



"""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


lowerCamelCase      =		False
class    lowercase__     (					unittest.TestCase   ):







							'''simple docstring'''


							pass



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







							'''simple docstring'''


							def   lowercase__       (							self    :						str					)				->		Optional[int]:


													'''simple docstring'''




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



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


													'''simple docstring'''




													UpperCAmelCase_    				=							VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"	,       torch_dtype=torch.floataa					)
													pipe.to(_UpperCAmelCase					)
													pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

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

													UpperCAmelCase_    				=							torch.manual_seed(0					)
													UpperCAmelCase_    				=							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					)
																			UpperCAmelCase_    				=							VersatileDiffusionPipeline.from_pretrained(_UpperCAmelCase	,       torch_dtype=torch.floataa					)
													pipe.to(_UpperCAmelCase					)
													pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

													UpperCAmelCase_    				=							generator.manual_seed(0					)
													UpperCAmelCase_    				=							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   lowercase__       (							self    :						Tuple					)				->		List[str]:


													'''simple docstring'''




													UpperCAmelCase_    				=							VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"	,       torch_dtype=torch.floataa					)
													pipe.to(_UpperCAmelCase					)
													pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

													UpperCAmelCase_    				=							"cyberpunk 2077"
													UpperCAmelCase_    				=							load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"					)
													UpperCAmelCase_    				=							torch.manual_seed(0					)
													UpperCAmelCase_    				=							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

													UpperCAmelCase_    				=							image[0, 253:256, 253:256, -1]

													assert image.shape == (1, 512, 512, 3)
													UpperCAmelCase_    				=							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

													UpperCAmelCase_    				=							"A painting of a squirrel eating a burger "
													UpperCAmelCase_    				=							torch.manual_seed(0					)
													UpperCAmelCase_    				=							pipe.text_to_image(
													    prompt=_UpperCAmelCase	,       generator=_UpperCAmelCase	,       guidance_scale=7.5	,       num_inference_steps=50	,       output_type="numpy"					).images

													UpperCAmelCase_    				=							image[0, 253:256, 253:256, -1]

													assert image.shape == (1, 512, 512, 3)
													UpperCAmelCase_    				=							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

													UpperCAmelCase_    				=							pipe.image_variation(_UpperCAmelCase	,       generator=_UpperCAmelCase	,       output_type="numpy"					).images

													UpperCAmelCase_    				=							image[0, 253:256, 253:256, -1]

													assert image.shape == (1, 512, 512, 3)
													UpperCAmelCase_    				=							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


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''convbert'''



       def __init__(							self    :						Any	,       _UpperCAmelCase    :						Optional[int]=30522	,       _UpperCAmelCase    :						Tuple=768	,       _UpperCAmelCase    :						Optional[int]=12	,       _UpperCAmelCase    :						Dict=12	,       _UpperCAmelCase    :						Any=3072	,       _UpperCAmelCase    :						Dict="gelu"	,       _UpperCAmelCase    :						Optional[int]=0.1	,       _UpperCAmelCase    :						str=0.1	,       _UpperCAmelCase    :						Tuple=512	,       _UpperCAmelCase    :						Optional[Any]=2	,       _UpperCAmelCase    :						Union[str, Any]=0.02	,       _UpperCAmelCase    :						Dict=1e-12	,       _UpperCAmelCase    :						Optional[int]=1	,       _UpperCAmelCase    :						int=0	,       _UpperCAmelCase    :						Optional[Any]=2	,       _UpperCAmelCase    :						List[Any]=768	,       _UpperCAmelCase    :						Optional[int]=2	,       _UpperCAmelCase    :						str=9	,       _UpperCAmelCase    :						List[Any]=1	,       _UpperCAmelCase    :						Optional[Any]=None	,       **_UpperCAmelCase    :						str	,       )				->		List[Any]:


             '''simple docstring'''




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

             UpperCAmelCase_    				=							vocab_size
             UpperCAmelCase_    				=							hidden_size
             UpperCAmelCase_    				=							num_hidden_layers
             UpperCAmelCase_    				=							num_attention_heads
             UpperCAmelCase_    				=							intermediate_size
             UpperCAmelCase_    				=							hidden_act
             UpperCAmelCase_    				=							hidden_dropout_prob
             UpperCAmelCase_    				=							attention_probs_dropout_prob
             UpperCAmelCase_    				=							max_position_embeddings
             UpperCAmelCase_    				=							type_vocab_size
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							layer_norm_eps
             UpperCAmelCase_    				=							embedding_size
             UpperCAmelCase_    				=							head_ratio
             UpperCAmelCase_    				=							conv_kernel_size
             UpperCAmelCase_    				=							num_groups
             UpperCAmelCase_    				=							classifier_dropout



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


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


             '''simple docstring'''




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




