


'''simple docstring'''





import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Union[str, Any]:
						__A=	[
						    'safety_checker/pytorch_model.bin',
						    'safety_checker/model.safetensors',
						    'vae/diffusion_pytorch_model.bin',
						    'vae/diffusion_pytorch_model.safetensors',
						    'text_encoder/pytorch_model.bin',
						    'text_encoder/model.safetensors',
						    'unet/diffusion_pytorch_model.bin',
						    'unet/diffusion_pytorch_model.safetensors',
						]
						self.assertTrue(is_safetensors_compatible(lowerCAmelCase_	)	)
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		int:
						__A=	[
						    'unet/diffusion_pytorch_model.bin',
						    'unet/diffusion_pytorch_model.safetensors',
						]
						self.assertTrue(is_safetensors_compatible(lowerCAmelCase_	)	)
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Dict:
						__A=	[
						    'safety_checker/pytorch_model.bin',
						    'safety_checker/model.safetensors',
						    'vae/diffusion_pytorch_model.bin',
						    'vae/diffusion_pytorch_model.safetensors',
						    'text_encoder/pytorch_model.bin',
						    'text_encoder/model.safetensors',
						    'unet/diffusion_pytorch_model.bin',
						    # Removed: 'unet/diffusion_pytorch_model.safetensors',
						]
						self.assertFalse(is_safetensors_compatible(lowerCAmelCase_	)	)
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		List[str]:
						__A=	[
						    'text_encoder/pytorch_model.bin',
						    'text_encoder/model.safetensors',
						]
						self.assertTrue(is_safetensors_compatible(lowerCAmelCase_	)	)
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Union[str, Any]:
						__A=	[
						    'safety_checker/pytorch_model.bin',
						    'safety_checker/model.safetensors',
						    'vae/diffusion_pytorch_model.bin',
						    'vae/diffusion_pytorch_model.safetensors',
						    'text_encoder/pytorch_model.bin',
						    # Removed: 'text_encoder/model.safetensors',
						    'unet/diffusion_pytorch_model.bin',
						    'unet/diffusion_pytorch_model.safetensors',
						]
						self.assertFalse(is_safetensors_compatible(lowerCAmelCase_	)	)
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Tuple:
						__A=	[
						    'safety_checker/pytorch_model.fp16.bin',
						    'safety_checker/model.fp16.safetensors',
						    'vae/diffusion_pytorch_model.fp16.bin',
						    'vae/diffusion_pytorch_model.fp16.safetensors',
						    'text_encoder/pytorch_model.fp16.bin',
						    'text_encoder/model.fp16.safetensors',
						    'unet/diffusion_pytorch_model.fp16.bin',
						    'unet/diffusion_pytorch_model.fp16.safetensors',
						]
						__A=	'fp16'
						self.assertTrue(is_safetensors_compatible(lowerCAmelCase_			,    variant=lowerCAmelCase_	)	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		List[Any]:
						__A=	[
						    'unet/diffusion_pytorch_model.fp16.bin',
						    'unet/diffusion_pytorch_model.fp16.safetensors',
						]
						__A=	'fp16'
						self.assertTrue(is_safetensors_compatible(lowerCAmelCase_			,    variant=lowerCAmelCase_	)	)
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Union[str, Any]:
						# pass variant but use the non-variant filenames
						__A=	[
						    'unet/diffusion_pytorch_model.bin',
						    'unet/diffusion_pytorch_model.safetensors',
						]
						__A=	'fp16'
						self.assertTrue(is_safetensors_compatible(lowerCAmelCase_			,    variant=lowerCAmelCase_	)	)
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Any:
						__A=	[
						    'safety_checker/pytorch_model.fp16.bin',
						    'safety_checker/model.fp16.safetensors',
						    'vae/diffusion_pytorch_model.fp16.bin',
						    'vae/diffusion_pytorch_model.fp16.safetensors',
						    'text_encoder/pytorch_model.fp16.bin',
						    'text_encoder/model.fp16.safetensors',
						    'unet/diffusion_pytorch_model.fp16.bin',
						    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
						]
						__A=	'fp16'
						self.assertFalse(is_safetensors_compatible(lowerCAmelCase_			,    variant=lowerCAmelCase_	)	)
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Dict:
						__A=	[
						    'text_encoder/pytorch_model.fp16.bin',
						    'text_encoder/model.fp16.safetensors',
						]
						__A=	'fp16'
						self.assertTrue(is_safetensors_compatible(lowerCAmelCase_			,    variant=lowerCAmelCase_	)	)
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Tuple:
						# pass variant but use the non-variant filenames
						__A=	[
						    'text_encoder/pytorch_model.bin',
						    'text_encoder/model.safetensors',
						]
						__A=	'fp16'
						self.assertTrue(is_safetensors_compatible(lowerCAmelCase_			,    variant=lowerCAmelCase_	)	)

