

'''simple docstring'''

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      lowerCAmelCase   (					lowerCAmelCase_      ,	unittest.TestCase      ):
       lowerCAmelCase_				=       RobertaTokenizer
       lowerCAmelCase_				=       RobertaTokenizerFast
       lowerCAmelCase_				=       True
       lowerCAmelCase_				=       {"""cls_token""": """<s>"""}







       def 	snake_case      (							self	:       List[Any]					):







            """simple docstring"""







            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            __lowercase							=[
                'l',
                'o',
                'w',
                'e',
                'r',
                's',
                't',
                'i',
                'd',
                'n',
                '\u0120',
                '\u0120l',
                '\u0120n',
                '\u0120lo',
                '\u0120low',
                'er',
                '\u0120lowest',
                '\u0120newer',
                '\u0120wider',
                '<unk>',
            ]
            __lowercase							=dict(zip(snake_case_  ,					range(len(snake_case_					)					)					)					)
            __lowercase							=['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
            __lowercase							={'unk_token': '<unk>'}

            __lowercase							=os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['vocab_file']					)
            __lowercase							=os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['merges_file']					)
            with open(self.vocab_file  ,					'w'  ,					encoding='utf-8'					) as fp:
                 fp.write(json.dumps(snake_case_					) + '\n'					)
            with open(self.merges_file  ,					'w'  ,					encoding='utf-8'					) as fp:
                 fp.write('\n'.join(snake_case_					)					)







       def 	snake_case      (							self	:       Optional[Any]  ,					**__lowercase	:       Dict					):







            """simple docstring"""







            kwargs.update(self.special_tokens_map					)
            return self.tokenizer_class.from_pretrained(self.tmpdirname  ,					**snake_case_					)







       def 	snake_case      (							self	:       Optional[int]  ,					**__lowercase	:       Any					):







            """simple docstring"""







            kwargs.update(self.special_tokens_map					)
            return RobertaTokenizerFast.from_pretrained(self.tmpdirname  ,					**snake_case_					)







       def 	snake_case      (							self	:       int  ,					__lowercase	:       Dict					):







            """simple docstring"""







            __lowercase							='lower newer'
            __lowercase							='lower newer'
            return input_text, output_text







       def 	snake_case      (							self	:       Dict					):







            """simple docstring"""







            __lowercase							=self.tokenizer_class(self.vocab_file  ,					self.merges_file  ,					**self.special_tokens_map					)
            __lowercase							='lower newer'
            __lowercase							=['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
            __lowercase							=tokenizer.tokenize(snake_case_					)  # , add_prefix_space=True)
            self.assertListEqual(snake_case_  ,					snake_case_					)

            __lowercase							=tokens + [tokenizer.unk_token]
            __lowercase							=[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_					)  ,					snake_case_					)







       def 	snake_case      (							self	:       Tuple					):







            """simple docstring"""







            __lowercase							=self.get_tokenizer()

            self.assertListEqual(tokenizer.encode('Hello world!'  ,					add_special_tokens=snake_case_					)  ,					[0, 31414, 232, 328, 2]					)
            self.assertListEqual(
                tokenizer.encode('Hello world! cécé herlolip 418'  ,					add_special_tokens=snake_case_					)  ,					[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]  ,					)







       @slow
       def 	snake_case      (							self	:       str					):







            """simple docstring"""







            __lowercase							=self.tokenizer_class.from_pretrained('roberta-base'					)

            __lowercase							=tokenizer.encode('sequence builders'  ,					add_special_tokens=snake_case_					)
            __lowercase							=tokenizer.encode('multi-sequence build'  ,					add_special_tokens=snake_case_					)

            __lowercase							=tokenizer.encode(
                'sequence builders'  ,					add_special_tokens=snake_case_  ,					add_prefix_space=snake_case_					)
            __lowercase							=tokenizer.encode(
                'sequence builders'  ,					'multi-sequence build'  ,					add_special_tokens=snake_case_  ,					add_prefix_space=snake_case_					)

