



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        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  int       )		->     int:
											with tempfile.TemporaryDirectory() as tmpdirname:
																		# pipeline has Flax weights
																		A      						=    FlaxDiffusionPipeline.from_pretrained(
																		    'hf-internal-testing/tiny-stable-diffusion-pipe'						, safety_checker=__UpperCamelCase						, cache_dir=__UpperCamelCase       )

																		A      						=    [t[-1] for t in os.walk(os.path.join(__UpperCamelCase						, os.listdir(__UpperCamelCase       )[0]						, 'snapshots'       )       )]
																		A      						=    [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        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     int:
											A						,   A      						=    FlaxStableDiffusionPipeline.from_pretrained(
											    'hf-internal-testing/tiny-stable-diffusion-pipe'						, safety_checker=__UpperCamelCase       )

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

											A      						=    jax.random.PRNGKey(0       )
											A      						=    4

											A      						=    jax.device_count()
											A      						=    num_samples * [prompt]
											A      						=    pipeline.prepare_inputs(__UpperCamelCase       )

											# shard inputs and rng
											A      						=    replicate(__UpperCamelCase       )
											A      						=    jax.random.split(__UpperCamelCase						, __UpperCamelCase       )
											A      						=    shard(__UpperCamelCase       )

											A      						=    pipeline(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, jit=__UpperCamelCase       ).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_5_1_4_7_4_5       ) < 1e-3
																		assert np.abs(np.abs(__UpperCamelCase						, dtype=np.floataa       ).sum() - 4_9_9_4_7.8_7_5       ) < 5e-1

											A      						=    pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]       )       )       )
											assert len(__UpperCamelCase       ) == num_samples


				def __UpperCamelCase						(							self      :  str       )		->     Optional[int]:
											A						,   A      						=    FlaxStableDiffusionPipeline.from_pretrained(
											    'CompVis/stable-diffusion-v1-4'						, revision='flax'						, safety_checker=__UpperCamelCase       )

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

											A      						=    jax.random.PRNGKey(0       )
											A      						=    50

											A      						=    jax.device_count()
											A      						=    num_samples * [prompt]
											A      						=    pipeline.prepare_inputs(__UpperCamelCase       )

											# shard inputs and rng
											A      						=    replicate(__UpperCamelCase       )
											A      						=    jax.random.split(__UpperCamelCase						, __UpperCamelCase       )
											A      						=    shard(__UpperCamelCase       )

											A      						=    pipeline(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, jit=__UpperCamelCase       ).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() - 0.0_5_6_5_2_4_0_1)       ) < 1e-3
																		assert np.abs((np.abs(__UpperCamelCase						, dtype=np.floataa       ).sum() - 2_3_8_3_8_0_8.2)       ) < 5e-1


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Tuple:
											A						,   A      						=    FlaxStableDiffusionPipeline.from_pretrained(
											    'CompVis/stable-diffusion-v1-4'						, revision='bf16'						, dtype=jnp.bfloataa						, safety_checker=__UpperCamelCase       )

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

											A      						=    jax.random.PRNGKey(0       )
											A      						=    50

											A      						=    jax.device_count()
											A      						=    num_samples * [prompt]
											A      						=    pipeline.prepare_inputs(__UpperCamelCase       )

											# shard inputs and rng
											A      						=    replicate(__UpperCamelCase       )
											A      						=    jax.random.split(__UpperCamelCase						, __UpperCamelCase       )
											A      						=    shard(__UpperCamelCase       )

											A      						=    pipeline(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, jit=__UpperCamelCase       ).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() - 0.0_4_0_0_3_9_0_6)       ) < 1e-3
																		assert np.abs((np.abs(__UpperCamelCase						, dtype=np.floataa       ).sum() - 2_3_7_3_5_1_6.7_5)       ) < 5e-1


				def __UpperCamelCase						(							self      :  Dict       )		->     Tuple:
											A						,   A      						=    FlaxStableDiffusionPipeline.from_pretrained(
											    'CompVis/stable-diffusion-v1-4'						, revision='bf16'						, dtype=jnp.bfloataa       )

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

											A      						=    jax.random.PRNGKey(0       )
											A      						=    50

											A      						=    jax.device_count()
											A      						=    num_samples * [prompt]
											A      						=    pipeline.prepare_inputs(__UpperCamelCase       )

											# shard inputs and rng
											A      						=    replicate(__UpperCamelCase       )
											A      						=    jax.random.split(__UpperCamelCase						, __UpperCamelCase       )
											A      						=    shard(__UpperCamelCase       )

											A      						=    pipeline(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, jit=__UpperCamelCase       ).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() - 0.0_4_0_0_3_9_0_6)       ) < 1e-3
																		assert np.abs((np.abs(__UpperCamelCase						, dtype=np.floataa       ).sum() - 2_3_7_3_5_1_6.7_5)       ) < 5e-1


				def __UpperCamelCase						(							self      :  str       )		->     Dict:
											A      						=    FlaxDDIMScheduler(
											    beta_start=0.0_0_0_8_5						, beta_end=0.0_1_2						, beta_schedule='scaled_linear'						, set_alpha_to_one=__UpperCamelCase						, steps_offset=1						, )

