





"""simple docstring"""




import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
		import jax
		import jax.numpy as jnp
		from flax.jax_utils import replicate
		from flax.training.common_utils import shard

		from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline


@require_flax
class        __UpperCamelCase				(						unittest.TestCase						):






							def     __a   (   self      )	-> Optional[Any]:
								with tempfile.TemporaryDirectory() as tmpdirname:
									# pipeline has Flax weights
									a      :		Optional[Any]	=				FlaxDiffusionPipeline.from_pretrained(
									    "hf-internal-testing/tiny-stable-diffusion-pipe"      ,					safety_checker=lowerCAmelCase__      ,					cache_dir=lowerCAmelCase__      )

									a      :		Dict	=				[t[-1] for t in os.walk(os.path.join(lowerCAmelCase__      ,					os.listdir(lowerCAmelCase__      )[0]      ,					"snapshots"      )      )]
									a      :		Any	=				[item for sublist in all_root_files for item in sublist]

									# None of the downloaded files should be a PyTorch file even if we have some here:
									# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
									assert not any(f.endswith(".bin"      ) for f in files      )


@slow
@require_flax
class        __UpperCamelCase				(						unittest.TestCase						):






							def     __a   (   self      )	-> int:
								a,       a      :		Any	=				FlaxStableDiffusionPipeline.from_pretrained(
								    "hf-internal-testing/tiny-stable-diffusion-pipe"      ,					safety_checker=lowerCAmelCase__      )

								a      :		List[Any]	=				(
								    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
								    " field, close up, split lighting, cinematic"
								)

								a      :		int	=				jax.random.PRNGKey(0      )
								a      :		Dict	=				4

								a      :		List[str]	=				jax.device_count()
								a      :		Optional[Any]	=				num_samples * [prompt]
								a      :		str	=				pipeline.prepare_inputs(lowerCAmelCase__      )

								# shard inputs and rng
								a      :		Tuple	=				replicate(lowerCAmelCase__      )
								a      :		Optional[Any]	=				jax.random.split(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		Optional[int]	=				shard(lowerCAmelCase__      )

								a      :		List[Any]	=				pipeline(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					jit=lowerCAmelCase__      ).images

								assert images.shape == (num_samples, 1, 64, 64, 3)
								if jax.device_count() == 8:
									assert np.abs(np.abs(images[0, 0, :2, :2, -2:]      ,					dtype=np.floataa      ).sum() - 4.1_514_745      ) < 1E-3
									assert np.abs(np.abs(lowerCAmelCase__      ,					dtype=np.floataa      ).sum() - 49_947.875      ) < 5E-1

								a      :		Optional[Any]	=				pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]      )      )      )
								assert len(lowerCAmelCase__      ) == num_samples






							def     __a   (   self      )	-> Optional[Any]:
								a,       a      :		str	=				FlaxStableDiffusionPipeline.from_pretrained(
								    "CompVis/stable-diffusion-v1-4"      ,					revision="flax"      ,					safety_checker=lowerCAmelCase__      )

								a      :		Optional[int]	=				(
								    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
								    " field, close up, split lighting, cinematic"
								)

								a      :		Dict	=				jax.random.PRNGKey(0      )
								a      :		List[Any]	=				50

								a      :		Optional[int]	=				jax.device_count()
								a      :		List[str]	=				num_samples * [prompt]
								a      :		str	=				pipeline.prepare_inputs(lowerCAmelCase__      )

								# shard inputs and rng
								a      :		Tuple	=				replicate(lowerCAmelCase__      )
								a      :		Optional[int]	=				jax.random.split(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		int	=				shard(lowerCAmelCase__      )

								a      :		int	=				pipeline(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					jit=lowerCAmelCase__      ).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
									assert np.abs((np.abs(images[0, 0, :2, :2, -2:]      ,					dtype=np.floataa      ).sum() - 0.05_652_401)      ) < 1E-3
									assert np.abs((np.abs(lowerCAmelCase__      ,					dtype=np.floataa      ).sum() - 2_383_808.2)      ) < 5E-1






							def     __a   (   self      )	-> int:
								a,       a      :		Tuple	=				FlaxStableDiffusionPipeline.from_pretrained(
								    "CompVis/stable-diffusion-v1-4"      ,					revision="bf16"      ,					dtype=jnp.bfloataa      ,					safety_checker=lowerCAmelCase__      )

								a      :		str	=				(
								    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
								    " field, close up, split lighting, cinematic"
								)

								a      :		int	=				jax.random.PRNGKey(0      )
								a      :		Union[str, Any]	=				50

								a      :		str	=				jax.device_count()
								a      :		str	=				num_samples * [prompt]
								a      :		Union[str, Any]	=				pipeline.prepare_inputs(lowerCAmelCase__      )

