
'''simple docstring'''







import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
_lowercase				:						Tuple												= logging.getLogger(__name__)

_lowercase				:						str												= int(1_0_0_0_0)  # Hardcoded max length to avoid infinite loop

_lowercase				:						Tuple												= {
    "gpt2": (GPTaLMHeadModel, GPTaTokenizer),
    "ctrl": (CTRLLMHeadModel, CTRLTokenizer),
    "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    "xlnet": (XLNetLMHeadModel, XLNetTokenizer),
    "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer),
    "xlm": (XLMWithLMHeadModel, XLMTokenizer),
    "gptj": (GPTJForCausalLM, AutoTokenizer),
    "bloom": (BloomForCausalLM, BloomTokenizerFast),
    "llama": (LlamaForCausalLM, LlamaTokenizer),
    "opt": (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
_lowercase				:						str												= "In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>"
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Optional[Any]							):



					"""simple docstring"""





					np.random.seed(args.seed							)
					torch.manual_seed(args.seed							)
					if args.n_gpu > 0:
										torch.cuda.manual_seed_all(args.seed							)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				List[str]   ,   __SCREAMING_SNAKE_CASE      :				Tuple   ,   __SCREAMING_SNAKE_CASE      :				Optional[int]   ,   __SCREAMING_SNAKE_CASE      :				Dict							):



					"""simple docstring"""





					if args.temperature > 0.7:
										logger.info('''CTRL typically works better with lower temperatures (and lower top_k).'''							)

					lowercase_			:  int									= tokenizer.encode(UpperCamelCase__   ,   add_special_tokens=UpperCamelCase__							)
					if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()							):
										logger.info('''WARNING! You are not starting your generation from a control code so you won\'t get good results'''							)
					return prompt_text
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Optional[Any]   ,   __SCREAMING_SNAKE_CASE      :				List[str]   ,   __SCREAMING_SNAKE_CASE      :				Optional[int]   ,   __SCREAMING_SNAKE_CASE      :				List[str]							):



					"""simple docstring"""





					lowercase_			:  Union[str, Any]									= hasattr(model.config   ,   '''use_lang_emb'''							) and model.config.use_lang_emb
					if hasattr(model.config   ,   '''lang2id'''							) and use_lang_emb:
										lowercase_			:  Optional[Any]									= model.config.langaid.keys()
										if args.xlm_language in available_languages:
															lowercase_			:  Tuple									= args.xlm_language
										else:
															lowercase_			:  Optional[int]									= None
															while language not in available_languages:
																				lowercase_			:  Tuple									= input('''Using XLM. Select language in ''' + str(list(UpperCamelCase__							)							) + ''' >>> '''							)

										lowercase_			:  List[Any]									= model.config.langaid[language]
										# kwargs["language"] = tokenizer.lang2id[language]

					# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
					# XLM masked-language modeling (MLM) models need masked token
					# is_xlm_mlm = "mlm" in args.model_name_or_path
					# if is_xlm_mlm:
					#     kwargs["mask_token_id"] = tokenizer.mask_token_id

					return prompt_text
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Optional[Any]   ,   __SCREAMING_SNAKE_CASE      :				Any   ,   __SCREAMING_SNAKE_CASE      :				Dict   ,   __SCREAMING_SNAKE_CASE      :				List[Any]							):



					"""simple docstring"""





					lowercase_			:  List[Any]									= args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
					lowercase_			:  Optional[int]									= prefix + prompt_text
					return prompt_text
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Union[str, Any]   ,   __SCREAMING_SNAKE_CASE      :				List[str]   ,   __SCREAMING_SNAKE_CASE      :				List[str]   ,   __SCREAMING_SNAKE_CASE      :				Tuple							):



					"""simple docstring"""





					lowercase_			:  Optional[Any]									= args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
					lowercase_			:  Any									= prefix + prompt_text
					return prompt_text


