




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase      :  str      						=						logging.get_logger(__name__)




def 							_UpperCAmelCase  (UpperCamelCase_ : int      ,    UpperCamelCase_ : Optional[Any]=False      ,    UpperCamelCase_ : str=False      ,    UpperCamelCase_ : Union[str, Any]=False       ):
    '''simple docstring'''
    _lowerCAmelCase    :    Optional[int]										=      []
    for i in range(config.num_hidden_layers       ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((F"transformer.blocks.{i}.norm1.weight", F"vilt.encoder.layer.{i}.layernorm_before.weight")       )
        rename_keys.append((F"transformer.blocks.{i}.norm1.bias", F"vilt.encoder.layer.{i}.layernorm_before.bias")       )
        rename_keys.append(
            (F"transformer.blocks.{i}.attn.proj.weight", F"vilt.encoder.layer.{i}.attention.output.dense.weight")       )
        rename_keys.append(
            (F"transformer.blocks.{i}.attn.proj.bias", F"vilt.encoder.layer.{i}.attention.output.dense.bias")       )
        rename_keys.append((F"transformer.blocks.{i}.norm2.weight", F"vilt.encoder.layer.{i}.layernorm_after.weight")       )
        rename_keys.append((F"transformer.blocks.{i}.norm2.bias", F"vilt.encoder.layer.{i}.layernorm_after.bias")       )
        rename_keys.append(
            (F"transformer.blocks.{i}.mlp.fc1.weight", F"vilt.encoder.layer.{i}.intermediate.dense.weight")       )
        rename_keys.append((F"transformer.blocks.{i}.mlp.fc1.bias", F"vilt.encoder.layer.{i}.intermediate.dense.bias")       )
        rename_keys.append((F"transformer.blocks.{i}.mlp.fc2.weight", F"vilt.encoder.layer.{i}.output.dense.weight")       )
        rename_keys.append((F"transformer.blocks.{i}.mlp.fc2.bias", F"vilt.encoder.layer.{i}.output.dense.bias")       )

    # embeddings
    rename_keys.extend(
        [
            # text embeddings
            ("""text_embeddings.word_embeddings.weight""", """vilt.embeddings.text_embeddings.word_embeddings.weight"""),
            (
                """text_embeddings.position_embeddings.weight""",
                """vilt.embeddings.text_embeddings.position_embeddings.weight""",
            ),
            ("""text_embeddings.position_ids""", """vilt.embeddings.text_embeddings.position_ids"""),
            (
                """text_embeddings.token_type_embeddings.weight""",
                """vilt.embeddings.text_embeddings.token_type_embeddings.weight""",
            ),
            ("""text_embeddings.LayerNorm.weight""", """vilt.embeddings.text_embeddings.LayerNorm.weight"""),
            ("""text_embeddings.LayerNorm.bias""", """vilt.embeddings.text_embeddings.LayerNorm.bias"""),
            # patch embeddings
            ("""transformer.cls_token""", """vilt.embeddings.cls_token"""),
            ("""transformer.patch_embed.proj.weight""", """vilt.embeddings.patch_embeddings.projection.weight"""),
            ("""transformer.patch_embed.proj.bias""", """vilt.embeddings.patch_embeddings.projection.bias"""),
            ("""transformer.pos_embed""", """vilt.embeddings.position_embeddings"""),
            # token type embeddings
            ("""token_type_embeddings.weight""", """vilt.embeddings.token_type_embeddings.weight"""),
        ]       )

    # final layernorm + pooler
    rename_keys.extend(
        [
            ("""transformer.norm.weight""", """vilt.layernorm.weight"""),
            ("""transformer.norm.bias""", """vilt.layernorm.bias"""),
            ("""pooler.dense.weight""", """vilt.pooler.dense.weight"""),
            ("""pooler.dense.bias""", """vilt.pooler.dense.bias"""),
        ]       )

