






"""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


lowercase__          = {
    """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 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase       )						->    Optional[int]:

		"""simple docstring"""







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

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    Union[str, Any]:

		"""simple docstring"""







		lowerCAmelCase_   :							str									=			torch.sin(t * math.pi / 2       ) ** 2
		lowerCAmelCase_   :							Any									=			(1 - sigma**2) ** 0.5
		return alpha_sigma_to_t(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__       )


class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		pass







class 		__lowerCamelCase				( nn.Module			):





		'''simple docstring'''






		def __init__(   self							:	Any						,			a_							:	Union[str, Any]	):
				super().__init__()

				lowerCAmelCase_   :							List[str]									=			DiffusionAttnUnetaD(a_						,			n_attn_layers=4	)
				lowerCAmelCase_   :							int									=			deepcopy(self.diffusion	)
				lowerCAmelCase_   :							Dict									=			torch.quasirandom.SobolEngine(1						,			scramble=a_	)







def 		__lowerCamelCase	(  __UpperCamelCase       )						->    Optional[Any]:

		"""simple docstring"""







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

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


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

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

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

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    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 		__lowerCamelCase	(  __UpperCamelCase       )						->    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 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase=13       )						->    Tuple:

		"""simple docstring"""







		lowerCAmelCase_   :							Optional[int]									=			input_string

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

		lowerCAmelCase_   :							Optional[int]									=			0
		if string.startswith("net.3."       ):
				depth += 1
				lowerCAmelCase_   :							List[str]									=			string[6:]
		elif string.startswith("net."       ):
				lowerCAmelCase_   :							str									=			string[4:]

		while string.startswith("main.7."       ):
				depth += 1
				lowerCAmelCase_   :							Union[str, Any]									=			string[7:]

		if string.startswith("main."       ):
				lowerCAmelCase_   :							List[str]									=			string[5:]

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

		if depth == max_depth:
				lowerCAmelCase_   :							str									=			MID_NUM_TO_LAYER[layer_num]
				lowerCAmelCase_   :							Dict									=			"""mid_block"""
		elif depth > 0 and int(SCREAMING_SNAKE_CASE__       ) < 7:
				lowerCAmelCase_   :							str									=			DOWN_NUM_TO_LAYER[layer_num]
				lowerCAmelCase_   :							int									=			f'''down_blocks.{depth}'''
		elif depth > 0 and int(SCREAMING_SNAKE_CASE__       ) > 7:
				lowerCAmelCase_   :							Dict									=			UP_NUM_TO_LAYER[layer_num]
				lowerCAmelCase_   :							Any									=			f'''up_blocks.{max_depth - depth - 1}'''
		elif depth == 0:
				lowerCAmelCase_   :							Dict									=			DEPTH_0_TO_LAYER[layer_num]
				lowerCAmelCase_   :							str									=			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}.'''       )

		lowerCAmelCase_   :							Any									=			string_left[1:]

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

		if not isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__       ):
				lowerCAmelCase_   :							Dict									=			prefix + """.""" + new_layer + """.""" + string_left
		else:
				lowerCAmelCase_   :							int									=			[prefix + """.""" + new_layer + """.""" + s for s in string_left]
		return new_string

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    Union[str, Any]:

		"""simple docstring"""







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

				lowerCAmelCase_   :							List[str]									=			rename(SCREAMING_SNAKE_CASE__       )

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

		return new_state_dict

def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase       )						->    List[Any]:

		"""simple docstring"""







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

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    List[str]:

		"""simple docstring"""







		lowerCAmelCase_   :							List[Any]									=			torch.device("cuda" if torch.cuda.is_available() else "cpu"       )

		lowerCAmelCase_   :							List[Any]									=			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()}'''
				lowerCAmelCase_   :							List[str]									=			download(SCREAMING_SNAKE_CASE__       )

		lowerCAmelCase_   :							Any									=			MODELS_MAP[model_name]["""sample_rate"""]
		lowerCAmelCase_   :							str									=			MODELS_MAP[model_name]["""sample_size"""]

		lowerCAmelCase_   :							List[Any]									=			Object()
		lowerCAmelCase_   :							str									=			sample_size
		lowerCAmelCase_   :							List[str]									=			sample_rate
		lowerCAmelCase_   :							Any									=			0

		lowerCAmelCase_   :							Dict									=			UNetaDModel(sample_size=SCREAMING_SNAKE_CASE__      ,       sample_rate=SCREAMING_SNAKE_CASE__       )
		lowerCAmelCase_   :							Optional[int]									=			diffusers_model.state_dict()

