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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = None  ,			):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		{}

    if train_file is not None:
        __UpperCamelCase     :Optional[Any]			  =		[train_file]
    if eval_file is not None:
        __UpperCamelCase     :Any			  =		[eval_file]
    if test_file is not None:
        __UpperCamelCase     :List[str]			  =		[test_file]

    __UpperCamelCase     :int			  =		datasets.load_dataset('''csv'''  ,			data_files=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Optional[Any]			  =		list(ds[list(files.keys()	)[0]].features.keys()	)
    __UpperCamelCase     :int			  =		features_name.pop(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Union[str, Any]			  =		list(set(ds[list(files.keys()	)[0]][label_name]	)	)
    __UpperCamelCase     :List[Any]			  =		{label: i for i, label in enumerate(SCREAMING_SNAKE_CASE	)}
    __UpperCamelCase     :str			  =		tokenizer.model_input_names
    __UpperCamelCase     :Optional[int]			  =		{}

    if len(SCREAMING_SNAKE_CASE	) == 1:
        for k in files.keys():
            __UpperCamelCase     :str			  =		ds[k].map(
                lambda SCREAMING_SNAKE_CASE	: tokenizer.batch_encode_plus(
                    example[features_name[0]]  ,			truncation=SCREAMING_SNAKE_CASE  ,			max_length=SCREAMING_SNAKE_CASE  ,			padding='''max_length'''	)  ,			batched=SCREAMING_SNAKE_CASE  ,			)
    elif len(SCREAMING_SNAKE_CASE	) == 2:
        for k in files.keys():
            __UpperCamelCase     :int			  =		ds[k].map(
                lambda SCREAMING_SNAKE_CASE	: tokenizer.batch_encode_plus(
                    (example[features_name[0]], example[features_name[1]])  ,			truncation=SCREAMING_SNAKE_CASE  ,			max_length=SCREAMING_SNAKE_CASE  ,			padding='''max_length'''  ,			)  ,			batched=SCREAMING_SNAKE_CASE  ,			)

    def gen_train():
        for ex in transformed_ds[datasets.Split.TRAIN]:
            __UpperCamelCase     :List[Any]			  =		{k: v for k, v in ex.items() if k in input_names}
            __UpperCamelCase     :Tuple			  =		labelaid[ex[label_name]]
            yield (d, label)

    def gen_val():
        for ex in transformed_ds[datasets.Split.VALIDATION]:
            __UpperCamelCase     :List[str]			  =		{k: v for k, v in ex.items() if k in input_names}
            __UpperCamelCase     :Dict			  =		labelaid[ex[label_name]]
            yield (d, label)

    def gen_test():
        for ex in transformed_ds[datasets.Split.TEST]:
            __UpperCamelCase     :Dict			  =		{k: v for k, v in ex.items() if k in input_names}
            __UpperCamelCase     :Optional[int]			  =		labelaid[ex[label_name]]
            yield (d, label)

    __UpperCamelCase     :Optional[int]			  =		(
        tf.data.Dataset.from_generator(
            SCREAMING_SNAKE_CASE  ,			({k: tf.intaa for k in input_names}, tf.intaa)  ,			({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))  ,			)
        if datasets.Split.TRAIN in transformed_ds
        else None
    )

    if train_ds is not None:
        __UpperCamelCase     :List[Any]			  =		train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

    __UpperCamelCase     :str			  =		(
        tf.data.Dataset.from_generator(
            SCREAMING_SNAKE_CASE  ,			({k: tf.intaa for k in input_names}, tf.intaa)  ,			({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))  ,			)
        if datasets.Split.VALIDATION in transformed_ds
        else None
    )

    if val_ds is not None:
        __UpperCamelCase     :List[Any]			  =		val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

    __UpperCamelCase     :int			  =		(
        tf.data.Dataset.from_generator(
            SCREAMING_SNAKE_CASE  ,			({k: tf.intaa for k in input_names}, tf.intaa)  ,			({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))  ,			)
        if datasets.Split.TEST in transformed_ds
        else None
    )

    if test_ds is not None:
        __UpperCamelCase     :Tuple			  =		test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

    return train_ds, val_ds, test_ds, labelaid


__lowercase							 =					logging.getLogger(__name__)


@dataclass
class 	lowerCamelCase_     :






