




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()
UpperCamelCase__         =      logging.get_logger("""transformers.models.speecht5""")

UpperCamelCase__         =      {
    """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""",
}
UpperCamelCase__         =      {
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
UpperCamelCase__         =      {
    """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""",
}
UpperCamelCase__         =      {
    """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""",
}
UpperCamelCase__         =      {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
UpperCamelCase__         =      {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
UpperCamelCase__         =      {
    """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""",
}
UpperCamelCase__         =      {
    """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""",
}
UpperCamelCase__         =      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
UpperCamelCase__         =      {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCamelCase__         =      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCamelCase__         =      []
UpperCamelCase__         =      [
    """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""",
]
UpperCamelCase__         =      IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
UpperCamelCase__         =      IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
UpperCamelCase__         =      IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]







def      _a				(						SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    Dict				,				SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    List[Any]					):
					for attribute in key.split("."					):
										__lowerCAmelCase           =	getattr(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)

					if weight_type is not None:
										__lowerCAmelCase           =	getattr(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					).shape
					else:
										__lowerCAmelCase           =	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":
										__lowerCAmelCase           =	value
					elif weight_type == "weight_g":
										__lowerCAmelCase           =	value
					elif weight_type == "weight_v":
										__lowerCAmelCase           =	value
					elif weight_type == "bias":
										__lowerCAmelCase           =	value
					elif weight_type == "running_mean":
										__lowerCAmelCase           =	value
					elif weight_type == "running_var":
										__lowerCAmelCase           =	value
					elif weight_type == "num_batches_tracked":
										__lowerCAmelCase           =	value
					else:
										__lowerCAmelCase           =	value

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







def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    str					):
					for key in ignore_keys:
										if key.endswith(".*"					):
															if name.startswith(key[:-1]					):
																				return True
										elif ".*." in key:
															__lowerCAmelCase       ,							__lowerCAmelCase           =	key.split(".*."					)
															if prefix in name and suffix in name:
																				return True
										elif key in name:
															return True
					return False







def      _a				(						SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    str					):
					__lowerCAmelCase           =	[]

					if task == "s2t":
										__lowerCAmelCase           =	hf_model.speechta.encoder.prenet.feature_encoder
										__lowerCAmelCase           =	MAPPING_S2T
										__lowerCAmelCase           =	IGNORE_KEYS_S2T
					elif task == "t2s":
										__lowerCAmelCase           =	None
										__lowerCAmelCase           =	MAPPING_T2S
										__lowerCAmelCase           =	IGNORE_KEYS_T2S
					elif task == "s2s":
										__lowerCAmelCase           =	hf_model.speechta.encoder.prenet.feature_encoder
										__lowerCAmelCase           =	MAPPING_S2S
										__lowerCAmelCase           =	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

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

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																				if key in name:
																									__lowerCAmelCase           =	True
																									if "*" in mapped_key:
																														__lowerCAmelCase           =	name.split(SCREAMING_SNAKE_CASE_					)[0].split("."					)[-2]
																														__lowerCAmelCase           =	mapped_key.replace("*"				,				SCREAMING_SNAKE_CASE_					)
																									if "weight_g" in name:
																														__lowerCAmelCase           =	"weight_g"
																									elif "weight_v" in name:
																														__lowerCAmelCase           =	"weight_v"
																									elif "bias" in name:
																														__lowerCAmelCase           =	"bias"
																									elif "weight" in name:
																														__lowerCAmelCase           =	"weight"
																									elif "running_mean" in name:
																														__lowerCAmelCase           =	"running_mean"
																									elif "running_var" in name:
																														__lowerCAmelCase           =	"running_var"
																									elif "num_batches_tracked" in name:
																														__lowerCAmelCase           =	"num_batches_tracked"
																									else:
																														__lowerCAmelCase           =	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      _a				(						SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    Dict					):
					__lowerCAmelCase           =	full_name.split("conv_layers."					)[-1]
					__lowerCAmelCase           =	name.split("."					)
					__lowerCAmelCase           =	int(items[0]					)
					__lowerCAmelCase           =	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."""					)
															__lowerCAmelCase           =	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."""					)
															__lowerCAmelCase           =	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."""					)
															__lowerCAmelCase           =	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."""					)
															__lowerCAmelCase           =	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      _a				(						SCREAMING_SNAKE_CASE_							:    Dict				,				SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    Any=None				,				SCREAMING_SNAKE_CASE_							:    List[Any]=None				,				SCREAMING_SNAKE_CASE_							:    str=None				,				):
					if config_path is not None:
										__lowerCAmelCase           =	SpeechTaConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
					else:
										__lowerCAmelCase           =	SpeechTaConfig()

					if task == "s2t":
										__lowerCAmelCase           =	config.max_text_positions
										__lowerCAmelCase           =	SpeechTaForSpeechToText(SCREAMING_SNAKE_CASE_					)
					elif task == "t2s":
										__lowerCAmelCase           =	18_76
										__lowerCAmelCase           =	6_00
										__lowerCAmelCase           =	config.max_speech_positions
										__lowerCAmelCase           =	SpeechTaForTextToSpeech(SCREAMING_SNAKE_CASE_					)
					elif task == "s2s":
										__lowerCAmelCase           =	18_76
										__lowerCAmelCase           =	config.max_speech_positions
										__lowerCAmelCase           =	SpeechTaForSpeechToSpeech(SCREAMING_SNAKE_CASE_					)
					else:
										raise ValueError(F"""Unknown task name: {task}"""					)

					if vocab_path:
										__lowerCAmelCase           =	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
										__lowerCAmelCase           =	AddedToken("<mask>"				,				lstrip=SCREAMING_SNAKE_CASE_				,				rstrip=SCREAMING_SNAKE_CASE_					)
										__lowerCAmelCase           =	mask_token
										tokenizer.add_special_tokens({"mask_token": mask_token}					)
										tokenizer.add_tokens(["<ctc_blank>"]					)

