


"""simple docstring"""






import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
						from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
						from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
						from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


lowerCAmelCase__						:						Any		  =			DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

lowerCAmelCase__						:						Optional[Any]		  =			'main'
# Default branch name
lowerCAmelCase__						:						Optional[int]		  =			'f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'
# One particular commit (not the top of `main`)
lowerCAmelCase__						:						Optional[int]		  =			'aaaaaaa'
# This commit does not exist, so we should 404.

lowerCAmelCase__						:						Any		  =			'd9e9f15bc825e4b2c9249e9578f884bbcb5e3684'
# Sha-1 of config.json on the top of `main`, for checking purposes
lowerCAmelCase__						:						int		  =			'4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'



@contextlib.contextmanager
def 						a_		(      ):
				print('Welcome!' )
				yield
				print('Bye!' )



@contextlib.contextmanager
def 						a_		(      ):
				print('Bonjour!' )
				yield
				print('Au revoir!' )
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									# If the spec is missing, importlib would not be able to import the module dynamically.
									assert transformers.__spec__ is not None
									assert importlib.util.find_spec('transformers'    ) is not None






class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					@unittest.mock.patch('sys.stdout'					,new_callable=io.StringIO    )
					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			Optional[Any]    ):
									with ContextManagers([]    ):
													print('Transformers are awesome!'    )
									# The print statement adds a new line at the end of the output
									self.assertEqual(mock_stdout.getvalue()					,'Transformers are awesome!\n'    )







					@unittest.mock.patch('sys.stdout'					,new_callable=io.StringIO    )
					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			Optional[int]    ):
									with ContextManagers([context_en()]    ):
													print('Transformers are awesome!'    )
									# The output should be wrapped with an English welcome and goodbye
									self.assertEqual(mock_stdout.getvalue()					,'Welcome!\nTransformers are awesome!\nBye!\n'    )







					@unittest.mock.patch('sys.stdout'					,new_callable=io.StringIO    )
					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			List[Any]    ):
									with ContextManagers([context_fr(), context_en()]    ):
													print('Transformers are awesome!'    )
									# The output should be wrapped with an English and French welcome and goodbye
									self.assertEqual(mock_stdout.getvalue()					,'Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n'    )







					@require_torch
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									self.assertEqual(find_labels(a_    )					,['labels']    )
									self.assertEqual(find_labels(a_    )					,['labels', 'next_sentence_label']    )
									self.assertEqual(find_labels(a_    )					,['start_positions', 'end_positions']    )
									class        snake_case (				__lowerCamelCase       ):




														"""simple docstring"""

														pass






									self.assertEqual(find_labels(a_    )					,['labels']    )







					@require_tf
					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									self.assertEqual(find_labels(a_    )					,['labels']    )
									self.assertEqual(find_labels(a_    )					,['labels', 'next_sentence_label']    )
									self.assertEqual(find_labels(a_    )					,['start_positions', 'end_positions']    )
									class        snake_case (				__lowerCamelCase       ):




														"""simple docstring"""

														pass






									self.assertEqual(find_labels(a_    )					,['labels']    )







					@require_flax
					def 	__lowerCAmelCase		(       self     :			int    ):
									# Flax models don't have labels
									self.assertEqual(find_labels(a_    )					,[]    )
									self.assertEqual(find_labels(a_    )					,[]    )
									self.assertEqual(find_labels(a_    )					,[]    )
									class        snake_case (				__lowerCamelCase       ):




														"""simple docstring"""

														pass






									self.assertEqual(find_labels(a_    )					,[]    )




"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCAmelCase__						:						Optional[int]		  =			False
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					pass






@slow
@require_torch_gpu
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									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(
									    image=lowerCamelCase__					,generator=lowerCamelCase__					,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([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2



