




'''simple docstring'''



import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


UpperCamelCase_    	=				trt.Logger(trt.Logger.WARNING)
UpperCamelCase_    	=				absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

UpperCamelCase_    	=				logging.getLogger(__name__)

UpperCamelCase_    	=				argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    "--onnx_model_path",
    default=None,
    type=str,
    required=True,
    help="Path to ONNX model: ",
)

parser.add_argument(
    "--output_dir",
    default=None,
    type=str,
    required=True,
    help="The output directory where the model checkpoints and predictions will be written.",
)

# Other parameters

parser.add_argument(
    "--tokenizer_name",
    default="",
    type=str,
    required=True,
    help="Pretrained tokenizer name or path if not the same as model_name",
)

parser.add_argument(
    "--version_2_with_negative",
    action="store_true",
    help="If true, the SQuAD examples contain some that do not have an answer.",
)
parser.add_argument(
    "--null_score_diff_threshold",
    type=float,
    default=0.0,
    help="If null_score - best_non_null is greater than the threshold predict null.",
)

parser.add_argument(
    "--max_seq_length",
    default=3_8_4,
    type=int,
    help=(
        "The maximum total input sequence length after WordPiece tokenization. Sequences "
        "longer than this will be truncated, and sequences shorter than this will be padded."
    ),
)
parser.add_argument(
    "--doc_stride",
    default=1_2_8,
    type=int,
    help="When splitting up a long document into chunks, how much stride to take between chunks.",
)

parser.add_argument("--per_device_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.")

parser.add_argument(
    "--n_best_size",
    default=2_0,
    type=int,
    help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
)
parser.add_argument(
    "--max_answer_length",
    default=3_0,
    type=int,
    help=(
        "The maximum length of an answer that can be generated. This is needed because the start "
        "and end predictions are not conditioned on one another."
    ),
)

parser.add_argument("--seed", type=int, default=4_2, help="random seed for initialization")

parser.add_argument(
    "--dataset_name",
    type=str,
    default=None,
    required=True,
    help="The name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--dataset_config_name",
    type=str,
    default=None,
    help="The configuration name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--preprocessing_num_workers", type=int, default=4, help="A csv or a json file containing the training data."
)
parser.add_argument("--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets")
parser.add_argument(
    "--fp16",
    action="store_true",
    help="Whether to use 16-bit (mixed) precision instead of 32-bit",
)
parser.add_argument(
    "--int8",
    action="store_true",
    help="Whether to use INT8",
)

UpperCamelCase_    	=				parser.parse_args()

if args.tokenizer_name:
      UpperCamelCase_    	=				AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
      raise ValueError(
          "You are instantiating a new tokenizer from scratch. This is not supported by this script."
          "You can do it from another script, save it, and load it from here, using --tokenizer_name."
      )

logger.info("Training/evaluation parameters %s", args)

UpperCamelCase_    	=				args.per_device_eval_batch_size

UpperCamelCase_    	=				(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
UpperCamelCase_    	=				True

UpperCamelCase_    	=				"temp_engine/bert-fp32.engine"
if args.fpaa:
      UpperCamelCase_    	=				"temp_engine/bert-fp16.engine"
if args.inta:
      UpperCamelCase_    	=				"temp_engine/bert-int8.engine"

# import ONNX file
if not os.path.exists("temp_engine"):
      os.makedirs("temp_engine")

UpperCamelCase_    	=				1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
      with open(args.onnx_model_path, "rb") as model:
            if not parser.parse(model.read()):
                  for error in range(parser.num_errors):
                        print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
      UpperCamelCase_    	=				[network.get_input(i) for i in range(network.num_inputs)]
      UpperCamelCase_    	=				[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

      with builder.create_builder_config() as config:
            UpperCamelCase_    	=				1 << 5_0
            if STRICT_TYPES:
                  config.set_flag(trt.BuilderFlag.STRICT_TYPES)
            if args.fpaa:
                  config.set_flag(trt.BuilderFlag.FPaa)
            if args.inta:
                  config.set_flag(trt.BuilderFlag.INTa)
            UpperCamelCase_    	=				builder.create_optimization_profile()
            config.add_optimization_profile(profile)
            for i in range(len(input_names)):
                  profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
            UpperCamelCase_    	=				builder.build_engine(network, config)

