






"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFXLMForMultipleChoice,
     TFXLMForQuestionAnsweringSimple,
     TFXLMForSequenceClassification,
     TFXLMForTokenClassification,
     TFXLMModel,
     TFXLMWithLMHeadModel,
     XLMConfig,
 )




class        lowerCAmelCase__       :
   def __init__(						self	:     str				,       snake_case__	:     Union[str, Any]				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Dict				=				parent
        UpperCAmelCase__		:  Dict				=				1_3
        UpperCAmelCase__		:  Optional[Any]				=				7
        UpperCAmelCase__		:  Optional[Any]				=				True
        UpperCAmelCase__		:  str				=				True
        UpperCAmelCase__		:  Optional[Any]				=				True
        UpperCAmelCase__		:  Dict				=				True
        UpperCAmelCase__		:  Tuple				=				True
        UpperCAmelCase__		:  int				=				False
        UpperCAmelCase__		:  Optional[Any]				=				False
        UpperCAmelCase__		:  List[Any]				=				False
        UpperCAmelCase__		:  Any				=				2
        UpperCAmelCase__		:  int				=				9_9
        UpperCAmelCase__		:  List[Any]				=				0
        UpperCAmelCase__		:  Optional[Any]				=				3_2
        UpperCAmelCase__		:  Tuple				=				2
        UpperCAmelCase__		:  int				=				4
        UpperCAmelCase__		:  Dict				=				0.1
        UpperCAmelCase__		:  Any				=				0.1
        UpperCAmelCase__		:  List[Any]				=				5_1_2
        UpperCAmelCase__		:  Optional[int]				=				1_6
        UpperCAmelCase__		:  List[Any]				=				2
        UpperCAmelCase__		:  Optional[int]				=				0.02
        UpperCAmelCase__		:  Dict				=				3
        UpperCAmelCase__		:  Union[str, Any]				=				4
        UpperCAmelCase__		:  Union[str, Any]				=				"""last"""
        UpperCAmelCase__		:  List[Any]				=				True
        UpperCAmelCase__		:  str				=				None
        UpperCAmelCase__		:  Union[str, Any]				=				0
   def 	__a				(						self	:     Union[str, Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[Any]				=				ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size				)
        UpperCAmelCase__		:  Union[str, Any]				=				random_attention_mask([self.batch_size, self.seq_length]				,       dtype=tf.floataa				)

        UpperCAmelCase__		:  Optional[Any]				=				None
        if self.use_input_lengths:
             UpperCAmelCase__		:  str				=				(
                 ids_tensor([self.batch_size]				,       vocab_size=2				) + self.seq_length - 2
             )  # small variation of seq_length

        UpperCAmelCase__		:  List[str]				=				None
        if self.use_token_type_ids:
             UpperCAmelCase__		:  str				=				ids_tensor([self.batch_size, self.seq_length]				,       self.n_langs				)

        UpperCAmelCase__		:  str				=				None
        UpperCAmelCase__		:  Optional[Any]				=				None
        UpperCAmelCase__		:  Tuple				=				None
        if self.use_labels:
             UpperCAmelCase__		:  Optional[int]				=				ids_tensor([self.batch_size]				,       self.type_sequence_label_size				)
             UpperCAmelCase__		:  List[str]				=				ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels				)
             UpperCAmelCase__		:  str				=				ids_tensor([self.batch_size]				,       2				,       dtype=tf.floataa				)
             UpperCAmelCase__		:  str				=				ids_tensor([self.batch_size]				,       self.num_choices				)

        UpperCAmelCase__		:  Any				=				XLMConfig(
            vocab_size=self.vocab_size				,       n_special=self.n_special				,       emb_dim=self.hidden_size				,       n_layers=self.num_hidden_layers				,       n_heads=self.num_attention_heads				,       dropout=self.hidden_dropout_prob				,       attention_dropout=self.attention_probs_dropout_prob				,       gelu_activation=self.gelu_activation				,       sinusoidal_embeddings=self.sinusoidal_embeddings				,       asm=self.asm				,       causal=self.causal				,       n_langs=self.n_langs				,       max_position_embeddings=self.max_position_embeddings				,       initializer_range=self.initializer_range				,       summary_type=self.summary_type				,       use_proj=self.use_proj				,       bos_token_id=self.bos_token_id				,       )

