




"""simple docstring"""



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       lowerCamelCase					(a_						:Tuple				,				a_						:Dict				,				a_						:str				,				a_						:int)     ->							Optional[Any]:
       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       lowerCamelCase					(a_						:Dict				,				a_						:str				,				a_						:Optional[int]				,				a_						:Optional[Any]				,				a_						:List[str]=True)     ->							Union[str, Any]:
       model.train()
       lowercase		:Optional[Any]       							=				model(a_)
       lowercase		:List[str]       							=				F.mse_loss(a_				,				target.to(output.device))
       if not do_backward:
              loss /= accelerator.gradient_accumulation_steps
              loss.backward()
       else:
              accelerator.backward(a_)






def       lowerCamelCase					(a_						:Optional[Any]				,				a_						:Optional[Any]=False)     ->							Dict:
       set_seed(42)
       lowercase		:Union[str, Any]       							=				RegressionModel()
       lowercase		:List[Any]       							=				deepcopy(a_)
       lowercase		:Union[str, Any]       							=				RegressionDataset(length=80)
       lowercase		:List[str]       							=				DataLoader(a_				,				batch_size=16)
       model.to(accelerator.device)
       if sched:
              lowercase		:Optional[Any]       							=				AdamW(params=model.parameters()				,				lr=1E-3)
              lowercase		:List[Any]       							=				AdamW(params=ddp_model.parameters()				,				lr=1E-3)
              lowercase		:List[str]       							=				LambdaLR(a_				,				lr_lambda=lambda a_: epoch**0.65)
              lowercase		:Any       							=				LambdaLR(a_				,				lr_lambda=lambda a_: epoch**0.65)
       # Make a copy of `model`
       if sched:
              lowercase		:Tuple       							=				accelerator.prepare(a_				,				a_				,				a_				,				a_)
       else:
              lowercase		:List[str]       							=				accelerator.prepare(a_				,				a_)
       if sched:
              return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
       return model, ddp_model, dataloader






def       lowerCamelCase					(a_						:Union[str, Any])     ->							int:
       # Test when on a single CPU or GPU that the context manager does nothing
       lowercase		:int       							=				get_training_setup(a_)
       # Use a single batch
       lowercase		:str       							=				next(iter(a_)).values()
       for iteration in range(3):
              # Gather the distributed inputs and targs for the base model
              lowercase		:List[Any]       							=				accelerator.gather((ddp_input, ddp_target))
              lowercase		:int       							=				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(1337 + iteration)
              lowercase		:Dict       							=				ddp_input[torch.randperm(len(a_))]






def       lowerCamelCase					(a_						:Optional[Any])     ->							str:
       # Test on distributed setup that context manager behaves properly
       lowercase		:Any       							=				get_training_setup(a_)
       # Use a single batch
       lowercase		:Optional[int]       							=				next(iter(a_)).values()
       for iteration in range(3):
              # Gather the distributed inputs and targs for the base model
              lowercase		:Union[str, Any]       							=				accelerator.gather((ddp_input, ddp_target))
              lowercase		:Any       							=				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(1337 + iteration)
              lowercase		:Tuple       							=				ddp_input[torch.randperm(len(a_))]






def       lowerCamelCase					(a_						:Union[str, Any]=False				,				a_						:List[str]=False)     ->							List[Any]:
       lowercase		:List[Any]       							=				Accelerator(
           split_batches=a_				,				dispatch_batches=a_				,				gradient_accumulation_steps=2)
       # Test that context manager behaves properly
       lowercase		:Dict       							=				get_training_setup(a_)
       for iteration, batch in enumerate(a_):
              lowercase		:Dict       							=				batch.values()
              # Gather the distributed inputs and targs for the base model
              lowercase		:Any       							=				accelerator.gather((ddp_input, ddp_target))
              lowercase		:Optional[int]       							=				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(1337 + iteration)
              lowercase		:List[str]       							=				ddp_input[torch.randperm(len(a_))]
       GradientState._reset_state()






def       lowerCamelCase					(a_						:Union[str, Any]=False				,				a_						:List[Any]=False)     ->							Any:
       lowercase		:int       							=				Accelerator(
           split_batches=a_				,				dispatch_batches=a_				,				gradient_accumulation_steps=2)
       # Test that context manager behaves properly
       lowercase		:Optional[int]       							=				get_training_setup(a_				,				a_)
       for iteration, batch in enumerate(a_):
              lowercase		:Union[str, Any]       							=				batch.values()
              # Gather the distributed inputs and targs for the base model
              lowercase		:List[str]       							=				accelerator.gather((ddp_input, ddp_target))
              lowercase		:Optional[Any]       							=				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"""
              lowercase		:Optional[int]       							=				(((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(1337 + iteration)
       GradientState._reset_state()






