

import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
				from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


_a:    str 							=			logging.get_logger(__name__)  # pylint: disable=invalid-name

_a:    Union[str, Any] 							=			256

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     ['melgan']




	def __init__(		self	:						str		,	lowerCAmelCase	:						SpectrogramNotesEncoder		,	lowerCAmelCase	:						SpectrogramContEncoder		,	lowerCAmelCase	:						TaFilmDecoder		,	lowerCAmelCase	:						DDPMScheduler		,	lowerCAmelCase	:						OnnxRuntimeModel if is_onnx_available() else Any		,	):


		'''simple docstring'''







		super().__init__()

		# From MELGAN
		UpperCAmelCase_       				=			math.log(1e-5							)  # Matches MelGAN training.
		UpperCAmelCase_       				=			4.0  # Largest value for most examples
		UpperCAmelCase_       				=			128

		self.register_modules(
		    notes_encoder=lowerCAmelCase		,	continuous_encoder=lowerCAmelCase		,	decoder=lowerCAmelCase		,	scheduler=lowerCAmelCase		,	melgan=lowerCAmelCase		,	)




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						str=(-1.0, 1.0)		,	lowerCAmelCase	:						Union[str, Any]=False							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			output_range
		if clip:
			UpperCAmelCase_       				=			torch.clip(lowerCAmelCase		,	self.min_value		,	self.max_value							)
		# Scale to [0, 1].
		UpperCAmelCase_       				=			(features - self.min_value) / (self.max_value - self.min_value)
		# Scale to [min_out, max_out].
		return zero_one * (max_out - min_out) + min_out




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						Dict=(-1.0, 1.0)		,	lowerCAmelCase	:						List[str]=False							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			input_range
		UpperCAmelCase_       				=			torch.clip(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							) if clip else outputs
		# Scale to [0, 1].
		UpperCAmelCase_       				=			(outputs - min_out) / (max_out - min_out)
		# Scale to [self.min_value, self.max_value].
		return zero_one * (self.max_value - self.min_value) + self.min_value




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			input_tokens > 0
		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.notes_encoder(
		    encoder_input_tokens=lowerCAmelCase		,	encoder_inputs_mask=lowerCAmelCase							)

		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.continuous_encoder(
		    encoder_inputs=lowerCAmelCase		,	encoder_inputs_mask=lowerCAmelCase							)

		return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			noise_time
		if not torch.is_tensor(lowerCAmelCase							):
			UpperCAmelCase_       				=			torch.tensor([timesteps]		,	dtype=torch.long		,	device=input_tokens.device							)
		elif torch.is_tensor(lowerCAmelCase							) and len(timesteps.shape							) == 0:
			UpperCAmelCase_       				=			timesteps[None].to(input_tokens.device							)

		# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
		UpperCAmelCase_       				=			timesteps * torch.ones(input_tokens.shape[0]		,	dtype=timesteps.dtype		,	device=timesteps.device							)

		UpperCAmelCase_       				=			self.decoder(
		    encodings_and_masks=lowerCAmelCase		,	decoder_input_tokens=lowerCAmelCase		,	decoder_noise_time=lowerCAmelCase							)
		return logits




	@torch.no_grad()
	def __call__(		self	:						str		,	lowerCAmelCase	:						List[List[int]]		,	lowerCAmelCase	:						Optional[torch.Generator] = None		,	lowerCAmelCase	:						int = 100		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						str = "numpy"		,	lowerCAmelCase	:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,	lowerCAmelCase	:						int = 1		,	):


		'''simple docstring'''







		if (callback_steps is None) or (
		    callback_steps is not None and (not isinstance(lowerCAmelCase		,	lowerCAmelCase							) or callback_steps <= 0)
		):
			raise ValueError(
			    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
			    F" {type(lowerCAmelCase							)}."							)

