

from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import (
          TF_MODEL_FOR_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_MASKED_LM_MAPPING,
          TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
          TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          TFSharedEmbeddings,
      )

      if _tf_gpu_memory_limit is not None:
            _SCREAMING_SNAKE_CASE							 =			tf.config.list_physical_devices('''GPU''')
            for gpu in gpus:
                  # Restrict TensorFlow to only allocate x GB of memory on the GPUs
                  try:
                        tf.config.set_logical_device_configuration(
                            gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                        )
                        _SCREAMING_SNAKE_CASE							 =			tf.config.list_logical_devices('''GPU''')
                        print('''Logical GPUs''', logical_gpus)
                  except RuntimeError as e:
                        # Virtual devices must be set before GPUs have been initialized
                        print(e)


@require_tf
class      lowerCamelCase_ :
 _lowerCAmelCase									=	None
 _lowerCAmelCase									=	()
 _lowerCAmelCase									=	()
 _lowerCAmelCase									=	True
 _lowerCAmelCase									=	True
 _lowerCAmelCase									=	True
 _lowerCAmelCase									=	False




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase=False							):
   __UpperCAmelCase	:  str		=     copy.deepcopy(A_							)

   if model_class in get_values(A_							):
     __UpperCAmelCase	:  List[str]		=     {
         k: tf.tile(tf.expand_dims(A_  ,  1							)  ,  (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)							)
         if isinstance(A_  ,  tf.Tensor							) and v.ndim > 0
         else v
         for k, v in inputs_dict.items()
     }

   if return_labels:
     if model_class in get_values(A_							):
       __UpperCAmelCase	:  Tuple		=     tf.ones(self.model_tester.batch_size  ,  dtype=tf.intaa							)
     elif model_class in get_values(A_							):
       __UpperCAmelCase	:  str		=     tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa							)
       __UpperCAmelCase	:  Optional[Any]		=     tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa							)
     elif model_class in [
         *get_values(A_							),
         *get_values(A_							),
     ]:
       __UpperCAmelCase	:  Dict		=     tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa							)
     elif model_class in get_values(A_							):
       __UpperCAmelCase	:  Dict		=     tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa							)
     elif model_class in [
         *get_values(A_							),
         *get_values(A_							),
         *get_values(A_							),
         *get_values(A_							),
         *get_values(A_							),
     ]:
       __UpperCAmelCase	:  Any		=     tf.zeros(
           (self.model_tester.batch_size, self.model_tester.seq_length)  ,  dtype=tf.intaa							)
   return inputs_dict




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  List[str]		=     self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  Union[str, Any]		=     self._prepare_for_class(A_  ,  A_							)
     __UpperCAmelCase	:  int		=     model_class(A_							)

     @tf.function
     def run_in_graph_mode():
       return model(A_							)

     __UpperCAmelCase	:  Tuple		=     run_in_graph_mode()
     self.assertIsNotNone(A_							)




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  Dict		=     self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  int		=     self._prepare_for_class(A_  ,  A_							)
     __UpperCAmelCase	:  List[Any]		=     model_class(A_							)

     @tf.function(experimental_compile=A_							)
     def run_in_graph_mode():
       return model(A_							)

     __UpperCAmelCase	:  Tuple		=     run_in_graph_mode()
     self.assertIsNotNone(A_							)




 @slow
 def     a       (			self							):
   # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
   __UpperCAmelCase,			__UpperCAmelCase	:  int		=     self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  int		=     model_class(A_							)
     if getattr(A_  ,  'hf_compute_loss'  ,  A_							):
       # Test that model correctly compute the loss with kwargs
       __UpperCAmelCase	:  Union[str, Any]		=     self._prepare_for_class(inputs_dict.copy()  ,  A_  ,  return_labels=A_							)
       # Is there a better way to remove these decoder inputs?
       __UpperCAmelCase	:  Optional[int]		=     {
           key: val
           for key, val in prepared_for_class.items()
           if key not in ('head_mask', 'decoder_head_mask', 'cross_attn_head_mask', 'decoder_input_ids')
       }

       __UpperCAmelCase	:  Optional[Any]		=     {
           'labels',
           'label',
           'label_ids',
           'start_positions',
           'start_position',
           'end_positions',
           'end_position',
           'next_sentence_label',
       }
       __UpperCAmelCase	:  List[Any]		=     possible_label_cols.intersection(set(A_							)							)
       self.assertGreater(len(A_							)  ,  0  ,  msg='No matching label names found!'							)
       __UpperCAmelCase	:  Union[str, Any]		=     {key: val for key, val in prepared_for_class.items() if key in label_names}
       __UpperCAmelCase	:  Optional[Any]		=     {key: val for key, val in prepared_for_class.items() if key not in label_names}
       self.assertGreater(len(A_							)  ,  0							)

