
"""simple docstring"""







import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


lowerCamelCase__        =		{
    """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    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	return torch.atana(_UpperCamelCase	,  _UpperCamelCase		) / math.pi * 2
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						List[str]						      =				torch.sin(t * math.pi / 2		) ** 2
	__lowerCAmelCase      :						Optional[int]						      =				(1 - sigma**2) ** 0.5
	return alpha_sigma_to_t(_UpperCamelCase	,  _UpperCamelCase		)




class 			A__	(     _lowerCamelCase):
		pass




class 			A__	(     nn.Module):

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

			__lowerCAmelCase      :						Union[str, Any]						      =				DiffusionAttnUnetaD(_SCREAMING_SNAKE_CASE	,		n_attn_layers=4			)
			__lowerCAmelCase      :						Dict						      =				deepcopy(self.diffusion			)
			__lowerCAmelCase      :						Union[str, Any]						      =				torch.quasirandom.SobolEngine(1	,		scramble=_SCREAMING_SNAKE_CASE			)



def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						Tuple						      =				MODELS_MAP[model_name]['url']
	os.system(F"wget {url} ./"		)

	return F"./{model_name}.ckpt"


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

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

lowerCamelCase__        =		{
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}
def    __lowerCAmelCase    (_UpperCamelCase		):
	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		):
	for key, value in ATTN_MAP.items():
		if name.startswith(_UpperCamelCase		) and not isinstance(_UpperCamelCase	,  _UpperCamelCase		):
			return name.replace(_UpperCamelCase	,  _UpperCamelCase		)
		elif name.startswith(_UpperCamelCase		):
			return [name.replace(_UpperCamelCase	,  _UpperCamelCase		) for v in value]
	raise ValueError(F"Attn error with {name}"		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase=13		):
	__lowerCAmelCase      :						Any						      =				input_string

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

	__lowerCAmelCase      :						Tuple						      =				0
	if string.startswith('net.3.'		):
		depth += 1
		__lowerCAmelCase      :						Union[str, Any]						      =				string[6:]
	elif string.startswith('net.'		):
		__lowerCAmelCase      :						Dict						      =				string[4:]

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

	if string.startswith('main.'		):
		__lowerCAmelCase      :						Optional[Any]						      =				string[5:]

	# mid block
	if string[:2].isdigit():
		__lowerCAmelCase      :						Union[str, Any]						      =				string[:2]
		__lowerCAmelCase      :						Union[str, Any]						      =				string[2:]
	else:
		__lowerCAmelCase      :						Dict						      =				string[0]
		__lowerCAmelCase      :						List[str]						      =				string[1:]

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

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

	__lowerCAmelCase      :						List[str]						      =				string_left[1:]

	if "resnets" in new_layer:
		__lowerCAmelCase      :						int						      =				convert_resconv_naming(_UpperCamelCase		)
	elif "attentions" in new_layer:
		__lowerCAmelCase      :						Tuple						      =				convert_attn_naming(_UpperCamelCase		)
		__lowerCAmelCase      :						Optional[int]						      =				new_string_left

	if not isinstance(_UpperCamelCase	,  _UpperCamelCase		):
		__lowerCAmelCase      :						Any						      =				prefix + '.' + new_layer + '.' + string_left
	else:
		__lowerCAmelCase      :						int						      =				[prefix + '.' + new_layer + '.' + s for s in string_left]
	return new_string
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						Union[str, Any]						      =				{}
	for k, v in state_dict.items():
		if k.endswith('kernel'		):
			# up- and downsample layers, don't have trainable weights
			continue

		__lowerCAmelCase      :						Optional[Any]						      =				rename(_UpperCamelCase		)

		# check if we need to transform from Conv => Linear for attention
		if isinstance(_UpperCamelCase	,  _UpperCamelCase		):
			__lowerCAmelCase      :						Union[str, Any]						      =				transform_conv_attns(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)
		else:
			__lowerCAmelCase      :						List[Any]						      =				v

	return new_state_dict
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	if len(_UpperCamelCase		) == 1:
		if len(v.shape		) == 3:
			# weight
			__lowerCAmelCase      :						List[Any]						      =				v[:, :, 0]
		else:
			# bias
			__lowerCAmelCase      :						str						      =				v
	else:
		# qkv matrices
		__lowerCAmelCase      :						Union[str, Any]						      =				v.shape[0]
		__lowerCAmelCase      :						List[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      :						Optional[Any]						      =				v[i * single_shape : (i + 1) * single_shape]
	return new_state_dict
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						Optional[Any]						      =				torch.device('cuda' if torch.cuda.is_available() else 'cpu'		)

	__lowerCAmelCase      :						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      :						int						      =				download(_UpperCamelCase		)

	__lowerCAmelCase      :						Dict						      =				MODELS_MAP[model_name]['sample_rate']
	__lowerCAmelCase      :						Tuple						      =				MODELS_MAP[model_name]['sample_size']

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

	__lowerCAmelCase      :						Optional[Any]						      =				UNetaDModel(sample_size=_UpperCamelCase	,  sample_rate=_UpperCamelCase		)
	__lowerCAmelCase      :						Union[str, Any]						      =				diffusers_model.state_dict()