    # classifier head(s)
    if vqa_model:
        # classification head
        rename_keys.extend(
            [
                ("""vqa_classifier.0.weight""", """classifier.0.weight"""),
                ("""vqa_classifier.0.bias""", """classifier.0.bias"""),
                ("""vqa_classifier.1.weight""", """classifier.1.weight"""),
                ("""vqa_classifier.1.bias""", """classifier.1.bias"""),
                ("""vqa_classifier.3.weight""", """classifier.3.weight"""),
                ("""vqa_classifier.3.bias""", """classifier.3.bias"""),
            ]       )
    elif nlvr_model:
        # classification head
        rename_keys.extend(
            [
                ("""nlvr2_classifier.0.weight""", """classifier.0.weight"""),
                ("""nlvr2_classifier.0.bias""", """classifier.0.bias"""),
                ("""nlvr2_classifier.1.weight""", """classifier.1.weight"""),
                ("""nlvr2_classifier.1.bias""", """classifier.1.bias"""),
                ("""nlvr2_classifier.3.weight""", """classifier.3.weight"""),
                ("""nlvr2_classifier.3.bias""", """classifier.3.bias"""),
            ]       )
    else:
        pass

    return rename_keys




def 							_UpperCAmelCase  (UpperCamelCase_ : Optional[int]      ,    UpperCamelCase_ : List[Any]       ):
    '''simple docstring'''
    for i in range(config.num_hidden_layers       ):
        _lowerCAmelCase    :    Any										=      """vilt."""
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        _lowerCAmelCase    :    Any										=      state_dict.pop(F"transformer.blocks.{i}.attn.qkv.weight"       )
        _lowerCAmelCase    :    Optional[Any]										=      state_dict.pop(F"transformer.blocks.{i}.attn.qkv.bias"       )
        # next, add query, keys and values (in that order) to the state dict
        _lowerCAmelCase    :    Tuple										=      in_proj_weight[
            : config.hidden_size, :
        ]
        _lowerCAmelCase    :    Union[str, Any]										=      in_proj_bias[: config.hidden_size]
        _lowerCAmelCase    :    Any										=      in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        _lowerCAmelCase    :    Optional[int]										=      in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        _lowerCAmelCase    :    Optional[int]										=      in_proj_weight[
            -config.hidden_size :, :
        ]
        _lowerCAmelCase    :    int										=      in_proj_bias[-config.hidden_size :]




def 							_UpperCAmelCase  (UpperCamelCase_ : List[Any]       ):
    '''simple docstring'''
    _lowerCAmelCase    :    Optional[Any]										=      ["""head.weight""", """head.bias"""]
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : Tuple      ,    UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : int       ):
    '''simple docstring'''
    _lowerCAmelCase    :    Any										=      dct.pop(SCREAMING_SNAKE_CASE_       )
    _lowerCAmelCase    :    int										=      val




@torch.no_grad()
def 							_UpperCAmelCase  (UpperCamelCase_ : Dict      ,    UpperCamelCase_ : List[Any]       ):
    '''simple docstring'''
    _lowerCAmelCase    :    int										=      ViltConfig(image_size=384      ,    patch_size=32      ,    tie_word_embeddings=SCREAMING_SNAKE_CASE_       )
    _lowerCAmelCase    :    Tuple										=      False
    _lowerCAmelCase    :    List[Any]										=      False
    _lowerCAmelCase    :    List[str]										=      False
    _lowerCAmelCase    :    Optional[int]										=      False
    if "vqa" in checkpoint_url:
        _lowerCAmelCase    :    Union[str, Any]										=      True
        _lowerCAmelCase    :    Dict										=      3129
        _lowerCAmelCase    :    List[Any]										=      """huggingface/label-files"""
        _lowerCAmelCase    :    Dict										=      """vqa2-id2label.json"""
        _lowerCAmelCase    :    str										=      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    repo_type="""dataset"""       )      ,    """r"""       )       )
        _lowerCAmelCase    :    List[str]										=      {int(SCREAMING_SNAKE_CASE_       ): v for k, v in idalabel.items()}
        _lowerCAmelCase    :    str										=      idalabel
        _lowerCAmelCase    :    str										=      {v: k for k, v in idalabel.items()}
        _lowerCAmelCase    :    int										=      ViltForQuestionAnswering(SCREAMING_SNAKE_CASE_       )
    elif "nlvr" in checkpoint_url:
        _lowerCAmelCase    :    str										=      True
        _lowerCAmelCase    :    Any										=      2
        _lowerCAmelCase    :    Optional[int]										=      {0: """False""", 1: """True"""}
        _lowerCAmelCase    :    Dict										=      {v: k for k, v in config.idalabel.items()}
        _lowerCAmelCase    :    List[str]										=      3
        _lowerCAmelCase    :    List[str]										=      ViltForImagesAndTextClassification(SCREAMING_SNAKE_CASE_       )
    elif "irtr" in checkpoint_url:
        _lowerCAmelCase    :    Tuple										=      True
        _lowerCAmelCase    :    Any										=      ViltForImageAndTextRetrieval(SCREAMING_SNAKE_CASE_       )
    elif "mlm_itm" in checkpoint_url:
        _lowerCAmelCase    :    Optional[int]										=      True
        _lowerCAmelCase    :    Optional[Any]										=      ViltForMaskedLM(SCREAMING_SNAKE_CASE_       )
    else:
        raise ValueError("""Unknown model type"""       )

