"""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


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__							=				{
    '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'
        ),
    },
}
lowerCAmelCase__							=				{
    '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'
        ),
    },
}
lowerCAmelCase__							=				{
    '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'
        ),
    },
}

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


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





class  snake_case     (							__lowercase   ):
							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  snake_case     (							__lowercase   ):
							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







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

lowerCAmelCase__							=				collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


lowerCAmelCase__							=				r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '





@add_start_docstrings(__lowercase   )
class  snake_case     :

							def __call__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									if titles is None and texts is None:
											return super().__call__(
											    SCREAMING_SNAKE_CASE_       ,       padding=SCREAMING_SNAKE_CASE_       ,       truncation=SCREAMING_SNAKE_CASE_       ,       max_length=SCREAMING_SNAKE_CASE_       ,       return_tensors=SCREAMING_SNAKE_CASE_       ,       return_attention_mask=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_       ,       )
									elif titles is None or texts is None:
											SCREAMING_SNAKE_CASE_						 = titles if texts is None else texts
											return super().__call__(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       padding=SCREAMING_SNAKE_CASE_       ,       truncation=SCREAMING_SNAKE_CASE_       ,       max_length=SCREAMING_SNAKE_CASE_       ,       return_tensors=SCREAMING_SNAKE_CASE_       ,       return_attention_mask=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_       ,       )
									SCREAMING_SNAKE_CASE_						 = titles if not isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) else [titles]
									SCREAMING_SNAKE_CASE_						 = texts if not isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) else [texts]
									SCREAMING_SNAKE_CASE_						 = len(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = questions if not isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) else [questions] * n_passages
									assert len(SCREAMING_SNAKE_CASE_    ) == len(
									    SCREAMING_SNAKE_CASE_    ), f'There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE_    )} titles and {len(SCREAMING_SNAKE_CASE_    )} texts.'
									SCREAMING_SNAKE_CASE_						 = super().__call__(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       padding=SCREAMING_SNAKE_CASE_       ,       truncation=SCREAMING_SNAKE_CASE_    )['''input_ids''']
									SCREAMING_SNAKE_CASE_						 = super().__call__(SCREAMING_SNAKE_CASE_       ,       add_special_tokens=SCREAMING_SNAKE_CASE_       ,       padding=SCREAMING_SNAKE_CASE_       ,       truncation=SCREAMING_SNAKE_CASE_    )['''input_ids''']
									SCREAMING_SNAKE_CASE_						 = {
									    '''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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									    ]
									}
									if return_attention_mask is not False:
											SCREAMING_SNAKE_CASE_						 = []
											for input_ids in encoded_inputs["input_ids"]:
													attention_mask.append([int(input_id != self.pad_token_id    ) for input_id in input_ids]    )
											SCREAMING_SNAKE_CASE_						 = attention_mask
									return self.pad(SCREAMING_SNAKE_CASE_       ,       padding=SCREAMING_SNAKE_CASE_       ,       max_length=SCREAMING_SNAKE_CASE_       ,       return_tensors=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = 16       ,       SCREAMING_SNAKE_CASE_ = 64       ,       SCREAMING_SNAKE_CASE_ = 4       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = reader_input['''input_ids''']
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = reader_output[:3]
									SCREAMING_SNAKE_CASE_						 = len(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = sorted(range(SCREAMING_SNAKE_CASE_    )       ,       reverse=SCREAMING_SNAKE_CASE_       ,       key=relevance_logits.__getitem__    )
									SCREAMING_SNAKE_CASE_						 = []
									for doc_id in sorted_docs:
											SCREAMING_SNAKE_CASE_						 = list(input_ids[doc_id]    )
											# assuming question & title information is at the beginning of the sequence
											SCREAMING_SNAKE_CASE_						 = sequence_ids.index(self.sep_token_id       ,       2    ) + 1  # second sep id
											if sequence_ids[-1] == self.pad_token_id:
													SCREAMING_SNAKE_CASE_						 = sequence_ids.index(self.pad_token_id    )
											else:
													SCREAMING_SNAKE_CASE_						 = len(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = 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=SCREAMING_SNAKE_CASE_       ,       top_spans=SCREAMING_SNAKE_CASE_       ,       )
											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=SCREAMING_SNAKE_CASE_       ,       start_index=SCREAMING_SNAKE_CASE_       ,       end_index=SCREAMING_SNAKE_CASE_       ,       text=self.decode(sequence_ids[start_index : end_index + 1]    )       ,       )    )
											if len(SCREAMING_SNAKE_CASE_    ) >= num_spans:
													break
									return nbest_spans_predictions[:num_spans]







