
import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
	import bitsandbytes as bnb

from copy import deepcopy


UpperCAmelCase	      = logging.getLogger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE = None ,							__SCREAMING_SNAKE_CASE = None ,							__SCREAMING_SNAKE_CASE = None ,							__SCREAMING_SNAKE_CASE = None ,							__SCREAMING_SNAKE_CASE = None ,							__SCREAMING_SNAKE_CASE = False ,							):
	lowercase       =    bnb_quantization_config.load_in_abit
	lowercase       =    bnb_quantization_config.load_in_abit

	if load_in_abit and not is_abit_bnb_available():
		raise ImportError(
		    'You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'
		    ' make sure you have the latest version of `bitsandbytes` installed.'							)
	if load_in_abit and not is_abit_bnb_available():
		raise ValueError(
		    'You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'
		    'make sure you have the latest version of `bitsandbytes` installed.'							)

	lowercase       =    []
	# custom device map
	if isinstance(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							) and len(device_map.keys()							) > 1:
		lowercase       =    [key for key, value in device_map.items() if value in ['disk', 'cpu']]

	# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
	if bnb_quantization_config.skip_modules is None:
		lowercase       =    get_keys_to_not_convert(__SCREAMING_SNAKE_CASE							)

	# add cpu modules to skip modules only for 4-bit modules
	if load_in_abit:
		bnb_quantization_config.skip_modules.extend(__SCREAMING_SNAKE_CASE							)
	lowercase       =    bnb_quantization_config.skip_modules

	# We add the modules we want to keep in full precision
	if bnb_quantization_config.keep_in_fpaa_modules is None:
		lowercase       =    []
	lowercase       =    bnb_quantization_config.keep_in_fpaa_modules
	modules_to_not_convert.extend(__SCREAMING_SNAKE_CASE							)

	# compatibility with peft
	lowercase       =    load_in_abit
	lowercase       =    load_in_abit

	lowercase       =    get_parameter_device(__SCREAMING_SNAKE_CASE							)
	if model_device.type != "meta":
		# quantization of an already loaded model
		logger.warning(
		    'It is not recommended to quantize a loaded model. '
		    'The model should be instantiated under the `init_empty_weights` context manager.'							)
		lowercase       =    replace_with_bnb_layers(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							modules_to_not_convert=__SCREAMING_SNAKE_CASE							)
		# convert param to the right dtype
		lowercase       =    bnb_quantization_config.torch_dtype
		for name, param in model.state_dict().items():
			if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules							):
				param.to(torch.floataa							)
				if param.dtype != torch.floataa:
					lowercase       =    name.replace('.weight' ,							''							).replace('.bias' ,							''							)
					lowercase       =    getattr(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
					if param is not None:
						param.to(torch.floataa							)
			elif torch.is_floating_point(__SCREAMING_SNAKE_CASE							):
				param.to(__SCREAMING_SNAKE_CASE							)
		if model_device.type == "cuda":
			# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
			model.cuda(torch.cuda.current_device()							)
			torch.cuda.empty_cache()
		elif torch.cuda.is_available():
			model.to(torch.cuda.current_device()							)
		else:
			raise RuntimeError('No GPU found. A GPU is needed for quantization.'							)
		logger.info(
		    F'''The model device type is {model_device.type}. However, cuda is needed for quantization.'''
		    'We move the model to cuda.'							)
		return model

	elif weights_location is None:
		raise RuntimeError(
		    F'''`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} '''							)

	else:
		with init_empty_weights():
			lowercase       =    replace_with_bnb_layers(
			    __SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							modules_to_not_convert=__SCREAMING_SNAKE_CASE							)

		lowercase       =    get_quantized_model_device_map(
		    __SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							max_memory=__SCREAMING_SNAKE_CASE ,							no_split_module_classes=__SCREAMING_SNAKE_CASE ,							)
		if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
			lowercase       =    True

		lowercase       =    any(x in list(device_map.values()							) for x in ['cpu', 'disk']							)

		load_checkpoint_in_model(
		    __SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							dtype=bnb_quantization_config.torch_dtype ,							offload_folder=__SCREAMING_SNAKE_CASE ,							offload_state_dict=__SCREAMING_SNAKE_CASE ,							keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules ,							offload_abit_bnb=load_in_abit and offload ,							)
		return dispatch_model(__SCREAMING_SNAKE_CASE ,							device_map=__SCREAMING_SNAKE_CASE ,							offload_dir=__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=None ,							__SCREAMING_SNAKE_CASE=None ,							__SCREAMING_SNAKE_CASE=None							):
	if device_map is None:
		if torch.cuda.is_available():
			lowercase       =    {'': torch.cuda.current_device()}
		else:
			raise RuntimeError('No GPU found. A GPU is needed for quantization.'							)
		logger.info('The device_map was not initialized.' 'Setting device_map to `{\'\':torch.cuda.current_device()}`.'							)

	if isinstance(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
		if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
			raise ValueError(
			    'If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '
			    '\'sequential\'.'							)

		lowercase       =    {}
		special_dtypes.update(
		    {
		        name: bnb_quantization_config.torch_dtype
		        for name, _ in model.named_parameters()
		        if any(m in name for m in bnb_quantization_config.skip_modules							)
		    }							)
		special_dtypes.update(
		    {
		        name: torch.floataa
		        for name, _ in model.named_parameters()
		        if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules							)
		    }							)

		lowercase       =    {}
		lowercase       =    special_dtypes
		lowercase       =    no_split_module_classes
		lowercase       =    bnb_quantization_config.target_dtype