        return (
            config,
            input_ids,
            token_type_ids,
            input_lengths,
            sequence_labels,
            token_labels,
            is_impossible_labels,
            choice_labels,
            input_mask,
        )
   def 	__a				(						self	:     Any				,       snake_case__	:     Dict				,       snake_case__	:     Optional[int]				,       snake_case__	:     Optional[int]				,       snake_case__	:     Any				,       snake_case__	:     Union[str, Any]				,       snake_case__	:     Dict				,       snake_case__	:     Union[str, Any]				,       snake_case__	:     str				,       snake_case__	:     Optional[int]				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Tuple				=				TFXLMModel(config=lowerCAmelCase__				)
        UpperCAmelCase__		:  Union[str, Any]				=				{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
        UpperCAmelCase__		:  Tuple				=				model(lowerCAmelCase__				)

        UpperCAmelCase__		:  List[Any]				=				[input_ids, input_mask]
        UpperCAmelCase__		:  List[Any]				=				model(lowerCAmelCase__				)
        self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)				)
   def 	__a				(						self	:     int				,       snake_case__	:     List[Any]				,       snake_case__	:     Dict				,       snake_case__	:     List[Any]				,       snake_case__	:     Union[str, Any]				,       snake_case__	:     Any				,       snake_case__	:     List[str]				,       snake_case__	:     Dict				,       snake_case__	:     List[str]				,       snake_case__	:     str				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Tuple				=				TFXLMWithLMHeadModel(lowerCAmelCase__				)

        UpperCAmelCase__		:  Optional[Any]				=				{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
        UpperCAmelCase__		:  List[Any]				=				model(lowerCAmelCase__				)

        UpperCAmelCase__		:  Tuple				=				outputs

        self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)				)
   def 	__a				(						self	:     str				,       snake_case__	:     Dict				,       snake_case__	:     Dict				,       snake_case__	:     List[Any]				,       snake_case__	:     Dict				,       snake_case__	:     str				,       snake_case__	:     str				,       snake_case__	:     List[str]				,       snake_case__	:     Any				,       snake_case__	:     Optional[int]				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Tuple				=				TFXLMForQuestionAnsweringSimple(lowerCAmelCase__				)

        UpperCAmelCase__		:  Any				=				{"""input_ids""": input_ids, """lengths""": input_lengths}

        UpperCAmelCase__		:  Any				=				model(lowerCAmelCase__				)

        self.parent.assertEqual(result.start_logits.shape				,       (self.batch_size, self.seq_length)				)
        self.parent.assertEqual(result.end_logits.shape				,       (self.batch_size, self.seq_length)				)
   def 	__a				(						self	:     Optional[int]				,       snake_case__	:     Optional[int]				,       snake_case__	:     List[Any]				,       snake_case__	:     int				,       snake_case__	:     List[Any]				,       snake_case__	:     str				,       snake_case__	:     Tuple				,       snake_case__	:     Tuple				,       snake_case__	:     Any				,       snake_case__	:     str				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Union[str, Any]				=				TFXLMForSequenceClassification(lowerCAmelCase__				)

        UpperCAmelCase__		:  int				=				{"""input_ids""": input_ids, """lengths""": input_lengths}

        UpperCAmelCase__		:  List[Any]				=				model(lowerCAmelCase__				)