            __lowercase							=tokenizer.build_inputs_with_special_tokens(snake_case_					)
            __lowercase							=tokenizer.build_inputs_with_special_tokens(snake_case_  ,					snake_case_					)

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode







       def 	snake_case      (							self	:       Optional[int]					):







            """simple docstring"""







            __lowercase							=self.get_tokenizer()

            __lowercase							='Encode this sequence.'
            __lowercase							=tokenizer.byte_encoder[' '.encode('utf-8'					)[0]]

            # Testing encoder arguments
            __lowercase							=tokenizer.encode(snake_case_  ,					add_special_tokens=snake_case_  ,					add_prefix_space=snake_case_					)
            __lowercase							=tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
            self.assertNotEqual(snake_case_  ,					snake_case_					)

            __lowercase							=tokenizer.encode(snake_case_  ,					add_special_tokens=snake_case_  ,					add_prefix_space=snake_case_					)
            __lowercase							=tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
            self.assertEqual(snake_case_  ,					snake_case_					)

            tokenizer.add_special_tokens({'bos_token': '<s>'}					)
            __lowercase							=tokenizer.encode(snake_case_  ,					add_special_tokens=snake_case_					)
            __lowercase							=tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
            self.assertNotEqual(snake_case_  ,					snake_case_					)

            # Testing spaces after special tokens
            __lowercase							='<mask>'
            tokenizer.add_special_tokens(
                {'mask_token': AddedToken(snake_case_  ,					lstrip=snake_case_  ,					rstrip=snake_case_					)}					)  # mask token has a left space
            __lowercase							=tokenizer.convert_tokens_to_ids(snake_case_					)

            __lowercase							='Encode <mask> sequence'
            __lowercase							='Encode <mask>sequence'

            __lowercase							=tokenizer.encode(snake_case_					)
            __lowercase							=encoded.index(snake_case_					)
            __lowercase							=tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
            self.assertEqual(snake_case_  ,					snake_case_					)

            __lowercase							=tokenizer.encode(snake_case_					)
            __lowercase							=encoded.index(snake_case_					)
            __lowercase							=tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
            self.assertNotEqual(snake_case_  ,					snake_case_					)







       def 	snake_case      (							self	:       Dict					):







            """simple docstring"""







            pass







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







            """simple docstring"""







            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
                      __lowercase							=self.rust_tokenizer_class.from_pretrained(snake_case_  ,					**snake_case_					)
                      __lowercase							=self.tokenizer_class.from_pretrained(snake_case_  ,					**snake_case_					)
                      __lowercase							='A, <mask> AllenNLP sentence.'
                      __lowercase							=tokenizer_r.encode_plus(snake_case_  ,					add_special_tokens=snake_case_  ,					return_token_type_ids=snake_case_					)
                      __lowercase							=tokenizer_p.encode_plus(snake_case_  ,					add_special_tokens=snake_case_  ,					return_token_type_ids=snake_case_					)

                      # token_type_ids should put 0 everywhere
                      self.assertEqual(sum(tokens_r['token_type_ids']					)  ,					sum(tokens_p['token_type_ids']					)					)

                      # attention_mask should put 1 everywhere, so sum over length should be 1
                      self.assertEqual(
                          sum(tokens_r['attention_mask']					) / len(tokens_r['attention_mask']					)  ,					sum(tokens_p['attention_mask']					) / len(tokens_p['attention_mask']					)  ,					)

                      __lowercase							=tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
                      __lowercase							=tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']					)

                      # Rust correctly handles the space before the mask while python doesnt
                      self.assertSequenceEqual(tokens_p['input_ids']  ,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)
                      self.assertSequenceEqual(tokens_r['input_ids']  ,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)

                      self.assertSequenceEqual(
                          snake_case_  ,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)
                      self.assertSequenceEqual(
                          snake_case_  ,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)







       def 	snake_case      (							self	:       Dict					):







            """simple docstring"""







            for trim_offsets, add_prefix_space in itertools.product([True, False]  ,					repeat=2					):
                 __lowercase							=self.rust_tokenizer_class.from_pretrained(
                     self.tmpdirname  ,					use_fast=snake_case_  ,					add_prefix_space=snake_case_  ,					trim_offsets=snake_case_					)

