

import argparse
import gc
import json
import os

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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


A_       : int         =			16
A_       : str         =			32

def 						UpperCamelCase					(lowercase_: Optional[Any]					)     ->      List[Any]:
	return int(x / 2**20					)



class     _a						:


					'''simple docstring'''






					def __enter__( self			):
						gc.collect()
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
						A__							:    Any        =		torch.cuda.memory_allocated()
						return self






					def __exit__( self    ,       *A__			):
						gc.collect()
						torch.cuda.empty_cache()
						A__							:    Optional[int]        =		torch.cuda.memory_allocated()
						A__							:    str        =		torch.cuda.max_memory_allocated()
						A__							:    str        =		bamb(self.end - self.begin			)
						A__							:    Any        =		bamb(self.peak - self.begin			)
						# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")



def 						UpperCamelCase					(lowercase_: Accelerator    ,      lowercase_: int = 16    ,      lowercase_: str = "bert-base-cased"    ,      lowercase_: int = 320    ,      lowercase_: int = 160    ,      )     ->      Dict:
	A__							:    List[Any]        =		AutoTokenizer.from_pretrained(lowercase_					)
	A__							:    List[Any]        =		load_dataset(
	    """glue"""    ,      """mrpc"""    ,      split={"""train""": f"""train[:{n_train}]""", """validation""": f"""validation[:{n_val}]"""}					)

	def tokenize_function(lowercase_: int					):
		# max_length=None => use the model max length (it's actually the default)
		A__							:    Optional[Any]        =		tokenizer(examples["""sentence1"""]    ,      examples["""sentence2"""]    ,      truncation=lowercase_    ,      max_length=lowercase_					)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	A__							:    str        =		datasets.map(
	    lowercase_    ,      batched=lowercase_    ,      remove_columns=["""idx""", """sentence1""", """sentence2"""]    ,      load_from_cache_file=lowercase_					)

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

	def collate_fn(lowercase_: Optional[Any]					):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(lowercase_    ,      padding="""max_length"""    ,      max_length=128    ,      return_tensors="""pt"""					)
		return tokenizer.pad(lowercase_    ,      padding="""longest"""    ,      return_tensors="""pt"""					)

	# Instantiate dataloaders.
	A__							:    Tuple        =		DataLoader(
	    tokenized_datasets["""train"""]    ,      shuffle=lowercase_    ,      collate_fn=lowercase_    ,      batch_size=lowercase_					)
	A__							:    List[str]        =		DataLoader(
	    tokenized_datasets["""validation"""]    ,      shuffle=lowercase_    ,      collate_fn=lowercase_    ,      batch_size=lowercase_					)

	return train_dataloader, eval_dataloader

def 						UpperCamelCase					(lowercase_: Tuple    ,      lowercase_: Optional[int]					)     ->      Tuple:
	# Initialize accelerator
	A__							:    List[Any]        =		Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A__							:    Union[str, Any]        =		config["""lr"""]
	A__							:    Any        =		int(config["""num_epochs"""]					)
	A__							:    List[Any]        =		int(config["""seed"""]					)
	A__							:    Optional[Any]        =		int(config["""batch_size"""]					)
	A__							:    str        =		args.model_name_or_path

	set_seed(lowercase_					)
	A__   ,   A__							:    int        =		get_dataloaders(lowercase_    ,      lowercase_    ,      lowercase_    ,      args.n_train    ,      args.n_val					)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A__							:    Union[str, Any]        =		AutoModelForSequenceClassification.from_pretrained(lowercase_    ,      return_dict=lowercase_					)

	# Instantiate optimizer
	A__							:    Optional[int]        =		(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	A__							:    Any        =		optimizer_cls(params=model.parameters()    ,      lr=lowercase_					)

	if accelerator.state.deepspeed_plugin is not None:
		A__							:    Union[str, Any]        =		accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		A__							:    Optional[int]        =		1
	A__							:    Optional[Any]        =		(len(lowercase_					) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		A__							:    List[str]        =		get_linear_schedule_with_warmup(
		    optimizer=lowercase_    ,      num_warmup_steps=0    ,      num_training_steps=lowercase_    ,      )
	else:
		A__							:    int        =		DummyScheduler(lowercase_    ,      total_num_steps=lowercase_    ,      warmup_num_steps=0					)

	# 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.
	A__   ,   A__   ,   A__   ,   A__   ,   A__							:    Tuple        =		accelerator.prepare(
	    lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_					)

	# We need to keep track of how many total steps we have iterated over
	A__							:    Tuple        =		0
	# We also need to keep track of the stating epoch so files are named properly
	A__							:    str        =		0

