
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


snake_case_      :					Tuple				      =     logging.getLogger(__name__)





@dataclass
class 		__lowerCamelCase     :
							lowerCamelCase__:				str      =							field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}		)
							lowerCamelCase__:				Optional[str]      =							field(
							    default=lowercase							, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}		)
							lowerCamelCase__:				Optional[str]      =							field(
							    default=lowercase							, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}		)
							lowerCamelCase__:				bool      =							field(default=lowercase							, metadata={'''help''': '''Set this flag to use fast tokenization.'''}		)
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							lowerCamelCase__:				Optional[str]      =							field(
							    default=lowercase							, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							, )



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					Tuple 						=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)

	if len(sys.argv				) == 2 and sys.argv[1].endswith(".json"				):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
	else:
		UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     parser.parse_args_into_dataclasses()

	if (
	    os.path.exists(training_args.output_dir				)
	    and os.listdir(training_args.output_dir				)
	    and training_args.do_train
	    and not training_args.overwrite_output_dir
	):
		raise ValueError(
		    F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
		    " --overwrite_output_dir to overcome."				)

	# Setup logging
	logging.basicConfig(
	    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,					datefmt="%m/%d/%Y %H:%M:%S"				,					level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				,					)
	logger.warning(
	    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"				,					training_args.local_rank				,					training_args.device				,					training_args.n_gpu				,					bool(training_args.local_rank != -1				)				,					training_args.fpaa				,					)
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank				):
		transformers.utils.logging.set_verbosity_info()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
	logger.info("Training/evaluation parameters %s"				,					snake_case_				)

	# Prepare Question-Answering task
	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	UpperCAmelCase:					str 						=     AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path				,					cache_dir=model_args.cache_dir				,					)
	UpperCAmelCase:					Optional[Any] 						=     AutoTokenizer.from_pretrained(
	    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,					cache_dir=model_args.cache_dir				,					use_fast=snake_case_				,					)
	UpperCAmelCase:					Tuple 						=     AutoModelForQuestionAnswering.from_pretrained(
	    model_args.model_name_or_path				,					from_tf=bool(".ckpt" in model_args.model_name_or_path				)				,					config=snake_case_				,					cache_dir=model_args.cache_dir				,					)

	# Get datasets
	UpperCAmelCase:					Tuple 						=     hasattr(model.config				,					"lang2id"				)
	UpperCAmelCase:					Tuple 						=     (
	    SquadDataset(
	        snake_case_				,					tokenizer=snake_case_				,					is_language_sensitive=snake_case_				,					cache_dir=model_args.cache_dir				)
	    if training_args.do_train
	    else None
	)
	UpperCAmelCase:					int 						=     (
	    SquadDataset(
	        snake_case_				,					tokenizer=snake_case_				,					mode="dev"				,					is_language_sensitive=snake_case_				,					cache_dir=model_args.cache_dir				,					)
	    if training_args.do_eval
	    else None
	)

	# Data collator
	UpperCAmelCase:					int 						=     DataCollatorWithPadding(snake_case_				,					pad_to_multiple_of=8				) if training_args.fpaa else None

	# Initialize our Trainer
	UpperCAmelCase:					int 						=     Trainer(
	    model=snake_case_				,					args=snake_case_				,					train_dataset=snake_case_				,					eval_dataset=snake_case_				,					data_collator=snake_case_				,					)

	# Training
	if training_args.do_train:
		trainer.train(
		    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
		trainer.save_model()
		# For convenience, we also re-save the tokenizer to the same directory,
		# so that you can share your model easily on huggingface.co/models =)
		if trainer.is_world_master():
			tokenizer.save_pretrained(training_args.output_dir				)



def      __UpperCAmelCase    (  snake_case_							:		List[str]				):



	'''simple docstring'''



	main()


if __name__ == "__main__":
	main()


import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin





class 		__lowerCamelCase     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				int      =							UNetaDModel
							lowerCamelCase__:				Any      =							'''sample'''



							@property
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     4
								UpperCAmelCase:					Optional[Any] 						=     1_4
								UpperCAmelCase:					str 						=     1_6

								UpperCAmelCase:					Optional[int] 						=     floats_tensor((batch_size, num_features, seq_len)							).to(__snake_case							)
								UpperCAmelCase:					Any 						=     torch.tensor([1_0] * batch_size							).to(__snake_case							)

								return {"sample": noise, "timestep": time_step}



							@property
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								return (4, 1_4, 1_6)



							@property
							def     A__ (     self							)						->     Union[str, Any]:






								"""simple docstring"""



								return (4, 1_4, 1_6)



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								pass



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								super().test_determinism()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								super().test_outputs_equivalence()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								super().test_from_save_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     str:






								"""simple docstring"""



								super().test_from_save_pretrained_variant()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								super().test_model_from_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								super().test_output()



							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {
								    "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
								    "in_channels": 1_4,
								    "out_channels": 1_4,
								    "time_embedding_type": "positional",
								    "use_timestep_embedding": True,
								    "flip_sin_to_cos": False,
								    "freq_shift": 1.0,
								    "out_block_type": "OutConv1DBlock",
								    "mid_block_type": "MidResTemporalBlock1D",
								    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
								    "up_block_types": ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D"),
								    "act_fn": "mish",
								}
								UpperCAmelCase:					Tuple 						=     self.dummy_input
								return init_dict, inputs_dict



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="unet"							)
								self.assertIsNotNone(__snake_case							)
								self.assertEqual(len(loading_info["missing_keys"]							)	,				0							)

								model.to(__snake_case							)
								UpperCAmelCase:					Tuple 						=     model(**self.dummy_input							)

								assert image is not None, "Make sure output is not None"