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

		lowerCAmelCase_   :							Optional[Any]									=			set(renamed_state_dict.keys()       ) - set(diffusers_state_dict.keys()       )
		lowerCAmelCase_   :							Tuple									=			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":
						lowerCAmelCase_   :							str									=			value.squeeze()

				lowerCAmelCase_   :							int									=			value

		diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE__       )

		lowerCAmelCase_   :							List[Any]									=			100
		lowerCAmelCase_   :							int									=			33

		lowerCAmelCase_   :							Tuple									=			IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE__       )

		lowerCAmelCase_   :							List[str]									=			torch.manual_seed(SCREAMING_SNAKE_CASE__       )
		lowerCAmelCase_   :							str									=			torch.randn([1, 2, config.sample_size]      ,       generator=SCREAMING_SNAKE_CASE__       ).to(SCREAMING_SNAKE_CASE__       )

		lowerCAmelCase_   :							List[str]									=			torch.linspace(1      ,       0      ,       steps + 1      ,       device=SCREAMING_SNAKE_CASE__       )[:-1]
		lowerCAmelCase_   :							List[Any]									=			get_crash_schedule(SCREAMING_SNAKE_CASE__       )

		lowerCAmelCase_   :							Any									=			DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__       )

		lowerCAmelCase_   :							Dict									=			torch.manual_seed(33       )
		lowerCAmelCase_   :							List[str]									=			pipe(num_inference_steps=SCREAMING_SNAKE_CASE__      ,       generator=SCREAMING_SNAKE_CASE__       ).audios

		lowerCAmelCase_   :							int									=			sampling.iplms_sample(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       {}       )
		lowerCAmelCase_   :							str									=			generated.clamp(-1      ,       1       )

		lowerCAmelCase_   :							Any									=			(generated - audio).abs().sum()
		lowerCAmelCase_   :							Dict									=			(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__":
					lowercase__          = 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.""")
					lowercase__          = parser.parse_args()

					main(args)


import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Any:
       assert isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@require_sqlalchemy
@pytest.mark.parametrize("""keep_in_memory"""					,	[False, True]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     Union[str, Any]       	=      tmp_path / """cache"""
       lowercase							:     Optional[Any]       	=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              lowercase							:     Any       	=      SqlDatasetReader(
                  """dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__					,	keep_in_memory=SCREAMING_SNAKE_CASE__				).read()
       _check_sql_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







@require_sqlalchemy
@pytest.mark.parametrize(
    """features"""					,	[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]					,	)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       lowercase							:     Union[str, Any]       	=      tmp_path / """cache"""
       lowercase							:     int       	=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       lowercase							:     str       	=      features.copy() if features else default_expected_features
       lowercase							:     Optional[Any]       	=      (
           Features({feature: Value(SCREAMING_SNAKE_CASE__				) for feature, dtype in features.items()}				) if features is not None else None
       )
       lowercase							:     Optional[int]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	features=SCREAMING_SNAKE_CASE__					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       _check_sql_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       with contextlib.closing(sqlitea.connect(SCREAMING_SNAKE_CASE__				)				) as con:
              lowercase							:     Optional[int]       	=      con.cursor()
              cur.execute("""SELECT * FROM dataset"""				)
              for row in cur:
                     yield row







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       lowercase							:     Any       	=      tmp_path / """cache"""
       lowercase							:     int       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     List[str]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=1				).write()

       lowercase							:     List[str]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)

       for rowa, rowa in zip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              assert rowa == rowa







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Dict:
       lowercase							:     Dict       	=      tmp_path / """cache"""
       lowercase							:     List[str]       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     List[str]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=2				).write()

       lowercase							:     Optional[int]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)

       for rowa, rowa in zip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              assert rowa == rowa







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     str       	=      tmp_path / """cache"""
       lowercase							:     Optional[int]       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     Optional[Any]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       with pytest.raises(SCREAMING_SNAKE_CASE__				):
              SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=0				).write()