    # load state_dict of original model, remove and rename some keys
    _lowerCAmelCase    :    Dict										=      torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_      ,    map_location="""cpu"""       )["""state_dict"""]
    _lowerCAmelCase    :    Union[str, Any]										=      create_rename_keys(SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_       )
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_       )
    read_in_q_k_v(SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_       )
    if mlm_model or irtr_model:
        _lowerCAmelCase    :    List[str]										=      ["""itm_score.fc.weight""", """itm_score.fc.bias"""]
        for k in ignore_keys:
            state_dict.pop(SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_       )

    # load state dict into HuggingFace model
    model.eval()
    if mlm_model:
        _lowerCAmelCase					,    _lowerCAmelCase    :    Optional[Any]										=      model.load_state_dict(SCREAMING_SNAKE_CASE_      ,    strict=SCREAMING_SNAKE_CASE_       )
        assert missing_keys == ["mlm_score.decoder.bias"]
    else:
        model.load_state_dict(SCREAMING_SNAKE_CASE_       )

    # Define processor
    _lowerCAmelCase    :    Tuple										=      ViltImageProcessor(size=384       )
    _lowerCAmelCase    :    Union[str, Any]										=      BertTokenizer.from_pretrained("""bert-base-uncased"""       )
    _lowerCAmelCase    :    Optional[int]										=      ViltProcessor(SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_       )

    # Forward pass on example inputs (image + text)
    if nlvr_model:
        _lowerCAmelCase    :    Optional[Any]										=      Image.open(requests.get("""https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg"""      ,    stream=SCREAMING_SNAKE_CASE_       ).raw       )
        _lowerCAmelCase    :    Any										=      Image.open(requests.get("""https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg"""      ,    stream=SCREAMING_SNAKE_CASE_       ).raw       )
        _lowerCAmelCase    :    int										=      (
            """The left image contains twice the number of dogs as the right image, and at least two dogs in total are"""
            """ standing."""
        )
        _lowerCAmelCase    :    Tuple										=      processor(SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    return_tensors="""pt"""       )
        _lowerCAmelCase    :    Tuple										=      processor(SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    return_tensors="""pt"""       )
        _lowerCAmelCase    :    Union[str, Any]										=      model(
            input_ids=encoding_a.input_ids      ,    pixel_values=encoding_a.pixel_values      ,    pixel_values_a=encoding_a.pixel_values      ,    )
    else:
        _lowerCAmelCase    :    Dict										=      Image.open(requests.get("""http://images.cocodataset.org/val2017/000000039769.jpg"""      ,    stream=SCREAMING_SNAKE_CASE_       ).raw       )
        if mlm_model:
            _lowerCAmelCase    :    str										=      """a bunch of [MASK] laying on a [MASK]."""
        else:
            _lowerCAmelCase    :    Optional[int]										=      """How many cats are there?"""
        _lowerCAmelCase    :    List[Any]										=      processor(SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    return_tensors="""pt"""       )
        _lowerCAmelCase    :    Optional[Any]										=      model(**SCREAMING_SNAKE_CASE_       )

    # Verify outputs
    if mlm_model:
        _lowerCAmelCase    :    Tuple										=      torch.Size([1, 11, 30522]       )
        _lowerCAmelCase    :    int										=      torch.tensor([-12.5_061, -12.5_123, -12.5_174]       )
        assert outputs.logits.shape == expected_shape
        assert torch.allclose(outputs.logits[0, 0, :3]      ,    SCREAMING_SNAKE_CASE_      ,    atol=1E-4       )

