


"""simple docstring"""



import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__snake_case         =					False







class _lowerCAmelCase       (   unittest.TestCase			):
   pass







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







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case					:   List[Any]         =					VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,			torch_dtype=torch.floataa		)
       pipe.to(UpperCamelCase_		)
       pipe.set_progress_bar_config(disable=UpperCamelCase_		)

       snake_case					:   Tuple         =					load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)

       snake_case					:   Union[str, Any]         =					torch.manual_seed(0		)
       snake_case					:   List[str]         =					pipe.dual_guided(
           prompt="first prompt"		,			image=UpperCamelCase_		,			text_to_image_strength=0.75		,			generator=UpperCamelCase_		,			guidance_scale=7.5		,			num_inference_steps=2		,			output_type="numpy"		,			).images

       with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(UpperCamelCase_		)
           snake_case					:   Union[str, Any]         =					VersatileDiffusionPipeline.from_pretrained(UpperCamelCase_		,			torch_dtype=torch.floataa		)
       pipe.to(UpperCamelCase_		)
       pipe.set_progress_bar_config(disable=UpperCamelCase_		)

       snake_case					:   Tuple         =					generator.manual_seed(0		)
       snake_case					:   int         =					pipe.dual_guided(
           prompt="first prompt"		,			image=UpperCamelCase_		,			text_to_image_strength=0.75		,			generator=UpperCamelCase_		,			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		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,			torch_dtype=torch.floataa		)
       pipe.to(UpperCamelCase_		)
       pipe.set_progress_bar_config(disable=UpperCamelCase_		)

       snake_case					:   Optional[int]         =					"cyberpunk 2077"
       snake_case					:   int         =					load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
       snake_case					:   str         =					torch.manual_seed(0		)
       snake_case					:   int         =					pipe.dual_guided(
           prompt=UpperCamelCase_		,			image=UpperCamelCase_		,			text_to_image_strength=0.75		,			generator=UpperCamelCase_		,			guidance_scale=7.5		,			num_inference_steps=50		,			output_type="numpy"		,			).images

       snake_case					:   int         =					image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       snake_case					:   Dict         =					np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]		)

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

       snake_case					:   str         =					"A painting of a squirrel eating a burger "
       snake_case					:   Dict         =					torch.manual_seed(0		)
       snake_case					:   Union[str, Any]         =					pipe.text_to_image(
           prompt=UpperCamelCase_		,			generator=UpperCamelCase_		,			guidance_scale=7.5		,			num_inference_steps=50		,			output_type="numpy"		).images

       snake_case					:   Any         =					image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       snake_case					:   Optional[Any]         =					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-1

       snake_case					:   Dict         =					pipe.image_variation(UpperCamelCase_		,			generator=UpperCamelCase_		,			output_type="numpy"		).images

       snake_case					:   Optional[int]         =					image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       snake_case					:   Optional[int]         =					np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]		)

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









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

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


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """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 _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Union[str, Any]													=						'''xmod'''







   def __init__(			self		,			UpperCamelCase__=3_0522		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=1		,			UpperCamelCase__=0		,			UpperCamelCase__=2		,			UpperCamelCase__="absolute"		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			UpperCamelCase__=False		,			UpperCamelCase__=2		,			UpperCamelCase__=False		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=("en_XX",)		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	List[str]:

       '''simple docstring'''




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

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







class _lowerCAmelCase       (   snake_case_			):







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

       '''simple docstring'''




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





