




from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_		      = logging.get_logger(__name__)

lowerCAmelCase_		      = {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}
class 						_A					(  _lowerCamelCase      ):
  _UpperCamelCase   :			Optional[Any] =			'''xmod'''






  def __init__(  self		:	int      ,						_A		:	Any=30_522      ,						_A		:	Tuple=768      ,						_A		:	Tuple=12      ,						_A		:	Optional[Any]=12      ,						_A		:	Optional[int]=3_072      ,						_A		:	Optional[int]="gelu"      ,						_A		:	Optional[Any]=0.1      ,						_A		:	List[Any]=0.1      ,						_A		:	List[str]=512      ,						_A		:	Dict=2      ,						_A		:	List[Any]=0.02      ,						_A		:	Optional[Any]=1E-12      ,						_A		:	Optional[int]=1      ,						_A		:	Tuple=0      ,						_A		:	Union[str, Any]=2      ,						_A		:	Optional[Any]="absolute"      ,						_A		:	Any=True      ,						_A		:	List[Any]=None      ,						_A		:	Optional[Any]=False      ,						_A		:	Union[str, Any]=2      ,						_A		:	Optional[int]=False      ,						_A		:	List[Any]=True      ,						_A		:	Optional[int]=True      ,						_A		:	List[Any]=("en_XX",)      ,						_A		:	str=None      ,						**_A		:	Union[str, Any]      ,						)		->    int:






       """simple docstring"""







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

       lowercase					:   int								= vocab_size
       lowercase					:   int								= hidden_size
       lowercase					:   Optional[int]								= num_hidden_layers
       lowercase					:   Dict								= num_attention_heads
       lowercase					:   Dict								= hidden_act
       lowercase					:   int								= intermediate_size
       lowercase					:   List[Any]								= hidden_dropout_prob
       lowercase					:   Union[str, Any]								= attention_probs_dropout_prob
       lowercase					:   Tuple								= max_position_embeddings
       lowercase					:   str								= type_vocab_size
       lowercase					:   Optional[Any]								= initializer_range
       lowercase					:   Union[str, Any]								= layer_norm_eps
       lowercase					:   Tuple								= position_embedding_type
       lowercase					:   Optional[int]								= use_cache
       lowercase					:   Any								= classifier_dropout
       lowercase					:   Dict								= pre_norm
       lowercase					:   int								= adapter_reduction_factor
       lowercase					:   str								= adapter_layer_norm
       lowercase					:   Union[str, Any]								= adapter_reuse_layer_norm
       lowercase					:   List[Any]								= ln_before_adapter
       lowercase					:   Tuple								= list(lowerCAmelCase_      )
       lowercase					:   List[str]								= default_language


class 						_A					(  _lowerCamelCase      ):






  @property
  def 					__a			(  self		:	Optional[int]      )		->    Optional[int]:






       """simple docstring"""







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

'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowercase    :							Optional[Any]      =					False


class 						__UpperCAmelCase      (					unittest.TestCase						):
							pass


@nightly
@require_torch_gpu
class 						__UpperCAmelCase      (					unittest.TestCase						):


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'  )
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(lowerCAmelCase_  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		'A painting of a squirrel eating a burger '
								_snake_case   						=		torch.manual_seed(0  )
								_snake_case   						=		pipe(
								    prompt=lowerCAmelCase_    ,   generator=lowerCAmelCase_    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type='numpy'  ).images

								with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(lowerCAmelCase_  )
									_snake_case   						=		VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCAmelCase_  )
								pipe.to(lowerCAmelCase_  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		generator.manual_seed(0  )
								_snake_case   						=		pipe(
								    prompt=lowerCAmelCase_    ,   generator=lowerCAmelCase_    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type='numpy'  ).images

								assert np.abs(image - new_image  ).sum() < 1E-5, "Models don't have the same forward pass"




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		VersatileDiffusionTextToImagePipeline.from_pretrained(
								    'shi-labs/versatile-diffusion'    ,   torch_dtype=torch.floataa  )
								pipe.to(lowerCAmelCase_  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		'A painting of a squirrel eating a burger '
								_snake_case   						=		torch.manual_seed(0  )
								_snake_case   						=		pipe(
								    prompt=lowerCAmelCase_    ,   generator=lowerCAmelCase_    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type='numpy'  ).images

								_snake_case   						=		image[0, 2_53:2_56, 2_53:2_56, -1]

								assert image.shape == (1, 5_12, 5_12, 3)
								_snake_case   						=		np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2

