


'''simple docstring'''



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class    UpperCAmelCase_  (__SCREAMING_SNAKE_CASE  ):





    """simple docstring"""






    lowerCamelCase  :					Any							     =			(DEISMultistepScheduler,)
    lowerCamelCase  :					Optional[Any]							     =			(("""num_inference_steps""", 2_5),)







    def       lowercase_				(							self			,     **SCREAMING_SNAKE_CASE_	)				->     Tuple:
         __lowerCamelCase   :	str								=		{
             """num_train_timesteps""": 10_00,
             """beta_start""": 0.0_0_0_1,
             """beta_end""": 0.0_2,
             """beta_schedule""": """linear""",
             """solver_order""": 2,
         }

         config.update(**SCREAMING_SNAKE_CASE_	)
         return config







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0			,     **SCREAMING_SNAKE_CASE_	)				->     Tuple:
         __lowerCamelCase   :	str								=		dict(self.forward_default_kwargs	)
         __lowerCamelCase   :	Any								=		kwargs.pop('num_inference_steps'			,     SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[str]								=		self.dummy_sample
         __lowerCamelCase   :	Dict								=		0.1 * sample
         __lowerCamelCase   :	List[Any]								=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
              __lowerCamelCase   :	Any								=		self.get_scheduler_config(**SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	Optional[Any]								=		scheduler_class(**SCREAMING_SNAKE_CASE_	)
              scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)
              # copy over dummy past residuals
              __lowerCamelCase   :	str								=		dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(SCREAMING_SNAKE_CASE_	)
                   __lowerCamelCase   :	Any								=		scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE_	)
                   new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)
                   # copy over dummy past residuals
                   __lowerCamelCase   :	Optional[Any]								=		dummy_past_residuals[: new_scheduler.config.solver_order]

              __lowerCamelCase   :	Optional[int]								=		sample, sample
              for t in range(SCREAMING_SNAKE_CASE_			,     time_step + scheduler.config.solver_order + 1	):
                   __lowerCamelCase   :	int								=		scheduler.step(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	).prev_sample
                   __lowerCamelCase   :	str								=		new_scheduler.step(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	).prev_sample

                   assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"







    def       lowercase_				(							self	)				->     str:
         pass







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0			,     **SCREAMING_SNAKE_CASE_	)				->     Any:
         __lowerCamelCase   :	Union[str, Any]								=		dict(self.forward_default_kwargs	)
         __lowerCamelCase   :	Optional[Any]								=		kwargs.pop('num_inference_steps'			,     SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Union[str, Any]								=		self.dummy_sample
         __lowerCamelCase   :	Tuple								=		0.1 * sample
         __lowerCamelCase   :	Optional[Any]								=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
              __lowerCamelCase   :	List[str]								=		self.get_scheduler_config()
              __lowerCamelCase   :	Dict								=		scheduler_class(**SCREAMING_SNAKE_CASE_	)
              scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

              # copy over dummy past residuals (must be after setting timesteps)
              __lowerCamelCase   :	Tuple								=		dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(SCREAMING_SNAKE_CASE_	)
                   __lowerCamelCase   :	int								=		scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE_	)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

                   # copy over dummy past residual (must be after setting timesteps)
                   __lowerCamelCase   :	int								=		dummy_past_residuals[: new_scheduler.config.solver_order]

              __lowerCamelCase   :	int								=		scheduler.step(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	).prev_sample
              __lowerCamelCase   :	Optional[Any]								=		new_scheduler.step(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	).prev_sample

              assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=None			,     **SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         if scheduler is None:
              __lowerCamelCase   :	List[Any]								=		self.scheduler_classes[0]
              __lowerCamelCase   :	str								=		self.get_scheduler_config(**SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	List[Any]								=		scheduler_class(**SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Optional[Any]								=		self.scheduler_classes[0]
         __lowerCamelCase   :	str								=		self.get_scheduler_config(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Tuple								=		scheduler_class(**SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Dict								=		10
         __lowerCamelCase   :	Optional[int]								=		self.dummy_model()
         __lowerCamelCase   :	Optional[Any]								=		self.dummy_sample_deter
         scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

         for i, t in enumerate(scheduler.timesteps	):
              __lowerCamelCase   :	str								=		model(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	List[str]								=		scheduler.step(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	).prev_sample

         return sample







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	int								=		dict(self.forward_default_kwargs	)