        self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.type_sequence_label_size)				)
   def 	__a				(						self	:     Any				,       snake_case__	:     Union[str, Any]				,       snake_case__	:     Union[str, Any]				,       snake_case__	:     Dict				,       snake_case__	:     Tuple				,       snake_case__	:     Optional[int]				,       snake_case__	:     List[str]				,       snake_case__	:     Tuple				,       snake_case__	:     str				,       snake_case__	:     int				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[Any]				=				self.num_labels
        UpperCAmelCase__		:  Tuple				=				TFXLMForTokenClassification(config=lowerCAmelCase__				)
        UpperCAmelCase__		:  int				=				{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
        UpperCAmelCase__		:  int				=				model(lowerCAmelCase__				)
        self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)				)
   def 	__a				(						self	:     int				,       snake_case__	:     Tuple				,       snake_case__	:     Optional[Any]				,       snake_case__	:     Union[str, Any]				,       snake_case__	:     Optional[int]				,       snake_case__	:     List[str]				,       snake_case__	:     Tuple				,       snake_case__	:     Dict				,       snake_case__	:     Any				,       snake_case__	:     str				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Any				=				self.num_choices
        UpperCAmelCase__		:  Union[str, Any]				=				TFXLMForMultipleChoice(config=lowerCAmelCase__				)
        UpperCAmelCase__		:  List[str]				=				tf.tile(tf.expand_dims(lowerCAmelCase__				,       1				)				,       (1, self.num_choices, 1)				)
        UpperCAmelCase__		:  Optional[Any]				=				tf.tile(tf.expand_dims(lowerCAmelCase__				,       1				)				,       (1, self.num_choices, 1)				)
        UpperCAmelCase__		:  Union[str, Any]				=				tf.tile(tf.expand_dims(lowerCAmelCase__				,       1				)				,       (1, self.num_choices, 1)				)
        UpperCAmelCase__		:  Tuple				=				{
            """input_ids""": multiple_choice_inputs_ids,
            """attention_mask""": multiple_choice_input_mask,
            """token_type_ids""": multiple_choice_token_type_ids,
        }
        UpperCAmelCase__		:  List[Any]				=				model(lowerCAmelCase__				)
        self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_choices)				)




   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        UpperCAmelCase__		:  Dict				=				self.prepare_config_and_inputs()
        (
            UpperCAmelCase__     
        )		:  Optional[int]				=				config_and_inputs
        UpperCAmelCase__		:  List[Any]				=				{
            """input_ids""": input_ids,
            """token_type_ids""": token_type_ids,
            """langs""": token_type_ids,
            """lengths""": input_lengths,
        }
        return config, inputs_dict




@require_tf
class        lowerCAmelCase__       (  __a				,		__a				,		unittest.TestCase					):
   SCREAMING_SNAKE_CASE_					      =(
       (
           TFXLMModel,
           TFXLMWithLMHeadModel,
           TFXLMForSequenceClassification,
           TFXLMForQuestionAnsweringSimple,
           TFXLMForTokenClassification,
           TFXLMForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   SCREAMING_SNAKE_CASE_					      =(
       (TFXLMWithLMHeadModel,) if is_tf_available() else ()
   )  # TODO (PVP): Check other models whether language generation is also applicable
   SCREAMING_SNAKE_CASE_					      =(
       {
           """feature-extraction""": TFXLMModel,
           """fill-mask""": TFXLMWithLMHeadModel,
           """question-answering""": TFXLMForQuestionAnsweringSimple,
           """text-classification""": TFXLMForSequenceClassification,
           """text-generation""": TFXLMWithLMHeadModel,
           """token-classification""": TFXLMForTokenClassification,
           """zero-shot""": TFXLMForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   SCREAMING_SNAKE_CASE_					      =False
   SCREAMING_SNAKE_CASE_					      =False
   def 	__a				(						self	:     Any				,       snake_case__	:     Any				,       snake_case__	:     str				,       snake_case__	:     Dict				,       snake_case__	:     Dict				,       snake_case__	:     Optional[int]				):


        '''simple docstring'''
        if (
            pipeline_test_casse_name == "QAPipelineTests"
            and tokenizer_name is not None
            and not tokenizer_name.endswith("Fast"				)
        ):
             # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
             # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
             # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
             return True

        return False
   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[Any]				=				TFXLMModelTester(self				)
        UpperCAmelCase__		:  Any				=				ConfigTester(self				,       config_class=lowerCAmelCase__				,       emb_dim=3_7				)
   def 	__a				(						self	:     Union[str, Any]				):


        '''simple docstring'''
        self.config_tester.run_common_tests()
   def 	__a				(						self	:     Tuple				):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[int]				=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_model(*lowerCAmelCase__				)
   def 	__a				(						self	:     Any				):


        '''simple docstring'''
        UpperCAmelCase__		:  Dict				=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_lm_head(*lowerCAmelCase__				)
   def 	__a				(						self	:     Tuple				):


