
'''simple docstring'''
import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
a_											=						logging.get_logger("transformers.models.speecht5")

a_											=						{
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
a_											=						{
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
a_											=						{
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
a_											=						{
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
a_											=						{
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
a_											=						{
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
a_											=						{
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
a_											=						{
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
a_											=						{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
a_											=						{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a_											=						{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a_											=						[]
a_											=						[
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
a_											=						IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
a_											=						IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
a_											=						IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    for attribute in key.split("."       ):
        snake_case_ :    Optional[int]     			=						getattr(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    if weight_type is not None:
        snake_case_ :    int     			=						getattr(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ).shape
    else:
        snake_case_ :    Union[str, Any]     			=						hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
            f' {value.shape} for {full_name}'       )

    if weight_type == "weight":
        snake_case_ :    Union[str, Any]     			=						value
    elif weight_type == "weight_g":
        snake_case_ :    List[str]     			=						value
    elif weight_type == "weight_v":
        snake_case_ :    str     			=						value
    elif weight_type == "bias":
        snake_case_ :    Optional[Any]     			=						value
    elif weight_type == "running_mean":
        snake_case_ :    Optional[Any]     			=						value
    elif weight_type == "running_var":
        snake_case_ :    Tuple     			=						value
    elif weight_type == "num_batches_tracked":
        snake_case_ :    Any     			=						value
    else:
        snake_case_ :    List[str]     			=						value

    logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    for key in ignore_keys:
        if key.endswith(".*"       ):
            if name.startswith(key[:-1]       ):
                return True
        elif ".*." in key:
            snake_case_			, snake_case_ :    Optional[Any]     			=						key.split(".*."       )
            if prefix in name and suffix in name:
                return True
        elif key in name:
            return True
    return False

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    int     			=						[]

    if task == "s2t":
        snake_case_ :    Optional[Any]     			=						hf_model.speechta.encoder.prenet.feature_encoder
        snake_case_ :    Dict     			=						MAPPING_S2T
        snake_case_ :    Any     			=						IGNORE_KEYS_S2T
    elif task == "t2s":
        snake_case_ :    Union[str, Any]     			=						None
        snake_case_ :    int     			=						MAPPING_T2S
        snake_case_ :    Optional[int]     			=						IGNORE_KEYS_T2S
    elif task == "s2s":
        snake_case_ :    Any     			=						hf_model.speechta.encoder.prenet.feature_encoder
        snake_case_ :    Any     			=						MAPPING_S2S
        snake_case_ :    str     			=						IGNORE_KEYS_S2S
    else:
        raise ValueError(f'Unsupported task: {task}'       )

    for name, value in fairseq_dict.items():
        if should_ignore(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
            logger.info(f'{name} was ignored'       )
            continue

        snake_case_ :    Any     			=						False
        if "conv_layers" in name:
            load_conv_layer(
                __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		hf_model.config.feat_extract_norm == "group",		)
            snake_case_ :    Dict     			=						True
        else:
            for key, mapped_key in MAPPING.items():
                # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                if "*" in key:
                    snake_case_			, snake_case_ :    Dict     			=						key.split(".*."       )
                    if prefix in name and suffix in name:
                        snake_case_ :    int     			=						suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                if key in name:
                    snake_case_ :    int     			=						True
                    if "*" in mapped_key:
                        snake_case_ :    str     			=						name.split(__SCREAMING_SNAKE_CASE       )[0].split("."       )[-2]
                        snake_case_ :    Optional[Any]     			=						mapped_key.replace("*",		__SCREAMING_SNAKE_CASE       )
                    if "weight_g" in name:
                        snake_case_ :    Any     			=						"weight_g"
                    elif "weight_v" in name:
                        snake_case_ :    int     			=						"weight_v"
                    elif "bias" in name:
                        snake_case_ :    Union[str, Any]     			=						"bias"
                    elif "weight" in name:
                        snake_case_ :    List[str]     			=						"weight"
                    elif "running_mean" in name:
                        snake_case_ :    Optional[Any]     			=						"running_mean"
                    elif "running_var" in name:
                        snake_case_ :    Dict     			=						"running_var"
                    elif "num_batches_tracked" in name:
                        snake_case_ :    List[Any]     			=						"num_batches_tracked"
                    else:
                        snake_case_ :    Optional[int]     			=						None
                    set_recursively(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
                continue
        if not is_used:
            unused_weights.append(__SCREAMING_SNAKE_CASE       )

