import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
					import onnxruntime as ort


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}
class     lowerCAmelCase_     :







							def __init__(   self      :     Any ,			_A      :     int=None ,			**_A      :     Optional[int]		):
												logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'''		)
												_UpperCamelCase			     =     model
												_UpperCamelCase			     =     kwargs.get('''model_save_dir''' ,			_A		)
												_UpperCamelCase			     =     kwargs.get('''latest_model_name''' ,			_A		)







							def __call__(   self      :     Union[str, Any] ,			**_A      :     Tuple		):
												_UpperCamelCase			     =     {k: np.array(_A		) for k, v in kwargs.items()}
												return self.model.run(_A ,			_A		)







							@staticmethod
							def  UpperCamelCase_						(   _A      :     Union[str, Path] ,			_A      :     Dict=None ,			_A      :     Tuple=None		):
												if provider is None:
																	logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''		)
																	_UpperCamelCase			     =     '''CPUExecutionProvider'''

												return ort.InferenceSession(_A ,			providers=[provider] ,			sess_options=_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Union[str, Path] ,			_A      :     Optional[str] = None ,			**_A      :     int		):
												_UpperCamelCase			     =     file_name if file_name is not None else ONNX_WEIGHTS_NAME

												_UpperCamelCase			     =     self.model_save_dir.joinpath(self.latest_model_name		)
												_UpperCamelCase			     =     Path(_A		).joinpath(_A		)
												try:
																	shutil.copyfile(_A ,			_A		)
												except shutil.SameFileError:
																	pass

												# copy external weights (for models >2GB)
												_UpperCamelCase			     =     self.model_save_dir.joinpath(_A		)
												if src_path.exists():
																	_UpperCamelCase			     =     Path(_A		).joinpath(_A		)
																	try:
																						shutil.copyfile(_A ,			_A		)
																	except shutil.SameFileError:
																						pass







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     Union[str, os.PathLike] ,			**_A      :     Dict ,			):
												if os.path.isfile(_A		):
																	logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""		)
																	return

												os.makedirs(_A ,			exist_ok=_A		)

												# saving model weights/files
												self._save_pretrained(_A ,			**_A		)







							@classmethod
							def  UpperCamelCase_						(   cls      :     Any ,			_A      :     Union[str, Path] ,			_A      :     Optional[Union[bool, str, None]] = None ,			_A      :     Optional[Union[str, None]] = None ,			_A      :     bool = False ,			_A      :     Optional[str] = None ,			_A      :     Optional[str] = None ,			_A      :     Optional[str] = None ,			_A      :     Optional["ort.SessionOptions"] = None ,			**_A      :     List[Any] ,			):
												_UpperCamelCase			     =     file_name if file_name is not None else ONNX_WEIGHTS_NAME
												# load model from local directory
												if os.path.isdir(_A		):
																	_UpperCamelCase			     =     OnnxRuntimeModel.load_model(
																	    os.path.join(_A ,			_A		) ,			provider=_A ,			sess_options=_A		)
																	_UpperCamelCase			     =     Path(_A		)
												# load model from hub
												else:
																	# download model
																	_UpperCamelCase			     =     hf_hub_download(
																	    repo_id=_A ,			filename=_A ,			use_auth_token=_A ,			revision=_A ,			cache_dir=_A ,			force_download=_A ,			)
																	_UpperCamelCase			     =     Path(_A		).parent
																	_UpperCamelCase			     =     Path(_A		).name
																	_UpperCamelCase			     =     OnnxRuntimeModel.load_model(_A ,			provider=_A ,			sess_options=_A		)
												return cls(model=_A ,			**_A		)







							@classmethod
							def  UpperCamelCase_						(   cls      :     Optional[int] ,			_A      :     Union[str, Path] ,			_A      :     bool = True ,			_A      :     Optional[str] = None ,			_A      :     Optional[str] = None ,			**_A      :     List[str] ,			):
												_UpperCamelCase			     =     None
												if len(str(_A		).split('''@'''		)		) == 2:
																	_UpperCamelCase     ,    _UpperCamelCase			     =     model_id.split('''@'''		)

												return cls._from_pretrained(
												    model_id=_A ,			revision=_A ,			cache_dir=_A ,			force_download=_A ,			use_auth_token=_A ,			**_A ,			)

import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
					from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
					_lowerCAmelCase       =		True
					from torch.cuda.amp import autocast