       # Make sure it works with XLA!
       model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)  ,  jit_compile=A_							)
       # Make sure the model fits without crashing regardless of where we pass the labels
       __UpperCAmelCase	:  int		=     model.fit(
           A_  ,  validation_data=A_  ,  steps_per_epoch=1  ,  validation_steps=1  ,  shuffle=A_  ,  verbose=0  ,  )
       __UpperCAmelCase	:  int		=     history.history['loss'][0]
       self.assertTrue(not isnan(A_							)							)
       __UpperCAmelCase	:  Dict		=     history.history['val_loss'][0]
       self.assertTrue(not isnan(A_							)							)

       # Now test it with separate labels, to make sure that path works in XLA too.
       __UpperCAmelCase	:  Optional[Any]		=     model_class(A_							)
       model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)  ,  jit_compile=A_							)
       __UpperCAmelCase	:  Tuple		=     model.fit(
           A_  ,  A_  ,  validation_data=(inputs_minus_labels, labels)  ,  steps_per_epoch=1  ,  validation_steps=1  ,  shuffle=A_  ,  verbose=0  ,  )

       __UpperCAmelCase	:  Optional[int]		=     history.history['loss'][0]
       self.assertTrue(not isnan(A_							)							)
       __UpperCAmelCase	:  Any		=     history.history['val_loss'][0]
       self.assertTrue(not isnan(A_							)							)




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  List[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()
   __UpperCAmelCase	:  str		=     True
   __UpperCAmelCase	:  List[str]		=     True

   if hasattr(A_  ,  'use_cache'							):
     __UpperCAmelCase	:  int		=     True

   __UpperCAmelCase	:  int		=     getattr(self.model_tester  ,  'encoder_seq_length'  ,  self.model_tester.seq_length							)
   __UpperCAmelCase	:  str		=     getattr(self.model_tester  ,  'key_length'  ,  A_							)

   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  str		=     self._prepare_for_class(A_  ,  A_							)
     __UpperCAmelCase	:  Tuple		=     model_class(A_							)
     model.build()
     __UpperCAmelCase	:  Union[str, Any]		=     len(model(A_							)							)

     for key in list(class_inputs_dict.keys()							):
       # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
       if key not in model.input_signature:
         del class_inputs_dict[key]
       # Check it's a tensor, in case the inputs dict has some bools in it too
       elif isinstance(class_inputs_dict[key]  ,  tf.Tensor							) and class_inputs_dict[key].dtype.is_integer:
         __UpperCAmelCase	:  Optional[int]		=     tf.cast(class_inputs_dict[key]  ,  tf.intaa							)

     if set(class_inputs_dict.keys()							) != set(model.input_signature.keys()							):
       continue  # Some models have inputs that the preparation functions don't create, we skip those

     with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(A_  ,  saved_model=A_							)
       __UpperCAmelCase	:  Any		=     os.path.join(A_  ,  'saved_model'  ,  '1'							)
       __UpperCAmelCase	:  Dict		=     tf.keras.models.load_model(A_							)
       __UpperCAmelCase	:  Union[str, Any]		=     model(A_							)

       if self.is_encoder_decoder:
         __UpperCAmelCase	:  Dict		=     outputs['encoder_hidden_states']
         __UpperCAmelCase	:  Tuple		=     outputs['encoder_attentions']
       else:
         __UpperCAmelCase	:  Optional[Any]		=     outputs['hidden_states']
         __UpperCAmelCase	:  List[Any]		=     outputs['attentions']

       self.assertEqual(len(A_							)  ,  A_							)

       __UpperCAmelCase	:  Dict		=     getattr(
           self.model_tester  ,  'expected_num_hidden_layers'  ,  self.model_tester.num_hidden_layers + 1							)

       self.assertEqual(len(A_							)  ,  A_							)
       self.assertListEqual(
           list(output_hidden_states[0].shape[-2:]							)  ,  [self.model_tester.seq_length, self.model_tester.hidden_size]  ,  )

       self.assertEqual(len(A_							)  ,  self.model_tester.num_hidden_layers							)
       self.assertListEqual(
           list(output_attentions[0].shape[-3:]							)  ,  [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]  ,  )




