




from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    for param, grad_param in zip(model_a.parameters()    ,		model_b.parameters()							):
        if not param.requires_grad:
            continue
        if not did_step:
            # Grads should not be in sync
            assert (
                torch.allclose(param.grad    ,		grad_param.grad							) is False
            ), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
        else:
            # Grads should be in sync
            assert (
                torch.allclose(param.grad    ,		grad_param.grad							) is True
            ), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase=True							):
    model.train()
    snake_case__						=				model(__A							)
    snake_case__						=				F.mse_loss(__A    ,		target.to(output.device							)							)
    if not do_backward:
        loss /= accelerator.gradient_accumulation_steps
        loss.backward()
    else:
        accelerator.backward(__A							)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase=False							):
    set_seed(42							)
    snake_case__						=				RegressionModel()
    snake_case__						=				deepcopy(__A							)
    snake_case__						=				RegressionDataset(length=80							)
    snake_case__						=				DataLoader(__A    ,		batch_size=16							)
    model.to(accelerator.device							)
    if sched:
        snake_case__						=				AdamW(params=model.parameters()    ,		lr=1E-3							)
        snake_case__						=				AdamW(params=ddp_model.parameters()    ,		lr=1E-3							)
        snake_case__						=				LambdaLR(__A    ,		lr_lambda=lambda __lowerCAmelCase							: epoch**0.65							)
        snake_case__						=				LambdaLR(__A    ,		lr_lambda=lambda __lowerCAmelCase							: epoch**0.65							)
    # Make a copy of `model`
    if sched:
        snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				accelerator.prepare(__A    ,		__A    ,		__A    ,		__A							)
    else:
        snake_case__						,					snake_case__						=				accelerator.prepare(__A    ,		__A							)
    if sched:
        return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
    return model, ddp_model, dataloader





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    # Test when on a single CPU or GPU that the context manager does nothing
    snake_case__						,					snake_case__						,					snake_case__						=				get_training_setup(__A							)
    # Use a single batch
    snake_case__						,					snake_case__						=				next(iter(__A							)							).values()
    for iteration in range(3							):
        # Gather the distributed inputs and targs for the base model
        snake_case__						,					snake_case__						=				accelerator.gather((ddp_input, ddp_target)							)
        snake_case__						,					snake_case__						=				input.to(accelerator.device							), target.to(accelerator.device							)
        # Perform our initial ground truth step in non "DDP"
        step_model(__A    ,		__A    ,		__A    ,		__A							)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(__A							):
                step_model(__A    ,		__A    ,		__A    ,		__A							)
        else:
            # Sync grads
            step_model(__A    ,		__A    ,		__A    ,		__A							)

        # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
        check_model_parameters(__A    ,		__A    ,		__A    ,		__A							)
        for param, ddp_param in zip(model.parameters()    ,		ddp_model.parameters()							):
            if not param.requires_grad:
                continue
            assert torch.allclose(
                param.grad    ,		ddp_param.grad							), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1_337 + iteration							)
        snake_case__						=				ddp_input[torch.randperm(len(__A							)							)]





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    # Test on distributed setup that context manager behaves properly
    snake_case__						,					snake_case__						,					snake_case__						=				get_training_setup(__A							)
    # Use a single batch
    snake_case__						,					snake_case__						=				next(iter(__A							)							).values()
    for iteration in range(3							):
        # Gather the distributed inputs and targs for the base model
        snake_case__						,					snake_case__						=				accelerator.gather((ddp_input, ddp_target)							)
        snake_case__						,					snake_case__						=				input.to(accelerator.device							), target.to(accelerator.device							)
        # Perform our initial ground truth step in non "DDP"
        step_model(__A    ,		__A    ,		__A    ,		__A							)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(__A							):
                step_model(__A    ,		__A    ,		__A    ,		__A							)
        else:
            # Sync grads
            step_model(__A    ,		__A    ,		__A    ,		__A							)

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters()    ,		ddp_model.parameters()							):
            if not param.requires_grad:
                continue
            if iteration % 2 == 0:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad    ,		ddp_param.grad							) is False
                ), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
            else:
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad    ,		ddp_param.grad							) is True
                ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

                # Shuffle ddp_input on each iteration
        torch.manual_seed(1_337 + iteration							)
        snake_case__						=				ddp_input[torch.randperm(len(__A							)							)]





