



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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_snake_case		=  logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")

_snake_case		=  list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
_snake_case		=  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class   UpperCAmelCase_    :
							lowerCamelCase__   			=		field(
							    default='cifar10'   ,					metadata={'help': 'Name of a dataset from the datasets package'})
							lowerCamelCase__   			=		field(
							    default=__A   ,					metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'})
							lowerCamelCase__   			=		field(
							    default=__A   ,					metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'}   ,					)
							lowerCamelCase__   			=		field(default=__A   ,					metadata={'help': 'A folder containing the training data.'})
							lowerCamelCase__   			=		field(default=__A   ,					metadata={'help': 'A folder containing the validation data.'})
							lowerCamelCase__   			=		field(
							    default=0.1_5   ,					metadata={'help': 'Percent to split off of train for validation.'})
							lowerCamelCase__   			=		field(default=32   ,					metadata={'help': 'The size of the square patches to use for masking.'})
							lowerCamelCase__   			=		field(
							    default=0.6   ,					metadata={'help': 'Percentage of patches to mask.'}   ,					)
							lowerCamelCase__   			=		field(
							    default=__A   ,					metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of training examples to this '
							            'value if set.'
							        )
							    }   ,					)
							lowerCamelCase__   			=		field(
							    default=__A   ,					metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
							            'value if set.'
							        )
							    }   ,					)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    {}
														if self.train_dir is not None:
																					_lowerCAmelCase     :							List[str]    =    self.train_dir
														if self.validation_dir is not None:
																					_lowerCAmelCase     :							Dict    =    self.validation_dir
														_lowerCAmelCase     :							int    =    data_files if data_files else None



@dataclass
class   UpperCAmelCase_    :
							lowerCamelCase__   			=		field(
							    default=__A   ,					metadata={
							        'help': (
							            'The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '
							            'checkpoint identifier on the hub. '
							            'Don\'t set if you want to train a model from scratch.'
							        )
							    }   ,					)
							lowerCamelCase__   			=		field(
							    default=__A   ,					metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__A)}   ,					)
							lowerCamelCase__   			=		field(
							    default=__A   ,					metadata={'help': 'Pretrained config name or path if not the same as model_name'})
							lowerCamelCase__   			=		field(
							    default=__A   ,					metadata={
							        'help': (
							            'Override some existing default config settings when a model is trained from scratch. Example: '
							            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
							        )
							    }   ,					)
							lowerCamelCase__   			=		field(
							    default=__A   ,					metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'}   ,					)
							lowerCamelCase__   			=		field(
							    default='main'   ,					metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}   ,					)
							lowerCamelCase__   			=		field(default=__A   ,					metadata={'help': 'Name or path of preprocessor config.'})
							lowerCamelCase__   			=		field(
							    default=__A   ,					metadata={
							        'help': (
							            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
							            'with private models).'
							        )
							    }   ,					)
							lowerCamelCase__   			=		field(
							    default=__A   ,					metadata={
							        'help': (
							            'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
							        )
							    }   ,					)
							lowerCamelCase__   			=		field(
							    default=__A   ,					metadata={
							        'help': (
							            'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
							        )
							    }   ,					)
							lowerCamelCase__   			=		field(
							    default=__A   ,					metadata={'help': 'Stride to use for the encoder.'}   ,					)



class   UpperCAmelCase_    :







							def __init__(   self,			__a=192,			__a=32,			__a=4,			__a=0.6):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    input_size
														_lowerCAmelCase     :							Any    =    mask_patch_size
														_lowerCAmelCase     :							List[str]    =    model_patch_size
														_lowerCAmelCase     :							Optional[Any]    =    mask_ratio

														if self.input_size % self.mask_patch_size != 0:
																					raise ValueError("Input size must be divisible by mask patch size")
														if self.mask_patch_size % self.model_patch_size != 0:
																					raise ValueError("Mask patch size must be divisible by model patch size")

														_lowerCAmelCase     :							List[Any]    =    self.input_size // self.mask_patch_size
														_lowerCAmelCase     :							int    =    self.mask_patch_size // self.model_patch_size

