






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


__UpperCamelCase       :		int							=    False


class 	__SCREAMING_SNAKE_CASE(						unittest.TestCase						):
							pass


@nightly
@require_torch_gpu
class 	__SCREAMING_SNAKE_CASE(						unittest.TestCase						):
							def lowerCAmelCase_			(  self:   Optional[Any]    )		->		int:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
							def lowerCAmelCase_			(  self:   Dict    )		->		List[str]:
											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:   str    )		->		List[Any]:
											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([0.3_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-2









'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case_					:      Union[str, Any]    			=    {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      str    			=    [
					    'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'AdaptiveEmbedding',
					    'TransfoXLForSequenceClassification',
					    'TransfoXLLMHeadModel',
					    'TransfoXLModel',
					    'TransfoXLPreTrainedModel',
					    'load_tf_weights_in_transfo_xl',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Any    			=    [
					    'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFAdaptiveEmbedding',
					    'TFTransfoXLForSequenceClassification',
					    'TFTransfoXLLMHeadModel',
					    'TFTransfoXLMainLayer',
					    'TFTransfoXLModel',
					    'TFTransfoXLPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
					from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_transfo_xl import (
										    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AdaptiveEmbedding,
										    TransfoXLForSequenceClassification,
										    TransfoXLLMHeadModel,
										    TransfoXLModel,
										    TransfoXLPreTrainedModel,
										    load_tf_weights_in_transfo_xl,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_transfo_xl import (
										    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFAdaptiveEmbedding,
										    TFTransfoXLForSequenceClassification,
										    TFTransfoXLLMHeadModel,
										    TFTransfoXLMainLayer,
										    TFTransfoXLModel,
										    TFTransfoXLPreTrainedModel,
										)

else:
					import sys

					snake_case_					:      Dict    			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