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase=False    ,		__lowerCAmelCase=False							):
    snake_case__						=				Accelerator(
        split_batches=__A    ,		dispatch_batches=__A    ,		gradient_accumulation_steps=2							)
    # Test that context manager behaves properly
    snake_case__						,					snake_case__						,					snake_case__						=				get_training_setup(__A							)
    for iteration, batch in enumerate(__A							):
        snake_case__						,					snake_case__						=				batch.values()
        # Gather the distributed inputs and targs for the base model
        snake_case__						,					snake_case__						=				accelerator.gather((ddp_input, ddp_target)							)
        snake_case__						,					snake_case__						=				input.to(accelerator.device							), target.to(accelerator.device							)
        # Perform our initial ground truth step in non "DDP"
        step_model(__A    ,		__A    ,		__A    ,		__A    ,		__A							)
        # Do "gradient accumulation" (noop)
        with accelerator.accumulate(__A							):
            step_model(__A    ,		__A    ,		__A    ,		__A							)

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters()    ,		ddp_model.parameters()							):
            if not param.requires_grad:
                continue
            if ((iteration + 1) % 2 == 0) or (iteration == len(__A							) - 1):
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad    ,		ddp_param.grad							) is True
                ), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
            else:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad    ,		ddp_param.grad							) is False
                ), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

                # Shuffle ddp_input on each iteration
        torch.manual_seed(1_337 + iteration							)
        snake_case__						=				ddp_input[torch.randperm(len(__A							)							)]
    GradientState._reset_state()





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase=False    ,		__lowerCAmelCase=False							):
    snake_case__						=				Accelerator(
        split_batches=__A    ,		dispatch_batches=__A    ,		gradient_accumulation_steps=2							)
    # Test that context manager behaves properly
    snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				get_training_setup(__A    ,		__A							)
    for iteration, batch in enumerate(__A							):
        snake_case__						,					snake_case__						=				batch.values()
        # Gather the distributed inputs and targs for the base model
        snake_case__						,					snake_case__						=				accelerator.gather((ddp_input, ddp_target)							)
        snake_case__						,					snake_case__						=				input.to(accelerator.device							), target.to(accelerator.device							)
        # Perform our initial ground truth step in non "DDP"
        model.train()
        ddp_model.train()
        step_model(__A    ,		__A    ,		__A    ,		__A    ,		__A							)
        opt.step()

        if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__A							)):
            if split_batches:
                sched.step()
            else:
                for _ in range(accelerator.num_processes							):
                    sched.step()
        opt.zero_grad()
        # Perform gradient accumulation under wrapper
        with accelerator.accumulate(__A							):
            step_model(__A    ,		__A    ,		__A    ,		__A							)
            ddp_opt.step()
            ddp_sched.step()
            ddp_opt.zero_grad()

        # Learning rates should be the same
        assert (
            opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
        ), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
        snake_case__						=				(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__A							))
        if accelerator.num_processes > 1:
            check_model_parameters(__A    ,		__A    ,		__A    ,		__A							)
        # Shuffle ddp_input on each iteration
        torch.manual_seed(1_337 + iteration							)
    GradientState._reset_state()





def 	SCREAMING_SNAKE_CASE__    (      ):
    snake_case__						=				Accelerator()

    snake_case__						=				RegressionDataset(length=80							)
    snake_case__						=				DataLoader(__A    ,		batch_size=16							)
    snake_case__						=				RegressionDataset(length=96							)
    snake_case__						=				DataLoader(__A    ,		batch_size=16							)
    snake_case__						,					snake_case__						=				accelerator.prepare(__A    ,		__A							)
    assert accelerator.gradient_state.active_dataloader is None
    for iteration, _ in enumerate(__A							):
        assert id(accelerator.gradient_state.active_dataloader							) == id(__A							)
        if iteration < len(__A							) - 1:
            assert not accelerator.gradient_state.end_of_dataloader
            if iteration == 1:
                for batch_num, _ in enumerate(__A							):
                    assert id(accelerator.gradient_state.active_dataloader							) == id(__A							)
                    if batch_num < len(__A							) - 1:
                        assert not accelerator.gradient_state.end_of_dataloader
                    else:
                        assert accelerator.gradient_state.end_of_dataloader
        else:
            assert accelerator.gradient_state.end_of_dataloader
    assert accelerator.gradient_state.active_dataloader is None