														_lowerCAmelCase     :							Union[str, Any]    =    self.rand_size**2
														_lowerCAmelCase     :							int    =    int(np.ceil(self.token_count * self.mask_ratio))







							def __call__(   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    np.random.permutation(self.token_count)[: self.mask_count]
														_lowerCAmelCase     :							str    =    np.zeros(self.token_count,			dtype=__UpperCAmelCase)
														_lowerCAmelCase     :							str    =    1

														_lowerCAmelCase     :							Union[str, Any]    =    mask.reshape((self.rand_size, self.rand_size))
														_lowerCAmelCase     :							str    =    mask.repeat(self.scale,			axis=0).repeat(self.scale,			axis=1)

														return torch.tensor(mask.flatten())






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    torch.stack([example["pixel_values"] for example in examples]  )
							_lowerCAmelCase     :							Tuple    =    torch.stack([example["mask"] for example in examples]  )
							return {"pixel_values": pixel_values, "bool_masked_pos": mask}






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    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.
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
							else:
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							str    =    parser.parse_args_into_dataclasses()

							# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
							# information sent is the one passed as arguments along with your Python/PyTorch versions.
							send_example_telemetry("run_mim"				, UpperCamelCase__				, UpperCamelCase__  )

							# Setup logging
							logging.basicConfig(
							    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				, datefmt="%m/%d/%Y %H:%M:%S"				, handlers=[logging.StreamHandler(sys.stdout  )]				, )

							if training_args.should_log:
														# The default of training_args.log_level is passive, so we set log level at info here to have that default.
														transformers.utils.logging.set_verbosity_info()

							_lowerCAmelCase     :							Optional[int]    =    training_args.get_process_log_level()
							logger.setLevel(UpperCamelCase__  )
							transformers.utils.logging.set_verbosity(UpperCamelCase__  )
							transformers.utils.logging.enable_default_handler()
							transformers.utils.logging.enable_explicit_format()

							# Log on each process the small summary:
							logger.warning(
							    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
							    + F"distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"  )
							logger.info(F"Training/evaluation parameters {training_args}"  )

							# Detecting last checkpoint.
							_lowerCAmelCase     :							Dict    =    None
							if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
														_lowerCAmelCase     :							List[Any]    =    get_last_checkpoint(training_args.output_dir  )
														if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
																					raise ValueError(
																					    F"Output directory ({training_args.output_dir}) already exists and is not empty. "
																					    "Use --overwrite_output_dir to overcome."  )
														elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
																					logger.info(
																					    F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
																					    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."  )

    # Initialize our dataset.
							_lowerCAmelCase     :							Optional[Any]    =    load_dataset(
							    data_args.dataset_name				, data_args.dataset_config_name				, data_files=data_args.data_files				, cache_dir=model_args.cache_dir				, use_auth_token=True if model_args.use_auth_token else None				, )

							# If we don't have a validation split, split off a percentage of train as validation.
							_lowerCAmelCase     :							Dict    =    None if "validation" in ds.keys() else data_args.train_val_split
							if isinstance(data_args.train_val_split				, UpperCamelCase__  ) and data_args.train_val_split > 0.0:
														_lowerCAmelCase     :							Any    =    ds["train"].train_test_split(data_args.train_val_split  )
														_lowerCAmelCase     :							Union[str, Any]    =    split["train"]
														_lowerCAmelCase     :							Tuple    =    split["test"]

							# Create config
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							_lowerCAmelCase     :							Dict    =    {
							    "cache_dir": model_args.cache_dir,
							    "revision": model_args.model_revision,
							    "use_auth_token": True if model_args.use_auth_token else None,
							}
							if model_args.config_name_or_path:
														_lowerCAmelCase     :							Union[str, Any]    =    AutoConfig.from_pretrained(model_args.config_name_or_path				, **UpperCamelCase__  )
							elif model_args.model_name_or_path:
														_lowerCAmelCase     :							List[str]    =    AutoConfig.from_pretrained(model_args.model_name_or_path				, **UpperCamelCase__  )
							else:
														_lowerCAmelCase     :							Optional[int]    =    CONFIG_MAPPING[model_args.model_type]()
														logger.warning("You are instantiating a new config instance from scratch."  )
														if model_args.config_overrides is not None:
																					logger.info(F"Overriding config: {model_args.config_overrides}"  )
																					config.update_from_string(model_args.config_overrides  )
																					logger.info(F"New config: {config}"  )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
							if hasattr(UpperCamelCase__				, "decoder_type"  ):
														_lowerCAmelCase     :							Optional[int]    =    "simmim"

