



from collections import OrderedDict
from typing import Mapping

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


__snake_case    :		List[Any]  =logging.get_logger(__name__)

__snake_case    :		Any  ={
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class 	lowerCamelCase__      (							lowercase_):




		'''simple docstring'''



		snake_case_     ="xlm-roberta"



		def __init__(self		,__lowerCamelCase=3_05_22		,__lowerCamelCase=7_68		,__lowerCamelCase=12		,__lowerCamelCase=12		,__lowerCamelCase=30_72		,__lowerCamelCase="gelu"		,__lowerCamelCase=0.1		,__lowerCamelCase=0.1		,__lowerCamelCase=5_12		,__lowerCamelCase=2		,__lowerCamelCase=0.02		,__lowerCamelCase=1e-12		,__lowerCamelCase=1		,__lowerCamelCase=0		,__lowerCamelCase=2		,__lowerCamelCase="absolute"		,__lowerCamelCase=True		,__lowerCamelCase=None		,**__lowerCamelCase		,)					->      Any:

						"""simple docstring"""






						super().__init__(pad_token_id=a__		,bos_token_id=a__		,eos_token_id=a__		,**a__			)

						lowerCAmelCase__					:    Tuple												=					vocab_size
						lowerCAmelCase__					:    Dict												=					hidden_size
						lowerCAmelCase__					:    Any												=					num_hidden_layers
						lowerCAmelCase__					:    Optional[Any]												=					num_attention_heads
						lowerCAmelCase__					:    Any												=					hidden_act
						lowerCAmelCase__					:    Any												=					intermediate_size
						lowerCAmelCase__					:    Dict												=					hidden_dropout_prob
						lowerCAmelCase__					:    Any												=					attention_probs_dropout_prob
						lowerCAmelCase__					:    Any												=					max_position_embeddings
						lowerCAmelCase__					:    List[Any]												=					type_vocab_size
						lowerCAmelCase__					:    str												=					initializer_range
						lowerCAmelCase__					:    Union[str, Any]												=					layer_norm_eps
						lowerCAmelCase__					:    List[str]												=					position_embedding_type
						lowerCAmelCase__					:    List[Any]												=					use_cache
						lowerCAmelCase__					:    Tuple												=					classifier_dropout



class 	lowerCamelCase__      (							lowercase_):




		'''simple docstring'''



		@property
		def 		lowerCAmelCase__				(self			)					->      Mapping[str, Mapping[int, str]]:

						"""simple docstring"""






						if self.task == "multiple-choice":
										lowerCAmelCase__					:    int												=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
						else:
										lowerCAmelCase__					:    Any												=					{0: '''batch''', 1: '''sequence'''}
						return OrderedDict(
						    [
						        ('''input_ids''', dynamic_axis),
						        ('''attention_mask''', dynamic_axis),
						    ]			)






import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class 	lowerCamelCase__      (							lowerCamelCase__				,    lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =IFInpaintingSuperResolutionPipeline
		snake_case_     =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
		snake_case_     =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""})
		snake_case_     =PipelineTesterMixin.required_optional_params - {"""latents"""}



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						return self._get_superresolution_dummy_components()



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      Dict:

						"""simple docstring"""






						if str(__lowerCamelCase			).startswith('''mps'''			):
										lowerCAmelCase__					:    Union[str, Any]												=					torch.manual_seed(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    int												=					torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)

						lowerCAmelCase__					:    str												=					floats_tensor((1, 3, 16, 16)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					floats_tensor((1, 3, 32, 32)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					floats_tensor((1, 3, 32, 32)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)

						lowerCAmelCase__					:    Dict												=					{
						    '''prompt''': '''A painting of a squirrel eating a burger''',
						    '''image''': image,
						    '''original_image''': original_image,
						    '''mask_image''': mask_image,
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''output_type''': '''numpy''',
						}

						return inputs



		@unittest.skipIf(
		    torch_device != '''cuda''' or not is_xformers_available()		,reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,)
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != '''cuda'''		,reason='''float16 requires CUDA'''			)
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						super().test_save_load_floataa(expected_max_diff=1e-1			)



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)



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

						"""simple docstring"""






						self._test_save_load_local()



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						self._test_inference_batch_single_identical(
						    expected_max_diff=1e-2		,)