								# shard inputs and rng
								a      :		Any	=				replicate(lowerCAmelCase__      )
								a      :		List[str]	=				jax.random.split(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		Optional[int]	=				shard(lowerCAmelCase__      )

								a      :		Tuple	=				pipeline(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					jit=lowerCAmelCase__      ).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
									assert np.abs((np.abs(images[0, 0, :2, :2, -2:]      ,					dtype=np.floataa      ).sum() - 0.04_003_906)      ) < 1E-3
									assert np.abs((np.abs(lowerCAmelCase__      ,					dtype=np.floataa      ).sum() - 2_373_516.75)      ) < 5E-1






							def     __a   (   self      )	-> Optional[int]:
								a,       a      :		str	=				FlaxStableDiffusionPipeline.from_pretrained(
								    "CompVis/stable-diffusion-v1-4"      ,					revision="bf16"      ,					dtype=jnp.bfloataa      )

								a      :		Tuple	=				(
								    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
								    " field, close up, split lighting, cinematic"
								)

								a      :		Optional[int]	=				jax.random.PRNGKey(0      )
								a      :		Optional[Any]	=				50

								a      :		List[str]	=				jax.device_count()
								a      :		Optional[int]	=				num_samples * [prompt]
								a      :		int	=				pipeline.prepare_inputs(lowerCAmelCase__      )

								# shard inputs and rng
								a      :		Union[str, Any]	=				replicate(lowerCAmelCase__      )
								a      :		Optional[int]	=				jax.random.split(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		Tuple	=				shard(lowerCAmelCase__      )

								a      :		List[Any]	=				pipeline(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					jit=lowerCAmelCase__      ).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
									assert np.abs((np.abs(images[0, 0, :2, :2, -2:]      ,					dtype=np.floataa      ).sum() - 0.04_003_906)      ) < 1E-3
									assert np.abs((np.abs(lowerCAmelCase__      ,					dtype=np.floataa      ).sum() - 2_373_516.75)      ) < 5E-1






							def     __a   (   self      )	-> Dict:
								a      :		List[str]	=				FlaxDDIMScheduler(
								    beta_start=0.00_085      ,					beta_end=0.012      ,					beta_schedule="scaled_linear"      ,					set_alpha_to_one=lowerCAmelCase__      ,					steps_offset=1      ,					)

								a,       a      :		str	=				FlaxStableDiffusionPipeline.from_pretrained(
								    "CompVis/stable-diffusion-v1-4"      ,					revision="bf16"      ,					dtype=jnp.bfloataa      ,					scheduler=lowerCAmelCase__      ,					safety_checker=lowerCAmelCase__      ,					)
								a      :		Union[str, Any]	=				scheduler.create_state()

								a      :		Tuple	=				scheduler_state

								a      :		List[str]	=				(
								    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
								    " field, close up, split lighting, cinematic"
								)

								a      :		List[str]	=				jax.random.PRNGKey(0      )
								a      :		List[str]	=				50

								a      :		Dict	=				jax.device_count()
								a      :		Tuple	=				num_samples * [prompt]
								a      :		Optional[int]	=				pipeline.prepare_inputs(lowerCAmelCase__      )

								# shard inputs and rng
								a      :		Optional[int]	=				replicate(lowerCAmelCase__      )
								a      :		Optional[Any]	=				jax.random.split(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		Union[str, Any]	=				shard(lowerCAmelCase__      )

								a      :		Tuple	=				pipeline(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					jit=lowerCAmelCase__      ).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
									assert np.abs((np.abs(images[0, 0, :2, :2, -2:]      ,					dtype=np.floataa      ).sum() - 0.045_043_945)      ) < 1E-3
									assert np.abs((np.abs(lowerCAmelCase__      ,					dtype=np.floataa      ).sum() - 2_347_693.5)      ) < 5E-1






							def     __a   (   self      )	-> str:
								a      :		Dict	=				(
								    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
								    " field, close up, split lighting, cinematic"
								)