		# get max_memory for each device.
		if device_map != "sequential":
			lowercase       =    get_balanced_memory(
			    __SCREAMING_SNAKE_CASE ,							low_zero=(device_map == 'balanced_low_0') ,							max_memory=__SCREAMING_SNAKE_CASE ,							**__SCREAMING_SNAKE_CASE ,							)

		lowercase       =    max_memory
		lowercase       =    infer_auto_device_map(__SCREAMING_SNAKE_CASE ,							**__SCREAMING_SNAKE_CASE							)

	if isinstance(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
		# check if don't have any quantized module on the cpu
		lowercase       =    bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

		lowercase       =    {
		    key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
		}
		for device in ["cpu", "disk"]:
			if device in device_map_without_some_modules.values():
				if bnb_quantization_config.load_in_abit:
					raise ValueError(
					    '\n                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n                        these modules in `torch_dtype`, you need to pass a custom `device_map` to\n                        `load_and_quantize_model`. Check\n                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n                        for more details.\n                        '							)
				else:
					logger.info(
					    'Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit'							)
		del device_map_without_some_modules
	return device_map






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=None ,							__SCREAMING_SNAKE_CASE=None							):

	if modules_to_not_convert is None:
		lowercase       =    []

	lowercase  ,					lowercase       =    _replace_with_bnb_layers(
	    __SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	if not has_been_replaced:
		logger.warning(
		    'You are loading your model in 8bit or 4bit but no linear modules were found in your model.'
		    ' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'
		    ' Please double check your model architecture, or submit an issue on github if you think this is'
		    ' a bug.'							)
	return model






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=None ,							__SCREAMING_SNAKE_CASE=None ,							):
	lowercase       =    False
	for name, module in model.named_children():
		if current_key_name is None:
			lowercase       =    []
		current_key_name.append(__SCREAMING_SNAKE_CASE							)
		if isinstance(__SCREAMING_SNAKE_CASE ,							nn.Linear							) and name not in modules_to_not_convert:
			# Check if the current key is not in the `modules_to_not_convert`
			lowercase       =    '.'.join(__SCREAMING_SNAKE_CASE							)
			lowercase       =    True
			for key in modules_to_not_convert:
				if (
				    (key in current_key_name_str) and (key + "." in current_key_name_str)
				) or key == current_key_name_str:
					lowercase       =    False
					break
			if proceed:
				# Load bnb module with empty weight and replace ``nn.Linear` module
				if bnb_quantization_config.load_in_abit:
					lowercase       =    bnb.nn.LinearabitLt(
					    module.in_features ,							module.out_features ,							module.bias is not None ,							has_fpaa_weights=__SCREAMING_SNAKE_CASE ,							threshold=bnb_quantization_config.llm_inta_threshold ,							)
				elif bnb_quantization_config.load_in_abit:
					lowercase       =    bnb.nn.Linearabit(
					    module.in_features ,							module.out_features ,							module.bias is not None ,							bnb_quantization_config.bnb_abit_compute_dtype ,							compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant ,							quant_type=bnb_quantization_config.bnb_abit_quant_type ,							)
				else:
					raise ValueError('load_in_8bit and load_in_4bit can\'t be both False'							)
				lowercase       =    module.weight.data
				if module.bias is not None:
					lowercase       =    module.bias.data
				bnb_module.requires_grad_(__SCREAMING_SNAKE_CASE							)
				setattr(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
				lowercase       =    True
		if len(list(module.children()							)							) > 0:
			lowercase  ,					lowercase       =    _replace_with_bnb_layers(
			    __SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
			lowercase       =    has_been_replaced | _has_been_replaced
		# Remove the last key for recursion
		current_key_name.pop(-1							)
	return model, has_been_replaced






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	# Create a copy of the model
	with init_empty_weights():
		lowercase       =    deepcopy(__SCREAMING_SNAKE_CASE							)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

	lowercase       =    find_tied_parameters(__SCREAMING_SNAKE_CASE							)
	# For compatibility with Accelerate < 0.18
	if isinstance(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
		lowercase       =    sum(list(tied_params.values()							) ,							[]							) + list(tied_params.keys()							)
	else:
		lowercase       =    sum(__SCREAMING_SNAKE_CASE ,							[]							)
	lowercase       =    len(__SCREAMING_SNAKE_CASE							) > 0

