



"""simple docstring"""


import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


snake_case_    					=	logging.get_logger(__name__)

snake_case_    					=	{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case_    					=	{
    """vocab_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
snake_case_    					=	{
    """vocab_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
snake_case_    					=	{
    """vocab_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case_    					=	{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
snake_case_    					=	{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
snake_case_    					=	{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


snake_case_    					=	{
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
snake_case_    					=	{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
snake_case_    					=	{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							VOCAB_FILES_NAMES
						__UpperCamelCase		    =							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase		    =							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase		    =							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
						__UpperCamelCase		    =							DPRContextEncoderTokenizer







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							VOCAB_FILES_NAMES
						__UpperCamelCase		    =							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase		    =							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase		    =							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
						__UpperCamelCase		    =							DPRQuestionEncoderTokenizer


snake_case_    					=	collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

snake_case_    					=	collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


snake_case_    					=	R"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """







@add_start_docstrings(SCREAMING_SNAKE_CASE_    )
class 				A_      :



						"""simple docstring"""






						def __call__( self			:Optional[int]       ,  lowercase_			:int       ,  lowercase_			:Optional[str] = None       ,  lowercase_			:Optional[str] = None       ,  lowercase_			:Union[bool, str] = False       ,  lowercase_			:Union[bool, str] = False       ,  lowercase_			:Optional[int] = None       ,  lowercase_			:Optional[Union[str, TensorType]] = None       ,  lowercase_			:Optional[bool] = None       ,  **lowercase_			:Optional[int]       ,  )    ->      BatchEncoding:
												if titles is None and texts is None:
																		return super().__call__(
																		    lowercase_       ,  padding=lowercase_       ,  truncation=lowercase_       ,  max_length=lowercase_       ,  return_tensors=lowercase_       ,  return_attention_mask=lowercase_       ,  **lowercase_       ,  )
												elif titles is None or texts is None:
																		UpperCAmelCase					=			titles if texts is None else texts
																		return super().__call__(
																		    lowercase_       ,  lowercase_       ,  padding=lowercase_       ,  truncation=lowercase_       ,  max_length=lowercase_       ,  return_tensors=lowercase_       ,  return_attention_mask=lowercase_       ,  **lowercase_       ,  )
												UpperCAmelCase					=			titles if not isinstance(lowercase_       ,  lowercase_ ) else [titles]
												UpperCAmelCase					=			texts if not isinstance(lowercase_       ,  lowercase_ ) else [texts]
												UpperCAmelCase					=			len(lowercase_ )
												UpperCAmelCase					=			questions if not isinstance(lowercase_       ,  lowercase_ ) else [questions] * n_passages
												assert len(lowercase_ ) == len(
												    lowercase_ ), f"""There should be as many titles than texts but got {len(lowercase_ )} titles and {len(lowercase_ )} texts."""
												UpperCAmelCase					=			super().__call__(lowercase_       ,  lowercase_       ,  padding=lowercase_       ,  truncation=lowercase_ )['input_ids']
												UpperCAmelCase					=			super().__call__(lowercase_       ,  add_special_tokens=lowercase_       ,  padding=lowercase_       ,  truncation=lowercase_ )['input_ids']
												UpperCAmelCase					=			{
												    'input_ids': [
												        (encoded_question_and_title + encoded_text)[:max_length]
												        if max_length is not None and truncation
												        else encoded_question_and_title + encoded_text
												        for encoded_question_and_title, encoded_text in zip(lowercase_       ,  lowercase_ )
												    ]
												}
												if return_attention_mask is not False:
																		UpperCAmelCase					=			[]
																		for input_ids in encoded_inputs["input_ids"]:
																								attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
																		UpperCAmelCase					=			attention_mask
												return self.pad(lowercase_       ,  padding=lowercase_       ,  max_length=lowercase_       ,  return_tensors=lowercase_ )






						def      UpperCAmelCase__					( self			:List[str]       ,  lowercase_			:BatchEncoding       ,  lowercase_			:DPRReaderOutput       ,  lowercase_			:int = 16       ,  lowercase_			:int = 64       ,  lowercase_			:int = 4       ,  )    ->      List[DPRSpanPrediction]:
												UpperCAmelCase					=			reader_input['input_ids']
												UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase					=			reader_output[:3]
												UpperCAmelCase					=			len(lowercase_ )
												UpperCAmelCase					=			sorted(range(lowercase_ )       ,  reverse=lowercase_       ,  key=relevance_logits.__getitem__ )
												UpperCAmelCase					=			[]
												for doc_id in sorted_docs:
																		UpperCAmelCase					=			list(input_ids[doc_id] )
																		# assuming question & title information is at the beginning of the sequence
																		UpperCAmelCase					=			sequence_ids.index(self.sep_token_id       ,  2 ) + 1  # second sep id
																		if sequence_ids[-1] == self.pad_token_id:
																								UpperCAmelCase					=			sequence_ids.index(self.pad_token_id )
																		else:
																								UpperCAmelCase					=			len(lowercase_ )