		UpperCAmelCase_       				=			np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]		,	dtype=np.floataa							)
		UpperCAmelCase_       				=			np.zeros([1, 0, self.n_dims]		,	np.floataa							)
		UpperCAmelCase_       				=			torch.ones((1, TARGET_FEATURE_LENGTH)		,	dtype=lowerCAmelCase		,	device=self.device							)

		for i, encoder_input_tokens in enumerate(lowerCAmelCase							):
			if i == 0:
				UpperCAmelCase_       				=			torch.from_numpy(pred_mel[:1].copy()							).to(
				    device=self.device		,	dtype=self.decoder.dtype							)
				# The first chunk has no previous context.
				UpperCAmelCase_       				=			torch.zeros((1, TARGET_FEATURE_LENGTH)		,	dtype=lowerCAmelCase		,	device=self.device							)
			else:
				# The full song pipeline does not feed in a context feature, so the mask
				# will be all 0s after the feature converter. Because we know we're
				# feeding in a full context chunk from the previous prediction, set it
				# to all 1s.
				UpperCAmelCase_       				=			ones

			UpperCAmelCase_       				=			self.scale_features(
			    lowerCAmelCase		,	output_range=[-1.0, 1.0]		,	clip=lowerCAmelCase							)

			UpperCAmelCase_       				=			self.encode(
			    input_tokens=torch.IntTensor([encoder_input_tokens]							).to(device=self.device							)		,	continuous_inputs=lowerCAmelCase		,	continuous_mask=lowerCAmelCase		,	)

			# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
			UpperCAmelCase_       				=			randn_tensor(
			    shape=encoder_continuous_inputs.shape		,	generator=lowerCAmelCase		,	device=self.device		,	dtype=self.decoder.dtype		,	)

			# set step values
			self.scheduler.set_timesteps(lowerCAmelCase							)

			# Denoising diffusion loop
			for j, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
				UpperCAmelCase_       				=			self.decode(
				    encodings_and_masks=lowerCAmelCase		,	input_tokens=lowerCAmelCase		,	noise_time=t / self.scheduler.config.num_train_timesteps		,	)

				# Compute previous output: x_t -> x_t-1
				UpperCAmelCase_       				=			self.scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	generator=lowerCAmelCase							).prev_sample

			UpperCAmelCase_       				=			self.scale_to_features(lowerCAmelCase		,	input_range=[-1.0, 1.0]							)
			UpperCAmelCase_       				=			mel[:1]
			UpperCAmelCase_       				=			mel.cpu().float().numpy()

			UpperCAmelCase_       				=			np.concatenate([full_pred_mel, pred_mel[:1]]		,	axis=1							)

			# call the callback, if provided
			if callback is not None and i % callback_steps == 0:
				callback(lowerCAmelCase		,	lowerCAmelCase							)

			logger.info("Generated segment"		,	lowerCAmelCase							)

		if output_type == "numpy" and not is_onnx_available():
			raise ValueError(
			    "Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."							)
		elif output_type == "numpy" and self.melgan is None:
			raise ValueError(
			    "Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."							)

		if output_type == "numpy":
			UpperCAmelCase_       				=			self.melgan(input_features=full_pred_mel.astype(np.floataa							)							)
		else:
			UpperCAmelCase_       				=			full_pred_mel

		if not return_dict:
			return (output,)

		return AudioPipelineOutput(audios=lowerCAmelCase							)


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_a:    int 							=			logging.getLogger(__name__)



def       __lowerCAmelCase      (						A       ,     A    ):
	return (preds == labels).mean()

@dataclass
class 						__UpperCamelCase    :
	SCREAMING_SNAKE_CASE__    				=     field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}     ,     )


@dataclass
class 						__UpperCamelCase    :
	SCREAMING_SNAKE_CASE__    				=     field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()						)}						)
	SCREAMING_SNAKE_CASE__    				=     field(metadata={'help': 'Should contain the data files for the task.'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=1_28     ,     metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }     ,     )
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Overwrite the cached training and evaluation sets'}						)



def       __lowerCAmelCase      (						):
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