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = []
									for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE_    ):
											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)    )
									SCREAMING_SNAKE_CASE_						 = sorted(SCREAMING_SNAKE_CASE_       ,       key=lambda SCREAMING_SNAKE_CASE_    : x[1]       ,       reverse=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = []
									for (start_index, end_index), score in scores:
											assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
											SCREAMING_SNAKE_CASE_						 = 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(SCREAMING_SNAKE_CASE_    ) == top_spans:
													break
									return chosen_span_intervals







@add_end_docstrings(__lowercase   )
class  snake_case     (							__lowercase       ,			__lowercase   ):
							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 math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def        _lowerCamelCase ( __a,							__a=0.9_9_9,							__a="cosine",							):
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(__a    ):
						return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2    ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(__a    ):
						return math.exp(t * -1_2.0    )

		else:
				raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'    )

		SCREAMING_SNAKE_CASE_						 = []
		for i in range(__a    ):
				SCREAMING_SNAKE_CASE_						 = i / num_diffusion_timesteps
				SCREAMING_SNAKE_CASE_						 = (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(__a    ) / alpha_bar_fn(__a    ),							__a    )    )
		return torch.tensor(__a,							dtype=torch.floataa    )





class  snake_case     (							__lowercase       ,			__lowercase   ):
							UpperCAmelCase__						     =      [e.name for e in KarrasDiffusionSchedulers]
							UpperCAmelCase__						     =      2

							@register_to_config
							def __init__(self       ,       SCREAMING_SNAKE_CASE_ = 10_00       ,       SCREAMING_SNAKE_CASE_ = 0.0_00_85       ,       SCREAMING_SNAKE_CASE_ = 0.0_12       ,       SCREAMING_SNAKE_CASE_ = "linear"       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = "epsilon"       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = "linspace"       ,       SCREAMING_SNAKE_CASE_ = 0       ,       ):

									"""simple docstring"""







									if trained_betas is not None:
											SCREAMING_SNAKE_CASE_						 = torch.tensor(SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    )
									elif beta_schedule == "linear":
											SCREAMING_SNAKE_CASE_						 = torch.linspace(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    )
									elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											SCREAMING_SNAKE_CASE_						 = (
											    torch.linspace(beta_start**0.5       ,       beta_end**0.5       ,       SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    ) ** 2
											)
									elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											SCREAMING_SNAKE_CASE_						 = betas_for_alpha_bar(SCREAMING_SNAKE_CASE_       ,       alpha_transform_type='''cosine'''    )
									elif beta_schedule == "exp":
											SCREAMING_SNAKE_CASE_						 = betas_for_alpha_bar(SCREAMING_SNAKE_CASE_       ,       alpha_transform_type='''exp'''    )
									else:
											raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'    )

									SCREAMING_SNAKE_CASE_						 = 1.0 - self.betas
									SCREAMING_SNAKE_CASE_						 = torch.cumprod(self.alphas       ,       dim=0    )

									#  set all values
									self.set_timesteps(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = use_karras_sigmas

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=None    ):

									"""simple docstring"""







									if schedule_timesteps is None:
											SCREAMING_SNAKE_CASE_						 = self.timesteps

									SCREAMING_SNAKE_CASE_						 = (schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter    ) == 0:
											SCREAMING_SNAKE_CASE_						 = 1 if len(SCREAMING_SNAKE_CASE_    ) > 1 else 0
									else:
											SCREAMING_SNAKE_CASE_						 = timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_    ) else timestep
											SCREAMING_SNAKE_CASE_						 = self._index_counter[timestep_int]

									return indices[pos].item()

