



"""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


__SCREAMING_SNAKE_CASE  :						Any    							=      False


class 							lowerCamelCase_(   unittest.TestCase    ):
					pass


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

					def 			snake_case__      (    self   ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''   )
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(lowerCamelCase__   )
									pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				'''A painting of a squirrel eating a burger '''
									_lowerCamelCase        =				torch.manual_seed(0   )
									_lowerCamelCase        =				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__   )
													_lowerCamelCase        =				VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase__   )
									pipe.to(lowerCamelCase__   )
									pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				generator.manual_seed(0   )
									_lowerCamelCase        =				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 			snake_case__      (    self   ):
									_lowerCamelCase        =				VersatileDiffusionTextToImagePipeline.from_pretrained(
									    '''shi-labs/versatile-diffusion'''				,       torch_dtype=torch.floataa   )
									pipe.to(lowerCamelCase__   )
									pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				'''A painting of a squirrel eating a burger '''
									_lowerCamelCase        =				torch.manual_seed(0   )
									_lowerCamelCase        =				pipe(
									    prompt=lowerCamelCase__				,       generator=lowerCamelCase__				,       guidance_scale=7.5				,       num_inference_steps=5_0				,       output_type='''numpy'''   ).images

									_lowerCamelCase        =				image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									_lowerCamelCase        =				np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]   )

									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


__SCREAMING_SNAKE_CASE  :						List[Any]    							=      {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						int    							=      [
			    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMForMultipleChoice''',
			    '''XLMForQuestionAnswering''',
			    '''XLMForQuestionAnsweringSimple''',
			    '''XLMForSequenceClassification''',
			    '''XLMForTokenClassification''',
			    '''XLMModel''',
			    '''XLMPreTrainedModel''',
			    '''XLMWithLMHeadModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      [
			    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMForMultipleChoice''',
			    '''TFXLMForQuestionAnsweringSimple''',
			    '''TFXLMForSequenceClassification''',
			    '''TFXLMForTokenClassification''',
			    '''TFXLMMainLayer''',
			    '''TFXLMModel''',
			    '''TFXLMPreTrainedModel''',
			    '''TFXLMWithLMHeadModel''',
			]


if TYPE_CHECKING:
			from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
			from .tokenization_xlm import XLMTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm import (
						    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMForMultipleChoice,
						    XLMForQuestionAnswering,
						    XLMForQuestionAnsweringSimple,
						    XLMForSequenceClassification,
						    XLMForTokenClassification,
						    XLMModel,
						    XLMPreTrainedModel,
						    XLMWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm import (
						    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMForMultipleChoice,
						    TFXLMForQuestionAnsweringSimple,
						    TFXLMForSequenceClassification,
						    TFXLMForTokenClassification,
						    TFXLMMainLayer,
						    TFXLMModel,
						    TFXLMPreTrainedModel,
						    TFXLMWithLMHeadModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						Tuple    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
