

from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)


if is_torch_available():
					import torch
					from torch import nn
					from torch.utils.data import Dataset

					from transformers import Trainer

					class 			a					(		UpperCAmelCase			):


											def __init__(    self  ,   A_ = 101		):







															'''simple docstring'''


															_UpperCAmelCase    :						int							=      length


											def __len__(    self		):







															'''simple docstring'''


															return self.length





											def __getitem__(    self  ,   A_		):







															'''simple docstring'''


															return i







					class 			a					:


											def __call__(    self  ,   A_		):







															'''simple docstring'''


															return {"input_ids": torch.tensor(A_		), "labels": torch.tensor(A_		)}







					class 			a					(		nn.Module			):


											def __init__(    self		):







															'''simple docstring'''


															super().__init__()
															# Add some (unused) params otherwise DDP will complain.
															_UpperCAmelCase    :						List[str]							=      nn.Linear(120  ,   80		)





											def     _UpperCAmelCase			(    self  ,   A_  ,   A_=None		):







															'''simple docstring'''


															if labels is not None:
																			return torch.tensor(0.0  ,   device=input_ids.device		), input_ids
															else:
																			return input_ids

class 			a					(		UpperCAmelCase			):


						@require_torch_neuroncore
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      f'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
										_UpperCAmelCase    :						Optional[int]							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						Optional[Any]							=      f'--output_dir {output_dir}'.split()
										_UpperCAmelCase    :						List[str]							=      ["torchrun"] + distributed_args + args
										execute_subprocess_async(A_  ,   env=self.get_env()		)
										# successful return here == success - any errors would have caused an error in the sub-call







class 			a					(		UpperCAmelCase			):


						@require_torch_multi_gpu
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      f'--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
										_UpperCAmelCase    :						Optional[Any]							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						Any							=      f'--output_dir {output_dir}'.split()
										_UpperCAmelCase    :						str							=      ["torchrun"] + distributed_args + args
										execute_subprocess_async(A_  ,   env=self.get_env()		)
										# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
					# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
					#
					# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

					SCREAMING_SNAKE_CASE_            = HfArgumentParser((TrainingArguments,))
					SCREAMING_SNAKE_CASE_            = parser.parse_args_into_dataclasses()[0]

					logger.warning(
					    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
					    F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
					)

					# Essentially, what we want to verify in the distributed case is that we get all samples back,
					# in the right order. (this is crucial for prediction for instance)
					for dataset_length in [101, 40, 7]:
										SCREAMING_SNAKE_CASE_            = DummyDataset(dataset_length)

										def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					EvalPrediction	)				->    Dict:
														_UpperCAmelCase    :						int							=      list(range(len(lowerCAmelCase	)	)	)
														_UpperCAmelCase    :						Union[str, Any]							=      p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
														if not success and training_args.local_rank == 0:
																		logger.warning(
																		    "Predictions and/or labels do not match expected results:\n  - predictions: "
																		    F'{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'	)
														return {"success": success}

										SCREAMING_SNAKE_CASE_            = Trainer(
										    model=DummyModel(),
										    args=training_args,
										    data_collator=DummyDataCollator(),
										    eval_dataset=dataset,
										    compute_metrics=compute_metrics,
										)
										SCREAMING_SNAKE_CASE_            = trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										SCREAMING_SNAKE_CASE_            = trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)

										SCREAMING_SNAKE_CASE_            = 2

										SCREAMING_SNAKE_CASE_            = trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										SCREAMING_SNAKE_CASE_            = trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)





										SCREAMING_SNAKE_CASE_            = None





import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = [
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    Tuple:
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								_UpperCAmelCase    :						Optional[int]							=      k.replace(lowerCAmelCase					, lowerCAmelCase	)

				if k.startswith("encoder"	):
								_UpperCAmelCase    :						Tuple							=      k.replace(".attn"					, ".self_attn"	)
								_UpperCAmelCase    :						str							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Optional[Any]							=      k.replace("norm2"					, "final_layer_norm"	)
				elif k.startswith("decoder"	):
								_UpperCAmelCase    :						Union[str, Any]							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Tuple							=      k.replace("norm2"					, "encoder_attn_layer_norm"	)
								_UpperCAmelCase    :						Any							=      k.replace("norm3"					, "final_layer_norm"	)
				return k

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						int							=      [
				    "model.encoder.layernorm_embedding.weight",
				    "model.encoder.layernorm_embedding.bias",
				    "model.decoder.layernorm_embedding.weight",
				    "model.decoder.layernorm_embedding.bias",
				]
				for k in keys:
								_UpperCAmelCase    :						Dict							=      sd.pop(lowerCAmelCase	)
								_UpperCAmelCase    :						str							=      k.replace("layernorm_embedding"					, "layer_norm"	)
								assert new_k not in sd
								_UpperCAmelCase    :						Dict							=      v


SCREAMING_SNAKE_CASE_            = ['START']

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						Union[str, Any]							=      torch.load(lowerCAmelCase					, map_location="cpu"	)
				_UpperCAmelCase    :						List[str]							=      model["model"]
				_UpperCAmelCase    :						List[str]							=      BlenderbotConfig.from_json_file(lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      BlenderbotForConditionalGeneration(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      m.model.state_dict().keys()
				_UpperCAmelCase    :						List[Any]							=      []
				_UpperCAmelCase    :						List[str]							=      {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

								_UpperCAmelCase    :						Tuple							=      rename_state_dict_key(lowerCAmelCase	)
								if new_k not in valid_keys:
												failures.append([k, new_k]	)
								else:
												_UpperCAmelCase    :						Tuple							=      v
				if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
								rename_layernorm_keys(lowerCAmelCase	)
				m.model.load_state_dict(lowerCAmelCase					, strict=lowerCAmelCase	)
				m.half()
				m.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
					parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
					parser.add_argument(
					    '--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