	__lowerCAmelCase      :						str						      =				DiffusionUncond(_UpperCamelCase		)
	orig_model.load_state_dict(torch.load(args.model_path	,  map_location=_UpperCamelCase		)['state_dict']		)
	__lowerCAmelCase      :						Any						      =				orig_model.diffusion_ema.eval()
	__lowerCAmelCase      :						Dict						      =				orig_model.state_dict()
	__lowerCAmelCase      :						Optional[int]						      =				rename_orig_weights(_UpperCamelCase		)

	__lowerCAmelCase      :						str						      =				set(renamed_state_dict.keys()		) - set(diffusers_state_dict.keys()		)
	__lowerCAmelCase      :						Optional[int]						      =				set(diffusers_state_dict.keys()		) - set(renamed_state_dict.keys()		)

	assert len(_UpperCamelCase		) == 0, F"Problem with {renamed_minus_diffusers}"
	assert all(k.endswith('kernel'		) for k in list(_UpperCamelCase		)		), 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      :						Optional[int]						      =				value.squeeze()

		__lowerCAmelCase      :						Dict						      =				value

	diffusers_model.load_state_dict(_UpperCamelCase		)

	__lowerCAmelCase      :						Tuple						      =				100
	__lowerCAmelCase      :						Tuple						      =				33

	__lowerCAmelCase      :						List[str]						      =				IPNDMScheduler(num_train_timesteps=_UpperCamelCase		)

	__lowerCAmelCase      :						int						      =				torch.manual_seed(_UpperCamelCase		)
	__lowerCAmelCase      :						Optional[int]						      =				torch.randn([1, 2, config.sample_size]	,  generator=_UpperCamelCase		).to(_UpperCamelCase		)

	__lowerCAmelCase      :						Tuple						      =				torch.linspace(1	,  0	,  steps + 1	,  device=_UpperCamelCase		)[:-1]
	__lowerCAmelCase      :						Dict						      =				get_crash_schedule(_UpperCamelCase		)

	__lowerCAmelCase      :						Optional[Any]						      =				DanceDiffusionPipeline(unet=_UpperCamelCase	,  scheduler=_UpperCamelCase		)

	__lowerCAmelCase      :						Any						      =				torch.manual_seed(33		)
	__lowerCAmelCase      :						List[Any]						      =				pipe(num_inference_steps=_UpperCamelCase	,  generator=_UpperCamelCase		).audios

	__lowerCAmelCase      :						Any						      =				sampling.iplms_sample(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  {}		)
	__lowerCAmelCase      :						List[str]						      =				generated.clamp(-1	,  1		)

	__lowerCAmelCase      :						Tuple						      =				(generated - audio).abs().sum()
	__lowerCAmelCase      :						List[str]						      =				(generated - audio).abs().max()

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

	print('Diff sum'	,  _UpperCamelCase		)
	print('Diff max'	,  _UpperCamelCase		)

	assert diff_max < 1e-3, F"Diff max: {diff_max} is too much :-/"

	print(F"Conversion for {model_name} successful!"		)


if __name__ == "__main__":
							lowerCamelCase__        =		argparse.ArgumentParser()

							parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
							parser.add_argument(
							    """--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
							)
							parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
							lowerCamelCase__        =		parser.parse_args()

							main(args)

"""simple docstring"""







import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCamelCase__        =		getLogger(__name__)


lowerCamelCase__        =		"""cuda""" if torch.cuda.is_available() else """cpu"""
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = 8	,  _UpperCamelCase = DEFAULT_DEVICE	,  _UpperCamelCase=False	,  _UpperCamelCase="summarization"	,  _UpperCamelCase=None	,  **_UpperCamelCase	,  ):
	__lowerCAmelCase      :						str						      =				Path(_UpperCamelCase		).open('w'	,  encoding='utf-8'		)
	__lowerCAmelCase      :						Union[str, Any]						      =				str(_UpperCamelCase		)
	__lowerCAmelCase      :						List[str]						      =				AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase		).to(_UpperCamelCase		)
	if fpaa:
		__lowerCAmelCase      :						Optional[Any]						      =				model.half()

	__lowerCAmelCase      :						List[str]						      =				AutoTokenizer.from_pretrained(_UpperCamelCase		)
	logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"		)  # if this is wrong, check config.model_type.