        '''simple docstring'''
        UpperCAmelCase__		:  int				=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_qa(*lowerCAmelCase__				)
   def 	__a				(						self	:     List[str]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Dict				=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_sequence_classif(*lowerCAmelCase__				)
   def 	__a				(						self	:     Any				):


        '''simple docstring'''
        UpperCAmelCase__		:  int				=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_for_token_classification(*lowerCAmelCase__				)
   def 	__a				(						self	:     Any				):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[int]				=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlm_for_multiple_choice(*lowerCAmelCase__				)




   @slow
   def 	__a				(						self	:     str				):


        '''simple docstring'''
        for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase__		:  Optional[Any]				=				TFXLMModel.from_pretrained(lowerCAmelCase__				)
             self.assertIsNotNone(lowerCAmelCase__				)




@require_tf
class        lowerCAmelCase__       (  unittest.TestCase					):
   @slow
   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        UpperCAmelCase__		:  Union[str, Any]				=				TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"				)
        UpperCAmelCase__		:  str				=				tf.convert_to_tensor([[1_4, 4_4_7]]				,       dtype=tf.intaa				)  # the president
        UpperCAmelCase__		:  Optional[int]				=				[
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
            1_4,
            4_4_7,
        ]  # the president the president the president the president the president the president the president the president the president the president
        # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
        UpperCAmelCase__		:  int				=				model.generate(lowerCAmelCase__				,       do_sample=lowerCAmelCase__				)
        self.assertListEqual(output_ids[0].numpy().tolist()				,       lowerCAmelCase__				)




'''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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_=0.999 ,       lowerCAmelCase_="cosine" ,       ):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(lowerCAmelCase_       ):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(lowerCAmelCase_       ):
               return math.exp(t * -12.0       )

     else:
          raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"       )

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







class 	__lowerCAmelCase    (  __a       ,					__a				):
     snake_case   :    Any							      =      [e.name for e in KarrasDiffusionSchedulers]
     snake_case   :    List[Any]							      =      2



     @register_to_config
     def __init__(self						,	lowerCAmelCase__ = 1_0_0_0						,	lowerCAmelCase__ = 0.0_0_0_8_5						,	lowerCAmelCase__ = 0.0_1_2						,	lowerCAmelCase__ = "linear"						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = "epsilon"						,	lowerCAmelCase__ = "linspace"						,	lowerCAmelCase__ = 0						,	):
          if trained_betas is not None:
               _UpperCAmelCase					:  Optional[int]         =    torch.tensor(lowerCAmelCase__						,	dtype=torch.floataa	)
          elif beta_schedule == "linear":
               _UpperCAmelCase					:  Optional[int]         =    torch.linspace(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	dtype=torch.floataa	)
          elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _UpperCAmelCase					:  Tuple         =    (
                   torch.linspace(beta_start**0.5						,	beta_end**0.5						,	lowerCAmelCase__						,	dtype=torch.floataa	) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _UpperCAmelCase					:  Union[str, Any]         =    betas_for_alpha_bar(lowerCAmelCase__	)
          else:
               raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"	)

          _UpperCAmelCase					:  Union[str, Any]         =    1.0 - self.betas
          _UpperCAmelCase					:  List[Any]         =    torch.cumprod(self.alphas						,	dim=0	)

          #  set all values
          self.set_timesteps(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__=None	):
          if schedule_timesteps is None:
               _UpperCAmelCase					:  Optional[Any]         =    self.timesteps

          _UpperCAmelCase					:  Optional[Any]         =    (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:
               _UpperCAmelCase					:  Dict         =    1 if len(lowerCAmelCase__	) > 1 else 0
          else:
               _UpperCAmelCase					:  str         =    timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__	) else timestep
               _UpperCAmelCase					:  Any         =    self._index_counter[timestep_int]

          return indices[pos].item()



     @property
     def     snake_case_ (self	):
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

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



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Dict         =    self.index_for_timestep(lowerCAmelCase__	)

          if self.state_in_first_order:
               _UpperCAmelCase					:  Any         =    self.sigmas[step_index]
          else:
               _UpperCAmelCase					:  Tuple         =    self.sigmas_interpol[step_index - 1]

          _UpperCAmelCase					:  Dict         =    sample / ((sigma**2 + 1) ** 0.5)
          return sample



