


from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase__							:						List[str]							     =   logging.get_logger(__name__)

lowerCamelCase__							:						Dict							     =   {
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							str	   =       'camembert'

     def __init__(					self:str     ,		_a:List[str]=3_05_22     ,		_a:Tuple=7_68     ,		_a:str=12     ,		_a:Optional[int]=12     ,		_a:Tuple=30_72     ,		_a:Tuple="gelu"     ,		_a:Any=0.1     ,		_a:str=0.1     ,		_a:Union[str, Any]=5_12     ,		_a:str=2     ,		_a:Dict=0.02     ,		_a:Tuple=1e-12     ,		_a:int=1     ,		_a:Dict=0     ,		_a:str=2     ,		_a:int="absolute"     ,		_a:Tuple=True     ,		_a:Dict=None     ,		**_a:Tuple     ,		):
       super().__init__(pad_token_id=_a     ,		bos_token_id=_a     ,		eos_token_id=_a     ,		**_a   )

       snake_case__						= vocab_size
       snake_case__						= hidden_size
       snake_case__						= num_hidden_layers
       snake_case__						= num_attention_heads
       snake_case__						= hidden_act
       snake_case__						= intermediate_size
       snake_case__						= hidden_dropout_prob
       snake_case__						= attention_probs_dropout_prob
       snake_case__						= max_position_embeddings
       snake_case__						= type_vocab_size
       snake_case__						= initializer_range
       snake_case__						= layer_norm_eps
       snake_case__						= position_embedding_type
       snake_case__						= use_cache
       snake_case__						= classifier_dropout




class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       if self.task == "multiple-choice":
         snake_case__						= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
         snake_case__						= {0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
           ]   )





import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 		__magic_name__						(snake_case_   ,snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							str	   =       IFImgaImgSuperResolutionPipeline
     __lowercase				:							Optional[Any]	   =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
     __lowercase				:							int	   =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}				)
     __lowercase				:							List[str]	   =       PipelineTesterMixin.required_optional_params - {'latents'}

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       return self._get_superresolution_dummy_components()

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:int     ,		_a:Optional[Any]=0   ):
       if str(_a   ).startswith('''mps'''   ):
         snake_case__						= torch.manual_seed(_a   )
       else:
         snake_case__						= torch.Generator(device=_a   ).manual_seed(_a   )

       snake_case__						= floats_tensor((1, 3, 32, 32)     ,		rng=random.Random(_a   )   ).to(_a   )
       snake_case__						= floats_tensor((1, 3, 16, 16)     ,		rng=random.Random(_a   )   ).to(_a   )

       snake_case__						= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''original_image''': original_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 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       self._test_save_load_optional_components()

     @unittest.skipIf(torch_device != '''cuda'''     ,		reason='''float16 requires CUDA'''   )
     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1e-1   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       self._test_save_load_local()







     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2     ,		)