                 __lowercase							=json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
                 __lowercase							=json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

                 self.assertEqual(pre_tokenizer_state['add_prefix_space']  ,					snake_case_					)

                 self.assertEqual(post_processor_state['add_prefix_space']  ,					snake_case_					)
                 self.assertEqual(post_processor_state['trim_offsets']  ,					snake_case_					)







       def 	snake_case      (							self	:       List[Any]					):







            """simple docstring"""







            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
                      __lowercase							='hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                      __lowercase							=f'''{text_of_1_token} {text_of_1_token}'''

                      __lowercase							=self.rust_tokenizer_class.from_pretrained(
                          snake_case_  ,					use_fast=snake_case_  ,					add_prefix_space=snake_case_  ,					trim_offsets=snake_case_					)
                      __lowercase							=tokenizer_r(snake_case_  ,					return_offsets_mapping=snake_case_  ,					add_special_tokens=snake_case_					)
                      self.assertEqual(encoding.offset_mapping[0]  ,					(0, len(snake_case_					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]  ,					(len(snake_case_					) + 1, len(snake_case_					) + 1 + len(snake_case_					))  ,					)

                      __lowercase							=self.rust_tokenizer_class.from_pretrained(
                          snake_case_  ,					use_fast=snake_case_  ,					add_prefix_space=snake_case_  ,					trim_offsets=snake_case_					)
                      __lowercase							=tokenizer_r(snake_case_  ,					return_offsets_mapping=snake_case_  ,					add_special_tokens=snake_case_					)
                      self.assertEqual(encoding.offset_mapping[0]  ,					(0, len(snake_case_					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]  ,					(len(snake_case_					) + 1, len(snake_case_					) + 1 + len(snake_case_					))  ,					)

                      __lowercase							=self.rust_tokenizer_class.from_pretrained(
                          snake_case_  ,					use_fast=snake_case_  ,					add_prefix_space=snake_case_  ,					trim_offsets=snake_case_					)
                      __lowercase							=tokenizer_r(snake_case_  ,					return_offsets_mapping=snake_case_  ,					add_special_tokens=snake_case_					)
                      self.assertEqual(encoding.offset_mapping[0]  ,					(0, len(snake_case_					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]  ,					(len(snake_case_					), len(snake_case_					) + 1 + len(snake_case_					))  ,					)

                      __lowercase							=self.rust_tokenizer_class.from_pretrained(
                          snake_case_  ,					use_fast=snake_case_  ,					add_prefix_space=snake_case_  ,					trim_offsets=snake_case_					)
                      __lowercase							=tokenizer_r(snake_case_  ,					return_offsets_mapping=snake_case_  ,					add_special_tokens=snake_case_					)
                      self.assertEqual(encoding.offset_mapping[0]  ,					(0, len(snake_case_					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]  ,					(len(snake_case_					), len(snake_case_					) + 1 + len(snake_case_					))  ,					)

                      __lowercase							=f''' {text}'''

                      # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                      #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                      # )
                      # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                      # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                      # self.assertEqual(
                      #     encoding.offset_mapping[1],
                      #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                      # )

                      __lowercase							=self.rust_tokenizer_class.from_pretrained(
                          snake_case_  ,					use_fast=snake_case_  ,					add_prefix_space=snake_case_  ,					trim_offsets=snake_case_					)
                      __lowercase							=tokenizer_r(snake_case_  ,					return_offsets_mapping=snake_case_  ,					add_special_tokens=snake_case_					)
                      self.assertEqual(encoding.offset_mapping[0]  ,					(1, 1 + len(snake_case_					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]  ,					(1 + len(snake_case_					) + 1, 1 + len(snake_case_					) + 1 + len(snake_case_					))  ,					)

                      __lowercase							=self.rust_tokenizer_class.from_pretrained(
                          snake_case_  ,					use_fast=snake_case_  ,					add_prefix_space=snake_case_  ,					trim_offsets=snake_case_					)
                      __lowercase							=tokenizer_r(snake_case_  ,					return_offsets_mapping=snake_case_  ,					add_special_tokens=snake_case_					)
                      self.assertEqual(encoding.offset_mapping[0]  ,					(0, 1 + len(snake_case_					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]  ,					(1 + len(snake_case_					), 1 + len(snake_case_					) + 1 + len(snake_case_					))  ,					)

                      __lowercase							=self.rust_tokenizer_class.from_pretrained(
                          snake_case_  ,					use_fast=snake_case_  ,					add_prefix_space=snake_case_  ,					trim_offsets=snake_case_					)
                      __lowercase							=tokenizer_r(snake_case_  ,					return_offsets_mapping=snake_case_  ,					add_special_tokens=snake_case_					)
                      self.assertEqual(encoding.offset_mapping[0]  ,					(0, 1 + len(snake_case_					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]  ,					(1 + len(snake_case_					), 1 + len(snake_case_					) + 1 + len(snake_case_					))  ,					)









'''simple docstring'''




__SCREAMING_SNAKE_CASE     :List[str]       =				'''0.18.2'''

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
  if not is_onnx_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_onnx_objects import *  # noqa F403
else:
  from .pipelines import OnnxRuntimeModel

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_pt_objects import *  # noqa F403
else:
  from .models import (
      AutoencoderKL,
      ControlNetModel,
      ModelMixin,
      PriorTransformer,
      TaFilmDecoder,
      TransformeraDModel,
      UNetaDModel,
      UNetaDConditionModel,
      UNetaDModel,
      UNetaDConditionModel,
      VQModel,
  )
  from .optimization import (
      get_constant_schedule,
      get_constant_schedule_with_warmup,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
      get_scheduler,
  )
  from .pipelines import (
      AudioPipelineOutput,
      ConsistencyModelPipeline,
      DanceDiffusionPipeline,
      DDIMPipeline,
      DDPMPipeline,
      DiffusionPipeline,
      DiTPipeline,
      ImagePipelineOutput,
      KarrasVePipeline,
      LDMPipeline,
      LDMSuperResolutionPipeline,
      PNDMPipeline,
      RePaintPipeline,
      ScoreSdeVePipeline,
  )
  from .schedulers import (
      CMStochasticIterativeScheduler,
      DDIMInverseScheduler,
      DDIMParallelScheduler,
      DDIMScheduler,
      DDPMParallelScheduler,
      DDPMScheduler,
      DEISMultistepScheduler,
      DPMSolverMultistepInverseScheduler,
      DPMSolverMultistepScheduler,
      DPMSolverSinglestepScheduler,
      EulerAncestralDiscreteScheduler,
      EulerDiscreteScheduler,
      HeunDiscreteScheduler,
      IPNDMScheduler,
      KarrasVeScheduler,
      KDPMaAncestralDiscreteScheduler,
      KDPMaDiscreteScheduler,
      PNDMScheduler,
      RePaintScheduler,
      SchedulerMixin,
      ScoreSdeVeScheduler,
      UnCLIPScheduler,
      UniPCMultistepScheduler,
      VQDiffusionScheduler,
  )
  from .training_utils import EMAModel

try:
  if not (is_torch_available() and is_scipy_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
  from .schedulers import LMSDiscreteScheduler

try:
  if not (is_torch_available() and is_torchsde_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
  from .schedulers import DPMSolverSDEScheduler

try:
  if not (is_torch_available() and is_transformers_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .pipelines import (
      AltDiffusionImgaImgPipeline,
      AltDiffusionPipeline,
      AudioLDMPipeline,
      CycleDiffusionPipeline,
      IFImgaImgPipeline,
      IFImgaImgSuperResolutionPipeline,
      IFInpaintingPipeline,
      IFInpaintingSuperResolutionPipeline,
      IFPipeline,
      IFSuperResolutionPipeline,
      ImageTextPipelineOutput,
      KandinskyImgaImgPipeline,
      KandinskyInpaintPipeline,
      KandinskyPipeline,
      KandinskyPriorPipeline,
      KandinskyVaaControlnetImgaImgPipeline,
      KandinskyVaaControlnetPipeline,
      KandinskyVaaImgaImgPipeline,
      KandinskyVaaInpaintPipeline,
      KandinskyVaaPipeline,
      KandinskyVaaPriorEmbaEmbPipeline,
      KandinskyVaaPriorPipeline,
      LDMTextToImagePipeline,
      PaintByExamplePipeline,
      SemanticStableDiffusionPipeline,
      ShapEImgaImgPipeline,
      ShapEPipeline,
      StableDiffusionAttendAndExcitePipeline,
      StableDiffusionControlNetImgaImgPipeline,
      StableDiffusionControlNetInpaintPipeline,
      StableDiffusionControlNetPipeline,
      StableDiffusionDepthaImgPipeline,
      StableDiffusionDiffEditPipeline,
      StableDiffusionImageVariationPipeline,
      StableDiffusionImgaImgPipeline,
      StableDiffusionInpaintPipeline,
      StableDiffusionInpaintPipelineLegacy,
      StableDiffusionInstructPixaPixPipeline,
      StableDiffusionLatentUpscalePipeline,
      StableDiffusionLDMaDPipeline,
      StableDiffusionModelEditingPipeline,
      StableDiffusionPanoramaPipeline,
      StableDiffusionParadigmsPipeline,
      StableDiffusionPipeline,
      StableDiffusionPipelineSafe,
      StableDiffusionPixaPixZeroPipeline,
      StableDiffusionSAGPipeline,
      StableDiffusionUpscalePipeline,
      StableUnCLIPImgaImgPipeline,
      StableUnCLIPPipeline,
      TextToVideoSDPipeline,
      TextToVideoZeroPipeline,
      UnCLIPImageVariationPipeline,
      UnCLIPPipeline,
      UniDiffuserModel,
      UniDiffuserPipeline,
      UniDiffuserTextDecoder,
      VersatileDiffusionDualGuidedPipeline,
      VersatileDiffusionImageVariationPipeline,
      VersatileDiffusionPipeline,
      VersatileDiffusionTextToImagePipeline,
      VideoToVideoSDPipeline,
      VQDiffusionPipeline,
  )

try:
  if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
  from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
  if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
  from .pipelines import StableDiffusionKDiffusionPipeline

try:
  if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
  from .pipelines import (
      OnnxStableDiffusionImgaImgPipeline,
      OnnxStableDiffusionInpaintPipeline,
      OnnxStableDiffusionInpaintPipelineLegacy,
      OnnxStableDiffusionPipeline,
      OnnxStableDiffusionUpscalePipeline,
      StableDiffusionOnnxPipeline,
  )

try:
  if not (is_torch_available() and is_librosa_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
  from .pipelines import AudioDiffusionPipeline, Mel

try:
  if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
  from .pipelines import SpectrogramDiffusionPipeline

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_flax_objects import *  # noqa F403
else:
  from .models.controlnet_flax import FlaxControlNetModel
  from .models.modeling_flax_utils import FlaxModelMixin
  from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
  from .models.vae_flax import FlaxAutoencoderKL
  from .pipelines import FlaxDiffusionPipeline
  from .schedulers import (
      FlaxDDIMScheduler,
      FlaxDDPMScheduler,
      FlaxDPMSolverMultistepScheduler,
      FlaxKarrasVeScheduler,
      FlaxLMSDiscreteScheduler,
      FlaxPNDMScheduler,
      FlaxSchedulerMixin,
      FlaxScoreSdeVeScheduler,
  )


try:
  if not (is_flax_available() and is_transformers_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
  from .pipelines import (
      FlaxStableDiffusionControlNetPipeline,
      FlaxStableDiffusionImgaImgPipeline,
      FlaxStableDiffusionInpaintPipeline,
      FlaxStableDiffusionPipeline,
  )

try:
  if not (is_note_seq_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from .utils.dummy_note_seq_objects import *  # noqa F403
else:
  from .pipelines import MidiProcessor