_lowercase				:						Optional[int]												= {
    "ctrl": prepare_ctrl_input,
    "xlm": prepare_xlm_input,
    "xlnet": prepare_xlnet_input,
    "transfo-xl": prepare_transfoxl_input,
}
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Dict   ,   __SCREAMING_SNAKE_CASE      :				int							):



					"""simple docstring"""





					if length < 0 and max_sequence_length > 0:
										lowercase_			:  Optional[int]									= max_sequence_length
					elif 0 < max_sequence_length < length:
										lowercase_			:  Tuple									= max_sequence_length  # No generation bigger than model size
					elif length < 0:
										lowercase_			:  Optional[Any]									= MAX_LENGTH  # avoid infinite loop
					return length
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Tuple							):



					"""simple docstring"""





					lowercase_			:  Dict									= None
					if hasattr(UpperCamelCase__   ,   '''hidden_size'''							):
										lowercase_			:  str									= model_config.hidden_size
					elif hasattr(UpperCamelCase__   ,   '''n_embed'''							):
										lowercase_			:  List[str]									= model_config.n_embed
					elif hasattr(UpperCamelCase__   ,   '''n_embd'''							):
										lowercase_			:  Optional[Any]									= model_config.n_embd

					lowercase_			:  Optional[Any]									= None
					if hasattr(UpperCamelCase__   ,   '''num_attention_heads'''							):
										lowercase_			:  Tuple									= model_config.num_attention_heads
					elif hasattr(UpperCamelCase__   ,   '''n_head'''							):
										lowercase_			:  Any									= model_config.n_head

					if embedding_size is None or num_head is None or num_head == 0:
										raise ValueError('''Check the model config'''							)

					lowercase_			:  int									= int(embedding_size / num_head							)
					if hasattr(UpperCamelCase__   ,   '''n_layer'''							):
										lowercase_			:  Tuple									= model_config.n_layer
					elif hasattr(UpperCamelCase__   ,   '''num_hidden_layers'''							):
										lowercase_			:  str									= model_config.num_hidden_layers
					else:
										raise ValueError('''Number of hidden layers couldn\'t be determined from the model config'''							)

					return num_layer, num_head, num_embedding_size_per_head
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Optional[int]   ,   __SCREAMING_SNAKE_CASE      :				Dict   ,   __SCREAMING_SNAKE_CASE      :				Tuple							):



					"""simple docstring"""





					lowercase_			:  Optional[int]									= sparse_model_config(model.config							)
					if model.config.model_type == "bloom":
										lowercase_			:  Any									= tuple(
										    (
										        torch.empty(int(num_attention_heads * batch_size							)   ,   UpperCamelCase__   ,   UpperCamelCase__							)
										        .to(model.dtype							)
										        .to(model.device							),
										        torch.empty(int(num_attention_heads * batch_size							)   ,   UpperCamelCase__   ,   UpperCamelCase__							)
										        .to(model.dtype							)
										        .to(model.device							),
										    )
										    for _ in range(UpperCamelCase__							)							)
					else:
										lowercase_			:  Union[str, Any]									= tuple(
										    (
										        torch.empty(UpperCamelCase__   ,   UpperCamelCase__   ,   UpperCamelCase__   ,   UpperCamelCase__							)
										        .to(model.dtype							)
										        .to(model.device							),
										        torch.empty(UpperCamelCase__   ,   UpperCamelCase__   ,   UpperCamelCase__   ,   UpperCamelCase__							)
										        .to(model.dtype							)
										        .to(model.device							),
										    )
										    for _ in range(UpperCamelCase__							)							)
					return past_key_values
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Optional[Any]   ,   __SCREAMING_SNAKE_CASE      :				List[str]   ,   __SCREAMING_SNAKE_CASE      :				Any							):