    logger.warning(f'Unused weights: {unused_weights}'       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[str]     			=						full_name.split("conv_layers."       )[-1]
    snake_case_ :    List[Any]     			=						name.split("."       )
    snake_case_ :    Union[str, Any]     			=						int(items[0]       )
    snake_case_ :    Union[str, Any]     			=						int(items[1]       )

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    f'{full_name} has size {value.shape}, but'
                    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'       )
            snake_case_ :    Union[str, Any]     			=						value
            logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'       )
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    f'{full_name} has size {value.shape}, but'
                    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'       )
            snake_case_ :    List[Any]     			=						value
            logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'       )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    f'{full_name} has size {value.shape}, but'
                    f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'       )
            snake_case_ :    Union[str, Any]     			=						value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    f'{full_name} has size {value.shape}, but'
                    f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'       )
            snake_case_ :    Any     			=						value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
    else:
        unused_weights.append(__SCREAMING_SNAKE_CASE       )

@torch.no_grad()
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=None,		__SCREAMING_SNAKE_CASE=None,		__SCREAMING_SNAKE_CASE=None,		):
    """simple docstring"""






    if config_path is not None:
        snake_case_ :    str     			=						SpeechTaConfig.from_pretrained(__SCREAMING_SNAKE_CASE       )
    else:
        snake_case_ :    Any     			=						SpeechTaConfig()

    if task == "s2t":
        snake_case_ :    Any     			=						config.max_text_positions
        snake_case_ :    int     			=						SpeechTaForSpeechToText(__SCREAMING_SNAKE_CASE       )
    elif task == "t2s":
        snake_case_ :    Optional[int]     			=						1_8_7_6
        snake_case_ :    int     			=						6_0_0
        snake_case_ :    List[str]     			=						config.max_speech_positions
        snake_case_ :    int     			=						SpeechTaForTextToSpeech(__SCREAMING_SNAKE_CASE       )
    elif task == "s2s":
        snake_case_ :    str     			=						1_8_7_6
        snake_case_ :    Optional[int]     			=						config.max_speech_positions
        snake_case_ :    str     			=						SpeechTaForSpeechToSpeech(__SCREAMING_SNAKE_CASE       )
    else:
        raise ValueError(f'Unknown task name: {task}'       )

    if vocab_path:
        snake_case_ :    Any     			=						SpeechTaTokenizer(__SCREAMING_SNAKE_CASE,		model_max_length=config.max_text_positions       )

        # Mask token behaves like a normal word, i.e. include the space before it
        snake_case_ :    Optional[Any]     			=						AddedToken("<mask>",		lstrip=__SCREAMING_SNAKE_CASE,		rstrip=__SCREAMING_SNAKE_CASE       )
        snake_case_ :    str     			=						mask_token
        tokenizer.add_special_tokens({"mask_token": mask_token}       )
        tokenizer.add_tokens(["<ctc_blank>"]       )

    snake_case_ :    List[Any]     			=						SpeechTaFeatureExtractor()
    snake_case_ :    Tuple     			=						SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE,		feature_extractor=__SCREAMING_SNAKE_CASE       )
    processor.save_pretrained(__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Dict     			=						torch.load(__SCREAMING_SNAKE_CASE       )
    recursively_load_weights(fairseq_checkpoint["model"],		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    model.save_pretrained(__SCREAMING_SNAKE_CASE       )

    if repo_id:
        print("Pushing to the hub..."       )
        processor.push_to_hub(__SCREAMING_SNAKE_CASE       )
        model.push_to_hub(__SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  parser.add_argument(
      "--task",
      default="s2t",
      type=str,
      help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
  )
  parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
  parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
  )

  a_											=						parser.parse_args()
  convert_speechta_checkpoint(
      args.task,
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.vocab_path,
      args.push_to_hub,
  )