            # serialize_engine and store in file (can be directly loaded and deserialized):
            with open(engine_name, "wb") as f:
                  f.write(engine.serialize())




def   lowercase__(			__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Tuple      ,__UpperCamelCase:					List[str]      ,__UpperCamelCase:					List[str]      ,__UpperCamelCase:					Optional[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Tuple    					=						np.asarray(inputs['input_ids']      ,dtype=np.intaa    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						np.asarray(inputs['attention_mask']      ,dtype=np.intaa    )
      SCREAMING_SNAKE_CASE   :      Dict    					=						np.asarray(inputs['token_type_ids']      ,dtype=np.intaa    )

      # Copy inputs
      cuda.memcpy_htod_async(d_inputs[0]      ,input_ids.ravel()      ,__UpperCamelCase    )
      cuda.memcpy_htod_async(d_inputs[1]      ,attention_mask.ravel()      ,__UpperCamelCase    )
      cuda.memcpy_htod_async(d_inputs[2]      ,token_type_ids.ravel()      ,__UpperCamelCase    )
      # start time
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						time.time()
      # Run inference
      context.execute_async(
          bindings=[int(__UpperCamelCase    ) for d_inp in d_inputs] + [int(__UpperCamelCase    ), int(__UpperCamelCase    )]      ,stream_handle=stream.handle    )
      # Transfer predictions back from GPU
      cuda.memcpy_dtoh_async(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
      cuda.memcpy_dtoh_async(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
      # Synchronize the stream and take time
      stream.synchronize()
      # end time
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						time.time()
      SCREAMING_SNAKE_CASE   :      Tuple    					=						end_time - start_time
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						(h_outputa, h_outputa)
      # print(outputs)
      return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
UpperCamelCase_    	=				Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_info()
else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
      set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
      # Downloading and loading a dataset from the hub.
      UpperCamelCase_    	=				load_dataset(args.dataset_name, args.dataset_config_name)
else:
      raise ValueError("Evaluation requires a dataset name")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

UpperCamelCase_    	=				raw_datasets["validation"].column_names

UpperCamelCase_    	=				"question" if "question" in column_names else column_names[0]
UpperCamelCase_    	=				"context" if "context" in column_names else column_names[1]
UpperCamelCase_    	=				"answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
UpperCamelCase_    	=				tokenizer.padding_side == "right"

if args.max_seq_length > tokenizer.model_max_length:
      logger.warning(
          F"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
          F"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
      )

UpperCamelCase_    	=				min(args.max_seq_length, tokenizer.model_max_length)




def   lowercase__(			__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[q.lstrip() for q in examples[question_column_name]]

      # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
      # in one example possible giving several features when a context is long, each of those features having a
      # context that overlaps a bit the context of the previous feature.
      SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer(
          examples[question_column_name if pad_on_right else context_column_name]      ,examples[context_column_name if pad_on_right else question_column_name]      ,truncation='only_second' if pad_on_right else 'only_first'      ,max_length=__UpperCamelCase      ,stride=args.doc_stride      ,return_overflowing_tokens=__UpperCamelCase      ,return_offsets_mapping=__UpperCamelCase      ,padding='max_length'      ,)

      # Since one example might give us several features if it has a long context, we need a map from a feature to
      # its corresponding example. This key gives us just that.
      SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenized_examples.pop('overflow_to_sample_mapping'    )

      # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
      # corresponding example_id and we will store the offset mappings.
      SCREAMING_SNAKE_CASE   :      List[str]    					=						[]

      for i in range(len(tokenized_examples['input_ids']    )    ):
            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tokenized_examples.sequence_ids(__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						1 if pad_on_right else 0