					"""simple docstring"""





					lowercase_			:  List[str]									= len(UpperCamelCase__							)
					lowercase_			:  Tuple									= tokenizer.batch_encode_plus(UpperCamelCase__   ,   return_tensors='''pt'''							)
					lowercase_			:  Any									= dummy_input.to(model.device							)
					if model.config.use_cache:
										lowercase_			:  Tuple									= generate_past_key_values(UpperCamelCase__   ,   UpperCamelCase__   ,   1							)
					lowercase_			:  Any									= torch.cat(
					    [
					        torch.zeros(dummy_input['''attention_mask'''].shape[0]   ,   1							)
					        .to(dummy_input['''attention_mask'''].dtype							)
					        .to(model.device							),
					        dummy_input['''attention_mask'''],
					    ]   ,   -1   ,   )
					return dummy_input






class 				lowerCAmelCase__     (						lowerCamelCase_  ):
					lowerCAmelCase_       =  ('''_optimized''', '''_default''')






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


										"""simple docstring"""





										lowercase_			:  Optional[int]									= optimized
										lowercase_			:  Optional[Any]									= default






					def __call__(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


										"""simple docstring"""





										if kwargs["past_key_values"] is None and self._default.config.use_cache:
															lowercase_			:  Optional[Any]									= generate_past_key_values(self._default       ,							kwargs['''input_ids'''].shape[0]       ,							0	)
										kwargs.pop('''position_ids'''       ,							__lowerCamelCase	)
										for k in list(kwargs.keys()	):
															if kwargs[k] is None or isinstance(kwargs[k]       ,							__lowerCamelCase	):
																				kwargs.pop(__lowerCamelCase	)
										lowercase_			:  List[Any]									= self._optimized(**__lowerCamelCase	)
										lowercase_			:  Any									= outputs[0]
										lowercase_			:  Any									= outputs[1]
										lowercase_			:  Tuple									= CausalLMOutputWithPast(
										    loss=__lowerCamelCase       ,							logits=__lowerCamelCase       ,							past_key_values=__lowerCamelCase       ,							hidden_states=__lowerCamelCase       ,							attentions=__lowerCamelCase       ,							)
										return fixed_output






					def __getattr__(     self       ,							__SCREAMING_SNAKE_CASE	):


										"""simple docstring"""





										return getattr(self._default       ,							__lowerCamelCase	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


										"""simple docstring"""





										return self._default.prepare_inputs_for_generation(
										    __lowerCamelCase       ,							past_key_values=__lowerCamelCase       ,							inputs_embeds=__lowerCamelCase       ,							use_cache=__lowerCamelCase       ,							**__lowerCamelCase	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


										"""simple docstring"""





										return self._default._reorder_cache(__lowerCamelCase       ,							__lowerCamelCase	)







def       snake_case_	(       ):



					"""simple docstring"""





					lowercase_			:  Union[str, Any]									= argparse.ArgumentParser()
					parser.add_argument(
					    '''--model_type'''   ,   default=UpperCamelCase__   ,   type=UpperCamelCase__   ,   required=UpperCamelCase__   ,   help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()							)   ,   )
					parser.add_argument(
					    '''--model_name_or_path'''   ,   default=UpperCamelCase__   ,   type=UpperCamelCase__   ,   required=UpperCamelCase__   ,   help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()							)   ,   )

					parser.add_argument('''--prompt'''   ,   type=UpperCamelCase__   ,   default=''''''							)
					parser.add_argument('''--length'''   ,   type=UpperCamelCase__   ,   default=20							)
					parser.add_argument('''--stop_token'''   ,   type=UpperCamelCase__   ,   default=UpperCamelCase__   ,   help='''Token at which text generation is stopped'''							)

					parser.add_argument(
					    '''--temperature'''   ,   type=UpperCamelCase__   ,   default=1.0   ,   help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''   ,   )
					parser.add_argument(
					    '''--repetition_penalty'''   ,   type=UpperCamelCase__   ,   default=1.0   ,   help='''primarily useful for CTRL model; in that case, use 1.2'''							)
					parser.add_argument('''--k'''   ,   type=UpperCamelCase__   ,   default=0							)
					parser.add_argument('''--p'''   ,   type=UpperCamelCase__   ,   default=0.9							)