    '''simple docstring'''






    a__	:						int						=      field(metadata={"""help""": """Which column contains the label"""} )
    a__	:						str						=      field(default=UpperCAmelCase_						,	metadata={"""help""": """The path of the training file"""} )
    a__	:						Optional[str]						=      field(default=UpperCAmelCase_						,	metadata={"""help""": """The path of the development file"""} )
    a__	:						Optional[str]						=      field(default=UpperCAmelCase_						,	metadata={"""help""": """The path of the test file"""} )
    a__	:						int						=      field(
        default=1_2_8						,	metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )
        }						,	)
    a__	:						bool						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )


@dataclass
class 	lowerCamelCase_     :






    '''simple docstring'''






    a__	:						str						=      field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
    a__	:						Optional[str]						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
    a__	:						Optional[str]						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
    a__	:						bool						=      field(default=UpperCAmelCase_						,	metadata={"""help""": """Set this flag to use fast tokenization."""} )
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    a__	:						Optional[str]						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}						,	)



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
    __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :str			  =		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  ,			)
    logger.info(
        f"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1	)}, """
        f"""16-bits training: {training_args.fpaa}"""	)
    logger.info(f"""Training/evaluation parameters {training_args}"""	)

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

    __UpperCamelCase     :List[Any]			  =		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  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Optional[int]			  =		get_tfds(
        train_file=data_args.train_file  ,			eval_file=data_args.dev_file  ,			test_file=data_args.test_file  ,			tokenizer=SCREAMING_SNAKE_CASE  ,			label_column_id=data_args.label_column_id  ,			max_seq_length=data_args.max_seq_length  ,			)

    __UpperCamelCase     :Dict			  =		AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,			num_labels=len(SCREAMING_SNAKE_CASE	)  ,			labelaid=SCREAMING_SNAKE_CASE  ,			idalabel={id: label for label, id in labelaid.items()}  ,			finetuning_task='''text-classification'''  ,			cache_dir=model_args.cache_dir  ,			)

    with training_args.strategy.scope():
        __UpperCamelCase     :str			  =		TFAutoModelForSequenceClassification.from_pretrained(
            model_args.model_name_or_path  ,			from_pt=bool('''.bin''' in model_args.model_name_or_path	)  ,			config=SCREAMING_SNAKE_CASE  ,			cache_dir=model_args.cache_dir  ,			)

    def compute_metrics(SCREAMING_SNAKE_CASE	) -> Dict:
        __UpperCamelCase     :Dict			  =		np.argmax(p.predictions  ,			axis=1	)

        return {"acc": (preds == p.label_ids).mean()}

    # Initialize our Trainer
    __UpperCamelCase     :Optional[Any]			  =		TFTrainer(
        model=SCREAMING_SNAKE_CASE  ,			args=SCREAMING_SNAKE_CASE  ,			train_dataset=SCREAMING_SNAKE_CASE  ,			eval_dataset=SCREAMING_SNAKE_CASE  ,			compute_metrics=SCREAMING_SNAKE_CASE  ,			)

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
    __UpperCamelCase     :int			  =		{}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''	)
        __UpperCamelCase     :List[str]			  =		trainer.evaluate()
        __UpperCamelCase     :Optional[Any]			  =		os.path.join(training_args.output_dir  ,			'''eval_results.txt'''	)

        with open(SCREAMING_SNAKE_CASE  ,			'''w'''	) as writer:
            logger.info('''***** Eval results *****'''	)

            for key, value in result.items():
                logger.info(f"""  {key} = {value}"""	)
                writer.write(f"""{key} = {value}\n"""	)

            results.update(SCREAMING_SNAKE_CASE	)

    return results


if __name__ == "__main__":
    main()






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


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

__lowercase							 =					256


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Tuple						=      ["""melgan"""]







    def __init__(	self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , )       ->	None:
        super().__init__()

        # From MELGAN
        __UpperCamelCase     :int			  =		math.log(1E-5)  # Matches MelGAN training.
        __UpperCamelCase     :int			  =		4.0  # Largest value for most examples
        __UpperCamelCase     :str			  =		128