         __lowerCamelCase   :	Optional[Any]								=		kwargs.pop('num_inference_steps'			,     SCREAMING_SNAKE_CASE_	)

         for scheduler_class in self.scheduler_classes:
              __lowerCamelCase   :	List[str]								=		self.get_scheduler_config()
              __lowerCamelCase   :	Union[str, Any]								=		scheduler_class(**SCREAMING_SNAKE_CASE_	)

              __lowerCamelCase   :	List[Any]								=		self.dummy_sample
              __lowerCamelCase   :	str								=		0.1 * sample

              if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE_			,     'set_timesteps'	):
                   scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)
              elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE_			,     'set_timesteps'	):
                   __lowerCamelCase   :	Union[str, Any]								=		num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              __lowerCamelCase   :	List[str]								=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
              __lowerCamelCase   :	Any								=		dummy_past_residuals[: scheduler.config.solver_order]

              __lowerCamelCase   :	Dict								=		scheduler.timesteps[5]
              __lowerCamelCase   :	Union[str, Any]								=		scheduler.timesteps[6]

              __lowerCamelCase   :	Union[str, Any]								=		scheduler.step(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	).prev_sample
              __lowerCamelCase   :	List[str]								=		scheduler.step(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	).prev_sample

              self.assertEqual(output_a.shape			,     sample.shape	)
              self.assertEqual(output_a.shape			,     output_a.shape	)







    def       lowercase_				(							self	)				->     List[str]:
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         __lowerCamelCase   :	Union[str, Any]								=		DEISMultistepScheduler(**self.get_scheduler_config()	)
         __lowerCamelCase   :	Dict								=		self.full_loop(scheduler=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Union[str, Any]								=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

         assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1E-3

         __lowerCamelCase   :	str								=		DPMSolverSinglestepScheduler.from_config(scheduler.config	)
         __lowerCamelCase   :	Dict								=		DPMSolverMultistepScheduler.from_config(scheduler.config	)
         __lowerCamelCase   :	Union[str, Any]								=		UniPCMultistepScheduler.from_config(scheduler.config	)
         __lowerCamelCase   :	Union[str, Any]								=		DEISMultistepScheduler.from_config(scheduler.config	)

         __lowerCamelCase   :	List[Any]								=		self.full_loop(scheduler=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Dict								=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

         assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1E-3







    def       lowercase_				(							self	)				->     Optional[int]:
         for timesteps in [25, 50, 1_00, 9_99, 10_00]:
              self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     Dict:
         self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_	)
         for order in [1, 2, 3]:
              for solver_type in ["logrho"]:
                   for threshold in [0.5, 1.0, 2.0]:
                        for prediction_type in ["epsilon", "sample"]:
                             self.check_over_configs(
                                 thresholding=SCREAMING_SNAKE_CASE_			,     prediction_type=SCREAMING_SNAKE_CASE_			,     sample_max_value=SCREAMING_SNAKE_CASE_			,     algorithm_type='deis'			,     solver_order=SCREAMING_SNAKE_CASE_			,     solver_type=SCREAMING_SNAKE_CASE_			,     )