					parser.add_argument('''--prefix'''   ,   type=UpperCamelCase__   ,   default=''''''   ,   help='''Text added prior to input.'''							)
					parser.add_argument('''--padding_text'''   ,   type=UpperCamelCase__   ,   default=''''''   ,   help='''Deprecated, the use of `--prefix` is preferred.'''							)
					parser.add_argument('''--xlm_language'''   ,   type=UpperCamelCase__   ,   default=''''''   ,   help='''Optional language when used with the XLM model.'''							)

					parser.add_argument('''--seed'''   ,   type=UpperCamelCase__   ,   default=42   ,   help='''random seed for initialization'''							)
					parser.add_argument('''--no_cuda'''   ,   action='''store_true'''   ,   help='''Avoid using CUDA when available'''							)
					parser.add_argument('''--num_return_sequences'''   ,   type=UpperCamelCase__   ,   default=1   ,   help='''The number of samples to generate.'''							)
					parser.add_argument(
					    '''--fp16'''   ,   action='''store_true'''   ,   help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''   ,   )
					parser.add_argument('''--jit'''   ,   action='''store_true'''   ,   help='''Whether or not to use jit trace to accelerate inference'''							)
					lowercase_			:  Union[str, Any]									= parser.parse_args()

					lowercase_			:  List[str]									= torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
					lowercase_			:  Any									= 0 if args.no_cuda else torch.cuda.device_count()

					logger.warning(F'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''							)

					set_seed(UpperCamelCase__							)

					# Initialize the model and tokenizer
					try:
										lowercase_			:  Tuple									= args.model_type.lower()
										lowercase_			:  str									= MODEL_CLASSES[args.model_type]
					except KeyError:
										raise KeyError('''the model {} you specified is not supported. You are welcome to add it and open a PR :)'''							)

					lowercase_			:  Tuple									= tokenizer_class.from_pretrained(args.model_name_or_path							)
					if tokenizer.pad_token is None:
										lowercase_			:  str									= tokenizer.eos_token
					lowercase_			:  Union[str, Any]									= model_class.from_pretrained(args.model_name_or_path							)
					model.to(args.device							)

					if args.fpaa:
										model.half()
					lowercase_			:  Dict									= getattr(model.config   ,   '''max_position_embeddings'''   ,   0							)
					lowercase_			:  List[Any]									= adjust_length_to_model(args.length   ,   max_sequence_length=UpperCamelCase__							)
					logger.info(UpperCamelCase__							)

					lowercase_			:  List[Any]									= args.prompt if args.prompt else input('''Model prompt >>> '''							)

					# Different models need different input formatting and/or extra arguments
					lowercase_			:  List[str]									= args.model_type in PREPROCESSING_FUNCTIONS.keys()
					if requires_preprocessing:
										lowercase_			:  List[Any]									= PREPROCESSING_FUNCTIONS.get(args.model_type							)
										lowercase_			:  Tuple									= prepare_input(UpperCamelCase__   ,   UpperCamelCase__   ,   UpperCamelCase__   ,   UpperCamelCase__							)

										if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
															lowercase_			:  Tuple									= {"add_space_before_punct_symbol": True}
										else:
															lowercase_			:  Dict									= {}

										lowercase_			:  List[Any]									= tokenizer.encode(
										    UpperCamelCase__   ,   add_special_tokens=UpperCamelCase__   ,   return_tensors='''pt'''   ,   **UpperCamelCase__							)
					else:
										lowercase_			:  int									= args.prefix if args.prefix else args.padding_text
										lowercase_			:  Any									= tokenizer.encode(prefix + prompt_text   ,   add_special_tokens=UpperCamelCase__   ,   return_tensors='''pt'''							)
					lowercase_			:  str									= encoded_prompt.to(args.device							)