            # One example can give several spans, this is the index of the example containing this span of text.
            SCREAMING_SNAKE_CASE   :      List[str]    					=						sample_mapping[i]
            tokenized_examples["example_id"].append(examples['id'][sample_index]    )

            # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
            # position is part of the context or not.
            SCREAMING_SNAKE_CASE   :      List[str]    					=						[
                (o if sequence_ids[k] == context_index else None)
                for k, o in enumerate(tokenized_examples['offset_mapping'][i]    )
            ]

      return tokenized_examples


UpperCamelCase_    	=				raw_datasets["validation"]
# Validation Feature Creation
UpperCamelCase_    	=				eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="Running tokenizer on validation dataset",
)

UpperCamelCase_    	=				default_data_collator

UpperCamelCase_    	=				eval_dataset.remove_columns(["example_id", "offset_mapping"])
UpperCamelCase_    	=				DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)




def   lowercase__(			__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Dict      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Dict="eval"    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[Any]    					=						postprocess_qa_predictions(
          examples=__UpperCamelCase      ,features=__UpperCamelCase      ,predictions=__UpperCamelCase      ,version_2_with_negative=args.version_2_with_negative      ,n_best_size=args.n_best_size      ,max_answer_length=args.max_answer_length      ,null_score_diff_threshold=args.null_score_diff_threshold      ,output_dir=args.output_dir      ,prefix=__UpperCamelCase      ,)
      # Format the result to the format the metric expects.
      if args.version_2_with_negative:
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						[
                {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
            ]
      else:
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

      SCREAMING_SNAKE_CASE   :      Any    					=						[{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
      return EvalPrediction(predictions=__UpperCamelCase      ,label_ids=__UpperCamelCase    )


UpperCamelCase_    	=				load_metric("squad_v2" if args.version_2_with_negative else "squad")

# Evaluation!
logger.info("Loading ONNX model %s for evaluation", args.onnx_model_path)
with open(engine_name, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
      # setup for TRT inferrence
      for i in range(len(input_names)):
            context.set_binding_shape(i, INPUT_SHAPE)
      assert context.all_binding_shapes_specified




      def   lowercase__(			__UpperCamelCase:					Any    ):

            """simple docstring"""






            return trt.volume(engine.get_binding_shape(__UpperCamelCase    )    ) * engine.get_binding_dtype(__UpperCamelCase    ).itemsize

      # Allocate device memory for inputs and outputs.
      UpperCamelCase_    	=				[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

      # Allocate output buffer
      UpperCamelCase_    	=				cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
      UpperCamelCase_    	=				cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
      UpperCamelCase_    	=				cuda.mem_alloc(h_outputa.nbytes)
      UpperCamelCase_    	=				cuda.mem_alloc(h_outputa.nbytes)

      # Create a stream in which to copy inputs/outputs and run inference.
      UpperCamelCase_    	=				cuda.Stream()

      # Evaluation
      logger.info("***** Running Evaluation *****")
      logger.info(F"""  Num examples = {len(eval_dataset)}""")
      logger.info(F"""  Batch size = {args.per_device_eval_batch_size}""")

      UpperCamelCase_    	=				0.0
      UpperCamelCase_    	=				0
      UpperCamelCase_    	=				timeit.default_timer()

      UpperCamelCase_    	=				None
      for step, batch in enumerate(eval_dataloader):
            UpperCamelCase_	,    UpperCamelCase_    	=				model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
            total_time += infer_time
            niter += 1

            UpperCamelCase_	,    UpperCamelCase_    	=				outputs
            UpperCamelCase_    	=				torch.tensor(start_logits)
            UpperCamelCase_    	=				torch.tensor(end_logits)

            # necessary to pad predictions and labels for being gathered
            UpperCamelCase_    	=				accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0)
            UpperCamelCase_    	=				accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

            UpperCamelCase_    	=				(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
            UpperCamelCase_    	=				logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_0_0)

      if all_preds is not None:
            UpperCamelCase_    	=				nested_truncate(all_preds, len(eval_dataset))