_lowerCAmelCase       =		logging.getLogger(__name__)
def 					_snake_case	(  __snake_case=None  ,     __snake_case=None						):
					return field(default_factory=lambda: default  ,     metadata=__snake_case						)
@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
							UpperCAmelCase				   =     field(
							    default=0.1,  metadata={"help": "The dropout ratio for the attention probabilities."} )
							UpperCAmelCase				   =     field(
							    default=0.1,  metadata={"help": "The dropout ratio for activations inside the fully connected layer."} )
							UpperCAmelCase				   =     field(
							    default=0.1,  metadata={
							        "help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
							    },  )
							UpperCAmelCase				   =     field(
							    default=0.1,  metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."},  )
							UpperCAmelCase				   =     field(
							    default=0.0_5,  metadata={
							        "help": (
							            "Propability of each feature vector along the time axis to be chosen as the start of the vector"
							            "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
							            "vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
							        )
							    },  )
							UpperCAmelCase				   =     field(default=0.0,  metadata={"help": "The LayerDrop probability."} )




@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
							UpperCAmelCase				   =     field(
							    default="train+validation",  metadata={
							        "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
							    },  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "The number of processes to use for the preprocessing."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    },  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of validation examples to this "
							            "value if set."
							        )
							    },  )
							UpperCAmelCase				   =     list_field(
							    default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"],  metadata={"help": "A list of characters to remove from the transcripts."},  )




@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     42
							UpperCAmelCase				   =     True
							UpperCAmelCase				   =     None
							UpperCAmelCase				   =     None
							UpperCAmelCase				   =     None
							UpperCAmelCase				   =     None







							def __call__(   self      :     Union[str, Any] ,			_A      :     List[Dict[str, Union[List[int], torch.Tensor]]]		):
												# split inputs and labels since they have to be of different lenghts and need
												# different padding methods
												_UpperCamelCase			     =     [{'''input_values''': feature['''input_values''']} for feature in features]
												_UpperCamelCase			     =     [{'''input_ids''': feature['''labels''']} for feature in features]

												_UpperCamelCase			     =     self.processor.pad(
												    _A ,			padding=self.padding ,			max_length=self.max_length ,			pad_to_multiple_of=self.pad_to_multiple_of ,			return_tensors='''pt''' ,			)
												_UpperCamelCase			     =     self.processor.pad(
												    labels=_A ,			padding=self.padding ,			max_length=self.max_length_labels ,			pad_to_multiple_of=self.pad_to_multiple_of_labels ,			return_tensors='''pt''' ,			)

												# replace padding with -100 to ignore loss correctly
												_UpperCamelCase			     =     labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1		) ,			-100		)

												_UpperCamelCase			     =     labels

												return batch




class     lowerCAmelCase_     (       __lowercase ):







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     nn.Module ,			_A      :     Dict[str, Union[torch.Tensor, Any]]		):
												model.train()
												_UpperCamelCase			     =     self._prepare_inputs(_A		)

												if self.use_amp:
																	with autocast():
																						_UpperCamelCase			     =     self.compute_loss(_A ,			_A		)
												else:
																	_UpperCamelCase			     =     self.compute_loss(_A ,			_A		)

												if self.args.n_gpu > 1:
																	if model.module.config.ctc_loss_reduction == "mean":
																						_UpperCamelCase			     =     loss.mean()
																	elif model.module.config.ctc_loss_reduction == "sum":
																						_UpperCamelCase			     =     loss.sum() / (inputs['''labels'''] >= 0).sum()
																	else:
																						raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"""		)

												if self.args.gradient_accumulation_steps > 1:
																	_UpperCamelCase			     =     loss / self.args.gradient_accumulation_steps

												if self.use_amp:
																	self.scaler.scale(_A		).backward()
												elif self.use_apex:
																	with amp.scale_loss(_A ,			self.optimizer		) as scaled_loss:
																						scaled_loss.backward()
												elif self.deepspeed:
																	self.deepspeed.backward(_A		)
												else:
																	loss.backward()

												return loss.detach()




def 					_snake_case	(  ):
					# 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)						)
					if len(sys.argv						) == 2 and sys.argv[1].endswith('''.json'''						):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
					else:
										_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     parser.parse_args_into_dataclasses()

					# Detecting last checkpoint.
					_UpperCamelCase			     =     None
					if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
										_UpperCamelCase			     =     get_last_checkpoint(training_args.output_dir						)
										if last_checkpoint is None and len(os.listdir(training_args.output_dir						)						) > 0:
															raise ValueError(
															    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
															    '''Use --overwrite_output_dir to overcome.'''						)
										elif last_checkpoint is not None:
															logger.info(
															    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
															    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''						)

    # Setup logging
					logging.basicConfig(
					    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''  ,     datefmt='''%m/%d/%Y %H:%M:%S'''  ,     handlers=[logging.StreamHandler(sys.stdout						)]  ,     )
					logger.setLevel(logging.INFO if is_main_process(training_args.local_rank						) else logging.WARN						)