					__lowerCAmelCase           =	SpeechTaFeatureExtractor()
					__lowerCAmelCase           =	SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_				,				feature_extractor=SCREAMING_SNAKE_CASE_					)
					processor.save_pretrained(SCREAMING_SNAKE_CASE_					)

					__lowerCAmelCase           =	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__":
					UpperCamelCase__         =      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."""
					)

					UpperCamelCase__         =      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,
					)










import argparse
import os

import evaluate
import torch
from datasets import load_dataset
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
# and perform gradient accumulation
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase__         =      16
UpperCamelCase__         =      32







def      _a				(						SCREAMING_SNAKE_CASE_							:    Accelerator				,				SCREAMING_SNAKE_CASE_							:    int = 16					):
					__lowerCAmelCase           =	AutoTokenizer.from_pretrained("bert-base-cased"					)
					__lowerCAmelCase           =	load_dataset("glue"				,				"mrpc"					)

					def tokenize_function(SCREAMING_SNAKE_CASE_							:    str					):
										# max_length=None => use the model max length (it's actually the default)
										__lowerCAmelCase           =	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():
										__lowerCAmelCase           =	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
					__lowerCAmelCase           =	tokenized_datasets.rename_column("label"				,				"labels"					)

					def collate_fn(SCREAMING_SNAKE_CASE_							:    int					):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										__lowerCAmelCase           =	1_28 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															__lowerCAmelCase           =	16
										elif accelerator.mixed_precision != "no":
															__lowerCAmelCase           =	8
										else:
															__lowerCAmelCase           =	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.
					__lowerCAmelCase           =	DataLoader(
					    tokenized_datasets["train"]				,				shuffle=SCREAMING_SNAKE_CASE_				,				collate_fn=SCREAMING_SNAKE_CASE_				,				batch_size=SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	DataLoader(
					    tokenized_datasets["validation"]				,				shuffle=SCREAMING_SNAKE_CASE_				,				collate_fn=SCREAMING_SNAKE_CASE_				,				batch_size=SCREAMING_SNAKE_CASE_					)

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					UpperCamelCase__         =      mocked_dataloaders  # noqa: F811







def      _a				(						SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    str					):
					# For testing only
					if os.environ.get("TESTING_MOCKED_DATALOADERS"				,				SCREAMING_SNAKE_CASE_					) == "1":
										__lowerCAmelCase           =	2
					# New Code #
					__lowerCAmelCase           =	int(args.gradient_accumulation_steps					)
					# Initialize accelerator
					__lowerCAmelCase           =	Accelerator(
					    cpu=args.cpu				,				mixed_precision=args.mixed_precision				,				gradient_accumulation_steps=SCREAMING_SNAKE_CASE_					)
					if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
										raise NotImplementedError(
										    "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"					)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__lowerCAmelCase           =	config["lr"]
					__lowerCAmelCase           =	int(config["num_epochs"]					)
					__lowerCAmelCase           =	int(config["seed"]					)
					__lowerCAmelCase           =	int(config["batch_size"]					)

					__lowerCAmelCase           =	evaluate.load("glue"				,				"mrpc"					)

					set_seed(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase       ,							__lowerCAmelCase           =	get_dataloaders(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__lowerCAmelCase           =	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).
					__lowerCAmelCase           =	model.to(accelerator.device					)

					# Instantiate optimizer
					__lowerCAmelCase           =	AdamW(params=model.parameters()				,				lr=SCREAMING_SNAKE_CASE_					)

					# Instantiate scheduler
					__lowerCAmelCase           =	get_linear_schedule_with_warmup(
					    optimizer=SCREAMING_SNAKE_CASE_				,				num_warmup_steps=1_00				,				num_training_steps=(len(SCREAMING_SNAKE_CASE_					) * num_epochs)				,				)

					# 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.
					__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase           =	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					)
															# New code #
															# We use the new `accumulate` context manager to perform gradient accumulation
															# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
															with accelerator.accumulate(SCREAMING_SNAKE_CASE_					):
																				__lowerCAmelCase           =	model(**SCREAMING_SNAKE_CASE_					)
																				__lowerCAmelCase           =	output.loss
																				accelerator.backward(SCREAMING_SNAKE_CASE_					)
																				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():
																				__lowerCAmelCase           =	model(**SCREAMING_SNAKE_CASE_					)
															__lowerCAmelCase           =	outputs.logits.argmax(dim=-1					)
															__lowerCAmelCase       ,							__lowerCAmelCase           =	accelerator.gather_for_metrics((predictions, batch["labels"])					)
															metric.add_batch(
															    predictions=SCREAMING_SNAKE_CASE_				,				references=SCREAMING_SNAKE_CASE_				,				)

										__lowerCAmelCase           =	metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"""epoch {epoch}:"""				,				SCREAMING_SNAKE_CASE_					)







def      _a				(						):
					__lowerCAmelCase           =	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."				,				)
					# New Code #
					parser.add_argument(
					    "--gradient_accumulation_steps"				,				type=SCREAMING_SNAKE_CASE_				,				default=1				,				help="The number of minibatches to be ran before gradients are accumulated."				,				)
					parser.add_argument("--cpu"				,				action="store_true"				,				help="If passed, will train on the CPU."					)
					__lowerCAmelCase           =	parser.parse_args()
					__lowerCAmelCase           =	{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
					main()