					if encoded_prompt.size()[-1] == 0:
										lowercase_			:  Union[str, Any]									= None
					else:
										lowercase_			:  Any									= encoded_prompt

					if args.jit:
										lowercase_			:  Union[str, Any]									= ["enable jit"]
										lowercase_			:  List[Any]									= prepare_jit_inputs(UpperCamelCase__   ,   UpperCamelCase__   ,   UpperCamelCase__							)
										torch._C._jit_set_texpr_fuser_enabled(UpperCamelCase__							)
										lowercase_			:  Union[str, Any]									= False
										if hasattr(UpperCamelCase__   ,   '''forward'''							):
															lowercase_			:  List[Any]									= inspect.signature(model.forward							)
										else:
															lowercase_			:  Union[str, Any]									= inspect.signature(model.__call__							)
										lowercase_			:  str									= tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(UpperCamelCase__   ,   UpperCamelCase__							) is not None							)
										lowercase_			:  Any									= torch.jit.trace(UpperCamelCase__   ,   UpperCamelCase__   ,   strict=UpperCamelCase__							)
										lowercase_			:  Dict									= torch.jit.freeze(traced_model.eval()							)
										traced_model(*UpperCamelCase__							)
										traced_model(*UpperCamelCase__							)

										lowercase_			:  int									= _ModelFallbackWrapper(UpperCamelCase__   ,   UpperCamelCase__							)

					lowercase_			:  Union[str, Any]									= model.generate(
					    input_ids=UpperCamelCase__   ,   max_length=args.length + len(encoded_prompt[0]							)   ,   temperature=args.temperature   ,   top_k=args.k   ,   top_p=args.p   ,   repetition_penalty=args.repetition_penalty   ,   do_sample=UpperCamelCase__   ,   num_return_sequences=args.num_return_sequences   ,   )

					# Remove the batch dimension when returning multiple sequences
					if len(output_sequences.shape							) > 2:
										output_sequences.squeeze_()

					lowercase_			:  List[str]									= []

					for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase__							):
										print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''							)
										lowercase_			:  Optional[Any]									= generated_sequence.tolist()

										# Decode text
										lowercase_			:  List[Any]									= tokenizer.decode(UpperCamelCase__   ,   clean_up_tokenization_spaces=UpperCamelCase__							)

										# Remove all text after the stop token
										lowercase_			:  int									= text[: text.find(args.stop_token							) if args.stop_token else None]

										# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
										lowercase_			:  int									= (
										    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]   ,   clean_up_tokenization_spaces=UpperCamelCase__							)							) :]
										)

										generated_sequences.append(UpperCamelCase__							)
										print(UpperCamelCase__							)

					return generated_sequences


if __name__ == "__main__":
							main()





'''simple docstring'''







import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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
#
########################################################################

_lowercase				:						Tuple												= 3_2
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Accelerator   ,   __SCREAMING_SNAKE_CASE      :				int = 16							):



						"""simple docstring"""





						lowercase_			:  int									= AutoTokenizer.from_pretrained('''bert-base-cased'''							)
						lowercase_			:  int									= load_dataset('''glue'''   ,   '''mrpc'''							)

						def tokenize_function(__SCREAMING_SNAKE_CASE      :				Optional[int]							):
												# max_length=None => use the model max length (it's actually the default)
												lowercase_			:  Union[str, Any]									= 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():
												lowercase_			:  Optional[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
						lowercase_			:  Any									= tokenized_datasets.rename_column('''label'''   ,   '''labels'''							)

						def collate_fn(__SCREAMING_SNAKE_CASE      :				List[Any]							):
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		lowercase_			:  List[str]									= 16
												elif accelerator.mixed_precision != "no":
																		lowercase_			:  List[Any]									= 8
												else:
																		lowercase_			:  List[Any]									= None

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

						# Instantiate dataloaders.
						lowercase_			:  List[str]									= DataLoader(
						    tokenized_datasets['''train''']   ,   shuffle=__SCREAMING_SNAKE_CASE   ,   collate_fn=__SCREAMING_SNAKE_CASE   ,   batch_size=__SCREAMING_SNAKE_CASE							)
						lowercase_			:  int									= 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

