
'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


lowercase    :							Optional[Any]      =					logging.get_logger(__name__)

lowercase    :							Dict      =					{
    "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 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       """xmod"""


							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_=False    ,   lowerCAmelCase_=2    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=("en_XX",)    ,   lowerCAmelCase_=None    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






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

								_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
								_snake_case   						=		pre_norm
								_snake_case   						=		adapter_reduction_factor
								_snake_case   						=		adapter_layer_norm
								_snake_case   						=		adapter_reuse_layer_norm
								_snake_case   						=		ln_before_adapter
								_snake_case   						=		list(lowerCAmelCase_  )
								_snake_case   						=		default_language


class 						__UpperCAmelCase      (					_lowerCamelCase						):


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								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),
								    ]  )



'''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

