"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a					:					Any=			{
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a					:					Tuple=			["ConvBertTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a					:					str=			[
	    "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ConvBertForMaskedLM",
	    "ConvBertForMultipleChoice",
	    "ConvBertForQuestionAnswering",
	    "ConvBertForSequenceClassification",
	    "ConvBertForTokenClassification",
	    "ConvBertLayer",
	    "ConvBertModel",
	    "ConvBertPreTrainedModel",
	    "load_tf_weights_in_convbert",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a					:					Union[str, Any]=			[
	    "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFConvBertForMaskedLM",
	    "TFConvBertForMultipleChoice",
	    "TFConvBertForQuestionAnswering",
	    "TFConvBertForSequenceClassification",
	    "TFConvBertForTokenClassification",
	    "TFConvBertLayer",
	    "TFConvBertModel",
	    "TFConvBertPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
	from .tokenization_convbert import ConvBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_convbert_fast import ConvBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_convbert import (
		    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConvBertForMaskedLM,
		    ConvBertForMultipleChoice,
		    ConvBertForQuestionAnswering,
		    ConvBertForSequenceClassification,
		    ConvBertForTokenClassification,
		    ConvBertLayer,
		    ConvBertModel,
		    ConvBertPreTrainedModel,
		    load_tf_weights_in_convbert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_convbert import (
		    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFConvBertForMaskedLM,
		    TFConvBertForMultipleChoice,
		    TFConvBertForQuestionAnswering,
		    TFConvBertForSequenceClassification,
		    TFConvBertForTokenClassification,
		    TFConvBertLayer,
		    TFConvBertModel,
		    TFConvBertPreTrainedModel,
		)


else:
	import sys

	_a					:					int=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






"""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


_a					:					int=			False

class 		UpperCamelCase      (   unittest.TestCase						):
				pass







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



				def  _lowercase					(self    :				Optional[Any])   ->     Tuple:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



				def  _lowercase					(self    :				int)   ->     Tuple:
					__snake_case      :							str						=			VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'     ,  torch_dtype=torch.floataa)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

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

					__snake_case      :							List[Any]						=			torch.manual_seed(0)
					__snake_case      :							Optional[int]						=			pipe.dual_guided(
					    prompt='first prompt'     ,  image=_A     ,  text_to_image_strength=0.75     ,  generator=_A     ,  guidance_scale=7.5     ,  num_inference_steps=2     ,  output_type='numpy'     ,  ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
						pipe.save_pretrained(_A)
						__snake_case      :							Optional[Any]						=			VersatileDiffusionPipeline.from_pretrained(_A     ,  torch_dtype=torch.floataa)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Tuple						=			generator.manual_seed(0)
					__snake_case      :							int						=			pipe.dual_guided(
					    prompt='first prompt'     ,  image=_A     ,  text_to_image_strength=0.75     ,  generator=_A     ,  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    :				Optional[Any])   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'     ,  torch_dtype=torch.floataa)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Tuple						=			'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      :							Union[str, Any]						=			pipe.dual_guided(
					    prompt=_A     ,  image=_A     ,  text_to_image_strength=0.75     ,  generator=_A     ,  guidance_scale=7.5     ,  num_inference_steps=50     ,  output_type='numpy'     ,  ).images

					__snake_case      :							Optional[Any]						=			image[0, 2_53:2_56, 2_53:2_56, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__snake_case      :							Tuple						=			np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001])

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

					__snake_case      :							List[str]						=			'A painting of a squirrel eating a burger '
					__snake_case      :							str						=			torch.manual_seed(0)
					__snake_case      :							str						=			pipe.text_to_image(
					    prompt=_A     ,  generator=_A     ,  guidance_scale=7.5     ,  num_inference_steps=50     ,  output_type='numpy').images

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

					assert image.shape == (1, 5_12, 5_12, 3)
					__snake_case      :							Any						=			np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778])

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

					__snake_case      :							List[str]						=			pipe.image_variation(_A     ,  generator=_A     ,  output_type='numpy').images

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

					assert image.shape == (1, 5_12, 5_12, 3)
					__snake_case      :							List[Any]						=			np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456])

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