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.index_for_timestep(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index]
									SCREAMING_SNAKE_CASE_						 = sample / ((sigma**2 + 1) ** 0.5)
									return sample

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = num_inference_steps

									SCREAMING_SNAKE_CASE_						 = num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
											SCREAMING_SNAKE_CASE_						 = np.linspace(0       ,       num_train_timesteps - 1       ,       SCREAMING_SNAKE_CASE_       ,       dtype=SCREAMING_SNAKE_CASE_    )[::-1].copy()
									elif self.config.timestep_spacing == "leading":
											SCREAMING_SNAKE_CASE_						 = num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											SCREAMING_SNAKE_CASE_						 = (np.arange(0       ,       SCREAMING_SNAKE_CASE_    ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_    )
											timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
											SCREAMING_SNAKE_CASE_						 = num_train_timesteps / self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											SCREAMING_SNAKE_CASE_						 = (np.arange(SCREAMING_SNAKE_CASE_       ,       0       ,       -step_ratio    )).round().copy().astype(SCREAMING_SNAKE_CASE_    )
											timesteps -= 1
									else:
											raise ValueError(
											    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'    )

									SCREAMING_SNAKE_CASE_						 = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
									SCREAMING_SNAKE_CASE_						 = np.log(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = np.interp(SCREAMING_SNAKE_CASE_       ,       np.arange(0       ,       len(SCREAMING_SNAKE_CASE_    )    )       ,       SCREAMING_SNAKE_CASE_    )

									if self.config.use_karras_sigmas:
											SCREAMING_SNAKE_CASE_						 = self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE_       ,       num_inference_steps=self.num_inference_steps    )
											SCREAMING_SNAKE_CASE_						 = np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) for sigma in sigmas]    )

									SCREAMING_SNAKE_CASE_						 = np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
									SCREAMING_SNAKE_CASE_						 = torch.from_numpy(SCREAMING_SNAKE_CASE_    ).to(device=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2    ), sigmas[-1:]]    )

									SCREAMING_SNAKE_CASE_						 = torch.from_numpy(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2    )]    )

									if str(SCREAMING_SNAKE_CASE_    ).startswith('''mps'''    ):
											# mps does not support float64
											SCREAMING_SNAKE_CASE_						 = timesteps.to(SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    )
									else:
											SCREAMING_SNAKE_CASE_						 = timesteps.to(device=SCREAMING_SNAKE_CASE_    )

									# empty dt and derivative
									SCREAMING_SNAKE_CASE_						 = None
									SCREAMING_SNAKE_CASE_						 = None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									SCREAMING_SNAKE_CASE_						 = defaultdict(SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = np.log(SCREAMING_SNAKE_CASE_    )

									# get distribution
									SCREAMING_SNAKE_CASE_						 = log_sigma - log_sigmas[:, np.newaxis]

									# get sigmas range
									SCREAMING_SNAKE_CASE_						 = np.cumsum((dists >= 0)       ,       axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
									SCREAMING_SNAKE_CASE_						 = low_idx + 1

									SCREAMING_SNAKE_CASE_						 = log_sigmas[low_idx]
									SCREAMING_SNAKE_CASE_						 = log_sigmas[high_idx]

									# interpolate sigmas
									SCREAMING_SNAKE_CASE_						 = (low - log_sigma) / (low - high)
									SCREAMING_SNAKE_CASE_						 = np.clip(SCREAMING_SNAKE_CASE_       ,       0       ,       1    )

									# transform interpolation to time range
									SCREAMING_SNAKE_CASE_						 = (1 - w) * low_idx + w * high_idx
									SCREAMING_SNAKE_CASE_						 = t.reshape(sigma.shape    )
									return t

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = in_sigmas[-1].item()
									SCREAMING_SNAKE_CASE_						 = in_sigmas[0].item()