	# Now we train the model
	A__							:    Tuple        =		{}
	for epoch in range(lowercase_    ,      lowercase_					):
		with TorchTracemalloc() as tracemalloc:
			model.train()
			for step, batch in enumerate(lowercase_					):
				A__							:    Optional[Any]        =		model(**lowercase_					)
				A__							:    Any        =		outputs.loss
				A__							:    Optional[int]        =		loss / gradient_accumulation_steps
				accelerator.backward(lowercase_					)
				if step % gradient_accumulation_steps == 0:
					optimizer.step()
					lr_scheduler.step()
					optimizer.zero_grad()

				overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
		accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin					)					)					)
		accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used					)					)
		accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked					)					)
		accelerator.print(
		    """Total Peak Memory consumed during the train (max): {}""".format(
		        tracemalloc.peaked + bamb(tracemalloc.begin					)					)					)
		A__							:    Dict        =		tracemalloc.peaked + bamb(tracemalloc.begin					)
		if args.peak_memory_upper_bound is not None:
			assert (
			    train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
			), "Peak memory usage exceeded the upper bound"

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir    ,      """peak_memory_utilization.json"""					)    ,      """w"""					) as f:
			json.dump(lowercase_    ,      lowercase_					)

def 						UpperCamelCase					()     ->      Dict:
	A__							:    Dict        =		argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""					)
	parser.add_argument(
	    """--model_name_or_path"""    ,      type=lowercase_    ,      default="""bert-base-cased"""    ,      help="""Path to pretrained model or model identifier from huggingface.co/models."""    ,      required=lowercase_    ,      )
	parser.add_argument(
	    """--output_dir"""    ,      type=lowercase_    ,      default="""."""    ,      help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""    ,      )
	parser.add_argument(
	    """--peak_memory_upper_bound"""    ,      type=lowercase_    ,      default=lowercase_    ,      help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""    ,      )
	parser.add_argument(
	    """--n_train"""    ,      type=lowercase_    ,      default=320    ,      help="""Number of training examples to use."""    ,      )
	parser.add_argument(
	    """--n_val"""    ,      type=lowercase_    ,      default=160    ,      help="""Number of validation examples to use."""    ,      )
	parser.add_argument(
	    """--num_epochs"""    ,      type=lowercase_    ,      default=1    ,      help="""Number of train epochs."""    ,      )
	A__							:    List[Any]        =		parser.parse_args()
	A__							:    List[Any]        =		{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
	training_function(lowercase_    ,      lowercase_					)


if __name__ == "__main__":
	main()






import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
	import tensorflow as tf

if is_torch_available():
	import torch


A_       : List[Any]         =			logging.get_logger(__name__)



class     _a						:


					'''simple docstring'''






					def __init__( self    ,       A__ = None    ,       A__ = None    ,       A__=None    ,       A__=None			):
						if not conversation_id:
							A__							:    List[Any]        =		uuid.uuida()
						if past_user_inputs is None:
							A__							:    Dict        =		[]
						if generated_responses is None:
							A__							:    int        =		[]

						A__							:    uuid.UUID        =		conversation_id
						A__							:    List[str]        =		past_user_inputs
						A__							:    List[str]        =		generated_responses
						A__							:    Optional[str]        =		text






					def __eq__( self    ,       A__			):
						if not isinstance(A__    ,       A__			):
							return False
						if self.uuid == other.uuid:
							return True
						return (
						    self.new_user_input == other.new_user_input
						    and self.past_user_inputs == other.past_user_inputs
						    and self.generated_responses == other.generated_responses
						)






					def   __A			( self    ,       A__    ,       A__ = False			):
						if self.new_user_input:
							if overwrite:
								logger.warning(
								    F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
								    F"""with: \"{text}\"."""			)
								A__							:    str        =		text
							else:
								logger.warning(
								    F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
								    F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"""			)
						else:
							A__							:    Tuple        =		text






					def   __A			( self			):
						if self.new_user_input:
							self.past_user_inputs.append(self.new_user_input			)
						A__							:    Tuple        =		None






					def   __A			( self    ,       A__			):
						self.generated_responses.append(A__			)






					def   __A			( self			):
						for user_input, generated_response in zip(self.past_user_inputs    ,       self.generated_responses			):
							yield True, user_input
							yield False, generated_response
						if self.new_user_input:
							yield True, self.new_user_input






					def __repr__( self			):
						A__							:    Optional[Any]        =		F"""Conversation id: {self.uuid} \n"""
						for is_user, text in self.iter_texts():
							A__							:    str        =		"""user""" if is_user else """bot"""
							output += F"""{name} >> {text} \n"""
						return output