	UpperCAmelCase_       				=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
	UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			parser.parse_args_into_dataclasses()

	if (
	    os.path.exists(training_args.output_dir    )
	    and os.listdir(training_args.output_dir    )
	    and training_args.do_train
	    and not training_args.overwrite_output_dir
	):
		raise ValueError(
		    F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
		    " --overwrite_output_dir to overcome."    )

	# Setup logging
	logging.basicConfig(
	    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"       ,     datefmt="%m/%d/%Y %H:%M:%S"       ,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,     )
	logger.warning(
	    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"       ,     training_args.local_rank       ,     training_args.device       ,     training_args.n_gpu       ,     bool(training_args.local_rank != -1    )       ,     training_args.fpaa       ,     )
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank    ):
		transformers.utils.logging.set_verbosity_info()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
	logger.info("Training/evaluation parameters %s"       ,     A    )

	# Set seed
	set_seed(training_args.seed    )

	try:
		UpperCAmelCase_       				=			processors[data_args.task_name]()
		UpperCAmelCase_       				=			processor.get_labels()
		UpperCAmelCase_       				=			len(A    )
	except KeyError:
		raise ValueError("Task not found: %s" % (data_args.task_name)    )

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	UpperCAmelCase_       				=			AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,     num_labels=A       ,     finetuning_task=data_args.task_name       ,     cache_dir=model_args.cache_dir       ,     )
	UpperCAmelCase_       				=			AutoTokenizer.from_pretrained(
	    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,     cache_dir=model_args.cache_dir       ,     )
	UpperCAmelCase_       				=			AutoModelForMultipleChoice.from_pretrained(
	    model_args.model_name_or_path       ,     from_tf=bool(".ckpt" in model_args.model_name_or_path    )       ,     config=A       ,     cache_dir=model_args.cache_dir       ,     )

	# Get datasets
	UpperCAmelCase_       				=			(
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir       ,     tokenizer=A       ,     task=data_args.task_name       ,     max_seq_length=data_args.max_seq_length       ,     overwrite_cache=data_args.overwrite_cache       ,     mode=Split.train       ,     )
	    if training_args.do_train
	    else None
	)
	UpperCAmelCase_       				=			(
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir       ,     tokenizer=A       ,     task=data_args.task_name       ,     max_seq_length=data_args.max_seq_length       ,     overwrite_cache=data_args.overwrite_cache       ,     mode=Split.dev       ,     )
	    if training_args.do_eval
	    else None
	)

	def compute_metrics(A    ) -> Dict:
		UpperCAmelCase_       				=			np.argmax(p.predictions       ,     axis=1    )
		return {"acc": simple_accuracy(A       ,     p.label_ids    )}

	# Data collator
	UpperCAmelCase_       				=			DataCollatorWithPadding(A       ,     pad_to_multiple_of=8    ) if training_args.fpaa else None

	# Initialize our Trainer
	UpperCAmelCase_       				=			Trainer(
	    model=A       ,     args=A       ,     train_dataset=A       ,     eval_dataset=A       ,     compute_metrics=A       ,     data_collator=A       ,     )

	# Training
	if training_args.do_train:
		trainer.train(
		    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
		trainer.save_model()
		# For convenience, we also re-save the tokenizer to the same directory,
		# so that you can share your model easily on huggingface.co/models =)
		if trainer.is_world_master():
			tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
	UpperCAmelCase_       				=			{}
	if training_args.do_eval:
		logger.info("*** Evaluate ***"    )

		UpperCAmelCase_       				=			trainer.evaluate()

		UpperCAmelCase_       				=			os.path.join(training_args.output_dir       ,     "eval_results.txt"    )
		if trainer.is_world_master():
			with open(A       ,     "w"    ) as writer:
				logger.info("***** Eval results *****"    )
				for key, value in result.items():
					logger.info("  %s = %s"       ,     A       ,     A    )
					writer.write("%s = %s\n" % (key, value)    )

				results.update(A    )

	return results



def       __lowerCAmelCase      (						A    ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
				main()