																		UpperCAmelCase					=			self._get_best_spans(
																		    start_logits=start_logits[doc_id][passage_offset:sequence_len]       ,  end_logits=end_logits[doc_id][passage_offset:sequence_len]       ,  max_answer_length=lowercase_       ,  top_spans=lowercase_       ,  )
																		for start_index, end_index in best_spans:
																								start_index += passage_offset
																								end_index += passage_offset
																								nbest_spans_predictions.append(
																								    DPRSpanPrediction(
																								        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]       ,  relevance_score=relevance_logits[doc_id]       ,  doc_id=lowercase_       ,  start_index=lowercase_       ,  end_index=lowercase_       ,  text=self.decode(sequence_ids[start_index : end_index + 1] )       ,  ) )
																		if len(lowercase_ ) >= num_spans:
																								break
												return nbest_spans_predictions[:num_spans]






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:List[int]       ,  lowercase_			:List[int]       ,  lowercase_			:int       ,  lowercase_			:int       ,  )    ->      List[DPRSpanPrediction]:
												UpperCAmelCase					=			[]
												for start_index, start_score in enumerate(lowercase_ ):
																		for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
																								scores.append(((start_index, start_index + answer_length), start_score + end_score) )
												UpperCAmelCase					=			sorted(lowercase_       ,  key=lambda lowercase_ : x[1]       ,  reverse=lowercase_ )
												UpperCAmelCase					=			[]
												for (start_index, end_index), score in scores:
																		assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
																		UpperCAmelCase					=			end_index - start_index + 1
																		assert length <= max_answer_length, f"""Span is too long: {length} > {max_answer_length}"""
																		if any(
																		    start_index <= prev_start_index <= prev_end_index <= end_index
																		    or prev_start_index <= start_index <= end_index <= prev_end_index
																		    for (prev_start_index, prev_end_index) in chosen_span_intervals ):
																								continue
																		chosen_span_intervals.append((start_index, end_index) )

																		if len(lowercase_ ) == top_spans:
																								break
												return chosen_span_intervals







@add_end_docstrings(SCREAMING_SNAKE_CASE_    )
class 				A_      (					SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							VOCAB_FILES_NAMES
						__UpperCamelCase		    =							READER_PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase		    =							READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase		    =							READER_PRETRAINED_INIT_CONFIGURATION
						__UpperCamelCase		    =							["""input_ids""", """attention_mask"""]
						__UpperCamelCase		    =							DPRReaderTokenizer










"""simple docstring"""


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							(UniPCMultistepScheduler,)
						__UpperCamelCase		    =							(("""num_inference_steps""", 25),)






						def      UpperCAmelCase__					( self			:Any       ,  **lowercase_			:Optional[Any] )    ->      Dict:
												UpperCAmelCase					=			{
												    'num_train_timesteps': 10_00,
												    'beta_start': 0.0001,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												    'solver_order': 2,
												    'solver_type': 'bh2',
												}

												config.update(**lowercase_ )
												return config






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:Optional[int]=0       ,  **lowercase_			:Optional[Any] )    ->      List[str]:
												UpperCAmelCase					=			dict(self.forward_default_kwargs )
												UpperCAmelCase					=			kwargs.pop('num_inference_steps'       ,  lowercase_ )
												UpperCAmelCase					=			self.dummy_sample
												UpperCAmelCase					=			0.1 * sample
												UpperCAmelCase					=			[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config(**lowercase_ )
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )
																		scheduler.set_timesteps(lowercase_ )
																		# copy over dummy past residuals
																		UpperCAmelCase					=			dummy_past_residuals[: scheduler.config.solver_order]

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

																		UpperCAmelCase				,   UpperCAmelCase					=			sample, sample
																		for t in range(lowercase_       ,  time_step + scheduler.config.solver_order + 1 ):
																								UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample
																								UpperCAmelCase					=			new_scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample

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






						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:Dict=0       ,  **lowercase_			:Optional[int] )    ->      List[str]:
												UpperCAmelCase					=			dict(self.forward_default_kwargs )
												UpperCAmelCase					=			kwargs.pop('num_inference_steps'       ,  lowercase_ )
												UpperCAmelCase					=			self.dummy_sample
												UpperCAmelCase					=			0.1 * sample
												UpperCAmelCase					=			[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config()
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )
																		scheduler.set_timesteps(lowercase_ )

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

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

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

																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample
																		UpperCAmelCase					=			new_scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample

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






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

												UpperCAmelCase					=			self.scheduler_classes[0]
												UpperCAmelCase					=			self.get_scheduler_config(**lowercase_ )
												UpperCAmelCase					=			scheduler_class(**lowercase_ )