        # verify masked token prediction equals "cats"
        _lowerCAmelCase    :    Tuple										=      outputs.logits[0, 4, :].argmax(-1       ).item()
        assert tokenizer.decode([predicted_id]       ) == "cats"
    elif vqa_model:
        _lowerCAmelCase    :    Tuple										=      torch.Size([1, 3129]       )
        _lowerCAmelCase    :    Optional[int]										=      torch.tensor([-15.9_495, -18.1_472, -10.3_041]       )
        assert torch.allclose(outputs.logits[0, :3]      ,    SCREAMING_SNAKE_CASE_      ,    atol=1E-4       )
        assert outputs.logits.shape == expected_shape
        assert torch.allclose(outputs.logits[0, 0, :3]      ,    SCREAMING_SNAKE_CASE_      ,    atol=1E-4       )

        # verify vqa prediction equals "2"
        _lowerCAmelCase    :    Dict										=      outputs.logits.argmax(-1       ).item()
        assert model.config.idalabel[predicted_idx] == "2"
    elif nlvr_model:
        _lowerCAmelCase    :    str										=      torch.Size([1, 2]       )
        _lowerCAmelCase    :    Optional[Any]										=      torch.tensor([-2.8_721, 2.1_291]       )
        assert torch.allclose(outputs.logits[0, :3]      ,    SCREAMING_SNAKE_CASE_      ,    atol=1E-4       )
        assert outputs.logits.shape == expected_shape

    Path(SCREAMING_SNAKE_CASE_       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_       )
    print(F"Saving model and processor to {pytorch_dump_folder_path}"       )
    model.save_pretrained(SCREAMING_SNAKE_CASE_       )
    processor.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
 _lowerCamelCase      :  List[Any]      						=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint_url",
     default="https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt",
     type=str,
     help="URL of the checkpoint you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )

 _lowerCamelCase      :  str      						=						parser.parse_args()
 convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class        __snake_case  (_a		):
				lowerCAmelCase__				=     (PNDMScheduler,)
				lowerCAmelCase__				=     (("num_inference_steps", 5_0),)



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	**_UpperCAmelCase      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      {
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**_UpperCAmelCase      )
								return config



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Union[str, Any]=0		,	**_UpperCAmelCase      :		str      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      dict(self.forward_default_kwargs      )
								_lowerCAmelCase    :    str										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      self.dummy_sample
								_lowerCAmelCase    :    List[str]										=      0.1 * sample
								_lowerCAmelCase    :    Tuple										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Any										=      self.get_scheduler_config(**_UpperCAmelCase      )
												_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )
												scheduler.set_timesteps(_UpperCAmelCase      )
												# copy over dummy past residuals
												_lowerCAmelCase    :    Tuple										=      dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_UpperCAmelCase      )
																_lowerCAmelCase    :    str										=      scheduler_class.from_pretrained(_UpperCAmelCase      )
																new_scheduler.set_timesteps(_UpperCAmelCase      )
																# copy over dummy past residuals
																_lowerCAmelCase    :    Any										=      dummy_past_residuals[:]

												_lowerCAmelCase    :    int										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    List[str]										=      new_scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Tuple										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[int]										=      new_scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    str:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		Any=0		,	**_UpperCAmelCase      :		Optional[int]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      dict(self.forward_default_kwargs      )
								_lowerCAmelCase    :    Any										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      self.dummy_sample
								_lowerCAmelCase    :    Dict										=      0.1 * sample
								_lowerCAmelCase    :    Dict										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Any										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )
												scheduler.set_timesteps(_UpperCAmelCase      )

												# copy over dummy past residuals (must be after setting timesteps)
												_lowerCAmelCase    :    str										=      dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_UpperCAmelCase      )
																_lowerCAmelCase    :    List[Any]										=      scheduler_class.from_pretrained(_UpperCAmelCase      )
																# copy over dummy past residuals
																new_scheduler.set_timesteps(_UpperCAmelCase      )

																# copy over dummy past residual (must be after setting timesteps)
																_lowerCAmelCase    :    Any										=      dummy_past_residuals[:]

												_lowerCAmelCase    :    Tuple										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    str										=      new_scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Any										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Any										=      new_scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	**_UpperCAmelCase      :		Dict      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    int										=      self.get_scheduler_config(**_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      scheduler_class(**_UpperCAmelCase      )

								_lowerCAmelCase    :    List[Any]										=      10
								_lowerCAmelCase    :    Any										=      self.dummy_model()
								_lowerCAmelCase    :    Tuple										=      self.dummy_sample_deter
								scheduler.set_timesteps(_UpperCAmelCase      )

								for i, t in enumerate(scheduler.prk_timesteps      ):
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    Tuple										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample

								for i, t in enumerate(scheduler.plms_timesteps      ):
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    Dict										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample

								return sample



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      dict(self.forward_default_kwargs      )