'''simple docstring'''
import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a_											=						16
a_											=						32

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = 1_6       ):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						AutoTokenizer.from_pretrained("bert-base-cased"       )
    snake_case_ :    int     			=						DatasetDict(
        {
            "train": dataset["train"].select(__SCREAMING_SNAKE_CASE       ),
            "validation": dataset["train"].select(__SCREAMING_SNAKE_CASE       ),
            "test": dataset["validation"],
        }       )

    def tokenize_function(__SCREAMING_SNAKE_CASE       ):
        # max_length=None => use the model max length (it's actually the default)
        snake_case_ :    str     			=						tokenizer(examples["sentence1"],		examples["sentence2"],		truncation=__SCREAMING_SNAKE_CASE,		max_length=__SCREAMING_SNAKE_CASE       )
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        snake_case_ :    List[Any]     			=						datasets.map(
            __SCREAMING_SNAKE_CASE,		batched=__SCREAMING_SNAKE_CASE,		remove_columns=["idx", "sentence1", "sentence2"],		)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    snake_case_ :    Dict     			=						tokenized_datasets.rename_column("label",		"labels"       )

    def collate_fn(__SCREAMING_SNAKE_CASE       ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        snake_case_ :    Dict     			=						1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            snake_case_ :    Optional[int]     			=						1_6
        elif accelerator.mixed_precision != "no":
            snake_case_ :    Tuple     			=						8
        else:
            snake_case_ :    Union[str, Any]     			=						None

        return tokenizer.pad(
            __SCREAMING_SNAKE_CASE,		padding="longest",		max_length=__SCREAMING_SNAKE_CASE,		pad_to_multiple_of=__SCREAMING_SNAKE_CASE,		return_tensors="pt",		)

    # Instantiate dataloaders.
    snake_case_ :    Optional[int]     			=						DataLoader(
        tokenized_datasets["train"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    int     			=						DataLoader(
        tokenized_datasets["validation"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Tuple     			=						DataLoader(
        tokenized_datasets["test"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )

    return train_dataloader, eval_dataloader, test_dataloader

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[Any]     			=						[]
    # Download the dataset
    snake_case_ :    Tuple     			=						load_dataset("glue",		"mrpc"       )
    # Create our splits
    snake_case_ :    Union[str, Any]     			=						StratifiedKFold(n_splits=int(args.num_folds       )       )
    # Initialize accelerator
    snake_case_ :    Optional[Any]     			=						Accelerator(cpu=args.cpu,		mixed_precision=args.mixed_precision       )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case_ :    Optional[Any]     			=						config["lr"]
    snake_case_ :    str     			=						int(config["num_epochs"]       )
    snake_case_ :    Tuple     			=						int(config["seed"]       )
    snake_case_ :    Optional[Any]     			=						int(config["batch_size"]       )

    snake_case_ :    List[Any]     			=						evaluate.load("glue",		"mrpc"       )

    # If the batch size is too big we use gradient accumulation
    snake_case_ :    List[str]     			=						1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        snake_case_ :    Union[str, Any]     			=						batch_size // MAX_GPU_BATCH_SIZE
        snake_case_ :    Union[str, Any]     			=						MAX_GPU_BATCH_SIZE

    set_seed(__SCREAMING_SNAKE_CASE       )

    # New Code #
    # Create our folds:
    snake_case_ :    int     			=						kfold.split(np.zeros(datasets["train"].num_rows       ),		datasets["train"]["label"]       )
    snake_case_ :    Optional[int]     			=						[]
    # Iterate over them
    for i, (train_idxs, valid_idxs) in enumerate(__SCREAMING_SNAKE_CASE       ):
        snake_case_			, snake_case_			, snake_case_ :    Tuple     			=						get_fold_dataloaders(
            __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		)
        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        snake_case_ :    Optional[int]     			=						AutoModelForSequenceClassification.from_pretrained("bert-base-cased",		return_dict=__SCREAMING_SNAKE_CASE       )

        # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
        # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
        # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
        snake_case_ :    Union[str, Any]     			=						model.to(accelerator.device       )

        # Instantiate optimizer
        snake_case_ :    Any     			=						AdamW(params=model.parameters(),		lr=__SCREAMING_SNAKE_CASE       )