								a      :		List[str]	=				jax.device_count()
								a      :		Any	=				num_samples * [prompt]
								a      :		Optional[int]	=				jax.random.split(jax.random.PRNGKey(0      )      ,					lowerCAmelCase__      )

								a,       a      :		List[Any]	=				FlaxStableDiffusionPipeline.from_pretrained(
								    "CompVis/stable-diffusion-v1-4"      ,					revision="bf16"      ,					dtype=jnp.bfloataa      ,					safety_checker=lowerCAmelCase__      ,					)

								a      :		Optional[int]	=				replicate(lowerCAmelCase__      )
								a      :		Dict	=				pipeline.prepare_inputs(lowerCAmelCase__      )
								a      :		int	=				shard(lowerCAmelCase__      )
								a      :		str	=				pipeline(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					jit=lowerCAmelCase__      ).images
								assert images.shape == (num_samples, 1, 512, 512, 3)
								a      :		Any	=				images[2, 0, 256, 10:17, 1]

								# With memory efficient attention
								a,       a      :		List[str]	=				FlaxStableDiffusionPipeline.from_pretrained(
								    "CompVis/stable-diffusion-v1-4"      ,					revision="bf16"      ,					dtype=jnp.bfloataa      ,					safety_checker=lowerCAmelCase__      ,					use_memory_efficient_attention=lowerCAmelCase__      ,					)

								a      :		Optional[int]	=				replicate(lowerCAmelCase__      )
								a      :		Union[str, Any]	=				pipeline.prepare_inputs(lowerCAmelCase__      )
								a      :		Any	=				shard(lowerCAmelCase__      )
								a      :		List[str]	=				pipeline(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					jit=lowerCAmelCase__      ).images
								assert images_eff.shape == (num_samples, 1, 512, 512, 3)
								a      :		Union[str, Any]	=				images[2, 0, 256, 10:17, 1]

								# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
								# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
								assert abs(slice_eff - slice      ).max() < 1E-2









"""simple docstring"""




import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int = 8					) ->str:






	'''simple docstring'''






	a      :		List[Any]	=				ascii_letters + digits + punctuation
	return "".join(secrets.choice(_lowercase					) for _ in range(_lowercase					)					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int					) ->str:






	'''simple docstring'''






	i -= len(_lowercase					)
	a      :		List[str]	=				i // 3
	a      :		Any	=				i % 3
	# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
	#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
	a      :		int	=				(
	    chars_incl
	    + random(_lowercase    ,       quotient + remainder					)
	    + random(_lowercase    ,       _lowercase					)
	    + random(_lowercase    ,       _lowercase					)
	)
	a      :		List[str]	=				list(_lowercase					)
	shuffle(_lowercase					)
	return "".join(_lowercase					)

	# random is a generalised function for letters, characters and numbers






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int					) ->str:






	'''simple docstring'''






	return "".join(secrets.choice(_lowercase					) for _ in range(_lowercase					)					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]    ,       _lowercase						:    int					) ->List[str]:






	'''simple docstring'''






	pass  # Put your code here...






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Optional[Any]    ,       _lowercase						:    Optional[int]					) ->int:






	'''simple docstring'''






	pass  # Put your code here...






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int    ,       _lowercase						:    Optional[Any]					) ->Any:






	'''simple docstring'''






	pass  # Put your code here...






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int = 8					) ->bool:






	'''simple docstring'''






	if len(_lowercase					) < min_length:
		# Your Password must be at least 8 characters long
		return False

	a      :		List[str]	=				any(char in ascii_uppercase for char in password					)
	a      :		Optional[int]	=				any(char in ascii_lowercase for char in password					)
	a      :		List[str]	=				any(char in digits for char in password					)
	a      :		int	=				any(char in punctuation for char in password					)

	return upper and lower and num and spec_char
	# Passwords should contain UPPERCASE, lowerase
	# numbers, and special characters






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






	'''simple docstring'''






	a      :		Dict	=				int(input("Please indicate the max length of your password: "					).strip()					)
	a      :		str	=				input(
	    "Please indicate the characters that must be in your password: "					).strip()
	print("Password generated:"    ,       password_generator(_lowercase					)					)
	print(
	    "Alternative Password generated:"    ,       alternative_password_generator(_lowercase    ,       _lowercase					)    ,       )
	print("[If you are thinking of using this passsword, You better save it.]"					)


if __name__ == "__main__":
		main()


