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  :     str       =  {
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 4_8_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 4_8_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 4_8_0_0_0,
        """sample_size""": 1_3_1_0_7_2,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 1_6_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 1_6_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 1_6_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
}





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							torch.sin(t * math.pi / 2							) ** 2
				__lowercase 			=							(1 - sigma**2) ** 0.5
				return alpha_sigma_to_t(lowercase							,			lowercase							)




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			pass




class    _UpperCamelCase      (						nn.Module ):


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__						)     ->					Union[str, Any]:

							'''simple docstring'''


							super().__init__()

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





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							MODELS_MAP[model_name]['''url''']
				os.system(F"wget {url} ./"							)

				return F"./{model_name}.ckpt"


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

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

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





def 				UpperCAmelCase						(    lowercase							):







				"""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 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				for key, value in ATTN_MAP.items():
								if name.startswith(lowercase							) and not isinstance(lowercase							,			lowercase							):
												return name.replace(lowercase							,			lowercase							)
								elif name.startswith(lowercase							):
												return [name.replace(lowercase							,			lowercase							) for v in value]
				raise ValueError(F"Attn error with {name}"							)





def 				UpperCAmelCase						(    lowercase							,			lowercase=13							):







				"""simple docstring"""



				__lowercase 			=							input_string

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

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

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

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

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

				if depth == max_depth:
								__lowercase 			=							MID_NUM_TO_LAYER[layer_num]
								__lowercase 			=							'''mid_block'''
				elif depth > 0 and int(lowercase							) < 7:
								__lowercase 			=							DOWN_NUM_TO_LAYER[layer_num]
								__lowercase 			=							F"down_blocks.{depth}"
				elif depth > 0 and int(lowercase							) > 7:
								__lowercase 			=							UP_NUM_TO_LAYER[layer_num]
								__lowercase 			=							F"up_blocks.{max_depth - depth - 1}"
				elif depth == 0:
								__lowercase 			=							DEPTH_0_TO_LAYER[layer_num]
								__lowercase 			=							F"up_blocks.{max_depth - 1}" if int(lowercase							) > 3 else '''down_blocks.0'''

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

				__lowercase 			=							string_left[1:]

				if "resnets" in new_layer:
								__lowercase 			=							convert_resconv_naming(lowercase							)
				elif "attentions" in new_layer:
								__lowercase 			=							convert_attn_naming(lowercase							)
								__lowercase 			=							new_string_left

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





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



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

								__lowercase 			=							rename(lowercase							)

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

				return new_state_dict





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



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

				__lowercase 			=							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()}"
								__lowercase 			=							download(lowercase							)

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

				__lowercase 			=							Object()
				__lowercase 			=							sample_size
				__lowercase 			=							sample_rate
				__lowercase 			=							0

				__lowercase 			=							UNetaDModel(sample_size=lowercase							,			sample_rate=lowercase							)
				__lowercase 			=							diffusers_model.state_dict()

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

				__lowercase 			=							set(renamed_state_dict.keys()							) - set(diffusers_state_dict.keys()							)
				__lowercase 			=							set(diffusers_state_dict.keys()							) - set(renamed_state_dict.keys()							)

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

								__lowercase 			=							value

				diffusers_model.load_state_dict(lowercase							)

				__lowercase 			=							100
				__lowercase 			=							33

				__lowercase 			=							IPNDMScheduler(num_train_timesteps=lowercase							)

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

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

				__lowercase 			=							DanceDiffusionPipeline(unet=lowercase							,			scheduler=lowercase							)

				__lowercase 			=							torch.manual_seed(33							)
				__lowercase 			=							pipe(num_inference_steps=lowercase							,			generator=lowercase							).audios

				__lowercase 			=							sampling.iplms_sample(lowercase							,			lowercase							,			lowercase							,			{}							)
				__lowercase 			=							generated.clamp(-1							,			1							)

				__lowercase 			=							(generated - audio).abs().sum()
				__lowercase 			=							(generated - audio).abs().max()

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

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

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

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


if __name__ == "__main__":
		__a  :     int       =  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  :     int       =  parser.parse_args()

		main(args)
import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__a  :     List[Any]       =  logging.getLogger(__name__)





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							argparse.ArgumentParser(
				    description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''							)
				parser.add_argument(
				    '''--dataset_name'''							,			type=lowercase							,			default='''wikitext'''							,			help='''Name of the training. Explore datasets at: hf.co/datasets.'''							,			)
				parser.add_argument(
				    '''--dataset_config'''							,			type=lowercase							,			default='''wikitext-103-raw-v1'''							,			help='''Configuration name of the dataset.'''							)
				parser.add_argument(
				    '''--tokenizer_name_or_path'''							,			type=lowercase							,			default='''sayakpaul/unigram-tokenizer-wikitext'''							,			help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''							,			)
				parser.add_argument(
				    '''--shard_size'''							,			type=lowercase							,			default=1000							,			help='''Number of entries to go in a single shard.'''							,			)
				parser.add_argument('''--split'''							,			type=lowercase							,			default='''train'''							,			choices=['''train''', '''test''', '''validation''']							)
				parser.add_argument(
				    '''--limit'''							,			default=lowercase							,			type=lowercase							,			help='''Limit the number of shards (used for debugging).'''							,			)
				parser.add_argument(
				    '''--max_length'''							,			type=lowercase							,			default=512							,			help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
				    ''' sequence length that is a multiple of 8.'''							,			)
				parser.add_argument(
				    '''--output_dir'''							,			default='''tf-tpu'''							,			type=lowercase							,			help='''Output directory where the TFRecord shards will be saved. If the'''
				    ''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
				    ''' shards will be directly saved to a Google Cloud Storage bucket.'''							,			)