					# Log on each process the small summary:
					logger.warning(
					    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
					    + f"""distributed training: {bool(training_args.local_rank != -1						)}, 16-bits training: {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()
					logger.info('''Training/evaluation parameters %s'''  ,     __snake_case						)

					# Set seed before initializing model.
					set_seed(training_args.seed						)

					# Get the datasets:
					_UpperCamelCase			     =     datasets.load_dataset(
					    '''common_voice'''  ,     data_args.dataset_config_name  ,     split=data_args.train_split_name						)
					_UpperCamelCase			     =     datasets.load_dataset('''common_voice'''  ,     data_args.dataset_config_name  ,     split='''test'''						)

					# Create and save tokenizer
					_UpperCamelCase			     =     f"""[{"".join(data_args.chars_to_ignore						)}]"""

					def remove_special_characters(__snake_case						):
										_UpperCamelCase			     =     re.sub(__snake_case  ,     ''''''  ,     batch['''sentence''']						).lower() + ''' '''
										return batch

					_UpperCamelCase			     =     train_dataset.map(__snake_case  ,     remove_columns=['''sentence''']						)
					_UpperCamelCase			     =     eval_dataset.map(__snake_case  ,     remove_columns=['''sentence''']						)

					def extract_all_chars(__snake_case						):
										_UpperCamelCase			     =     ''' '''.join(batch['''text''']						)
										_UpperCamelCase			     =     list(set(__snake_case						)						)
										return {"vocab": [vocab], "all_text": [all_text]}

					_UpperCamelCase			     =     train_dataset.map(
					    __snake_case  ,     batched=__snake_case  ,     batch_size=-1  ,     keep_in_memory=__snake_case  ,     remove_columns=train_dataset.column_names  ,     )
					_UpperCamelCase			     =     train_dataset.map(
					    __snake_case  ,     batched=__snake_case  ,     batch_size=-1  ,     keep_in_memory=__snake_case  ,     remove_columns=eval_dataset.column_names  ,     )

					_UpperCamelCase			     =     list(set(vocab_train['''vocab'''][0]						) | set(vocab_test['''vocab'''][0]						)						)
					_UpperCamelCase			     =     {v: k for k, v in enumerate(__snake_case						)}
					_UpperCamelCase			     =     vocab_dict[''' ''']
					del vocab_dict[" "]
					_UpperCamelCase			     =     len(__snake_case						)
					_UpperCamelCase			     =     len(__snake_case						)

					with open('''vocab.json'''  ,     '''w'''						) as vocab_file:
										json.dump(__snake_case  ,     __snake_case						)

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					_UpperCamelCase			     =     WavaVecaCTCTokenizer(
					    '''vocab.json'''  ,     unk_token='''[UNK]'''  ,     pad_token='''[PAD]'''  ,     word_delimiter_token='''|'''  ,     )
					_UpperCamelCase			     =     WavaVecaFeatureExtractor(
					    feature_size=1  ,     sampling_rate=16000  ,     padding_value=0.0  ,     do_normalize=__snake_case  ,     return_attention_mask=__snake_case						)
					_UpperCamelCase			     =     WavaVecaProcessor(feature_extractor=__snake_case  ,     tokenizer=__snake_case						)
					_UpperCamelCase			     =     WavaVecaForCTC.from_pretrained(
					    model_args.model_name_or_path  ,     cache_dir=model_args.cache_dir  ,     activation_dropout=model_args.activation_dropout  ,     attention_dropout=model_args.attention_dropout  ,     hidden_dropout=model_args.hidden_dropout  ,     feat_proj_dropout=model_args.feat_proj_dropout  ,     mask_time_prob=model_args.mask_time_prob  ,     gradient_checkpointing=training_args.gradient_checkpointing  ,     layerdrop=model_args.layerdrop  ,     ctc_loss_reduction='''mean'''  ,     pad_token_id=processor.tokenizer.pad_token_id  ,     vocab_size=len(processor.tokenizer						)  ,     )

					if data_args.max_train_samples is not None:
										_UpperCamelCase			     =     min(len(__snake_case						)  ,     data_args.max_train_samples						)
										_UpperCamelCase			     =     train_dataset.select(range(__snake_case						)						)

					if data_args.max_val_samples is not None:
										_UpperCamelCase			     =     eval_dataset.select(range(data_args.max_val_samples						)						)

					_UpperCamelCase			     =     torchaudio.transforms.Resample(48000  ,     16000						)