												UpperCAmelCase					=			10
												UpperCAmelCase					=			self.dummy_model()
												UpperCAmelCase					=			self.dummy_sample_deter
												scheduler.set_timesteps(lowercase_ )

												for i, t in enumerate(scheduler.timesteps ):
																		UpperCAmelCase					=			model(lowercase_       ,  lowercase_ )
																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_ ).prev_sample

												return sample






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Tuple:
												UpperCAmelCase					=			dict(self.forward_default_kwargs )

												UpperCAmelCase					=			kwargs.pop('num_inference_steps'       ,  lowercase_ )

												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config()
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )

																		UpperCAmelCase					=			self.dummy_sample
																		UpperCAmelCase					=			0.1 * sample

																		if num_inference_steps is not None and hasattr(lowercase_       ,  'set_timesteps' ):
																								scheduler.set_timesteps(lowercase_ )
																		elif num_inference_steps is not None and not hasattr(lowercase_       ,  'set_timesteps' ):
																								UpperCAmelCase					=			num_inference_steps

																		# copy over dummy past residuals (must be done after set_timesteps)
																		UpperCAmelCase					=			[residual + 0.2, residual + 0.15, residual + 0.10]
																		UpperCAmelCase					=			dummy_past_residuals[: scheduler.config.solver_order]

																		UpperCAmelCase					=			scheduler.timesteps[5]
																		UpperCAmelCase					=			scheduler.timesteps[6]

																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample
																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample

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






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Optional[Any]:
												# make sure that iterating over schedulers with same config names gives same results
												# for defaults
												UpperCAmelCase					=			UniPCMultistepScheduler(**self.get_scheduler_config() )
												UpperCAmelCase					=			self.full_loop(scheduler=lowercase_ )
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.2464 ) < 1E-3

												UpperCAmelCase					=			DPMSolverSinglestepScheduler.from_config(scheduler.config )
												UpperCAmelCase					=			DEISMultistepScheduler.from_config(scheduler.config )
												UpperCAmelCase					=			DPMSolverMultistepScheduler.from_config(scheduler.config )
												UpperCAmelCase					=			UniPCMultistepScheduler.from_config(scheduler.config )

												UpperCAmelCase					=			self.full_loop(scheduler=lowercase_ )
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.2464 ) < 1E-3






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






						def      UpperCAmelCase__					( self			:int )    ->      str:
												self.check_over_configs(thresholding=lowercase_ )
												for order in [1, 2, 3]:
																		for solver_type in ["bh1", "bh2"]:
																								for threshold in [0.5, 1.0, 2.0]:
																														for prediction_type in ["epsilon", "sample"]:
																																				self.check_over_configs(
																																				    thresholding=lowercase_       ,  prediction_type=lowercase_       ,  sample_max_value=lowercase_       ,  solver_order=lowercase_       ,  solver_type=lowercase_       ,  )






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






						def      UpperCAmelCase__					( self			:str )    ->      Optional[Any]:
												for solver_type in ["bh1", "bh2"]:
																		for order in [1, 2, 3]:
																								for prediction_type in ["epsilon", "sample"]:
																														self.check_over_configs(
																														    solver_order=lowercase_       ,  solver_type=lowercase_       ,  prediction_type=lowercase_       ,  )
																														UpperCAmelCase					=			self.full_loop(
																														    solver_order=lowercase_       ,  solver_type=lowercase_       ,  prediction_type=lowercase_       ,  )
																														assert not torch.isnan(lowercase_ ).any(), "Samples have nan numbers"






						def      UpperCAmelCase__					( self			:Dict )    ->      List[Any]:
												self.check_over_configs(lower_order_final=lowercase_ )
												self.check_over_configs(lower_order_final=lowercase_ )






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






						def      UpperCAmelCase__					( self			:int )    ->      Union[str, Any]:
												UpperCAmelCase					=			self.full_loop()
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.2464 ) < 1E-3






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      str:
												UpperCAmelCase					=			self.full_loop(prediction_type='v_prediction' )
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.1014 ) < 1E-3






						def      UpperCAmelCase__					( self			:Tuple )    ->      Tuple:
												UpperCAmelCase					=			self.scheduler_classes[0]
												UpperCAmelCase					=			self.get_scheduler_config(thresholding=lowercase_       ,  dynamic_thresholding_ratio=0 )
												UpperCAmelCase					=			scheduler_class(**lowercase_ )

												UpperCAmelCase					=			10
												UpperCAmelCase					=			self.dummy_model()
												UpperCAmelCase					=			self.dummy_sample_deter.half()
												scheduler.set_timesteps(lowercase_ )

												for i, t in enumerate(scheduler.timesteps ):
																		UpperCAmelCase					=			model(lowercase_       ,  lowercase_ )
																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_ ).prev_sample

												assert sample.dtype == torch.floataa






						def      UpperCAmelCase__					( self			:List[Any]       ,  **lowercase_			:Optional[Any] )    ->      Dict:
												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config(**lowercase_ )
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )

																		scheduler.set_timesteps(scheduler.config.num_train_timesteps )
																		assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps





