


'''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   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             with tempfile.TemporaryDirectory() as tmpdirname:
                    # pipeline has Flax weights
                    lowercase__      			= FlaxDiffusionPipeline.from_pretrained(
                        '''hf-internal-testing/tiny-stable-diffusion-pipe'''   ,						safety_checker=UpperCamelCase   ,						cache_dir=UpperCamelCase )

                    lowercase__      			= [t[-1] for t in os.walk(os.path.join(UpperCamelCase   ,						os.listdir(UpperCamelCase )[0]   ,						'''snapshots''' ) )]
                    lowercase__      			= [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     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= FlaxStableDiffusionPipeline.from_pretrained(
                 '''hf-internal-testing/tiny-stable-diffusion-pipe'''   ,						safety_checker=UpperCamelCase )

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

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

             lowercase__      			= jax.device_count()
             lowercase__      			= num_samples * [prompt]
             lowercase__      			= pipeline.prepare_inputs(UpperCamelCase )

             # shard inputs and rng
             lowercase__      			= replicate(UpperCamelCase )
             lowercase__      			= jax.random.split(UpperCamelCase   ,						UpperCamelCase )
             lowercase__      			= shard(UpperCamelCase )

             lowercase__      			= 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_51_47_45 ) < 1E-3
                    assert np.abs(np.abs(UpperCamelCase   ,						dtype=np.floataa ).sum() - 4_99_47.8_75 ) < 5E-1

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



      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= FlaxStableDiffusionPipeline.from_pretrained(
                 '''CompVis/stable-diffusion-v1-4'''   ,						revision='''flax'''   ,						safety_checker=UpperCamelCase )

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

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

             lowercase__      			= jax.device_count()
             lowercase__      			= num_samples * [prompt]
             lowercase__      			= pipeline.prepare_inputs(UpperCamelCase )

             # shard inputs and rng
             lowercase__      			= replicate(UpperCamelCase )
             lowercase__      			= jax.random.split(UpperCamelCase   ,						UpperCamelCase )
             lowercase__      			= shard(UpperCamelCase )

             lowercase__      			= 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() - 0.05_65_24_01) ) < 1E-3
                    assert np.abs((np.abs(UpperCamelCase   ,						dtype=np.floataa ).sum() - 2_38_38_08.2) ) < 5E-1



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







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

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

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

             lowercase__      			= jax.device_count()
             lowercase__      			= num_samples * [prompt]
             lowercase__      			= pipeline.prepare_inputs(UpperCamelCase )

             # shard inputs and rng
             lowercase__      			= replicate(UpperCamelCase )
             lowercase__      			= jax.random.split(UpperCamelCase   ,						UpperCamelCase )
             lowercase__      			= shard(UpperCamelCase )

             lowercase__      			= 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() - 0.04_00_39_06) ) < 1E-3
                    assert np.abs((np.abs(UpperCamelCase   ,						dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







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

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

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

             lowercase__      			= jax.device_count()
             lowercase__      			= num_samples * [prompt]
             lowercase__      			= pipeline.prepare_inputs(UpperCamelCase )

             # shard inputs and rng
             lowercase__      			= replicate(UpperCamelCase )
             lowercase__      			= jax.random.split(UpperCamelCase   ,						UpperCamelCase )
             lowercase__      			= shard(UpperCamelCase )

             lowercase__      			= 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() - 0.04_00_39_06) ) < 1E-3
                    assert np.abs((np.abs(UpperCamelCase   ,						dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1



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

             '''simple docstring'''







             lowercase__      			= FlaxDDIMScheduler(
                 beta_start=0.0_00_85   ,						beta_end=0.0_12   ,						beta_schedule='''scaled_linear'''   ,						set_alpha_to_one=UpperCamelCase   ,						steps_offset=1   ,						)

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

             lowercase__      			= scheduler_state

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

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

             lowercase__      			= jax.device_count()
             lowercase__      			= num_samples * [prompt]
             lowercase__      			= pipeline.prepare_inputs(UpperCamelCase )

             # shard inputs and rng
             lowercase__      			= replicate(UpperCamelCase )
             lowercase__      			= jax.random.split(UpperCamelCase   ,						UpperCamelCase )
             lowercase__      			= shard(UpperCamelCase )

             lowercase__      			= 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_45_04_39_45) ) < 1E-3
                    assert np.abs((np.abs(UpperCamelCase   ,						dtype=np.floataa ).sum() - 2_34_76_93.5) ) < 5E-1






      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







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

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

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

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

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

             lowercase__      			= replicate(UpperCamelCase )
             lowercase__      			= pipeline.prepare_inputs(UpperCamelCase )
             lowercase__      			= shard(UpperCamelCase )
             lowercase__      			= pipeline(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						jit=UpperCamelCase ).images
             assert images_eff.shape == (num_samples, 1, 512, 512, 3)
             lowercase__      			= 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"""





from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

_a       =							_symbol_database.Default()


_a       =							_descriptor_pool.Default().AddSerializedFile(
    b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)

_a       =							globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
   _a       =							None
   _a       =							b'H\003'
   # (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
   # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
   # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
   # _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
   # _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
   _a       =							45
   _a       =							15_81
   _a       =							15_17
   _a       =							15_70
   _a       =							15_84
   _a       =							17_93
   _a       =							17_95
   _a       =							19_16
   _a       =							18_64
   _a       =							19_05
   _a       =							19_19
   _a       =							24_29
   _a       =							22_08
   _a       =							24_18
   _a       =							23_23
   _a       =							24_07
# @@protoc_insertion_point(module_scope)