					# Preprocessing the datasets.
					# We need to read the aduio files as arrays and tokenize the targets.
					def speech_file_to_array_fn(__snake_case						):
										_UpperCamelCase     ,    _UpperCamelCase			     =     torchaudio.load(batch['''path''']						)
										_UpperCamelCase			     =     resampler(__snake_case						).squeeze().numpy()
										_UpperCamelCase			     =     16000
										_UpperCamelCase			     =     batch['''text''']
										return batch

					_UpperCamelCase			     =     train_dataset.map(
					    __snake_case  ,     remove_columns=train_dataset.column_names  ,     num_proc=data_args.preprocessing_num_workers  ,     )
					_UpperCamelCase			     =     eval_dataset.map(
					    __snake_case  ,     remove_columns=eval_dataset.column_names  ,     num_proc=data_args.preprocessing_num_workers  ,     )

					def prepare_dataset(__snake_case						):
										# check that all files have the correct sampling rate
										assert (
										    len(set(batch['''sampling_rate''']						)						) == 1
										), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""

										_UpperCamelCase			     =     processor(
										    audio=batch['''speech''']  ,     text=batch['''target_text''']  ,     sampling_rate=batch['''sampling_rate'''][0]						)
										batch.update(__snake_case						)
										return batch

					_UpperCamelCase			     =     train_dataset.map(
					    __snake_case  ,     remove_columns=train_dataset.column_names  ,     batch_size=training_args.per_device_train_batch_size  ,     batched=__snake_case  ,     num_proc=data_args.preprocessing_num_workers  ,     )
					_UpperCamelCase			     =     eval_dataset.map(
					    __snake_case  ,     remove_columns=eval_dataset.column_names  ,     batch_size=training_args.per_device_train_batch_size  ,     batched=__snake_case  ,     num_proc=data_args.preprocessing_num_workers  ,     )

					# Metric
					_UpperCamelCase			     =     datasets.load_metric('''wer'''						)

					def compute_metrics(__snake_case						):
										_UpperCamelCase			     =     pred.predictions
										_UpperCamelCase			     =     np.argmax(__snake_case  ,     axis=-1						)

										_UpperCamelCase			     =     processor.tokenizer.pad_token_id

										_UpperCamelCase			     =     processor.batch_decode(__snake_case						)
										# we do not want to group tokens when computing the metrics
										_UpperCamelCase			     =     processor.batch_decode(pred.label_ids  ,     group_tokens=__snake_case						)

										_UpperCamelCase			     =     wer_metric.compute(predictions=__snake_case  ,     references=__snake_case						)

										return {"wer": wer}

					if model_args.freeze_feature_extractor:
										model.freeze_feature_extractor()

					# Data collator
					_UpperCamelCase			     =     DataCollatorCTCWithPadding(processor=__snake_case  ,     padding=__snake_case						)

					# Initialize our Trainer
					_UpperCamelCase			     =     CTCTrainer(
					    model=__snake_case  ,     data_collator=__snake_case  ,     args=__snake_case  ,     compute_metrics=__snake_case  ,     train_dataset=train_dataset if training_args.do_train else None  ,     eval_dataset=eval_dataset if training_args.do_eval else None  ,     tokenizer=processor.feature_extractor  ,     )

					# Training
					if training_args.do_train:
										if last_checkpoint is not None:
															_UpperCamelCase			     =     last_checkpoint
										elif os.path.isdir(model_args.model_name_or_path						):
															_UpperCamelCase			     =     model_args.model_name_or_path
										else:
															_UpperCamelCase			     =     None

										# Save the feature_extractor and the tokenizer
										if is_main_process(training_args.local_rank						):
															processor.save_pretrained(training_args.output_dir						)

										_UpperCamelCase			     =     trainer.train(resume_from_checkpoint=__snake_case						)
										trainer.save_model()

										_UpperCamelCase			     =     train_result.metrics
										_UpperCamelCase			     =     (
										    data_args.max_train_samples if data_args.max_train_samples is not None else len(__snake_case						)
										)
										_UpperCamelCase			     =     min(__snake_case  ,     len(__snake_case						)						)

										trainer.log_metrics('''train'''  ,     __snake_case						)
										trainer.save_metrics('''train'''  ,     __snake_case						)
										trainer.save_state()

					# Evaluation
					_UpperCamelCase			     =     {}
					if training_args.do_eval:
										logger.info('''*** Evaluate ***'''						)
										_UpperCamelCase			     =     trainer.evaluate()
										_UpperCamelCase			     =     data_args.max_val_samples if data_args.max_val_samples is not None else len(__snake_case						)
										_UpperCamelCase			     =     min(__snake_case  ,     len(__snake_case						)						)

										trainer.log_metrics('''eval'''  ,     __snake_case						)
										trainer.save_metrics('''eval'''  ,     __snake_case						)

					return results


if __name__ == "__main__":
					main()