							# adapt config
							_lowerCAmelCase     :							Optional[Any]    =    model_args.image_size if model_args.image_size is not None else config.image_size
							_lowerCAmelCase     :							Any    =    model_args.patch_size if model_args.patch_size is not None else config.patch_size
							_lowerCAmelCase     :							Tuple    =    (
							    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
							)

							config.update(
							    {
							        "image_size": model_args.image_size,
							        "patch_size": model_args.patch_size,
							        "encoder_stride": model_args.encoder_stride,
							    }  )

							# create image processor
							if model_args.image_processor_name:
														_lowerCAmelCase     :							Tuple    =    AutoImageProcessor.from_pretrained(model_args.image_processor_name				, **UpperCamelCase__  )
							elif model_args.model_name_or_path:
														_lowerCAmelCase     :							Tuple    =    AutoImageProcessor.from_pretrained(model_args.model_name_or_path				, **UpperCamelCase__  )
							else:
														_lowerCAmelCase     :							str    =    {
														    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
														}
														_lowerCAmelCase     :							Any    =    IMAGE_PROCESSOR_TYPES[model_args.model_type]()

							# create model
							if model_args.model_name_or_path:
														_lowerCAmelCase     :							Tuple    =    AutoModelForMaskedImageModeling.from_pretrained(
														    model_args.model_name_or_path				, from_tf=bool(".ckpt" in model_args.model_name_or_path  )				, config=UpperCamelCase__				, cache_dir=model_args.cache_dir				, revision=model_args.model_revision				, use_auth_token=True if model_args.use_auth_token else None				, )
							else:
														logger.info("Training new model from scratch"  )
														_lowerCAmelCase     :							Optional[int]    =    AutoModelForMaskedImageModeling.from_config(UpperCamelCase__  )

							if training_args.do_train:
														_lowerCAmelCase     :							int    =    ds["train"].column_names
							else:
														_lowerCAmelCase     :							str    =    ds["validation"].column_names

							if data_args.image_column_name is not None:
														_lowerCAmelCase     :							Dict    =    data_args.image_column_name
							elif "image" in column_names:
														_lowerCAmelCase     :							str    =    "image"
							elif "img" in column_names:
														_lowerCAmelCase     :							Tuple    =    "img"
							else:
														_lowerCAmelCase     :							Optional[int]    =    column_names[0]

							# transformations as done in original SimMIM paper
							# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
							_lowerCAmelCase     :							Optional[int]    =    Compose(
							    [
							        Lambda(lambda _lowerCamelCase  : img.convert("RGB"  ) if img.mode != "RGB" else img  ),
							        RandomResizedCrop(model_args.image_size				, scale=(0.67, 1.0)				, ratio=(3.0 / 4.0, 4.0 / 3.0)  ),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        Normalize(mean=image_processor.image_mean				, std=image_processor.image_std  ),
							    ]  )