def 	SCREAMING_SNAKE_CASE__    (      ):
    snake_case__						=				Accelerator()
    snake_case__						=				accelerator.state
    if state.local_process_index == 0:
        print("**Test `accumulate` gradient accumulation with dataloader break**"							)
    test_dataloader_break()
    if state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print("**Test NOOP `no_sync` context manager**"							)
        test_noop_sync(__A							)
    if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
        if state.local_process_index == 0:
            print("**Test Distributed `no_sync` context manager**"							)
        test_distributed_sync(__A							)
    if state.distributed_type == DistributedType.MULTI_GPU:
        for split_batch in [True, False]:
            for dispatch_batches in [True, False]:
                if state.local_process_index == 0:
                    print(
                        "**Test `accumulate` gradient accumulation, "    ,		F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""    ,		)
                test_gradient_accumulation(__A    ,		__A							)

        # Currently will break on torch 2.0 +, need to investigate why
    if is_torch_version("<"    ,		"2.0"							) or state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print(
                "**Test `accumulate` gradient accumulation with optimizer and scheduler, "    ,		"`split_batches=False`, `dispatch_batches=False`**"    ,		)
        test_gradient_accumulation_with_opt_and_scheduler()
        if state.distributed_type == DistributedType.MULTI_GPU:
            for split_batch in [True, False]:
                for dispatch_batches in [True, False]:
                    if not split_batch and not dispatch_batches:
                        continue
                    if state.local_process_index == 0:
                        print(
                            "**Test `accumulate` gradient accumulation with optimizer and scheduler, "    ,		F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""    ,		)
                    test_gradient_accumulation_with_opt_and_scheduler(__A    ,		__A							)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
     main()





import argparse
import struct
import unittest




class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase ):
          snake_case__						=				data

          # Initialize hash values
          snake_case__						=				[
              0X6A_09E_667,
              0XBB_67A_E85,
              0X3C_6EF_372,
              0XA5_4FF_53A,
              0X51_0E5_27F,
              0X9B_056_88C,
              0X1F_83D_9AB,
              0X5B_E0C_D19,
          ]

          # Initialize round constants
          snake_case__						=				[
              0X42_8A2_F98,
              0X71_374_491,
              0XB5_C0F_BCF,
              0XE9_B5D_BA5,
              0X39_56C_25B,
              0X59_F11_1F1,
              0X92_3F8_2A4,
              0XAB_1C5_ED5,
              0XD8_07A_A98,
              0X12_835_B01,
              0X24_318_5BE,
              0X55_0C7_DC3,
              0X72_BE5_D74,
              0X80_DEB_1FE,
              0X9B_DC0_6A7,
              0XC1_9BF_174,
              0XE4_9B6_9C1,
              0XEF_BE4_786,
              0X0F_C19_DC6,
              0X24_0CA_1CC,
              0X2D_E92_C6F,
              0X4A_748_4AA,
              0X5C_B0A_9DC,
              0X76_F98_8DA,
              0X98_3E5_152,
              0XA8_31C_66D,
              0XB0_032_7C8,
              0XBF_597_FC7,
              0XC6_E00_BF3,
              0XD5_A79_147,
              0X06_CA6_351,
              0X14_292_967,
              0X27_B70_A85,
              0X2E_1B2_138,
              0X4D_2C6_DFC,
              0X53_380_D13,
              0X65_0A7_354,
              0X76_6A0_ABB,
              0X81_C2C_92E,
              0X92_722_C85,
              0XA2_BFE_8A1,
              0XA8_1A6_64B,
              0XC2_4B8_B70,
              0XC7_6C5_1A3,
              0XD1_92E_819,
              0XD6_990_624,
              0XF4_0E3_585,
              0X10_6AA_070,
              0X19_A4C_116,
              0X1E_376_C08,
              0X27_487_74C,
              0X34_B0B_CB5,
              0X39_1C0_CB3,
              0X4E_D8A_A4A,
              0X5B_9CC_A4F,
              0X68_2E6_FF3,
              0X74_8F8_2EE,
              0X78_A56_36F,
              0X84_C87_814,
              0X8C_C70_208,
              0X90_BEF_FFA,
              0XA4_506_CEB,
              0XBE_F9A_3F7,
              0XC6_717_8F2,
          ]