									SCREAMING_SNAKE_CASE_						 = 7.0  # 7.0 is the value used in the paper
									SCREAMING_SNAKE_CASE_						 = np.linspace(0       ,       1       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = sigma_min ** (1 / rho)
									SCREAMING_SNAKE_CASE_						 = sigma_max ** (1 / rho)
									SCREAMING_SNAKE_CASE_						 = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self.dt is None

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = True       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.index_for_timestep(SCREAMING_SNAKE_CASE_    )

									# advance index counter by 1
									SCREAMING_SNAKE_CASE_						 = timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_    ) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index]
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index + 1]
									else:
											# 2nd order / Heun's method
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index - 1]
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									SCREAMING_SNAKE_CASE_						 = 0
									SCREAMING_SNAKE_CASE_						 = sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
											SCREAMING_SNAKE_CASE_						 = sigma_hat if self.state_in_first_order else sigma_next
											SCREAMING_SNAKE_CASE_						 = sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
											SCREAMING_SNAKE_CASE_						 = sigma_hat if self.state_in_first_order else sigma_next
											SCREAMING_SNAKE_CASE_						 = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
											    sample / (sigma_input**2 + 1)
											)
									elif self.config.prediction_type == "sample":
											SCREAMING_SNAKE_CASE_						 = model_output
									else:
											raise ValueError(
											    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'    )

									if self.config.clip_sample:
											SCREAMING_SNAKE_CASE_						 = pred_original_sample.clamp(
											    -self.config.clip_sample_range       ,       self.config.clip_sample_range    )

									if self.state_in_first_order:
											# 2. Convert to an ODE derivative for 1st order
											SCREAMING_SNAKE_CASE_						 = (sample - pred_original_sample) / sigma_hat
											# 3. delta timestep
											SCREAMING_SNAKE_CASE_						 = sigma_next - sigma_hat

											# store for 2nd order step
											SCREAMING_SNAKE_CASE_						 = derivative
											SCREAMING_SNAKE_CASE_						 = dt
											SCREAMING_SNAKE_CASE_						 = sample
									else:
											# 2. 2nd order / Heun's method
											SCREAMING_SNAKE_CASE_						 = (sample - pred_original_sample) / sigma_next
											SCREAMING_SNAKE_CASE_						 = (self.prev_derivative + derivative) / 2

											# 3. take prev timestep & sample
											SCREAMING_SNAKE_CASE_						 = self.dt
											SCREAMING_SNAKE_CASE_						 = self.sample

											# free dt and derivative
											# Note, this puts the scheduler in "first order mode"
											SCREAMING_SNAKE_CASE_						 = None
											SCREAMING_SNAKE_CASE_						 = None
											SCREAMING_SNAKE_CASE_						 = None

									SCREAMING_SNAKE_CASE_						 = sample + derivative * dt

									if not return_dict:
											return (prev_sample,)

									return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.sigmas.to(device=original_samples.device       ,       dtype=original_samples.dtype    )
									if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_    ):
											# mps does not support float64
											SCREAMING_SNAKE_CASE_						 = self.timesteps.to(original_samples.device       ,       dtype=torch.floataa    )
											SCREAMING_SNAKE_CASE_						 = timesteps.to(original_samples.device       ,       dtype=torch.floataa    )
									else:
											SCREAMING_SNAKE_CASE_						 = self.timesteps.to(original_samples.device    )
											SCREAMING_SNAKE_CASE_						 = timesteps.to(original_samples.device    )

									SCREAMING_SNAKE_CASE_						 = [self.index_for_timestep(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) for t in timesteps]

									SCREAMING_SNAKE_CASE_						 = sigmas[step_indices].flatten()
									while len(sigma.shape    ) < len(original_samples.shape    ):
											SCREAMING_SNAKE_CASE_						 = sigma.unsqueeze(-1    )

									SCREAMING_SNAKE_CASE_						 = original_samples + noise * sigma
									return noisy_samples







							def __len__(self    ):

									"""simple docstring"""







									return self.config.num_train_timesteps