    def       lowercase_				(							self	)				->     Dict:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     Optional[Any]:
         for algorithm_type in ["deis"]:
              for solver_type in ["logrho"]:
                   for order in [1, 2, 3]:
                        for prediction_type in ["epsilon", "sample"]:
                             self.check_over_configs(
                                 solver_order=SCREAMING_SNAKE_CASE_			,     solver_type=SCREAMING_SNAKE_CASE_			,     prediction_type=SCREAMING_SNAKE_CASE_			,     algorithm_type=SCREAMING_SNAKE_CASE_			,     )
                             __lowerCamelCase   :	Union[str, Any]								=		self.full_loop(
                                 solver_order=SCREAMING_SNAKE_CASE_			,     solver_type=SCREAMING_SNAKE_CASE_			,     prediction_type=SCREAMING_SNAKE_CASE_			,     algorithm_type=SCREAMING_SNAKE_CASE_			,     )
                             assert not torch.isnan(SCREAMING_SNAKE_CASE_	).any(), "Samples have nan numbers"







    def       lowercase_				(							self	)				->     Any:
         self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE_	)
         self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     Optional[int]:
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
              self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE_			,     time_step=0	)







    def       lowercase_				(							self	)				->     str:
         __lowerCamelCase   :	Tuple								=		self.full_loop()
         __lowerCamelCase   :	Union[str, Any]								=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

         assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1E-3







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	List[Any]								=		self.full_loop(prediction_type='v_prediction'	)
         __lowerCamelCase   :	Tuple								=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

         assert abs(result_mean.item() - 0.0_9_1	) < 1E-3







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	Tuple								=		self.scheduler_classes[0]
         __lowerCamelCase   :	str								=		self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE_			,     dynamic_thresholding_ratio=0	)
         __lowerCamelCase   :	List[Any]								=		scheduler_class(**SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Dict								=		10
         __lowerCamelCase   :	Optional[int]								=		self.dummy_model()
         __lowerCamelCase   :	List[Any]								=		self.dummy_sample_deter.half()
         scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

         for i, t in enumerate(scheduler.timesteps	):
              __lowerCamelCase   :	Optional[int]								=		model(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	Union[str, Any]								=		scheduler.step(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	).prev_sample

         assert sample.dtype == torch.floataa








# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


_lowerCamelCase					   ="""pytorch_model.bin"""
_lowerCamelCase					   ="""pytorch_model.bin.index.json"""
_lowerCamelCase					   ="""adapter_config.json"""
_lowerCamelCase					   ="""adapter_model.bin"""
_lowerCamelCase					   ="""adapter_model.safetensors"""
_lowerCamelCase					   ="""tf_model.h5"""
_lowerCamelCase					   ="""tf_model.h5.index.json"""
_lowerCamelCase					   ="""model.ckpt"""
_lowerCamelCase					   ="""flax_model.msgpack"""
_lowerCamelCase					   ="""flax_model.msgpack.index.json"""
_lowerCamelCase					   ="""model.safetensors"""
_lowerCamelCase					   ="""model.safetensors.index.json"""
_lowerCamelCase					   ="""config.json"""
_lowerCamelCase					   ="""preprocessor_config.json"""
_lowerCamelCase					   =FEATURE_EXTRACTOR_NAME
_lowerCamelCase					   ="""generation_config.json"""
_lowerCamelCase					   ="""modelcard.json"""

_lowerCamelCase					   ="""▁"""
_lowerCamelCase					   =SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

_lowerCamelCase					   =[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
_lowerCamelCase					   =[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
_lowerCamelCase					   =[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]





def     _a					(						lowerCamelCase							):
 if version.parse(lowerCamelCase							) < version.parse(lowerCamelCase							):
  if "dev" in min_version:
   lowerCamelCase		:	Optional[int]							=      (
       """This example requires a source install from HuggingFace Transformers (see """
       """`https://huggingface.co/docs/transformers/installation#install-from-source`),"""
   )
  else:
   lowerCamelCase		:	int							=      F'''This example requires a minimum version of {min_version},'''
  error_message += F''' but the version found is {__version__}.\n'''
  raise ImportError(
      error_message
      + """Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other """
      """versions of HuggingFace Transformers."""							)