 @slow
 def     a       (			self							):
   tf.keras.mixed_precision.set_global_policy('mixed_float16'							)

   # try/finally block to ensure subsequent tests run in float32
   try:
     __UpperCAmelCase,			__UpperCAmelCase	:  int		=     self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes[:2]:
       __UpperCAmelCase	:  List[str]		=     self._prepare_for_class(A_  ,  A_							)
       __UpperCAmelCase	:  Optional[Any]		=     model_class(A_							)
       __UpperCAmelCase	:  Dict		=     model(A_							)

       self.assertIsNotNone(A_							)
   finally:
     tf.keras.mixed_precision.set_global_policy('float32'							)




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  Dict		=     self.model_tester.prepare_config_and_inputs_for_common()
   # head_mask and decoder_head_mask has different shapes than other input args
   if "head_mask" in inputs_dict:
     del inputs_dict["head_mask"]
   if "decoder_head_mask" in inputs_dict:
     del inputs_dict["decoder_head_mask"]
   if "cross_attn_head_mask" in inputs_dict:
     del inputs_dict["cross_attn_head_mask"]
   __UpperCAmelCase	:  Dict		=     {
       module_member
       for model_class in self.all_model_classes
       for module in (import_module(model_class.__module__							),)
       for module_member_name in dir(A_							)
       if module_member_name.endswith('MainLayer'							)
       for module_member in (getattr(A_  ,  A_							),)
       if isinstance(A_  ,  A_							)
       and tf.keras.layers.Layer in module_member.__bases__
       and getattr(A_  ,  '_keras_serializable'  ,  A_							)
   }

   for main_layer_class in tf_main_layer_classes:
     # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
     if "T5" in main_layer_class.__name__:
       # Take the same values than in TFT5ModelTester for this shared layer
       __UpperCAmelCase	:  List[str]		=     TFSharedEmbeddings(self.model_tester.vocab_size  ,  self.model_tester.hidden_size  ,  name='shared'							)
       __UpperCAmelCase	:  List[Any]		=     False
       __UpperCAmelCase	:  List[Any]		=     main_layer_class(A_  ,  embed_tokens=A_							)
     else:
       __UpperCAmelCase	:  Union[str, Any]		=     main_layer_class(A_							)

     __UpperCAmelCase	:  List[Any]		=     {
         name: tf.keras.Input(tensor.shape[1:]  ,  dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
     }

     if hasattr(self.model_tester  ,  'num_labels'							):
       __UpperCAmelCase	:  int		=     self.model_tester.num_labels
     else:
       __UpperCAmelCase	:  List[str]		=     2

     __UpperCAmelCase	:  Optional[int]		=     tf.data.Dataset.from_tensor_slices(
         (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)							))							).batch(1							)

     __UpperCAmelCase	:  Tuple		=     main_layer(A_							)[0]
     __UpperCAmelCase	:  Optional[int]		=     tf.keras.layers.Dense(A_  ,  activation='softmax'  ,  name='outputs'							)(A_							)
     __UpperCAmelCase	:  Any		=     tf.keras.models.Model(inputs=A_  ,  outputs=[outputs]							)

     model.compile(loss='binary_crossentropy'  ,  optimizer='adam'  ,  metrics=['binary_accuracy']							)
     model.fit(A_  ,  epochs=1							)

     with tempfile.TemporaryDirectory() as tmpdirname:
       __UpperCAmelCase	:  int		=     os.path.join(A_  ,  'keras_model.h5'							)
       model.save(A_							)
       if "T5" in main_layer_class.__name__:
         __UpperCAmelCase	:  List[Any]		=     tf.keras.models.load_model(
             A_  ,  custom_objects={
                 main_layer_class.__name__: main_layer_class,
                 'TFSharedEmbeddings': TFSharedEmbeddings,
             }  ,  )
       else:
         __UpperCAmelCase	:  Union[str, Any]		=     tf.keras.models.load_model(
             A_  ,  custom_objects={main_layer_class.__name__: main_layer_class}							)
       assert isinstance(A_  ,  tf.keras.Model							)
       model(A_							)




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  int		=     self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  Any		=     model_class(A_							)