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	):
          _UpperCAmelCase					:  Dict         =    num_inference_steps

          _UpperCAmelCase					:  int         =    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":
               _UpperCAmelCase					:  Dict         =    np.linspace(0						,	num_train_timesteps - 1						,	lowerCAmelCase__						,	dtype=lowerCAmelCase__	)[::-1].copy()
          elif self.config.timestep_spacing == "leading":
               _UpperCAmelCase					:  List[str]         =    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
               _UpperCAmelCase					:  Optional[Any]         =    (np.arange(0						,	lowerCAmelCase__	) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__	)
               timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
               _UpperCAmelCase					:  Any         =    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
               _UpperCAmelCase					:  List[Any]         =    (np.arange(lowerCAmelCase__						,	0						,	-step_ratio	)).round().copy().astype(lowerCAmelCase__	)
               timesteps -= 1
          else:
               raise ValueError(
                   F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."	)

          _UpperCAmelCase					:  List[str]         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
          _UpperCAmelCase					:  Dict         =    torch.from_numpy(np.log(lowerCAmelCase__	)	).to(lowerCAmelCase__	)

          _UpperCAmelCase					:  Optional[Any]         =    np.interp(lowerCAmelCase__						,	np.arange(0						,	len(lowerCAmelCase__	)	)						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  Dict         =    np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
          _UpperCAmelCase					:  Any         =    torch.from_numpy(lowerCAmelCase__	).to(device=lowerCAmelCase__	)

          # compute up and down sigmas
          _UpperCAmelCase					:  str         =    sigmas.roll(-1	)
          _UpperCAmelCase					:  List[Any]         =    0.0
          _UpperCAmelCase					:  Optional[int]         =    (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
          _UpperCAmelCase					:  Any         =    (sigmas_next**2 - sigmas_up**2) ** 0.5
          _UpperCAmelCase					:  Tuple         =    0.0

          # compute interpolated sigmas
          _UpperCAmelCase					:  Union[str, Any]         =    sigmas.log().lerp(sigmas_down.log()						,	0.5	).exp()
          _UpperCAmelCase					:  int         =    0.0

          # set sigmas
          _UpperCAmelCase					:  List[Any]         =    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
          _UpperCAmelCase					:  str         =    torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)
          _UpperCAmelCase					:  Optional[int]         =    torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2	), sigmas_up[-1:]]	)
          _UpperCAmelCase					:  List[Any]         =    torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2	), sigmas_down[-1:]]	)

          if str(lowerCAmelCase__	).startswith("""mps"""	):
               # mps does not support float64
               _UpperCAmelCase					:  List[str]         =    torch.from_numpy(lowerCAmelCase__	).to(lowerCAmelCase__						,	dtype=torch.floataa	)
          else:
               _UpperCAmelCase					:  int         =    torch.from_numpy(lowerCAmelCase__	).to(lowerCAmelCase__	)

          _UpperCAmelCase					:  int         =    self.sigma_to_t(lowerCAmelCase__	).to(lowerCAmelCase__						,	dtype=timesteps.dtype	)
          _UpperCAmelCase					:  Optional[int]         =    torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])						,	dim=-1	).flatten()

          _UpperCAmelCase					:  Any         =    torch.cat([timesteps[:1], interleaved_timesteps]	)

          _UpperCAmelCase					:  Dict         =    None

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



     def     snake_case_ (self						,	lowerCAmelCase__	):
          # get log sigma
          _UpperCAmelCase					:  Union[str, Any]         =    sigma.log()

          # get distribution
          _UpperCAmelCase					:  str         =    log_sigma - self.log_sigmas[:, None]

          # get sigmas range
          _UpperCAmelCase					:  str         =    dists.ge(0	).cumsum(dim=0	).argmax(dim=0	).clamp(max=self.log_sigmas.shape[0] - 2	)
          _UpperCAmelCase					:  List[Any]         =    low_idx + 1

          _UpperCAmelCase					:  Optional[int]         =    self.log_sigmas[low_idx]
          _UpperCAmelCase					:  Dict         =    self.log_sigmas[high_idx]

          # interpolate sigmas
          _UpperCAmelCase					:  Union[str, Any]         =    (low - log_sigma) / (low - high)
          _UpperCAmelCase					:  Union[str, Any]         =    w.clamp(0						,	1	)

