



"""simple docstring"""







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:
          lowercase__						:							List[Any]        =      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)]
                    )
                    lowercase__						:							Optional[int]        =      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  UpperCamelCase__  :




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	None
      _SCREAMING_SNAKE_CASE    							=	()
      _SCREAMING_SNAKE_CASE    							=	()
      _SCREAMING_SNAKE_CASE    							=	True
      _SCREAMING_SNAKE_CASE    							=	True
      _SCREAMING_SNAKE_CASE    							=	True
      _SCREAMING_SNAKE_CASE    							=	False



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[Any]       ,     SCREAMING_SNAKE_CASE_							: Dict       ,     SCREAMING_SNAKE_CASE_							: List[str]       ,     SCREAMING_SNAKE_CASE_							: Any=False			):
        lowerCAmelCase_  :							Optional[int]								=					copy.deepcopy(SCREAMING_SNAKE_CASE_			)

        if model_class in get_values(SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							Dict								=					{
              k: tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_       ,     1			)       ,     (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)			)
              if isinstance(SCREAMING_SNAKE_CASE_       ,     tf.Tensor			) and v.ndim > 0
              else v
              for k, v in inputs_dict.items()
          }

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



      @slow
      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							int								=					self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
          lowerCAmelCase_  :							Any								=					self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
          lowerCAmelCase_  :							int								=					model_class(SCREAMING_SNAKE_CASE_			)

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

          lowerCAmelCase_  :							Dict								=					run_in_graph_mode()
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)



      @slow
      def 	SCREAMING_SNAKE_CASE__					( self							: Union[str, Any]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							List[Any]								=					self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
          lowerCAmelCase_  :							str								=					self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
          lowerCAmelCase_  :							List[Any]								=					model_class(SCREAMING_SNAKE_CASE_			)

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

          lowerCAmelCase_  :							int								=					run_in_graph_mode()
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)



      @slow
      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[Any]			):
        # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
        lowerCAmelCase_		,lowerCAmelCase_  :							int								=					self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes[:2]:
          lowerCAmelCase_  :							str								=					model_class(SCREAMING_SNAKE_CASE_			)
          if getattr(SCREAMING_SNAKE_CASE_       ,     'hf_compute_loss'       ,     SCREAMING_SNAKE_CASE_			):
            # Test that model correctly compute the loss with kwargs
            lowerCAmelCase_  :							str								=					self._prepare_for_class(inputs_dict.copy()       ,     SCREAMING_SNAKE_CASE_       ,     return_labels=SCREAMING_SNAKE_CASE_			)
            # Is there a better way to remove these decoder inputs?
            lowerCAmelCase_  :							str								=					{
                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')
            }

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

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

            # Now test it with separate labels, to make sure that path works in XLA too.
            lowerCAmelCase_  :							str								=					model_class(SCREAMING_SNAKE_CASE_			)
            model.compile(optimizer=tf.keras.optimizers.SGD(0.0			)       ,     jit_compile=SCREAMING_SNAKE_CASE_			)
            lowerCAmelCase_  :							Dict								=					model.fit(
                SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     validation_data=(inputs_minus_labels, labels)       ,     steps_per_epoch=1       ,     validation_steps=1       ,     shuffle=SCREAMING_SNAKE_CASE_       ,     verbose=0       ,     )

            lowerCAmelCase_  :							Optional[Any]								=					history.history['loss'][0]
            self.assertTrue(not isnan(SCREAMING_SNAKE_CASE_			)			)
            lowerCAmelCase_  :							List[str]								=					history.history['val_loss'][0]
            self.assertTrue(not isnan(SCREAMING_SNAKE_CASE_			)			)



      @slow
      def 	SCREAMING_SNAKE_CASE__					( self							: Union[str, Any]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Union[str, Any]								=					self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase_  :							Tuple								=					True
        lowerCAmelCase_  :							Optional[Any]								=					True

        if hasattr(SCREAMING_SNAKE_CASE_       ,     'use_cache'			):
          lowerCAmelCase_  :							List[str]								=					True

        lowerCAmelCase_  :							List[Any]								=					getattr(self.model_tester       ,     'encoder_seq_length'       ,     self.model_tester.seq_length			)
        lowerCAmelCase_  :							Optional[int]								=					getattr(self.model_tester       ,     'key_length'       ,     SCREAMING_SNAKE_CASE_			)

        for model_class in self.all_model_classes[:2]:
          lowerCAmelCase_  :							int								=					self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
          lowerCAmelCase_  :							Optional[int]								=					model_class(SCREAMING_SNAKE_CASE_			)
          model.build()
          lowerCAmelCase_  :							Optional[Any]								=					len(model(SCREAMING_SNAKE_CASE_			)			)