     __UpperCAmelCase	:  Union[str, Any]		=     copy.deepcopy(A_							)

     if not self.is_encoder_decoder:
       __UpperCAmelCase	:  List[Any]		=     inputs['input_ids']
       del inputs["input_ids"]
     else:
       __UpperCAmelCase	:  Tuple		=     inputs['input_ids']
       __UpperCAmelCase	:  Union[str, Any]		=     inputs.get('decoder_input_ids'  ,  A_							)
       del inputs["input_ids"]
       inputs.pop('decoder_input_ids'  ,  A_							)

     if not self.is_encoder_decoder:
       __UpperCAmelCase	:  Dict		=     model.get_input_embeddings()(A_							)
     else:
       __UpperCAmelCase	:  Optional[Any]		=     model.get_input_embeddings()(A_							)
       __UpperCAmelCase	:  Dict		=     model.get_input_embeddings()(A_							)

     __UpperCAmelCase	:  int		=     self._prepare_for_class(A_  ,  A_							)

     @tf.function
     def run_in_graph_mode():
       return model(A_							)

     __UpperCAmelCase	:  Optional[int]		=     run_in_graph_mode()
     self.assertIsNotNone(A_							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   # special tokens cannot be bad tokens
   __UpperCAmelCase	:  List[Any]		=     []
   if model.config.bos_token_id is not None:
     special_tokens.append(model.config.bos_token_id							)
   if model.config.pad_token_id is not None:
     special_tokens.append(model.config.pad_token_id							)
   if model.config.eos_token_id is not None:
     special_tokens.append(model.config.eos_token_id							)

   # create random bad tokens that are not special tokens
   __UpperCAmelCase	:  int		=     []
   while len(A_							) < num_bad_tokens:
     __UpperCAmelCase	:  Dict		=     tf.squeeze(ids_tensor((1, 1)  ,  self.model_tester.vocab_size							)  ,  0							).numpy()[0]
     if token not in special_tokens:
       bad_tokens.append(A_							)
   return bad_tokens




 def     a       (			self  ,  _UpperCAmelCase							):
   for token_id in output_ids[0].numpy().tolist():
     self.assertGreaterEqual(A_  ,  0							)
     self.assertLess(A_  ,  self.model_tester.vocab_size							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   # for all bad word tokens
   for bad_word_ids in bad_words_ids:
     # for all slices in batch
     for generated_ids_slice in generated_ids:
       # for all word idx
       for i in range(len(A_							)  ,  len(A_							)							):
         # if tokens match
         if generated_ids_slice[i - len(A_							) : i] == bad_word_ids:
           return True
   return False








import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


A__										=      logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			torch.FloatTensor
  SCREAMING_SNAKE_CASE__					:			Optional[torch.FloatTensor]					    =   None





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

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

    elif alpha_transform_type == "exp":

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

    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  UpperCamelCase_      (	_snake_case		,  _snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   [e.name for e in KarrasDiffusionSchedulers]
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   1



  @register_to_config
  def __init__(						self     :					Tuple  ,	A_     :					int = 1000  ,	A_     :					float = 0.0_001  ,	A_     :					float = 0.02  ,	A_     :					str = "linear"  ,	A_     :					Optional[Union[np.ndarray, List[float]]] = None  ,	A_     :					str = "epsilon"  ,	A_     :					str = "linspace"  ,	A_     :					int = 0  ,	)       ->			Optional[Any]:
      if trained_betas is not None:
          SCREAMING_SNAKE_CASE__         =    torch.tensor(A_  ,	dtype=torch.floataa						)
      elif beta_schedule == "linear":
          SCREAMING_SNAKE_CASE__         =    torch.linspace(A_  ,	A_  ,	A_  ,	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  ,	A_  ,	dtype=torch.floataa						) ** 2
          )
      elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          SCREAMING_SNAKE_CASE__         =    betas_for_alpha_bar(A_						)
      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						)

      SCREAMING_SNAKE_CASE__         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
      SCREAMING_SNAKE_CASE__         =    np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
      SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						)

      # setable values
      SCREAMING_SNAKE_CASE__         =    None
      SCREAMING_SNAKE_CASE__         =    np.linspace(0  ,	num_train_timesteps - 1  ,	A_  ,	dtype=A_						)[::-1].copy()
      SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						)
      SCREAMING_SNAKE_CASE__         =    False