							# create mask generator
							_lowerCAmelCase     :							int    =    MaskGenerator(
							    input_size=model_args.image_size				, mask_patch_size=data_args.mask_patch_size				, model_patch_size=model_args.patch_size				, mask_ratio=data_args.mask_ratio				, )

							def preprocess_images(_lowerCamelCase  ):
														_lowerCAmelCase     :							Optional[Any]    =    [transforms(UpperCamelCase__  ) for image in examples[image_column_name]]
														_lowerCAmelCase     :							str    =    [mask_generator() for i in range(len(examples[image_column_name]  )  )]

														return examples

							if training_args.do_train:
														if "train" not in ds:
																					raise ValueError("--do_train requires a train dataset"  )
														if data_args.max_train_samples is not None:
																					_lowerCAmelCase     :							str    =    ds["train"].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
														# Set the training transforms
														ds["train"].set_transform(UpperCamelCase__  )

							if training_args.do_eval:
														if "validation" not in ds:
																					raise ValueError("--do_eval requires a validation dataset"  )
														if data_args.max_eval_samples is not None:
																					_lowerCAmelCase     :							int    =    (
																					    ds["validation"].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
																					)
														# Set the validation transforms
														ds["validation"].set_transform(UpperCamelCase__  )

							# Initialize our trainer
							_lowerCAmelCase     :							Optional[int]    =    Trainer(
							    model=UpperCamelCase__				, args=UpperCamelCase__				, train_dataset=ds["train"] if training_args.do_train else None				, eval_dataset=ds["validation"] if training_args.do_eval else None				, tokenizer=UpperCamelCase__				, data_collator=UpperCamelCase__				, )

							# Training
							if training_args.do_train:
														_lowerCAmelCase     :							List[Any]    =    None
														if training_args.resume_from_checkpoint is not None:
																					_lowerCAmelCase     :							Any    =    training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					_lowerCAmelCase     :							Optional[Any]    =    last_checkpoint
														_lowerCAmelCase     :							List[str]    =    trainer.train(resume_from_checkpoint=UpperCamelCase__  )
														trainer.save_model()
														trainer.log_metrics("train"				, train_result.metrics  )
														trainer.save_metrics("train"				, train_result.metrics  )
														trainer.save_state()

							# Evaluation
							if training_args.do_eval:
														_lowerCAmelCase     :							List[Any]    =    trainer.evaluate()
														trainer.log_metrics("eval"				, UpperCamelCase__  )
														trainer.save_metrics("eval"				, UpperCamelCase__  )

							# Write model card and (optionally) push to hub
							_lowerCAmelCase     :							int    =    {
							    "finetuned_from": model_args.model_name_or_path,
							    "tasks": "masked-image-modeling",
							    "dataset": data_args.dataset_name,
							    "tags": ["masked-image-modeling"],
							}
							if training_args.push_to_hub:
														trainer.push_to_hub(**UpperCamelCase__  )
							else:
														trainer.create_model_card(**UpperCamelCase__  )


if __name__ == "__main__":
						main()

"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class 			UpperCamelCase__:





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> int:
													return None






class 			UpperCamelCase__:





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Tuple:
													return None






class 			UpperCamelCase__(	unittest.TestCase       ):
						lowerCAmelCase__     :  Tuple  							=						[
						    # (model_name, model_kwargs)
						    ('bert-base-cased', {}),
						    ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
						]





						@require_tf
						@slow
						def  snake_case__		(				self   )    -> int:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(__UpperCAmelCase						,'tf'						,12						,**__UpperCAmelCase   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> int:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(__UpperCAmelCase						,'pt'						,12						,**__UpperCAmelCase   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import BertModel

													A__		   =							['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
													with NamedTemporaryFile(mode='w+t'   ) as vocab_file:
																				vocab_file.write('\n'.join(__UpperCAmelCase   )   )
																				vocab_file.flush()
																				A__		   =							BertTokenizerFast(vocab_file.name   )

													with TemporaryDirectory() as bert_save_dir:
																				A__		   =							BertModel(BertConfig(vocab_size=len(__UpperCAmelCase   )   )   )
																				model.save_pretrained(__UpperCAmelCase   )
																				self._test_export(__UpperCAmelCase						,'pt'						,12						,__UpperCAmelCase   )





						@require_tf
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				A__		   =							self._test_export(__UpperCAmelCase						,'tf'						,12						,**__UpperCAmelCase   )
																				A__		   =							quantize(Path(__UpperCAmelCase   )   )