          # transform interpolation to time range
          _UpperCAmelCase					:  Union[str, Any]         =    (1 - w) * low_idx + w * high_idx
          _UpperCAmelCase					:  str         =    t.view(sigma.shape	)
          return t



     @property
     def     snake_case_ (self	):
          return self.sample is None



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = True						,	):
          _UpperCAmelCase					:  Union[str, Any]         =    self.index_for_timestep(lowerCAmelCase__	)

          # advance index counter by 1
          _UpperCAmelCase					:  Optional[Any]         =    timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__	) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
               _UpperCAmelCase					:  Tuple         =    self.sigmas[step_index]
               _UpperCAmelCase					:  List[str]         =    self.sigmas_interpol[step_index]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_up[step_index]
               _UpperCAmelCase					:  Optional[int]         =    self.sigmas_down[step_index - 1]
          else:
               # 2nd order / KPDM2's method
               _UpperCAmelCase					:  List[Any]         =    self.sigmas[step_index - 1]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_interpol[step_index - 1]
               _UpperCAmelCase					:  List[Any]         =    self.sigmas_up[step_index - 1]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_down[step_index - 1]

          # 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
          _UpperCAmelCase					:  int         =    0
          _UpperCAmelCase					:  str         =    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          _UpperCAmelCase					:  Optional[int]         =    model_output.device
          _UpperCAmelCase					:  Tuple         =    randn_tensor(model_output.shape						,	dtype=model_output.dtype						,	device=lowerCAmelCase__						,	generator=lowerCAmelCase__	)

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
               _UpperCAmelCase					:  int         =    sigma_hat if self.state_in_first_order else sigma_interpol
               _UpperCAmelCase					:  List[Any]         =    sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
               _UpperCAmelCase					:  Tuple         =    sigma_hat if self.state_in_first_order else sigma_interpol
               _UpperCAmelCase					:  Dict         =    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**2 + 1)
               )
          elif self.config.prediction_type == "sample":
               raise NotImplementedError("""prediction_type not implemented yet: sample"""	)
          else:
               raise ValueError(
                   F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"	)

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

               # store for 2nd order step
               _UpperCAmelCase					:  List[str]         =    sample
               _UpperCAmelCase					:  Optional[Any]         =    dt
               _UpperCAmelCase					:  Optional[int]         =    sample + derivative * dt
          else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               _UpperCAmelCase					:  Optional[int]         =    (sample - pred_original_sample) / sigma_interpol
               # 3. delta timestep
               _UpperCAmelCase					:  List[Any]         =    sigma_down - sigma_hat

               _UpperCAmelCase					:  int         =    self.sample
               _UpperCAmelCase					:  Union[str, Any]         =    None

               _UpperCAmelCase					:  str         =    sample + derivative * dt
               _UpperCAmelCase					:  List[str]         =    prev_sample + noise * sigma_up

          if not return_dict:
               return (prev_sample,)

          return SchedulerOutput(prev_sample=lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          _UpperCAmelCase					:  List[Any]         =    self.sigmas.to(device=original_samples.device						,	dtype=original_samples.dtype	)
          if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__	):
               # mps does not support float64
               _UpperCAmelCase					:  Any         =    self.timesteps.to(original_samples.device						,	dtype=torch.floataa	)
               _UpperCAmelCase					:  Dict         =    timesteps.to(original_samples.device						,	dtype=torch.floataa	)
          else:
               _UpperCAmelCase					:  Dict         =    self.timesteps.to(original_samples.device	)
               _UpperCAmelCase					:  Tuple         =    timesteps.to(original_samples.device	)

          _UpperCAmelCase					:  Any         =    [self.index_for_timestep(lowerCAmelCase__						,	lowerCAmelCase__	) for t in timesteps]

          _UpperCAmelCase					:  List[Any]         =    sigmas[step_indices].flatten()
          while len(sigma.shape	) < len(original_samples.shape	):
               _UpperCAmelCase					:  Dict         =    sigma.unsqueeze(-1	)

          _UpperCAmelCase					:  str         =    original_samples + noise * sigma
          return noisy_samples



     def __len__(self	):
          return self.config.num_train_timesteps