											A						,   A      						=    FlaxStableDiffusionPipeline.from_pretrained(
											    'CompVis/stable-diffusion-v1-4'						, revision='bf16'						, dtype=jnp.bfloataa						, scheduler=__UpperCamelCase						, safety_checker=__UpperCamelCase						, )
											A      						=    scheduler.create_state()

											A      						=    scheduler_state

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

											A      						=    jax.random.PRNGKey(0       )
											A      						=    50

											A      						=    jax.device_count()
											A      						=    num_samples * [prompt]
											A      						=    pipeline.prepare_inputs(__UpperCamelCase       )

											# shard inputs and rng
											A      						=    replicate(__UpperCamelCase       )
											A      						=    jax.random.split(__UpperCamelCase						, __UpperCamelCase       )
											A      						=    shard(__UpperCamelCase       )

											A      						=    pipeline(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, jit=__UpperCamelCase       ).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() - 0.0_4_5_0_4_3_9_4_5)       ) < 1e-3
																		assert np.abs((np.abs(__UpperCamelCase						, dtype=np.floataa       ).sum() - 2_3_4_7_6_9_3.5)       ) < 5e-1


				def __UpperCamelCase						(							self      :  List[Any]       )		->     int:
											A      						=    (
											    'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
											    ' field, close up, split lighting, cinematic'
											)

											A      						=    jax.device_count()
											A      						=    num_samples * [prompt]
											A      						=    jax.random.split(jax.random.PRNGKey(0       )						, __UpperCamelCase       )

											A						,   A      						=    FlaxStableDiffusionPipeline.from_pretrained(
											    'CompVis/stable-diffusion-v1-4'						, revision='bf16'						, dtype=jnp.bfloataa						, safety_checker=__UpperCamelCase						, )

											A      						=    replicate(__UpperCamelCase       )
											A      						=    pipeline.prepare_inputs(__UpperCamelCase       )
											A      						=    shard(__UpperCamelCase       )
											A      						=    pipeline(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, jit=__UpperCamelCase       ).images
											assert images.shape == (num_samples, 1, 512, 512, 3)
											A      						=    images[2, 0, 256, 10:17, 1]

											# With memory efficient attention
											A						,   A      						=    FlaxStableDiffusionPipeline.from_pretrained(
											    'CompVis/stable-diffusion-v1-4'						, revision='bf16'						, dtype=jnp.bfloataa						, safety_checker=__UpperCamelCase						, use_memory_efficient_attention=__UpperCamelCase						, )

											A      						=    replicate(__UpperCamelCase       )
											A      						=    pipeline.prepare_inputs(__UpperCamelCase       )
											A      						=    shard(__UpperCamelCase       )
											A      						=    pipeline(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, jit=__UpperCamelCase       ).images
											assert images_eff.shape == (num_samples, 1, 512, 512, 3)
											A      						=    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




import math
import sys






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							A      						=    ''
							try:
														with open(lowerCAmelCase__			,	'rb'				) as binary_file:
																					A      						=    binary_file.read()
														for dat in data:
																					A      						=    F'''{dat:08b}'''
																					result += curr_byte
														return result
							except OSError:
														print('File not accessible'				)
														sys.exit()






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							A      						=    {'0': '0', '1': '1'}
							A						,   A      						=    '', ''
							A      						=    len(lowerCAmelCase__				)

							for i in range(len(lowerCAmelCase__				)				):
														curr_string += data_bits[i]
														if curr_string not in lexicon:
																					continue

														A      						=    lexicon[curr_string]
														result += last_match_id
														A      						=    last_match_id + '0'

														if math.loga(lowerCAmelCase__				).is_integer():
																					A      						=    {}
																					for curr_key in list(lowerCAmelCase__				):
																												A      						=    lexicon.pop(lowerCAmelCase__				)
																					A      						=    new_lex

														A      						=    last_match_id + '1'
														index += 1
														A      						=    ''
							return result






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	str				)       ->							None:

							'''simple docstring'''






							A      						=    8
							try:
														with open(lowerCAmelCase__			,	'wb'				) as opened_file:
																					A      						=    [
																					    to_write[i : i + byte_length]
																					    for i in range(0			,	len(lowerCAmelCase__				)			,	lowerCAmelCase__				)
																					]

																					if len(result_byte_array[-1]				) % byte_length == 0:
																												result_byte_array.append('10000000'				)
																					else:
																												result_byte_array[-1] += "1" + "0" * (
																												    byte_length - len(result_byte_array[-1]				) - 1
																												)

																					for elem in result_byte_array[:-1]:
																												opened_file.write(int(lowerCAmelCase__			,	2				).to_bytes(1			,	byteorder='big'				)				)
							except OSError:
														print('File not accessible'				)
														sys.exit()






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							A      						=    0
							for letter in data_bits:
														if letter == "1":
																					break
														counter += 1

							A      						=    data_bits[counter:]
							A      						=    data_bits[counter + 1 :]
							return data_bits






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	str				)       ->							None:

							'''simple docstring'''






							A      						=    read_file_binary(lowerCAmelCase__				)
							A      						=    remove_prefix(lowerCAmelCase__				)
							A      						=    decompress_data(lowerCAmelCase__				)
							write_file_binary(lowerCAmelCase__			,	lowerCAmelCase__				)


if __name__ == "__main__":
				compress(sys.argv[1], sys.argv[2])