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=(-1.0, 1.0) , __lowercase=False)       ->	Dict:
        __UpperCamelCase  ,       __UpperCamelCase     :str			  =		output_range
        if clip:
            __UpperCamelCase     :Union[str, Any]			  =		torch.clip(__lowercase , self.min_value , self.max_value)
        # Scale to [0, 1].
        __UpperCamelCase     :Union[str, Any]			  =		(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 			UpperCamelCase__   (	self , __lowercase , __lowercase=(-1.0, 1.0) , __lowercase=False)       ->	Optional[int]:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		input_range
        __UpperCamelCase     :Optional[int]			  =		torch.clip(__lowercase , __lowercase , __lowercase) if clip else outputs
        # Scale to [0, 1].
        __UpperCamelCase     :List[str]			  =		(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 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	List[Any]:
        __UpperCamelCase     :List[str]			  =		input_tokens > 0
        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.notes_encoder(
            encoder_input_tokens=__lowercase , encoder_inputs_mask=__lowercase)

        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.continuous_encoder(
            encoder_inputs=__lowercase , encoder_inputs_mask=__lowercase)

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	str:
        __UpperCamelCase     :Optional[int]			  =		noise_time
        if not torch.is_tensor(__lowercase):
            __UpperCamelCase     :str			  =		torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device)
        elif torch.is_tensor(__lowercase) and len(timesteps.shape) == 0:
            __UpperCamelCase     :Dict			  =		timesteps[None].to(input_tokens.device)

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

        __UpperCamelCase     :Tuple			  =		self.decoder(
            encodings_and_masks=__lowercase , decoder_input_tokens=__lowercase , decoder_noise_time=__lowercase)
        return logits







    @torch.no_grad()
    def __call__(	self , __lowercase , __lowercase = None , __lowercase = 100 , __lowercase = True , __lowercase = "numpy" , __lowercase = None , __lowercase = 1 , )       ->	Union[AudioPipelineOutput, Tuple]:
        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(__lowercase , __lowercase) or callback_steps <= 0)
        ):
            raise ValueError(
                f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                f""" {type(__lowercase)}.""")

        __UpperCamelCase     :Union[str, Any]			  =		np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa)
        __UpperCamelCase     :Union[str, Any]			  =		np.zeros([1, 0, self.n_dims] , np.floataa)
        __UpperCamelCase     :Union[str, Any]			  =		torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=__lowercase , device=self.device)

        for i, encoder_input_tokens in enumerate(__lowercase):
            if i == 0:
                __UpperCamelCase     :int			  =		torch.from_numpy(pred_mel[:1].copy()).to(
                    device=self.device , dtype=self.decoder.dtype)
                # The first chunk has no previous context.
                __UpperCamelCase     :int			  =		torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=__lowercase , 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     :Tuple			  =		ones

            __UpperCamelCase     :Optional[Any]			  =		self.scale_features(
                __lowercase , output_range=[-1.0, 1.0] , clip=__lowercase)

            __UpperCamelCase     :int			  =		self.encode(
                input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device) , continuous_inputs=__lowercase , continuous_mask=__lowercase , )

            # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
            __UpperCamelCase     :int			  =		randn_tensor(
                shape=encoder_continuous_inputs.shape , generator=__lowercase , device=self.device , dtype=self.decoder.dtype , )

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

            # Denoising diffusion loop
            for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
                __UpperCamelCase     :Optional[int]			  =		self.decode(
                    encodings_and_masks=__lowercase , input_tokens=__lowercase , noise_time=t / self.scheduler.config.num_train_timesteps , )

                # Compute previous output: x_t -> x_t-1
                __UpperCamelCase     :int			  =		self.scheduler.step(__lowercase , __lowercase , __lowercase , generator=__lowercase).prev_sample

            __UpperCamelCase     :Tuple			  =		self.scale_to_features(__lowercase , input_range=[-1.0, 1.0])
            __UpperCamelCase     :List[Any]			  =		mel[:1]
            __UpperCamelCase     :Optional[Any]			  =		mel.cpu().float().numpy()

            __UpperCamelCase     :Any			  =		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(__lowercase , __lowercase)

            logger.info('''Generated segment''' , __lowercase)

        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     :Optional[Any]			  =		self.melgan(input_features=full_pred_mel.astype(np.floataa))
        else:
            __UpperCamelCase     :List[str]			  =		full_pred_mel

        if not return_dict:
            return (output,)

        return AudioPipelineOutput(audios=__lowercase)