@add_end_docstrings(
    __magic_name__		,   r'''
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    '''		,   )
class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       *A__    ,       **A__			):
						super().__init__(*A__    ,       **A__			)
						if self.tokenizer.pad_token_id is None:
							A__							:    Tuple        =		self.tokenizer.eos_token






					def   __A			( self    ,       A__=None    ,       A__=None    ,       A__=None    ,       **A__			):
						A__							:    Tuple        =		{}
						A__							:    List[str]        =		{}
						A__							:    Union[str, Any]        =		{}

						if min_length_for_response is not None:
							A__							:    str        =		min_length_for_response
						if minimum_tokens is not None:
							A__							:    List[str]        =		minimum_tokens

						if "max_length" in generate_kwargs:
							A__							:    List[Any]        =		generate_kwargs["""max_length"""]
							# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
						if clean_up_tokenization_spaces is not None:
							A__							:    Optional[int]        =		clean_up_tokenization_spaces

						if generate_kwargs:
							forward_params.update(A__			)
						return preprocess_params, forward_params, postprocess_params






					def __call__( self    ,       A__    ,       A__=0    ,       **A__			):
						A__							:    Optional[Any]        =		super().__call__(A__    ,       num_workers=A__    ,       **A__			)
						if isinstance(A__    ,       A__			) and len(A__			) == 1:
							return outputs[0]
						return outputs






					def   __A			( self    ,       A__    ,       A__=32			):
						if not isinstance(A__    ,       A__			):
							raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""			)
						if conversation.new_user_input is None:
							raise ValueError(
							    F"""Conversation with UUID {type(conversation.uuid			)} does not contain new user input to process. """
							    """Add user inputs with the conversation's `add_user_input` method"""			)
						if hasattr(self.tokenizer    ,       """_build_conversation_input_ids"""			):
							A__							:    List[str]        =		self.tokenizer._build_conversation_input_ids(A__			)
						else:
							# If the tokenizer cannot handle conversations, we default to only the old version
							A__							:    Tuple        =		self._legacy_parse_and_tokenize(A__			)

						if self.framework == "pt":
							A__							:    List[str]        =		torch.LongTensor([input_ids]			)
						elif self.framework == "tf":
							A__							:    Optional[Any]        =		tf.constant([input_ids]			)
						return {"input_ids": input_ids, "conversation": conversation}






					def   __A			( self    ,       A__    ,       A__=10    ,       **A__			):
						A__							:    List[Any]        =		generate_kwargs.get("""max_length"""    ,       self.model.config.max_length			)

						A__							:    Optional[int]        =		model_inputs["""input_ids"""].shape[1]
						if max_length - minimum_tokens < n:
							logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"""			)
							A__							:    Dict        =		max_length - minimum_tokens
							A__							:    Optional[int]        =		model_inputs["""input_ids"""][:, -trim:]
							if "attention_mask" in model_inputs:
								A__							:    str        =		model_inputs["""attention_mask"""][:, -trim:]
						A__							:    List[str]        =		model_inputs.pop("""conversation"""			)
						A__							:    Dict        =		max_length
						A__							:    str        =		self.model.generate(**A__    ,       **A__			)
						if self.model.config.is_encoder_decoder:
							A__							:    Union[str, Any]        =		1
						else:
							A__							:    Optional[Any]        =		n
						return {"output_ids": output_ids[:, start_position:], "conversation": conversation}






					def   __A			( self    ,       A__    ,       A__=True			):
						A__							:    Dict        =		model_outputs["""output_ids"""]
						A__							:    str        =		self.tokenizer.decode(
						    output_ids[0]    ,       skip_special_tokens=A__    ,       clean_up_tokenization_spaces=A__    ,       )
						A__							:    Optional[int]        =		model_outputs["""conversation"""]
						conversation.mark_processed()
						conversation.append_response(A__			)
						return conversation






					def   __A			( self    ,       A__			):
						A__							:    str        =		self.tokenizer.eos_token_id
						A__							:    Tuple        =		[]
						for is_user, text in conversation.iter_texts():
							if eos_token_id is not None:
								input_ids.extend(self.tokenizer.encode(A__    ,       add_special_tokens=A__			) + [eos_token_id]			)
							else:
								input_ids.extend(self.tokenizer.encode(A__    ,       add_special_tokens=A__			)			)

						if len(A__			) > self.tokenizer.model_max_length:
							A__							:    str        =		input_ids[-self.tokenizer.model_max_length :]
						return input_ids



