

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a_      =     'true'

def 			lowerCamelCase__			(       _a				,      _a=82				,      _a=16):
	set_seed(42)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							RegressionModel()
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							deepcopy(_a)
	SCREAMING_SNAKE_CASE :   Any					  =							RegressionDataset(length=_a)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							DataLoader(_a				,      batch_size=_a)
	model.to(accelerator.device)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							accelerator.prepare(_a				,      _a)
	return model, ddp_model, dataloader

def 			lowerCamelCase__			(       _a				,      _a=False):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased")
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							load_dataset("glue"				,      "mrpc"				,      split="validation")

	def tokenize_function(_a):
		SCREAMING_SNAKE_CASE :   str					  =							tokenizer(examples["sentence1"]				,      examples["sentence2"]				,      truncation=_a				,      max_length=_a)
		return outputs

	with accelerator.main_process_first():
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							dataset.map(
		    _a				,      batched=_a				,      remove_columns=["idx", "sentence1", "sentence2"]				,      )

	SCREAMING_SNAKE_CASE :   Any					  =							tokenized_datasets.rename_column("label"				,      "labels")

	def collate_fn(_a):
		if use_longest:
			return tokenizer.pad(_a				,      padding="longest"				,      return_tensors="pt")
		return tokenizer.pad(_a				,      padding="max_length"				,      max_length=128				,      return_tensors="pt")

	return DataLoader(_a				,      shuffle=_a				,      collate_fn=_a				,      batch_size=16)

def 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   Any					  =							Accelerator(dispatch_batches=_a				,      split_batches=_a)
	SCREAMING_SNAKE_CASE :   str					  =							get_dataloader(_a				,      not dispatch_batches)
	SCREAMING_SNAKE_CASE :   Any					  =							AutoModelForSequenceClassification.from_pretrained(
	    "hf-internal-testing/mrpc-bert-base-cased"				,      return_dict=_a)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							accelerator.prepare(_a				,      _a)
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   int					  =							[]
	for batch in dataloader:
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							batch.values()
		with torch.no_grad():
			SCREAMING_SNAKE_CASE :   str					  =							model(_a)
			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Any					  =							accelerator.gather_for_metrics((logit, target))
			logits_and_targets.append((logit, target))
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							[], []
	for logit, targ in logits_and_targets:
		logits.append(_a)
		targs.append(_a)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[str]					  =							torch.cat(_a), torch.cat(_a)
	return logits, targs

def 			lowerCamelCase__			(       _a				,      _a=82				,      _a=False				,      _a=False				,      _a=16):
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							get_basic_setup(_a				,      _a				,      _a)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							generate_predictions(_a				,      _a				,      _a)
	assert (
	    len(_a) == num_samples
	), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_a)}"

def 			lowerCamelCase__			(       _a = False				,      _a = False):
	SCREAMING_SNAKE_CASE :   List[str]					  =							evaluate.load("glue"				,      "mrpc")
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							get_mrpc_setup(_a				,      _a)
	# First do baseline
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							setup["no"]
	model.to(_a)
	model.eval()
	for batch in dataloader:
		batch.to(_a)
		with torch.inference_mode():
			SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model(**_a)
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							outputs.logits.argmax(dim=-1)
		metric.add_batch(predictions=_a				,      references=batch["labels"])
	SCREAMING_SNAKE_CASE :   Dict					  =							metric.compute()

	# Then do distributed
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[Any]					  =							setup["ddp"]
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			SCREAMING_SNAKE_CASE :   Optional[int]					  =							model(**_a)
		SCREAMING_SNAKE_CASE :   Tuple					  =							outputs.logits.argmax(dim=-1)
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							batch["labels"]
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							accelerator.gather_for_metrics((preds, references))
		metric.add_batch(predictions=_a				,      references=_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]				,      distributed[key]), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"

def 			lowerCamelCase__			(       ):
	SCREAMING_SNAKE_CASE :   Tuple					  =							Accelerator(split_batches=_a				,      dispatch_batches=_a)
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print("**Testing gather_for_metrics**")
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`")
				test_mrpc(_a				,      _a)
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print("**Test torch metrics**")
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			SCREAMING_SNAKE_CASE :   Tuple					  =							Accelerator(split_batches=_a				,      dispatch_batches=_a)
			if accelerator.is_local_main_process:
				print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99")
			test_torch_metrics(_a				,      99)
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print("**Test last batch is not dropped when perfectly divisible**")
	SCREAMING_SNAKE_CASE :   str					  =							Accelerator()
	test_torch_metrics(_a				,      512)
	accelerator.state._reset_state()

def 			lowerCamelCase__			(       _a):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
			main()

import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation


def      UpperCamelCase   (       __lowerCamelCase	:					int = 8       ):
       snake_case			:  int     							= ascii_letters + digits + punctuation
       return "".join(secrets.choice(__lowerCamelCase       ) for _ in range(__lowerCamelCase       )       )


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int       ):
       # Password Generator = full boot with random_number, random_letters, and
       # random_character FUNCTIONS
       # Put your code here...
       i -= len(__lowerCamelCase       )
       snake_case			:  Any     							= i // 3
       snake_case			:  Optional[int]     							= i % 3
       # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
       #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
       snake_case			:  Tuple     							= (
           chars_incl
           + random(__lowerCamelCase							,   quotient + remainder       )
           + random(__lowerCamelCase							,   __lowerCamelCase       )
           + random(__lowerCamelCase							,   __lowerCamelCase       )
       )
       snake_case			:  Optional[Any]     							= list(__lowerCamelCase       )
       shuffle(__lowerCamelCase       )
       return "".join(__lowerCamelCase       )

       # random is a generalised function for letters, characters and numbers


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int       ):
       return "".join(secrets.choice(__lowerCamelCase       ) for _ in range(__lowerCamelCase       )       )


def      UpperCamelCase   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					str       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[int]							,   __lowerCamelCase	:					List[str]       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					Any							,   __lowerCamelCase	:					List[Any]       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int = 8       ):

       if len(__lowerCamelCase       ) < min_length:
              # Your Password must be at least 8 characters long
              return False

       snake_case			:  Dict     							= any(char in ascii_uppercase for char in password       )
       snake_case			:  Optional[int]     							= any(char in ascii_lowercase for char in password       )
       snake_case			:  str     							= any(char in digits for char in password       )
       snake_case			:  str     							= any(char in punctuation for char in password       )

       return upper and lower and num and spec_char
       # Passwords should contain UPPERCASE, lowerase
       # numbers, and special characters


def      UpperCamelCase   (       ):
       snake_case			:  int     							= int(input("Please indicate the max length of your password: "       ).strip()       )
       snake_case			:  Union[str, Any]     							= input(
           "Please indicate the characters that must be in your password: "       ).strip()
       print("Password generated:"							,   password_generator(__lowerCamelCase       )       )
       print(
           "Alternative Password generated:"							,   alternative_password_generator(__lowerCamelCase							,   __lowerCamelCase       )							,   )
       print("[If you are thinking of using this passsword, You better save it.]"       )


if __name__ == "__main__":
   main()