							_lowercase				:						Tuple												= mocked_dataloaders  # noqa: F811
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				str   ,   __SCREAMING_SNAKE_CASE      :				List[Any]							):



						"""simple docstring"""





						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,   __SCREAMING_SNAKE_CASE							) == "1":
												lowercase_			:  Dict									= 2
						# Initialize accelerator
						lowercase_			:  Any									= Accelerator(cpu=args.cpu   ,   mixed_precision=args.mixed_precision							)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowercase_			:  Optional[Any]									= config['''lr''']
						lowercase_			:  Optional[Any]									= int(config['''num_epochs''']							)
						lowercase_			:  str									= int(config['''seed''']							)
						lowercase_			:  Optional[Any]									= int(config['''batch_size''']							)

						lowercase_			:  int									= evaluate.load('''glue'''   ,   '''mrpc'''							)

						# New Code #
						# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
						# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
						# half each time. From this, we can calculate the number of gradient accumulation steps needed
						# and modify the Accelerator object as a result
						@find_executable_batch_size(starting_batch_size=int(__SCREAMING_SNAKE_CASE							)							)
						def inner_training_loop(__SCREAMING_SNAKE_CASE      :				Any							):
												# Since we need to modify the outside accelerator object, we need to bring it
												# to the local scope
												nonlocal accelerator

												# We can calculate the number of gradient accumulation steps based on the current
												# batch size vs the starting batch size
												lowercase_			:  Union[str, Any]									= observed_batch_size // batch_size

												# And then set it in the Accelerator directly:
												lowercase_			:  Tuple									= num_gradient_accumulation_steps

												# Next we need to free all of the stored model references in the Accelerator each time
												accelerator.free_memory()

												# And set the seed so our results are reproducable each reset
												set_seed(__SCREAMING_SNAKE_CASE							)

												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												lowercase_			:  Dict									= 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).
												lowercase_			:  Optional[int]									= model.to(accelerator.device							)

												# Instantiate optimizer
												lowercase_			:  int									= AdamW(params=model.parameters()   ,   lr=__SCREAMING_SNAKE_CASE							)
												lowercase_		,				lowercase_			:  List[str]									= get_dataloaders(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)

												# Instantiate scheduler
												lowercase_			:  str									= get_linear_schedule_with_warmup(
												    optimizer=__SCREAMING_SNAKE_CASE   ,   num_warmup_steps=100   ,   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.
												lowercase_		,				lowercase_		,				lowercase_		,				lowercase_		,				lowercase_			:  Any									= 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							):
																								# And perform gradient accumulation
																								with accelerator.accumulate(__SCREAMING_SNAKE_CASE							):
																														# We could avoid this line since we set the accelerator with `device_placement=True`.
																														batch.to(accelerator.device							)
																														lowercase_			:  List[str]									= model(**__SCREAMING_SNAKE_CASE							)
																														lowercase_			:  Any									= outputs.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():
																														lowercase_			:  Tuple									= model(**__SCREAMING_SNAKE_CASE							)
																								lowercase_			:  List[str]									= outputs.logits.argmax(dim=-1							)
																								lowercase_		,				lowercase_			:  int									= accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
																								metric.add_batch(
																								    predictions=__SCREAMING_SNAKE_CASE   ,   references=__SCREAMING_SNAKE_CASE   ,   )

																		lowercase_			:  Union[str, Any]									= metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(F'''epoch {epoch}:'''   ,   __SCREAMING_SNAKE_CASE							)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
						inner_training_loop()
def       snake_case_	(       ):



						"""simple docstring"""





						lowercase_			:  int									= 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.'''							)
						lowercase_			:  int									= parser.parse_args()
						# New Code #
						# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
						lowercase_			:  Union[str, Any]									= {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
						training_function(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							main()



