






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


__UpperCAmelCase								=						"true"




def      lowercase__      (     __snake_case    :							Optional[Any]     ,       __snake_case    :							Dict=82     ,       __snake_case    :							Dict=16  ):


					'''simple docstring'''


					set_seed(42  )
					UpperCAmelCase_    :      Optional[Any]            =						RegressionModel()
					UpperCAmelCase_    :      int            =						deepcopy(_snake_case  )
					UpperCAmelCase_    :      Optional[int]            =						RegressionDataset(length=_snake_case  )
					UpperCAmelCase_    :      Union[str, Any]            =						DataLoader(_snake_case     ,       batch_size=_snake_case  )
					model.to(accelerator.device  )
					UpperCAmelCase_       ,  UpperCAmelCase_    :      List[Any]            =						accelerator.prepare(_snake_case     ,       _snake_case  )
					return model, ddp_model, dataloader




def      lowercase__      (     __snake_case    :							Accelerator     ,       __snake_case    :							List[Any]=False  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Any            =						AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'  )
					UpperCAmelCase_    :      Union[str, Any]            =						load_dataset('glue'     ,       'mrpc'     ,       split='validation'  )

					def tokenize_function(__snake_case    :							Dict  ):
										UpperCAmelCase_    :      Optional[int]            =						tokenizer(examples['sentence1']     ,       examples['sentence2']     ,       truncation=_snake_case     ,       max_length=_snake_case  )
										return outputs

					with accelerator.main_process_first():
										UpperCAmelCase_    :      Dict            =						dataset.map(
										    _snake_case     ,       batched=_snake_case     ,       remove_columns=['idx', 'sentence1', 'sentence2']     ,       )

					UpperCAmelCase_    :      Tuple            =						tokenized_datasets.rename_column('label'     ,       'labels'  )

					def collate_fn(__snake_case    :							Union[str, Any]  ):
										if use_longest:
															return tokenizer.pad(_snake_case     ,       padding='longest'     ,       return_tensors='pt'  )
										return tokenizer.pad(_snake_case     ,       padding='max_length'     ,       max_length=128     ,       return_tensors='pt'  )

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




def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							Optional[int]  ):


					'''simple docstring'''


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




def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							List[str]     ,       __snake_case    :							Optional[int]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      str            =						[]
					for batch in dataloader:
										UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						batch.values()
										with torch.no_grad():
															UpperCAmelCase_    :      Union[str, Any]            =						model(_snake_case  )
															UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						accelerator.gather_for_metrics((logit, target)  )
															logits_and_targets.append((logit, target)  )
					UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						[], []
					for logit, targ in logits_and_targets:
										logits.append(_snake_case  )
										targs.append(_snake_case  )
					UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						torch.cat(_snake_case  ), torch.cat(_snake_case  )
					return logits, targs




def      lowercase__      (     __snake_case    :							Accelerator     ,       __snake_case    :							int=82     ,       __snake_case    :							Tuple=False     ,       __snake_case    :							int=False     ,       __snake_case    :							List[str]=16  ):


					'''simple docstring'''


					UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						get_basic_setup(_snake_case     ,       _snake_case     ,       _snake_case  )
					UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						generate_predictions(_snake_case     ,       _snake_case     ,       _snake_case  )
					assert (
					    len(_snake_case  ) == num_samples
					), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_snake_case  )}"




def      lowercase__      (     __snake_case    :							bool = False     ,       __snake_case    :							bool = False  ):


					'''simple docstring'''


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

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


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[int]            =						Accelerator(split_batches=_snake_case     ,       dispatch_batches=_snake_case  )
					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(_snake_case     ,       _snake_case  )
																				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]:
															UpperCAmelCase_    :      Union[str, Any]            =						Accelerator(split_batches=_snake_case     ,       dispatch_batches=_snake_case  )
															if accelerator.is_local_main_process:
																				print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"  )
															test_torch_metrics(_snake_case     ,       99  )
															accelerator.state._reset_state()
					if accelerator.is_local_main_process:
										print('**Test last batch is not dropped when perfectly divisible**'  )
					UpperCAmelCase_    :      Optional[Any]            =						Accelerator()
					test_torch_metrics(_snake_case     ,       512  )
					accelerator.state._reset_state()




def      lowercase__      (     __snake_case    :							Any  ):


					'''simple docstring'''


					main()


if __name__ == "__main__":
		main()









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			__a        =[
			    'decoder.version',
			    'decoder.output_projection.weight',
			    '_float_tensor',
			    'decoder.embed_positions._float_tensor',
			]
			for k in ignore_keys:
						state_dict.pop(_snake_case       ,     _snake_case	)






def        UpperCamelCase_(							_snake_case   :  Any	):




			"""simple docstring"""






			__a      ,    __a        =emb.weight.shape
			__a        =nn.Linear(_snake_case       ,     _snake_case       ,     bias=_snake_case	)
			__a        =emb.weight.data
			return lin_layer






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)
			__a        =Namespace(**checkpoint['cfg']['model']	)
			__a        =checkpoint['model']
			remove_ignore_keys_(_snake_case	)
			__a        =state_dict['decoder.embed_tokens.weight'].shape[0]

			__a        ={key.replace('decoder'       ,     'model'	): val for key, val in state_dict.items()}

			__a        =XGLMConfig(
			    vocab_size=_snake_case       ,     max_position_embeddings=args.max_target_positions       ,     num_layers=args.decoder_layers       ,     attention_heads=args.decoder_attention_heads       ,     ffn_dim=args.decoder_ffn_embed_dim       ,     d_model=args.decoder_embed_dim       ,     layerdrop=args.decoder_layerdrop       ,     dropout=args.dropout       ,     attention_dropout=args.attention_dropout       ,     activation_dropout=args.activation_dropout       ,     activation_function='gelu'       ,     scale_embedding=not args.no_scale_embedding       ,     tie_word_embeddings=args.share_decoder_input_output_embed       ,     )

			__a        =XGLMForCausalLM(_snake_case	)
			__a        =model.load_state_dict(_snake_case       ,     strict=_snake_case	)
			print(_snake_case	)
			__a        =make_linear_from_emb(model.model.embed_tokens	)

			return model


if __name__ == "__main__":
						_lowerCAmelCase					:			str  				=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
						parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						_lowerCAmelCase					:			str  				=			parser.parse_args()
						_lowerCAmelCase					:			Union[str, Any]  				=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)