			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		str:
						__A=	[
						    'safety_checker/pytorch_model.fp16.bin',
						    'safety_checker/model.fp16.safetensors',
						    'vae/diffusion_pytorch_model.fp16.bin',
						    'vae/diffusion_pytorch_model.fp16.safetensors',
						    'text_encoder/pytorch_model.fp16.bin',
						    # 'text_encoder/model.fp16.safetensors',
						    'unet/diffusion_pytorch_model.fp16.bin',
						    'unet/diffusion_pytorch_model.fp16.safetensors',
						]
						__A=	'fp16'
						self.assertFalse(is_safetensors_compatible(lowerCAmelCase_			,    variant=lowerCAmelCase_	)	)







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__  			=				logging.get_logger(__name__)

UpperCAmelCase__  			=				{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Tuple					      =			'''xlm-roberta-xl'''
			def __init__(  self							:			Union[str, Any]			,    lowerCAmelCase_							:			Tuple=250_880			,    lowerCAmelCase_							:			Optional[int]=2_560			,    lowerCAmelCase_							:			Optional[Any]=36			,    lowerCAmelCase_							:			List[str]=32			,    lowerCAmelCase_							:			Optional[Any]=10_240			,    lowerCAmelCase_							:			Union[str, Any]="gelu"			,    lowerCAmelCase_							:			Optional[Any]=0.1			,    lowerCAmelCase_							:			Optional[Any]=0.1			,    lowerCAmelCase_							:			Optional[int]=514			,    lowerCAmelCase_							:			List[str]=1			,    lowerCAmelCase_							:			Optional[Any]=0.02			,    lowerCAmelCase_							:			List[str]=1E-05			,    lowerCAmelCase_							:			int=1			,    lowerCAmelCase_							:			List[str]=0			,    lowerCAmelCase_							:			List[Any]=2			,    lowerCAmelCase_							:			Optional[int]="absolute"			,    lowerCAmelCase_							:			List[Any]=True			,    lowerCAmelCase_							:			Tuple=None			,    **lowerCAmelCase_							:			List[str]			,    )      ->		str:
						super().__init__(pad_token_id=lowerCAmelCase_			,    bos_token_id=lowerCAmelCase_			,    eos_token_id=lowerCAmelCase_			,    **lowerCAmelCase_	)
						__A=	vocab_size
						__A=	hidden_size
						__A=	num_hidden_layers
						__A=	num_attention_heads
						__A=	hidden_act
						__A=	intermediate_size
						__A=	hidden_dropout_prob
						__A=	attention_probs_dropout_prob
						__A=	max_position_embeddings
						__A=	type_vocab_size
						__A=	initializer_range
						__A=	layer_norm_eps
						__A=	position_embedding_type
						__A=	use_cache
						__A=	classifier_dropout


class 						a__					(					a_   ):





			'''simple docstring'''

			@property
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									__A=	{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									__A=	{0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]	)



