '''cv_example.py'''   )   )
							_UpperCAmelCase					=    [
							    ''' ''' * 1_6 + '''{\n\n''',
							    ''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
							    ''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
							    ''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
							    ''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
							    ''' ''' * 1_6 + '''},\n\n''',
							    ''' ''' * 1_6 + '''step=epoch,\n''',
							    ''' ''' * 1_2,
							    ''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
							]
							self.one_complete_example('''complete_cv_example.py'''				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )
							self.one_complete_example('''complete_cv_example.py'''				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )



@mock.patch.dict(os.environ			,							{"""TESTING_MOCKED_DATALOADERS""": """1"""})
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	False



			@classmethod
			def  lowercase__						(		cls     :    Optional[int]   )->Optional[Any]:
							super().setUpClass()
							_UpperCAmelCase					=    tempfile.mkdtemp()
							_UpperCAmelCase					=    os.path.join(cls._tmpdir				,   '''default_config.yml'''   )

							write_basic_config(save_location=cls.configPath   )
							_UpperCAmelCase					=    ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]



			@classmethod
			def  lowercase__						(		cls     :    Dict   )->Any:
							super().tearDownClass()
							shutil.rmtree(cls._tmpdir   )



			def  lowercase__						(		self     :    Optional[int]   )->Any:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
							run_command(self._launch_args + testargs   )
							self.assertTrue(os.path.exists(os.path.join(self.tmpdir				,   '''epoch_0'''   )   )   )



			def  lowercase__						(		self     :    Optional[int]   )->Optional[int]:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
							_UpperCAmelCase					=    run_command(self._launch_args + testargs   )
							self.assertTrue(os.path.exists(os.path.join(self.tmpdir				,   '''step_2'''   )   )   )



			def  lowercase__						(		self     :    Optional[Any]   )->List[Any]:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir				,   "epoch_0"   )}\n        '.split()
							_UpperCAmelCase					=    run_command(self._launch_args + testargs				,   return_stdout=__UpperCamelCase   )
							self.assertNotIn('''epoch 0:'''				,   __UpperCamelCase   )
							self.assertIn('''epoch 1:'''				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    List[str]   )->str:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir				,   "step_2"   )}\n        '.split()
							_UpperCAmelCase					=    run_command(self._launch_args + testargs				,   return_stdout=__UpperCamelCase   )
							if torch.cuda.is_available():
											_UpperCAmelCase					=    torch.cuda.device_count()
							else:
											_UpperCAmelCase					=    1
							if num_processes > 1:
											self.assertNotIn('''epoch 0:'''				,   __UpperCamelCase   )
											self.assertIn('''epoch 1:'''				,   __UpperCamelCase   )
							else:
											self.assertIn('''epoch 0:'''				,   __UpperCamelCase   )
											self.assertIn('''epoch 1:'''				,   __UpperCamelCase   )



			@slow
			def  lowercase__						(		self     :    Dict   )->List[Any]:
							_UpperCAmelCase					=    '''
        examples/by_feature/cross_validation.py
        --num_folds 2
        '''.split()
							with mock.patch.dict(os.environ				,   {'''TESTING_MOCKED_DATALOADERS''': '''0'''}   ):
											_UpperCAmelCase					=    run_command(self._launch_args + testargs				,   return_stdout=__UpperCamelCase   )
											_UpperCAmelCase					=    re.findall('''({.+})'''				,   __UpperCamelCase   )
											_UpperCAmelCase					=    [r for r in results if '''accuracy''' in r][-1]
											_UpperCAmelCase					=    ast.literal_eval(__UpperCamelCase   )
											self.assertGreaterEqual(results['''accuracy''']				,   0.7_5   )



			def  lowercase__						(		self     :    Any   )->List[Any]:
							_UpperCAmelCase					=    ['''examples/by_feature/multi_process_metrics.py''']
							run_command(self._launch_args + testargs   )



			@require_trackers
			@mock.patch.dict(os.environ				,   {'''WANDB_MODE''': '''offline'''}   )
			def  lowercase__						(		self     :    Optional[int]   )->Union[str, Any]:
							with tempfile.TemporaryDirectory() as tmpdir:
											_UpperCAmelCase					=    F'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
											run_command(self._launch_args + testargs   )
											self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase				,   '''tracking'''   )   )   )



			def  lowercase__						(		self     :    Dict   )->Dict:
							_UpperCAmelCase					=    ['''examples/by_feature/gradient_accumulation.py''']
							run_command(self._launch_args + testargs   )



			def  lowercase__						(		self     :    Union[str, Any]   )->Tuple:
							_UpperCAmelCase					=    ['''examples/by_feature/local_sgd.py''']
							run_command(self._launch_args + testargs   )



