






'''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


__lowerCamelCase						:    List[Any]     				=  False



class   lowerCAmelCase__ ( unittest.TestCase  ):
			pass






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

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







				"""simple docstring"""






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

			def 						__UpperCamelCase				(					self					:		Any	) ->  List[Any]:







				"""simple docstring"""






				lowerCamelCase_					:  Optional[int]						=    VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''	)
				# remove text_unet
				pipe.remove_unused_weights()
				pipe.to(UpperCamelCase__	)
				pipe.set_progress_bar_config(disable=UpperCamelCase__	)

				lowerCamelCase_					:  Union[str, Any]						=    '''A painting of a squirrel eating a burger '''
				lowerCamelCase_					:  List[str]						=    torch.manual_seed(0	)
				lowerCamelCase_					:  str						=    pipe(
				    prompt=UpperCamelCase__     ,		generator=UpperCamelCase__     ,		guidance_scale=7.5     ,		num_inference_steps=2     ,		output_type='''numpy'''	).images

				with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(UpperCamelCase__	)
					lowerCamelCase_					:  Any						=    VersatileDiffusionTextToImagePipeline.from_pretrained(UpperCamelCase__	)
				pipe.to(UpperCamelCase__	)
				pipe.set_progress_bar_config(disable=UpperCamelCase__	)

				lowerCamelCase_					:  Any						=    generator.manual_seed(0	)
				lowerCamelCase_					:  Optional[Any]						=    pipe(
				    prompt=UpperCamelCase__     ,		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 						__UpperCamelCase				(					self					:		Any	) ->  Dict:







				"""simple docstring"""






				lowerCamelCase_					:  Optional[int]						=    VersatileDiffusionTextToImagePipeline.from_pretrained(
				    '''shi-labs/versatile-diffusion'''     ,		torch_dtype=torch.floataa	)
				pipe.to(UpperCamelCase__	)
				pipe.set_progress_bar_config(disable=UpperCamelCase__	)

				lowerCamelCase_					:  int						=    '''A painting of a squirrel eating a burger '''
				lowerCamelCase_					:  Tuple						=    torch.manual_seed(0	)
				lowerCamelCase_					:  Dict						=    pipe(
				    prompt=UpperCamelCase__     ,		generator=UpperCamelCase__     ,		guidance_scale=7.5     ,		num_inference_steps=50     ,		output_type='''numpy'''	).images

				lowerCamelCase_					:  Dict						=    image[0, 253:256, 253:256, -1]

				assert image.shape == (1, 512, 512, 3)
				lowerCamelCase_					:  List[str]						=    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-2








'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


lowercase__						       =      get_logger(__name__)






class 	snake_case__       (      enum.Enum					):







		"""simple docstring"""

		lowerCamelCase     							=						"""all_checks"""
		lowerCamelCase     							=						"""basic_checks"""
		lowerCamelCase     							=						"""no_checks"""







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=None			)  ->							str:


		'''simple docstring'''


		if expected_checksums is None:
				logger.info('''Unable to verify checksums.'''			)
				return
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise ExpectedMoreDownloadedFiles(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise UnexpectedDownloadedFile(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		snake_case  :       Any     			=						[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
		snake_case  :       Union[str, Any]     			=						''' for ''' + verification_name if verification_name is not None else ''''''
		if len(SCREAMING_SNAKE_CASE__			) > 0:
				raise NonMatchingChecksumError(
				    F'Checksums didn\'t match{for_verification_name}:\n'
				    F'{bad_urls}\n'
				    '''Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'''			)
		logger.info('''All the checksums matched successfully''' + for_verification_name			)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		if expected_splits is None:
				logger.info('''Unable to verify splits sizes.'''			)
				return
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise ExpectedMoreSplits(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise UnexpectedSplits(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		snake_case  :       Dict     			=						[
		    {'''expected''': expected_splits[name], '''recorded''': recorded_splits[name]}
		    for name in expected_splits
		    if expected_splits[name].num_examples != recorded_splits[name].num_examples
		]
		if len(SCREAMING_SNAKE_CASE__			) > 0:
				raise NonMatchingSplitsSizesError(str(SCREAMING_SNAKE_CASE__			)			)
		logger.info('''All the splits matched successfully.'''			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ = True			)  ->							dict:


		'''simple docstring'''


		if record_checksum:
				snake_case  :       Any     			=						shaaaa()
				with open(SCREAMING_SNAKE_CASE__ ,       '''rb'''			) as f:
						for chunk in iter(lambda: f.read(1 << 20			) ,       B''''''			):
								m.update(SCREAMING_SNAKE_CASE__			)
						snake_case  :       Any     			=						m.hexdigest()
		else:
				snake_case  :       Tuple     			=						None
		return {"num_bytes": os.path.getsize(SCREAMING_SNAKE_CASE__			), "checksum": checksum}




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Dict:


		'''simple docstring'''


		if dataset_size and config.IN_MEMORY_MAX_SIZE:
				return dataset_size < config.IN_MEMORY_MAX_SIZE
		else:
				return False


