

import re

from filelock import FileLock


try:
					import nltk

					lowerCAmelCase__						:       Optional[Any]										=						True
except (ImportError, ModuleNotFoundError):
					lowerCAmelCase__						:       Any										=						False

if NLTK_AVAILABLE:
					with FileLock(""".lock""") as lock:
										nltk.download("""punkt""", quiet=True)




def 	_a       (				__lowerCAmelCase		:      str     ):

		"""simple docstring"""

		re.sub('''<n>'''   ,							''''''   ,							_lowerCamelCase     )  # remove pegasus newline char
		assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
		return "\n".join(nltk.sent_tokenize(_lowerCamelCase     )     )






'''simple docstring'''


import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
					import jax




@require_flax
class     a			(				SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    FlaxAutoencoderKL







						@property
						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  4
								snake_case__						:       Optional[Any]       						=  3
								snake_case__						:       Optional[int]       						=  (3_2, 3_2)

								snake_case__						:       Optional[int]       						=  jax.random.PRNGKey(0  )
								snake_case__						:       Union[str, Any]       						=  jax.random.uniform(snake_case_			,			((batch_size, num_channels) + sizes)  )

								return {"sample": image, "prng_key": prng_key}







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  {
								    '''block_out_channels''': [3_2, 6_4],
								    '''in_channels''': 3,
								    '''out_channels''': 3,
								    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
								    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
								    '''latent_channels''': 4,
								}
								snake_case__						:       Dict       						=  self.dummy_input
								return init_dict, inputs_dict