  @property
  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Dict:
      # 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   _UpperCamelCase     (						self     :					str  ,	A_     :					torch.FloatTensor  ,	A_     :					Union[float, torch.FloatTensor]						)       ->			torch.FloatTensor:
      if isinstance(A_  ,	torch.Tensor						):
          SCREAMING_SNAKE_CASE__         =    timestep.to(self.timesteps.device						)
      SCREAMING_SNAKE_CASE__         =    (self.timesteps == timestep).nonzero().item()
      SCREAMING_SNAKE_CASE__         =    self.sigmas[step_index]
      SCREAMING_SNAKE_CASE__         =    sample / ((sigma**2 + 1) ** 0.5)
      SCREAMING_SNAKE_CASE__         =    True
      return sample



  def   _UpperCamelCase     (						self     :					List[Any]  ,	A_     :					int  ,	A_     :					Union[str, torch.device] = None						)       ->			Optional[int]:
      SCREAMING_SNAKE_CASE__         =    num_inference_steps

      # "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  ,	self.config.num_train_timesteps - 1  ,	A_  ,	dtype=A_						)[
              ::-1
          ].copy()
      elif self.config.timestep_spacing == "leading":
          SCREAMING_SNAKE_CASE__         =    self.config.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  ,	A_						) * step_ratio).round()[::-1].copy().astype(A_						)
          timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
          SCREAMING_SNAKE_CASE__         =    self.config.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(self.config.num_train_timesteps  ,	0  ,	-step_ratio						)).round().copy().astype(A_						)
          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.interp(A_  ,	np.arange(0  ,	len(A_						)						)  ,	A_						)
      SCREAMING_SNAKE_CASE__         =    np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
      SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						).to(device=A_						)
      if str(A_						).startswith("mps"						):
          # mps does not support float64
          SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						).to(A_  ,	dtype=torch.floataa						)
      else:
          SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						).to(device=A_						)



  def   _UpperCamelCase     (						self     :					Tuple  ,	A_     :					torch.FloatTensor  ,	A_     :					Union[float, torch.FloatTensor]  ,	A_     :					torch.FloatTensor  ,	A_     :					Optional[torch.Generator] = None  ,	A_     :					bool = True  ,	)       ->			Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

      if (
          isinstance(A_  ,	A_						)
          or isinstance(A_  ,	torch.IntTensor						)
          or isinstance(A_  ,	torch.LongTensor						)
      ):
          raise ValueError(
              (
                  "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                  " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                  " one of the `scheduler.timesteps` as a timestep."
              )  ,	)

      if not self.is_scale_input_called:
          logger.warning(
              "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
              "See `StableDiffusionPipeline` for a usage example."						)

      if isinstance(A_  ,	torch.Tensor						):
          SCREAMING_SNAKE_CASE__         =    timestep.to(self.timesteps.device						)

      SCREAMING_SNAKE_CASE__         =    (self.timesteps == timestep).nonzero().item()
      SCREAMING_SNAKE_CASE__         =    self.sigmas[step_index]

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
          SCREAMING_SNAKE_CASE__         =    sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          SCREAMING_SNAKE_CASE__         =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`"						)

      SCREAMING_SNAKE_CASE__         =    self.sigmas[step_index]
      SCREAMING_SNAKE_CASE__         =    self.sigmas[step_index + 1]
      SCREAMING_SNAKE_CASE__         =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
      SCREAMING_SNAKE_CASE__         =    (sigma_to**2 - sigma_up**2) ** 0.5

      # 2. Convert to an ODE derivative
      SCREAMING_SNAKE_CASE__         =    (sample - pred_original_sample) / sigma

      SCREAMING_SNAKE_CASE__         =    sigma_down - sigma

      SCREAMING_SNAKE_CASE__         =    sample + derivative * dt

      SCREAMING_SNAKE_CASE__         =    model_output.device
      SCREAMING_SNAKE_CASE__         =    randn_tensor(model_output.shape  ,	dtype=model_output.dtype  ,	device=A_  ,	generator=A_						)

      SCREAMING_SNAKE_CASE__         =    prev_sample + noise * sigma_up

      if not return_dict:
          return (prev_sample,)

      return EulerAncestralDiscreteSchedulerOutput(
          prev_sample=A_  ,	pred_original_sample=A_						)



  def   _UpperCamelCase     (						self     :					Tuple  ,	A_     :					torch.FloatTensor  ,	A_     :					torch.FloatTensor  ,	A_     :					torch.FloatTensor  ,	)       ->			torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      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(A_						):
          # 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__         =    [(schedule_timesteps == t).nonzero().item() 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     :					List[Any]						)       ->			Tuple:
      return self.config.num_train_timesteps