	# Check if it is a base model
	lowercase       =    False
	if hasattr(__SCREAMING_SNAKE_CASE ,							'base_model_prefix'							):
		lowercase       =    not hasattr(__SCREAMING_SNAKE_CASE ,							model.base_model_prefix							)

	# Ignore this for base models (BertModel, GPT2Model, etc.)
	if (not has_tied_params) and is_base_model:
		return []

	# otherwise they have an attached head
	lowercase       =    list(model.named_children()							)
	lowercase       =    [list_modules[-1][0]]

	# add last module together with tied weights
	lowercase       =    set(__SCREAMING_SNAKE_CASE							) - set(__SCREAMING_SNAKE_CASE							)
	lowercase       =    list(set(__SCREAMING_SNAKE_CASE							)							) + list(__SCREAMING_SNAKE_CASE							)

	# remove ".weight" from the keys
	lowercase       =    ['.weight', '.bias']
	lowercase       =    []
	for name in list_untouched:
		for name_to_remove in names_to_remove:
			if name_to_remove in name:
				lowercase       =    name.replace(__SCREAMING_SNAKE_CASE ,							''							)
		filtered_module_names.append(__SCREAMING_SNAKE_CASE							)

	return filtered_module_names






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	for m in model.modules():
		if isinstance(__SCREAMING_SNAKE_CASE ,							bnb.nn.Linearabit							):
			return True
	return False






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	return next(parameter.parameters()							).device






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	# if it is not quantized, we quantize and offload the quantized weights and the SCB stats
	if fpaa_statistics is None:
		set_module_tensor_to_device(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							0 ,							dtype=__SCREAMING_SNAKE_CASE ,							value=__SCREAMING_SNAKE_CASE							)
		lowercase       =    param_name
		lowercase       =    model
		if "." in tensor_name:
			lowercase       =    tensor_name.split('.'							)
			for split in splits[:-1]:
				lowercase       =    getattr(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
				if new_module is None:
					raise ValueError(F'''{module} has no attribute {split}.'''							)
				lowercase       =    new_module
			lowercase       =    splits[-1]
		# offload weights
		lowercase       =    False
		offload_weight(module._parameters[tensor_name] ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							index=__SCREAMING_SNAKE_CASE							)
		if hasattr(module._parameters[tensor_name] ,							'SCB'							):
			offload_weight(
			    module._parameters[tensor_name].SCB ,							param_name.replace('weight' ,							'SCB'							) ,							__SCREAMING_SNAKE_CASE ,							index=__SCREAMING_SNAKE_CASE ,							)
	else:
		offload_weight(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							index=__SCREAMING_SNAKE_CASE							)
		offload_weight(__SCREAMING_SNAKE_CASE ,							param_name.replace('weight' ,							'SCB'							) ,							__SCREAMING_SNAKE_CASE ,							index=__SCREAMING_SNAKE_CASE							)

	set_module_tensor_to_device(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							'meta' ,							dtype=__SCREAMING_SNAKE_CASE ,							value=torch.empty(*param.size()							)							)



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        A_   :




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		return None







class        A_   :




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case       ):
		return None







class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     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 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(snake_case		,    'tf'		,    12		,    **snake_case       )





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(snake_case		,    'pt'		,    12		,    **snake_case       )





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import BertModel

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

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





	@require_tf
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			lowercase       =    self._test_export(snake_case		,    'tf'		,    12		,    **snake_case       )
			lowercase       =    quantize(Path(snake_case       )       )

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





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			lowercase       =    self._test_export(snake_case		,    'pt'		,    12		,    **snake_case       )
			lowercase       =    quantize(snake_case       )

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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case=None		,    **snake_case       ):
		try:
			# Compute path
			with TemporaryDirectory() as tempdir:
				lowercase       =    Path(snake_case       ).joinpath('model.onnx'       )

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

			# Export
			convert(snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    **snake_case       )

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





	@require_torch
	@require_tokenizers
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import BertModel

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





	@require_tf
	@require_tokenizers
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import TFBertModel

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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    FeatureExtractionPipeline(snake_case		,    snake_case       )

		lowercase       =    ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
		lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    infer_shapes(snake_case		,    snake_case       )

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

		# 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 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ['input_ids', 'attention_mask', 'token_type_ids']
		lowercase       =    {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
		lowercase  ,					lowercase       =    ensure_valid_input(FuncContiguousArgs()		,    snake_case		,    snake_case       )

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

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

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

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

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

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    generate_identified_filename(Path('/home/something/my_fake_model.onnx'       )		,    '-test'       )
		self.assertEqual('/home/something/my_fake_model-test.onnx'		,    generated.as_posix()       )