      UpperCamelCase_    	=				timeit.default_timer() - start_time
      logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(eval_dataset))
      # Inference time from TRT
      logger.info("Average Inference Time = {:.3f} ms".format(total_time * 1_0_0_0 / niter))
      logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 1_0_0_0))





      logger.info("Total Number of Inference =  %d", niter)

UpperCamelCase_    	=				post_processing_function(eval_examples, eval_dataset, all_preds)
UpperCamelCase_    	=				metric.compute(predictions=prediction.predictions, references=prediction.label_ids)





logger.info(F"""Evaluation metrics: {eval_metric}""")





'''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


_UpperCamelCase						:  Union[str, Any]							       =				{
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 4_8000,
        "sample_size": 6_5536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 4_8000,
        "sample_size": 6_5536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 4_8000,
        "sample_size": 13_1072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 1_6000,
        "sample_size": 6_5536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 1_6000,
        "sample_size": 6_5536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 1_6000,
        "sample_size": 6_5536,
    },
}


def       snake_case   (	snake_case      : int					,		snake_case      : Tuple			) ->  Optional[int]:


    """simple docstring"""


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


def       snake_case   (	snake_case      : str			) ->  Optional[int]:


    """simple docstring"""


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



class 				_snake_case					(   a_			):
  pass



class 				_snake_case					(   nn.Module			):

  def __init__(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      super().__init__()

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



def       snake_case   (	snake_case      : Any			) ->  List[Any]:


    """simple docstring"""


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

    return F'./{model_name}.ckpt'


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

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

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


def       snake_case   (	snake_case      : str			) ->  List[str]:


    """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       snake_case   (	snake_case      : Optional[Any]			) ->  Optional[Any]:


    """simple docstring"""


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


def       snake_case   (	snake_case      : str					,		snake_case      : Union[str, Any]=13			) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							input_string

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

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

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

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

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

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

    if "resnets" in new_layer:
        lowerCAmelCase 						=							convert_resconv_naming(snake_case			)
    elif "attentions" in new_layer:
        lowerCAmelCase 						=							convert_attn_naming(snake_case			)
        lowerCAmelCase 						=							new_string_left

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


def       snake_case   (	snake_case      : str			) ->  Dict:


    """simple docstring"""


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

        lowerCAmelCase 						=							rename(snake_case			)

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

    return new_state_dict


def       snake_case   (	snake_case      : int					,		snake_case      : int					,		snake_case      : Optional[int]			) ->  Any:


    """simple docstring"""


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


def       snake_case   (	snake_case      : Optional[int]			) ->  Dict:


    """simple docstring"""


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

    lowerCAmelCase 						=							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 						=							download(snake_case			)

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

    lowerCAmelCase 						=							Object()
    lowerCAmelCase 						=							sample_size
    lowerCAmelCase 						=							sample_rate
    lowerCAmelCase 						=							0

    lowerCAmelCase 						=							UNetaDModel(sample_size=snake_case					,		sample_rate=snake_case			)
    lowerCAmelCase 						=							diffusers_model.state_dict()

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

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

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

        lowerCAmelCase 						=							value

    diffusers_model.load_state_dict(snake_case			)

    lowerCAmelCase 						=							100
    lowerCAmelCase 						=							33

    lowerCAmelCase 						=							IPNDMScheduler(num_train_timesteps=snake_case			)

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

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

    lowerCAmelCase 						=							DanceDiffusionPipeline(unet=snake_case					,		scheduler=snake_case			)

    lowerCAmelCase 						=							torch.manual_seed(33			)
    lowerCAmelCase 						=							pipe(num_inference_steps=snake_case					,		generator=snake_case			).audios

    lowerCAmelCase 						=							sampling.iplms_sample(snake_case					,		snake_case					,		snake_case					,		{}			)
    lowerCAmelCase 						=							generated.clamp(-1					,		1			)

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

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

    print('Diff sum'					,		snake_case			)
    print('Diff max'					,		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__":
     _UpperCamelCase						:  Optional[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.")
     _UpperCamelCase						:  List[str]							       =				parser.parse_args()

     main(args)





