




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					:       Dict    					=      {
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 65_536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 65_536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 131_072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
}





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       Tuple:
	return torch.atana(__lowerCamelCase							,			__lowerCamelCase    ) / math.pi * 2





def 		__A		(	__lowerCamelCase    )      ->       Any:
	a										=							torch.sin(t * math.pi / 2    ) ** 2
	a										=							(1 - sigma**2) ** 0.5
	return alpha_sigma_to_t(__lowerCamelCase							,			__lowerCamelCase    )

class 		__lowerCAmelCase	(	__magic_name__ ):
							pass







class 		__lowerCAmelCase	(	nn.Module ):
							def __init__(						self						:List[str]		,      __magic_name__						:Optional[int]				):







								'''simple docstring'''







								super().__init__()

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







def 		__A		(	__lowerCamelCase    )      ->       str:
	a										=							MODELS_MAP[model_name]["""url"""]
	os.system(f'wget {url} ./'    )

	return f'./{model_name}.ckpt'


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

__UpperCamelCase					:       Optional[int]    					=      {
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

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





def 		__A		(	__lowerCamelCase    )      ->       Union[str, 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 		__A		(	__lowerCamelCase    )      ->       Dict:
	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    )      ->       Union[str, Any]:
	a										=							input_string

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

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

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

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

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

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

	if "resnets" in new_layer:
		a										=							convert_resconv_naming(__lowerCamelCase    )
	elif "attentions" in new_layer:
		a										=							convert_attn_naming(__lowerCamelCase    )
		a										=							new_string_left

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





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

		a										=							rename(__lowerCamelCase    )

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

	return new_state_dict





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Tuple:
	if len(__lowerCamelCase    ) == 1:
		if len(v.shape    ) == 3:
			# weight
			a										=							v[:, :, 0]
		else:
			# bias
			a										=							v
	else:
		# qkv matrices
		a										=							v.shape[0]
		a										=							trippled_shape // 3
		for i in range(3    ):
			if len(v.shape    ) == 3:
				a										=							v[i * single_shape : (i + 1) * single_shape, :, 0]
			else:
				a										=							v[i * single_shape : (i + 1) * single_shape]
	return new_state_dict





def 		__A		(	__lowerCamelCase    )      ->       Any:
	a										=							torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""    )

	a										=							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										=							download(__lowerCamelCase    )

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

	a										=							Object()
	a										=							sample_size
	a										=							sample_rate
	a										=							0

	a										=							UNetaDModel(sample_size=__lowerCamelCase							,			sample_rate=__lowerCamelCase    )
	a										=							diffusers_model.state_dict()

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

	a										=							set(renamed_state_dict.keys()    ) - set(diffusers_state_dict.keys()    )
	a										=							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										=							value.squeeze()

		a										=							value

	diffusers_model.load_state_dict(__lowerCamelCase    )

	a										=							100
	a										=							33

	a										=							IPNDMScheduler(num_train_timesteps=__lowerCamelCase    )

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

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

	a										=							DanceDiffusionPipeline(unet=__lowerCamelCase							,			scheduler=__lowerCamelCase    )

	a										=							torch.manual_seed(33    )
	a										=							pipe(num_inference_steps=__lowerCamelCase							,			generator=__lowerCamelCase    ).audios

	a										=							sampling.iplms_sample(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			{}    )
	a										=							generated.clamp(-1							,			1    )

	a										=							(generated - audio).abs().sum()
	a										=							(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__":
		__UpperCamelCase					:       List[str]    					=      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					:       Any    					=      parser.parse_args()

		main(args)







import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 		__lowerCAmelCase	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  CLIPTokenizer
							UpperCamelCase__					     =  CLIPTokenizerFast
							UpperCamelCase__					     =  True
							UpperCamelCase__					     =  {}
							UpperCamelCase__					     =  False
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								super().setUp()

								# fmt: off
								a										=							["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
								# fmt: on
								a										=							dict(zip(__magic_name__		,      range(len(__magic_name__				)				)				)				)
								a										=							["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>"""]
								a										=							{"""unk_token""": """<unk>"""}

								a										=							os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""vocab_file"""]				)
								a										=							os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""merges_file"""]				)
								with open(self.vocab_file		,      """w"""		,      encoding="""utf-8"""				) as fp:
									fp.write(json.dumps(__magic_name__				) + """\n"""				)
								with open(self.merges_file		,      """w"""		,      encoding="""utf-8"""				) as fp:
									fp.write("""\n""".join(__magic_name__				)				)
							def       lowerCamelCase__							(						self						:Optional[Any]		,      **__magic_name__						:Union[str, Any]				):







								'''simple docstring'''







								kwargs.update(self.special_tokens_map				)
								return CLIPTokenizer.from_pretrained(self.tmpdirname		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:int		,      **__magic_name__						:Optional[Any]				):