        # Instantiate scheduler
        snake_case_ :    List[str]     			=						get_linear_schedule_with_warmup(
            optimizer=__SCREAMING_SNAKE_CASE,		num_warmup_steps=1_0_0,		num_training_steps=(len(__SCREAMING_SNAKE_CASE       ) * num_epochs) // gradient_accumulation_steps,		)

        # Prepare everything
        # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
        # prepare method.
        snake_case_			, snake_case_			, snake_case_			, snake_case_			, snake_case_ :    str     			=						accelerator.prepare(
            __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

        # Now we train the model
        for epoch in range(__SCREAMING_SNAKE_CASE       ):
            model.train()
            for step, batch in enumerate(__SCREAMING_SNAKE_CASE       ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device       )
                snake_case_ :    List[Any]     			=						model(**__SCREAMING_SNAKE_CASE       )
                snake_case_ :    Union[str, Any]     			=						outputs.loss
                snake_case_ :    Union[str, Any]     			=						loss / gradient_accumulation_steps
                accelerator.backward(__SCREAMING_SNAKE_CASE       )
                if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(__SCREAMING_SNAKE_CASE       ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device       )
                with torch.no_grad():
                    snake_case_ :    Dict     			=						model(**__SCREAMING_SNAKE_CASE       )
                snake_case_ :    str     			=						outputs.logits.argmax(dim=-1       )
                snake_case_			, snake_case_ :    Dict     			=						accelerator.gather_for_metrics((predictions, batch["labels"])       )
                metric.add_batch(
                    predictions=__SCREAMING_SNAKE_CASE,		references=__SCREAMING_SNAKE_CASE,		)

            snake_case_ :    str     			=						metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'epoch {epoch}:',		__SCREAMING_SNAKE_CASE       )

        # New Code #
        # We also run predictions on the test set at the very end
        snake_case_ :    Any     			=						[]
        for step, batch in enumerate(__SCREAMING_SNAKE_CASE       ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device       )
            with torch.no_grad():
                snake_case_ :    int     			=						model(**__SCREAMING_SNAKE_CASE       )
            snake_case_ :    Tuple     			=						outputs.logits
            snake_case_			, snake_case_ :    List[Any]     			=						accelerator.gather_for_metrics((predictions, batch["labels"])       )
            fold_predictions.append(predictions.cpu()       )
            if i == 0:
                # We need all of the test predictions
                test_references.append(references.cpu()       )
        # Use accelerator.print to print only on the main process.
        test_predictions.append(torch.cat(__SCREAMING_SNAKE_CASE,		dim=0       )       )
        # We now need to release all our memory and get rid of the current model, optimizer, etc
        accelerator.free_memory()
    # New Code #
    # Finally we check the accuracy of our folded results:
    snake_case_ :    List[Any]     			=						torch.cat(__SCREAMING_SNAKE_CASE,		dim=0       )
    snake_case_ :    Any     			=						torch.stack(__SCREAMING_SNAKE_CASE,		dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
    snake_case_ :    Tuple     			=						metric.compute(predictions=__SCREAMING_SNAKE_CASE,		references=__SCREAMING_SNAKE_CASE       )
    accelerator.print("Average test metrics from all folds:",		__SCREAMING_SNAKE_CASE       )

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Tuple     			=						argparse.ArgumentParser(description="Simple example of training script."       )
    parser.add_argument(
        "--mixed_precision",		type=__SCREAMING_SNAKE_CASE,		default=__SCREAMING_SNAKE_CASE,		choices=["no", "fp16", "bf16", "fp8"],		help="Whether to use mixed precision. Choose"
        "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
        "and an Nvidia Ampere GPU.",		)
    parser.add_argument("--cpu",		action="store_true",		help="If passed, will train on the CPU."       )
    # New Code #
    parser.add_argument("--num_folds",		type=__SCREAMING_SNAKE_CASE,		default=3,		help="The number of splits to perform across the dataset"       )
    snake_case_ :    List[Any]     			=						parser.parse_args()
    snake_case_ :    Optional[Any]     			=						{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
    training_function(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  main()