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     UNetaDModel.from_pretrained("bglick13/hopper-medium-v2-value-function-hor32"	,				subfolder="unet"							)
								torch.manual_seed(0							)
								if torch.cuda.is_available():
									torch.cuda.manual_seed_all(0							)

								UpperCAmelCase:					int 						=     model.config.in_channels
								UpperCAmelCase:					List[str] 						=     1_6
								UpperCAmelCase:					Union[str, Any] 						=     torch.randn((1, seq_len, num_features)							).permute(
								    0	,				2	,				1							)  # match original, we can update values and remove
								UpperCAmelCase:					List[str] 						=     torch.full((num_features,)	,				0							)

								with torch.no_grad():
									UpperCAmelCase:					Union[str, Any] 						=     model(__snake_case	,				__snake_case							).sample.permute(0	,				2	,				1							)

								UpperCAmelCase:					str 						=     output[0, -3:, -3:].flatten()
								# fmt: off
								UpperCAmelCase:					Dict 						=     torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]							)
								# fmt: on
								self.assertTrue(torch.allclose(__snake_case	,				__snake_case	,				rtol=1e-3							)							)



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								pass




							@slow
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     "harmonai/maestro-150k"
								UpperCAmelCase:					Optional[Any] 						=     UNetaDModel.from_pretrained(__snake_case	,				subfolder="unet"							)
								model.to(__snake_case							)

								UpperCAmelCase:					Any 						=     6_5_5_3_6
								UpperCAmelCase:					str 						=     torch.sin(torch.arange(__snake_case							)[None, None, :].repeat(1	,				2	,				1							)							).to(__snake_case							)
								UpperCAmelCase:					Dict 						=     torch.tensor([1]							).to(__snake_case							)

								with torch.no_grad():
									UpperCAmelCase:					Optional[Any] 						=     model(__snake_case	,				__snake_case							).sample

								UpperCAmelCase:					List[Any] 						=     output.abs().sum()
								UpperCAmelCase:					Dict 						=     output.abs().max()

								assert (output_sum - 2_24.08_96).abs() < 0.5
								assert (output_max - 0.06_07).abs() < 4e-4





class 		__lowerCamelCase     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				Dict      =							UNetaDModel
							lowerCamelCase__:				int      =							'''sample'''



							@property
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     4
								UpperCAmelCase:					int 						=     1_4
								UpperCAmelCase:					Optional[int] 						=     1_6

								UpperCAmelCase:					List[str] 						=     floats_tensor((batch_size, num_features, seq_len)							).to(__snake_case							)
								UpperCAmelCase:					Any 						=     torch.tensor([1_0] * batch_size							).to(__snake_case							)

								return {"sample": noise, "timestep": time_step}



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






								"""simple docstring"""



								return (4, 1_4, 1_6)



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






								"""simple docstring"""



								return (4, 1_4, 1)



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								super().test_determinism()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								super().test_outputs_equivalence()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								super().test_from_save_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								super().test_from_save_pretrained_variant()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								super().test_model_from_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     str:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Optional[int] 						=     self.prepare_init_args_and_inputs_for_common()
								UpperCAmelCase:					Tuple 						=     self.model_class(**__snake_case							)
								model.to(__snake_case							)
								model.eval()

								with torch.no_grad():
									UpperCAmelCase:					Union[str, Any] 						=     model(**__snake_case							)

									if isinstance(__snake_case	,				__snake_case							):
										UpperCAmelCase:					List[str] 						=     output.sample

								self.assertIsNotNone(__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     torch.Size((inputs_dict["sample"].shape[0], 1)							)
								self.assertEqual(output.shape	,				__snake_case	,				"Input and output shapes do not match"							)



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {
								    "in_channels": 1_4,
								    "out_channels": 1_4,
								    "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
								    "up_block_types": [],
								    "out_block_type": "ValueFunction",
								    "mid_block_type": "ValueFunctionMidBlock1D",
								    "block_out_channels": [3_2, 6_4, 1_2_8, 2_5_6],
								    "layers_per_block": 1,
								    "downsample_each_block": True,
								    "use_timestep_embedding": True,
								    "freq_shift": 1.0,
								    "flip_sin_to_cos": False,
								    "time_embedding_type": "positional",
								    "act_fn": "mish",
								}
								UpperCAmelCase:					Union[str, Any] 						=     self.dummy_input
								return init_dict, inputs_dict



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Optional[Any] 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="value_function"							)
								self.assertIsNotNone(__snake_case							)
								self.assertEqual(len(vf_loading_info["missing_keys"]							)	,				0							)

								value_function.to(__snake_case							)
								UpperCAmelCase:					Union[str, Any] 						=     value_function(**self.dummy_input							)

								assert image is not None, "Make sure output is not None"



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="value_function"							)
								torch.manual_seed(0							)
								if torch.cuda.is_available():
									torch.cuda.manual_seed_all(0							)

								UpperCAmelCase:					str 						=     value_function.config.in_channels
								UpperCAmelCase:					int 						=     1_4
								UpperCAmelCase:					int 						=     torch.randn((1, seq_len, num_features)							).permute(
								    0	,				2	,				1							)  # match original, we can update values and remove
								UpperCAmelCase:					Union[str, Any] 						=     torch.full((num_features,)	,				0							)

								with torch.no_grad():
									UpperCAmelCase:					Dict 						=     value_function(__snake_case	,				__snake_case							).sample

								# fmt: off
								UpperCAmelCase:					Tuple 						=     torch.tensor([1_65.25] * seq_len							)
								# fmt: on
								self.assertTrue(torch.allclose(__snake_case	,				__snake_case	,				rtol=1e-3							)							)




							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								pass