          snake_case__						=				self.preprocessing(self.data )
          self.final_hash()
      @staticmethod
      def A_			(			lowerCamelCase ):
          snake_case__						=				B"\x80" + (B"\x00" * (63 - (len(lowerCamelCase ) + 8) % 64))
          snake_case__						=				struct.pack(">Q"		,	(len(lowerCamelCase ) * 8) )
          return data + padding + big_endian_integer
      def A_			(			self ):
          # Convert into blocks of 64 bytes
          snake_case__						=				[
              self.preprocessed_data[x : x + 64]
              for x in range(0		,	len(self.preprocessed_data )		,	64 )
          ]

          for block in self.blocks:
              # Convert the given block into a list of 4 byte integers
              snake_case__						=				list(struct.unpack(">16L"		,	lowerCamelCase ) )
              # add 48 0-ed integers
              words += [0] * 48

              snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				self.hashes

              for index in range(0		,	64 ):
                  if index > 15:
                      # modify the zero-ed indexes at the end of the array
                      snake_case__						=				(
                          self.ror(words[index - 15]		,	7 )
                          ^ self.ror(words[index - 15]		,	18 )
                          ^ (words[index - 15] >> 3)
                      )
                      snake_case__						=				(
                          self.ror(words[index - 2]		,	17 )
                          ^ self.ror(words[index - 2]		,	19 )
                          ^ (words[index - 2] >> 10)
                      )

                      snake_case__						=				(
                          words[index - 16] + sa + words[index - 7] + sa
                      ) % 0X100_000_000

                  # Compression
                  snake_case__						=				self.ror(lowerCamelCase		,	6 ) ^ self.ror(lowerCamelCase		,	11 ) ^ self.ror(lowerCamelCase		,	25 )
                  snake_case__						=				(e & f) ^ ((~e & 0XFF_FFF_FFF) & g)
                  snake_case__						=				(
                      h + sa + ch + self.round_constants[index] + words[index]
                  ) % 0X100_000_000
                  snake_case__						=				self.ror(lowerCamelCase		,	2 ) ^ self.ror(lowerCamelCase		,	13 ) ^ self.ror(lowerCamelCase		,	22 )
                  snake_case__						=				(a & b) ^ (a & c) ^ (b & c)
                  snake_case__						=				(sa + maj) % 0X100_000_000

                  snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				(
                      g,
                      f,
                      e,
                      ((d + tempa) % 0X100_000_000),
                      c,
                      b,
                      a,
                      ((tempa + tempa) % 0X100_000_000),
                  )

              snake_case__						=				[a, b, c, d, e, f, g, h]

              # Modify final values
              snake_case__						=				[
                  ((element + mutated_hash_values[index]) % 0X100_000_000)
                  for index, element in enumerate(self.hashes )
              ]

          snake_case__						=				"".join([hex(lowerCamelCase )[2:].zfill(8 ) for value in self.hashes] )



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          return 0XFF_FFF_FFF & (value << (32 - rotations)) | (value >> rotations)





class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          import hashlib

          snake_case__						=				bytes("Test String"		,	"utf-8" )
          self.assertEqual(SHAaaa(lowerCamelCase ).hash		,	hashlib.shaaaa(lowerCamelCase ).hexdigest() )





def 	SCREAMING_SNAKE_CASE__    (      ):
    import doctest

    doctest.testmod()

    snake_case__						=				argparse.ArgumentParser()
    parser.add_argument(
        "-s"    ,		"--string"    ,		dest="input_string"    ,		default="Hello World!! Welcome to Cryptography"    ,		help="Hash the string"    ,		)
    parser.add_argument(
        "-f"    ,		"--file"    ,		dest="input_file"    ,		help="Hash contents of a file"							)

    snake_case__						=				parser.parse_args()

    snake_case__						=				args.input_string

    # hash input should be a bytestring
    if args.input_file:
        with open(args.input_file    ,		"rb"							) as f:
            snake_case__						=				f.read()
    else:
        snake_case__						=				bytes(__lowerCAmelCase    ,		"utf-8"							)

    print(SHAaaa(__lowerCAmelCase							).hash							)


if __name__ == "__main__":
     main()



