
"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser



@dataclass
class __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				field(
			    metadata={"help": "The output directory where the model will be written."}     ,				)
			snake_case	      =				field(
			    metadata={
			        "help": (
			            "The encoder model checkpoint for weights initialization."
			            "Don't set if you want to train an encoder model from scratch."
			        )
			    }     ,				)
			snake_case	      =				field(
			    metadata={
			        "help": (
			            "The decoder model checkpoint for weights initialization."
			            "Don't set if you want to train a decoder model from scratch."
			        )
			    }     ,				)
			snake_case	      =				field(
			    default=_lowercase     ,				metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}      )
			snake_case	      =				field(
			    default=_lowercase     ,				metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}      )





def 							A_     ():

					'''simple docstring'''





					A_				 =						HfArgumentParser((ModelArguments,)				)
					((A_) ,    )				 =						parser.parse_args_into_dataclasses()

					# Load pretrained model and tokenizer

					# Use explicit specified encoder config
					if model_args.encoder_config_name:
										A_				 =						AutoConfig.from_pretrained(model_args.encoder_config_name				)
					# Use pretrained encoder model's config
					else:
										A_				 =						AutoConfig.from_pretrained(model_args.encoder_model_name_or_path				)

					# Use explicit specified decoder config
					if model_args.decoder_config_name:
										A_				 =						AutoConfig.from_pretrained(model_args.decoder_config_name				)
					# Use pretrained decoder model's config
					else:
										A_				 =						AutoConfig.from_pretrained(model_args.decoder_model_name_or_path				)

					# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
					A_				 =						True
					A_				 =						True

					A_				 =						FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
					    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path				,   decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path				,   encoder_config=__a				,   decoder_config=__a				,   )

					# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
					A_				 =						decoder_config.decoder_start_token_id
					A_				 =						decoder_config.pad_token_id
					if decoder_start_token_id is None:
										A_				 =						decoder_config.bos_token_id
					if pad_token_id is None:
										A_				 =						decoder_config.eos_token_id

					# This is necessary to make Flax's generate() work
					A_				 =						decoder_config.eos_token_id
					A_				 =						decoder_start_token_id
					A_				 =						pad_token_id

					A_				 =						AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path				)

					A_				 =						AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path				)
					A_				 =						tokenizer.convert_ids_to_tokens(model.config.pad_token_id				)

					model.save_pretrained(model_args.output_dir				)
					image_processor.save_pretrained(model_args.output_dir				)
					tokenizer.save_pretrained(model_args.output_dir				)


if __name__ == "__main__":
		main()






"""simple docstring"""





import os

import pytest
from attr import dataclass


UpperCamelCase_					:						str				= '''us-east-1'''  # defaults region



@dataclass
class __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				42
			snake_case	      =				"arn:aws:iam::558105141721:role/sagemaker_execution_role"
			snake_case	      =				{
			    "task_name": "mnli",
			    "per_device_train_batch_size": 16,
			    "per_device_eval_batch_size": 16,
			    "do_train": True,
			    "do_eval": True,
			    "do_predict": True,
			    "output_dir": "/opt/ml/model",
			    "overwrite_output_dir": True,
			    "max_steps": 5_00,
			    "save_steps": 55_00,
			}
			snake_case	      =				{**hyperparameters, "max_steps": 10_00}




			@property
			def      lowerCamelCase__     (				self						:					int	)      -> str:







								"""simple docstring"""
								if self.framework == "pytorch":
													return [
													    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
													    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
													    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
													]
								else:
													return [
													    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
													    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
													    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
													]




			@property
			def      lowerCamelCase__     (				self						:					Any	)      -> str:







								"""simple docstring"""
								return F'{self.framework}-transfromers-test'




			@property
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> str:







								"""simple docstring"""
								return F'./tests/sagemaker/scripts/{self.framework}'






			@property
			def      lowerCamelCase__     (				self						:					Any	)      -> str:







								"""simple docstring"""
								if self.framework == "pytorch":
													return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
								else:
													return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"





@pytest.fixture(scope="class"				)
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						SageMakerTestEnvironment(framework=request.cls.framework				)