																				# Ensure the actual quantized model is not bigger than the original one
																				if quantized_path.stat().st_size >= Path(__UpperCAmelCase   ).stat().st_size:
																											self.fail('Quantized model is bigger than initial ONNX model'   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> str:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				A__		   =							self._test_export(__UpperCAmelCase						,'pt'						,12						,**__UpperCAmelCase   )
																				A__		   =							quantize(__UpperCAmelCase   )

																				# Ensure the actual quantized model is not bigger than the original one
																				if quantized_path.stat().st_size >= Path(__UpperCAmelCase   ).stat().st_size:
																											self.fail('Quantized model is bigger than initial ONNX model'   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=None						,**__UpperCAmelCase   )    -> Union[str, Any]:
													try:
																				# Compute path
																				with TemporaryDirectory() as tempdir:
																											A__		   =							Path(__UpperCAmelCase   ).joinpath('model.onnx'   )

																				# Remove folder if exists
																				if path.parent.exists():
																											path.parent.rmdir()

																				# Export
																				convert(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,**__UpperCAmelCase   )

																				return path
													except Exception as e:
																				self.fail(__UpperCAmelCase   )





						@require_torch
						@require_tokenizers
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import BertModel

													A__		   =							BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
													A__		   =							BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
													self._test_infer_dynamic_axis(__UpperCAmelCase						,__UpperCAmelCase						,'pt'   )





						@require_tf
						@require_tokenizers
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import TFBertModel

													A__		   =							TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
													A__		   =							BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
													self._test_infer_dynamic_axis(__UpperCAmelCase						,__UpperCAmelCase						,'tf'   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> str:
													A__		   =							FeatureExtractionPipeline(__UpperCAmelCase						,__UpperCAmelCase   )

													A__		   =							['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
													A__   ,     A__   ,     A__   ,     A__		   =							infer_shapes(__UpperCAmelCase						,__UpperCAmelCase   )

													# Assert all variables are present
													self.assertEqual(len(__UpperCAmelCase   )						,len(__UpperCAmelCase   )   )
													self.assertTrue(all(var_name in shapes for var_name in variable_names   )   )
													self.assertSequenceEqual(variable_names[:3]						,__UpperCAmelCase   )
													self.assertSequenceEqual(variable_names[3:]						,__UpperCAmelCase   )

													# Assert inputs are {0: batch, 1: sequence}
													for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
																				self.assertDictEqual(shapes[var_name]						,{0: 'batch', 1: 'sequence'}   )

													# Assert outputs are {0: batch, 1: sequence} and {0: batch}
													self.assertDictEqual(shapes['output_0']						,{0: 'batch', 1: 'sequence'}   )
													self.assertDictEqual(shapes['output_1']						,{0: 'batch'}   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							['input_ids', 'attention_mask', 'token_type_ids']
													A__		   =							{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
													A__   ,     A__		   =							ensure_valid_input(FuncContiguousArgs()						,__UpperCAmelCase						,__UpperCAmelCase   )

													# Should have exactly the same number of args (all are valid)
													self.assertEqual(len(__UpperCAmelCase   )						,3   )

													# Should have exactly the same input names
													self.assertEqual(set(__UpperCAmelCase   )						,set(__UpperCAmelCase   )   )

													# Parameter should be reordered according to their respective place in the function:
													# (input_ids, token_type_ids, attention_mask)
													self.assertEqual(__UpperCAmelCase						,(tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask'])   )

													# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
													A__   ,     A__		   =							ensure_valid_input(FuncNonContiguousArgs()						,__UpperCAmelCase						,__UpperCAmelCase   )

													# Should have exactly the one arg (all before the one not provided "some_other_args")
													self.assertEqual(len(__UpperCAmelCase   )						,1   )
													self.assertEqual(len(__UpperCAmelCase   )						,1   )

													# Should have only "input_ids"
													self.assertEqual(inputs_args[0]						,tokens['input_ids']   )
													self.assertEqual(ordered_input_names[0]						,'input_ids'   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							generate_identified_filename(Path('/home/something/my_fake_model.onnx'   )						,'-test'   )
													self.assertEqual('/home/something/my_fake_model-test.onnx'						,generated.as_posix()   )