				__lowercase 			=							parser.parse_args()
				return args





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				def fn(lowercase							):
								return tokenizer(examples['''text''']							)

				return fn





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							[]
				for i in range(len(tokenized_data['''input_ids''']							)							):
								__lowercase 			=							{
								    '''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i]							)							),
								    '''attention_mask''': tf.train.Feature(
								        intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i]							)							),
								}
								__lowercase 			=							tf.train.Features(feature=lowercase							)
								__lowercase 			=							tf.train.Example(features=lowercase							)
								__lowercase 			=							example.SerializeToString()
								records.append(lowercase							)
				return records





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							datasets.load_dataset(args.dataset_name							,			args.dataset_config							,			split=args.split							)

				if args.limit is not None:
								__lowercase 			=							min(len(lowercase							)							,			args.limit							)
								__lowercase 			=							dataset.select(range(lowercase							)							)
								print(F"Limiting the dataset to {args.limit} entries."							)

				__lowercase 			=							AutoTokenizer.from_pretrained(args.tokenizer_name_or_path							)

				# Handle output directory creation.
				# For serializing into a Google Cloud Storage Bucket, one needs to first
				# create a bucket.
				if "gs" not in args.output_dir:
								if not os.path.exists(args.output_dir							):
												os.makedirs(args.output_dir							)
								__lowercase 			=							os.path.join(args.output_dir							,			args.split							)
								if not os.path.exists(lowercase							):
												os.makedirs(lowercase							)
				else:
								__lowercase 			=							os.path.join(args.output_dir							,			args.split							)

				# Tokenize the whole dataset at once.
				__lowercase 			=							tokenize_function(lowercase							)
				__lowercase 			=							dataset.map(lowercase							,			batched=lowercase							,			num_proc=4							,			remove_columns=['''text''']							)

				# We need to concatenate all our texts together, and then split the result
				# into chunks of a fixed size, which we will call block_size. To do this, we
				# will use the map method again, with the option batched=True. When we use batched=True,
				# the function we pass to map() will be passed multiple inputs at once, allowing us
				# to group them into more or fewer examples than we had in the input.
				# This allows us to create our new fixed-length samples. The advantage of this
				# method is that we don't lose a whole lot of content from the dataset compared to the
				# case where we simply tokenize with a pre-defined max_length.

				def group_texts(lowercase							):
								# Concatenate all texts.
								__lowercase 			=							{k: sum(examples[k]							,			[]							) for k in examples.keys()}
								__lowercase 			=							len(concatenated_examples[list(examples.keys()							)[0]]							)
								# We drop the small remainder, though you could add padding instead if the model supports it
								# In this, as in all things, we advise you to follow your heart 🫀
								__lowercase 			=							(total_length // args.max_length) * args.max_length
								# Split by chunks of max_len.
								__lowercase 			=							{
								    k: [t[i : i + args.max_length] for i in range(0							,			lowercase							,			args.max_length							)]
								    for k, t in concatenated_examples.items()
								}
								return result

				__lowercase 			=							dataset_tokenized.map(lowercase							,			batched=lowercase							,			batch_size=1000							,			num_proc=4							)

				__lowercase 			=							0
				__lowercase 			=							0
				for shard in range(0							,			len(lowercase							)							,			args.shard_size							):
								__lowercase 			=							grouped_dataset[shard : shard + args.shard_size]
								__lowercase 			=							len(dataset_snapshot['''input_ids''']							)
								__lowercase 			=							os.path.join(lowercase							,			F"dataset-{shard_count}-{records_containing}.tfrecord"							)
								__lowercase 			=							get_serialized_examples(lowercase							)

								with tf.io.TFRecordWriter(lowercase							) as out_file:
												for i in range(len(lowercase							)							):
																__lowercase 			=							serialized_examples[i]
																out_file.write(lowercase							)
												print('''Wrote file {} containing {} records'''.format(lowercase							,			lowercase							)							)

								shard_count += 1
								total_records += records_containing

				with open(F"split-{args.split}-records-count.txt"							,			'''w'''							) as f:
								print(F"Total {args.split} records: {total_records}"							,			file=lowercase							)


if __name__ == "__main__":
		__a  :     Optional[Any]       =  parse_args()
		main(args)