	__lowerCAmelCase      :						List[Any]						      =				time.time()
	# update config with task specific params
	use_task_specific_params(_UpperCamelCase	,  _UpperCamelCase		)
	if prefix is None:
		__lowerCAmelCase      :						Optional[int]						      =				prefix or getattr(model.config	,  'prefix'	,  ''		) or ''
	for examples_chunk in tqdm(list(chunks(_UpperCamelCase	,  _UpperCamelCase		)		)		):
		__lowerCAmelCase      :						List[str]						      =				[prefix + text for text in examples_chunk]
		__lowerCAmelCase      :						List[str]						      =				tokenizer(_UpperCamelCase	,  return_tensors='pt'	,  truncation=_UpperCamelCase	,  padding='longest'		).to(_UpperCamelCase		)
		__lowerCAmelCase      :						str						      =				model.generate(
		    input_ids=batch.input_ids	,  attention_mask=batch.attention_mask	,  **_UpperCamelCase	,  )
		__lowerCAmelCase      :						str						      =				tokenizer.batch_decode(_UpperCamelCase	,  skip_special_tokens=_UpperCamelCase	,  clean_up_tokenization_spaces=_UpperCamelCase		)
		for hypothesis in dec:
			fout.write(hypothesis + '\n'		)
			fout.flush()
	fout.close()
	__lowerCAmelCase      :						Optional[int]						      =				int(time.time() - start_time		)  # seconds
	__lowerCAmelCase      :						List[Any]						      =				len(_UpperCamelCase		)
	return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs	,  4		)}
def    __lowerCAmelCase    ():
	return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'		)
def    __lowerCAmelCase    (_UpperCamelCase=True		):
	__lowerCAmelCase      :						List[Any]						      =				argparse.ArgumentParser()
	parser.add_argument('model_name'	,  type=_UpperCamelCase	,  help='like facebook/bart-large-cnn,t5-base, etc.'		)
	parser.add_argument('input_path'	,  type=_UpperCamelCase	,  help='like cnn_dm/test.source'		)
	parser.add_argument('save_path'	,  type=_UpperCamelCase	,  help='where to save summaries'		)
	parser.add_argument('--reference_path'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  help='like cnn_dm/test.target'		)
	parser.add_argument('--score_path'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default='metrics.json'	,  help='where to save metrics'		)
	parser.add_argument('--device'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default=_UpperCamelCase	,  help='cuda, cuda:1, cpu etc.'		)
	parser.add_argument(
	    '--prefix'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default=_UpperCamelCase	,  help='will be added to the begininng of src examples'		)
	parser.add_argument('--task'	,  type=_UpperCamelCase	,  default='summarization'	,  help='used for task_specific_params + metrics'		)
	parser.add_argument('--bs'	,  type=_UpperCamelCase	,  default=8	,  required=_UpperCamelCase	,  help='batch size'		)
	parser.add_argument(
	    '--n_obs'	,  type=_UpperCamelCase	,  default=-1	,  required=_UpperCamelCase	,  help='How many observations. Defaults to all.'		)
	parser.add_argument('--fp16'	,  action='store_true'		)
	parser.add_argument('--dump-args'	,  action='store_true'	,  help='print the custom hparams with the results'		)
	parser.add_argument(
	    '--info'	,  nargs='?'	,  type=_UpperCamelCase	,  const=datetime_now()	,  help=(
	        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
	        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
	    )	,  )
	# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
	__lowerCAmelCase      ,       __lowerCAmelCase      :						Optional[int]						      =				parser.parse_known_args()
	__lowerCAmelCase      :						Optional[int]						      =				parse_numeric_n_bool_cl_kwargs(_UpperCamelCase		)
	if parsed_args and verbose:
		print(F"parsed the following generate kwargs: {parsed_args}"		)
	__lowerCAmelCase      :						Dict						      =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path		).readlines()]
	if args.n_obs > 0:
		__lowerCAmelCase      :						int						      =				examples[: args.n_obs]
	Path(args.save_path		).parent.mkdir(exist_ok=_UpperCamelCase		)

	if args.reference_path is None and Path(args.score_path		).exists():
		warnings.warn(F"score_path {args.score_path} will be overwritten unless you type ctrl-c."		)

	if args.device == "cpu" and args.fpaa:
		# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
		raise ValueError('Can\'t mix --fp16 and --device cpu'		)

	__lowerCAmelCase      :						Optional[Any]						      =				generate_summaries_or_translations(
	    _UpperCamelCase	,  args.save_path	,  args.model_name	,  batch_size=args.bs	,  device=args.device	,  fpaa=args.fpaa	,  task=args.task	,  prefix=args.prefix	,  **_UpperCamelCase	,  )

	if args.reference_path is None:
		return {}

	# Compute scores
	__lowerCAmelCase      :						str						      =				calculate_bleu if 'translation' in args.task else calculate_rouge
	__lowerCAmelCase      :						Dict						      =				[x.rstrip() for x in open(args.save_path		).readlines()]
	__lowerCAmelCase      :						Dict						      =				[x.rstrip() for x in open(args.reference_path		).readlines()][: len(_UpperCamelCase		)]
	__lowerCAmelCase      :						dict						      =				score_fn(_UpperCamelCase	,  _UpperCamelCase		)
	scores.update(_UpperCamelCase		)

	if args.dump_args:
		scores.update(_UpperCamelCase		)
	if args.info:
		__lowerCAmelCase      :						Optional[Any]						      =				args.info

	if verbose:
		print(_UpperCamelCase		)

	if args.score_path is not None:
		json.dump(_UpperCamelCase	,  open(args.score_path	,  'w'		)		)

	return scores


if __name__ == "__main__":
							# Usage for MT:
							# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
							run_generate(verbose=True)