								'''simple docstring'''







								kwargs.update(self.special_tokens_map				)
								return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:int		,      __magic_name__						:List[str]				):







								'''simple docstring'''







								a										=							"""lower newer"""
								a										=							"""lower newer"""
								return input_text, output_text
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							CLIPTokenizer(self.vocab_file		,      self.merges_file		,      **self.special_tokens_map				)
								a										=							"""lower newer"""
								a										=							["""lo""", """w""", """er</w>""", """n""", """e""", """w""", """er</w>"""]
								a										=							tokenizer.tokenize(__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								a										=							tokens + [tokenizer.unk_token]
								a										=							[10, 2, 16, 9, 3, 2, 16, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__				)		,      __magic_name__				)
							@require_ftfy
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
										a										=							self.tokenizer_class.from_pretrained(__magic_name__		,      **__magic_name__				)
										a										=							self.rust_tokenizer_class.from_pretrained(__magic_name__		,      **__magic_name__				)

										a										=							"""A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."""
										a										=							tokenizer_s.tokenize(__magic_name__				)
										a										=							tokenizer_r.tokenize(__magic_name__				)

										self.assertListEqual(__magic_name__		,      __magic_name__				)

										# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
										# with Tilde) encoded in 2 different ways
										a										=							"""xa\u0303y""" + """ """ + """x\xe3y"""
										a										=							tokenizer_s.tokenize(__magic_name__				)
										a										=							tokenizer_r.tokenize(__magic_name__				)

										self.assertListEqual(__magic_name__		,      __magic_name__				)

										# Test that the tokenization is identical on unicode of space type
										a										=							[
										    """\u0009""",  # (horizontal tab, '\t')
										    """\u000B""",  # (vertical tab)
										    """\u000C""",  # (form feed)
										    """\u0020""",  # (space, ' ')
										    """\u200E""",  # (left-to-right mark):w
										    """\u200F""",  # (right-to-left mark)
										]
										for unicode_seq in spaces_unicodes:
											a										=							tokenizer_s.tokenize(__magic_name__				)
											a										=							tokenizer_r.tokenize(__magic_name__				)

											self.assertListEqual(__magic_name__		,      __magic_name__				)

										# Test that the tokenization is identical on unicode of line break type
										a										=							[
										    """\u000A""",  # (line feed, '\n')
										    """\r\n""",  # (carriage return and line feed, '\r\n')
										    """\u000D""",  # (carriage return, '\r')
										    """\r""",  # (carriage return, '\r')
										    """\u000D""",  # (carriage return, '\r')
										    """\u2028""",  # (line separator)
										    """\u2029""",  # (paragraph separator)
										    # "\u0085", # (next line)
										]

										# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
										# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
										# space (and thus into an empty list).

										for unicode_seq in line_break_unicodes:
											a										=							tokenizer_s.tokenize(__magic_name__				)
											a										=							tokenizer_r.tokenize(__magic_name__				)

											self.assertListEqual(__magic_name__		,      __magic_name__				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
										a										=							"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
										a										=							F'{text_of_1_token} {text_of_1_token}'

										a										=							self.rust_tokenizer_class.from_pretrained(
										    __magic_name__		,      use_fast=__magic_name__		,      )
										a										=							tokenizer_r(__magic_name__		,      return_offsets_mapping=__magic_name__		,      add_special_tokens=__magic_name__				)
										self.assertEqual(encoding.offset_mapping[0]		,      (0, len(__magic_name__				))				)
										self.assertEqual(
										    encoding.offset_mapping[1]		,      (len(__magic_name__				) + 1, len(__magic_name__				) + 1 + len(__magic_name__				))		,      )

										a										=							F' {text}'

										a										=							self.rust_tokenizer_class.from_pretrained(
										    __magic_name__		,      use_fast=__magic_name__		,      )
										a										=							tokenizer_r(__magic_name__		,      return_offsets_mapping=__magic_name__		,      add_special_tokens=__magic_name__				)
										self.assertEqual(encoding.offset_mapping[0]		,      (1, 1 + len(__magic_name__				))				)
										self.assertEqual(
										    encoding.offset_mapping[1]		,      (1 + len(__magic_name__				) + 1, 1 + len(__magic_name__				) + 1 + len(__magic_name__				))		,      )
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								with self.assertRaises(__magic_name__				) as context:
									self.rust_tokenizer_class.from_pretrained("""robot-test/old-clip-tokenizer"""				)

								self.assertTrue(
								    context.exception.args[0].startswith(
								        """The `backend_tokenizer` provided does not match the expected format."""				)				)
							@require_ftfy
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								super().test_tokenization_python_rust_equals()





							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								pass