								_lowerCAmelCase    :    int										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Tuple										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Union[str, Any]										=      scheduler_class(**_UpperCAmelCase      )

												_lowerCAmelCase    :    str										=      self.dummy_sample
												_lowerCAmelCase    :    str										=      0.1 * sample

												if num_inference_steps is not None and hasattr(_UpperCAmelCase		,	"""set_timesteps"""      ):
																scheduler.set_timesteps(_UpperCAmelCase      )
												elif num_inference_steps is not None and not hasattr(_UpperCAmelCase		,	"""set_timesteps"""      ):
																_lowerCAmelCase    :    Tuple										=      num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												_lowerCAmelCase    :    List[Any]										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												_lowerCAmelCase    :    List[str]										=      dummy_past_residuals[:]

												_lowerCAmelCase    :    Optional[int]										=      scheduler.step_prk(_UpperCAmelCase		,	0		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[int]										=      scheduler.step_prk(_UpperCAmelCase		,	1		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Tuple										=      scheduler.step_plms(_UpperCAmelCase		,	0		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[Any]										=      scheduler.step_plms(_UpperCAmelCase		,	1		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[Any]:





								'''simple docstring'''




								for timesteps in [100, 1000]:
												self.check_over_configs(num_train_timesteps=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Optional[int]:





								'''simple docstring'''




								for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=_UpperCAmelCase      )

								_lowerCAmelCase    :    Dict										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    Any										=      self.get_scheduler_config(steps_offset=1      )
								_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )
								scheduler.set_timesteps(10      )
								assert torch.equal(
								    scheduler.timesteps		,	torch.LongTensor(
								        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]      )		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[Any]:





								'''simple docstring'''




								for beta_start, beta_end in zip([0.0001, 0.001]		,	[0.002, 0.02]      ):
												self.check_over_configs(beta_start=_UpperCAmelCase		,	beta_end=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    int:





								'''simple docstring'''




								for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=_UpperCAmelCase      )



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





								'''simple docstring'''




								for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[str]:





								'''simple docstring'''




								for t in [1, 5, 10]:
												self.check_over_forward(time_step=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    List[Any]:





								'''simple docstring'''




								for t, num_inference_steps in zip([1, 5, 10]		,	[10, 50, 100]      ):
												self.check_over_forward(num_inference_steps=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      27

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Optional[Any]										=      self.dummy_sample
												_lowerCAmelCase    :    Any										=      0.1 * sample

												_lowerCAmelCase    :    List[str]										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )

												scheduler.set_timesteps(_UpperCAmelCase      )

												# before power of 3 fix, would error on first step, so we only need to do two
												for i, t in enumerate(scheduler.prk_timesteps[:2]      ):
																_lowerCAmelCase    :    List[Any]										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Union[str, Any]:





								'''simple docstring'''




								with self.assertRaises(_UpperCAmelCase      ):
												_lowerCAmelCase    :    Tuple										=      self.scheduler_classes[0]
												_lowerCAmelCase    :    List[str]										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )

												scheduler.step_plms(self.dummy_sample		,	1		,	self.dummy_sample      ).prev_sample



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.full_loop()
								_lowerCAmelCase    :    str										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    List[Any]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 198.1318      ) < 1E-2
								assert abs(result_mean.item() - 0.2580      ) < 1E-3



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.full_loop(prediction_type="""v_prediction"""      )
								_lowerCAmelCase    :    Optional[int]										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Optional[int]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 67.3986      ) < 1E-2
								assert abs(result_mean.item() - 0.0878      ) < 1E-3



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      self.full_loop(set_alpha_to_one=_UpperCAmelCase		,	beta_start=0.01      )
								_lowerCAmelCase    :    Any										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Optional[Any]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 230.0399      ) < 1E-2
								assert abs(result_mean.item() - 0.2995      ) < 1E-3



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





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.full_loop(set_alpha_to_one=_UpperCAmelCase		,	beta_start=0.01      )
								_lowerCAmelCase    :    List[Any]										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Tuple										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 186.9482      ) < 1E-2
								assert abs(result_mean.item() - 0.2434      ) < 1E-3