def       lowerCamelCase					()     ->							List[str]:
       lowercase		:Union[str, Any]       							=				Accelerator()

       lowercase		:Any       							=				RegressionDataset(length=80)
       lowercase		:Optional[int]       							=				DataLoader(a_				,				batch_size=16)
       lowercase		:Optional[int]       							=				RegressionDataset(length=96)
       lowercase		:List[Any]       							=				DataLoader(a_				,				batch_size=16)
       lowercase		:List[str]       							=				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       lowerCamelCase					()     ->							Union[str, Any]:
       lowercase		:Union[str, Any]       							=				Accelerator()
       lowercase		:int       							=				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       lowerCamelCase					(a_						:Dict)     ->							Optional[int]:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
 main()





"""simple docstring"""



import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCAmelCase      		=			[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]






def       lowerCamelCase					(a_						:Union[str, Any])     ->							Optional[Any]:
       for pegasus_name, hf_name in PATTERNS:
              lowercase		:List[Any]       							=				k.replace(a_				,				a_)
       return k






def       lowerCamelCase					(a_						:dict				,				a_						:dict)     ->							PegasusForConditionalGeneration:
       lowercase		:Union[str, Any]       							=				DEFAULTS.copy()
       cfg_kwargs.update(a_)
       lowercase		:Optional[Any]       							=				PegasusConfig(**a_)
       lowercase		:int       							=				PegasusForConditionalGeneration(a_)
       lowercase		:Optional[int]       							=				torch_model.model.state_dict()
       lowercase		:Tuple       							=				{}
       for k, v in tf_weights.items():
              lowercase		:Dict       							=				rename_state_dict_key(a_)
              if new_k not in sd:
                     raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""")

              if "dense" in k or "proj" in new_k:
                     lowercase		:Union[str, Any]       							=				v.T
              lowercase		:List[str]       							=				torch.tensor(a_				,				dtype=sd[new_k].dtype)
              assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
       # make sure embedding.padding_idx is respected
       lowercase		:Optional[int]       							=				torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1])
       lowercase		:Union[str, Any]       							=				mapping['''shared.weight''']
       lowercase		:Dict       							=				mapping['''shared.weight''']
       lowercase		:int       							=				{k: torch.zeros_like(a_) for k, v in sd.items() if k.endswith('''bias''') and k not in mapping}
       mapping.update(**a_)
       lowercase							,  lowercase		:Tuple       							=				torch_model.model.load_state_dict(a_				,				strict=a_)
       lowercase		:Dict       							=				[
           k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
       ]
       assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
       assert extra == [], F"""no matches found for the following tf keys {extra}"""
       return torch_model






def       lowerCamelCase					(a_						:Union[str, Any]="./ckpt/aeslc/model.ckpt-32000")     ->							Dict:
       lowercase		:Any       							=				tf.train.list_variables(a_)
       lowercase		:str       							=				{}
       lowercase		:Optional[Any]       							=				['''Adafactor''', '''global_step''']
       for name, shape in tqdm(a_				,				desc='''converting tf checkpoint to dict'''):
              lowercase		:str       							=				any(pat in name for pat in ignore_name)
              if skip_key:
                     continue
              lowercase		:str       							=				tf.train.load_variable(a_				,				a_)
              lowercase		:Dict       							=				array
       return tf_weights






def       lowerCamelCase					(a_						:str				,				a_						:str)     ->							List[str]:
       # save tokenizer first
       lowercase		:List[str]       							=				Path(a_).parent.name
       lowercase		:List[Any]       							=				task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
       lowercase		:List[Any]       							=				PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''				,				model_max_length=a_)
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(a_)

       # convert model
       lowercase		:Any       							=				get_tf_weights_as_numpy(a_)
       lowercase		:str       							=				task_specific_params[F"""summarization_{dataset}"""]
       if dataset == "large":
              lowercase		:int       							=				task_specific_params
       lowercase		:List[Any]       							=				convert_pegasus(a_				,				a_)
       torch_model.save_pretrained(a_)
       lowercase		:Union[str, Any]       							=				torch_model.state_dict()
       sd.pop('''model.decoder.embed_positions.weight''')
       sd.pop('''model.encoder.embed_positions.weight''')
       torch.save(a_				,				Path(a_) / '''pytorch_model.bin''')


if __name__ == "__main__":
 UpperCAmelCase      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
 parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
 UpperCAmelCase      		=			parser.parse_args()
 if args.save_dir is None:
  UpperCAmelCase      		=			Path(args.tf_ckpt_path).parent.name
  UpperCAmelCase      		=			os.path.join('''pegasus''', dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)