          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:
              lowerCAmelCase_  :							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(SCREAMING_SNAKE_CASE_       ,     saved_model=SCREAMING_SNAKE_CASE_			)
            lowerCAmelCase_  :							Tuple								=					os.path.join(SCREAMING_SNAKE_CASE_       ,     'saved_model'       ,     '1'			)
            lowerCAmelCase_  :							List[str]								=					tf.keras.models.load_model(SCREAMING_SNAKE_CASE_			)
            lowerCAmelCase_  :							Union[str, Any]								=					model(SCREAMING_SNAKE_CASE_			)

            if self.is_encoder_decoder:
              lowerCAmelCase_  :							Any								=					outputs['encoder_hidden_states']
              lowerCAmelCase_  :							Optional[Any]								=					outputs['encoder_attentions']
            else:
              lowerCAmelCase_  :							int								=					outputs['hidden_states']
              lowerCAmelCase_  :							Any								=					outputs['attentions']

            self.assertEqual(len(SCREAMING_SNAKE_CASE_			)       ,     SCREAMING_SNAKE_CASE_			)

            lowerCAmelCase_  :							Optional[Any]								=					getattr(
                self.model_tester       ,     'expected_num_hidden_layers'       ,     self.model_tester.num_hidden_layers + 1			)

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

            self.assertEqual(len(SCREAMING_SNAKE_CASE_			)       ,     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 	SCREAMING_SNAKE_CASE__					( self							: str			):
        tf.keras.mixed_precision.set_global_policy('mixed_float16'			)

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

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



      @slow
      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[Any]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Optional[int]								=					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"]
        lowerCAmelCase_  :							List[Any]								=					{
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__			),)
            for module_member_name in dir(SCREAMING_SNAKE_CASE_			)
            if module_member_name.endswith('MainLayer'			)
            for module_member in (getattr(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			),)
            if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(SCREAMING_SNAKE_CASE_       ,     '_keras_serializable'       ,     SCREAMING_SNAKE_CASE_			)
        }

        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
            lowerCAmelCase_  :							Tuple								=					TFSharedEmbeddings(self.model_tester.vocab_size       ,     self.model_tester.hidden_size       ,     name='shared'			)
            lowerCAmelCase_  :							Dict								=					False
            lowerCAmelCase_  :							Optional[Any]								=					main_layer_class(SCREAMING_SNAKE_CASE_       ,     embed_tokens=SCREAMING_SNAKE_CASE_			)
          else:
            lowerCAmelCase_  :							Optional[int]								=					main_layer_class(SCREAMING_SNAKE_CASE_			)

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

          if hasattr(self.model_tester       ,     'num_labels'			):
            lowerCAmelCase_  :							int								=					self.model_tester.num_labels
          else:
            lowerCAmelCase_  :							str								=					2

          lowerCAmelCase_  :							List[str]								=					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			)

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

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

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



      @slow
      def 	SCREAMING_SNAKE_CASE__					( self							: int			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Any								=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:2]:
          lowerCAmelCase_  :							Union[str, Any]								=					model_class(SCREAMING_SNAKE_CASE_			)

          lowerCAmelCase_  :							str								=					copy.deepcopy(SCREAMING_SNAKE_CASE_			)

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

          if not self.is_encoder_decoder:
            lowerCAmelCase_  :							Any								=					model.get_input_embeddings()(SCREAMING_SNAKE_CASE_			)
          else:
            lowerCAmelCase_  :							Tuple								=					model.get_input_embeddings()(SCREAMING_SNAKE_CASE_			)
            lowerCAmelCase_  :							str								=					model.get_input_embeddings()(SCREAMING_SNAKE_CASE_			)

          lowerCAmelCase_  :							Tuple								=					self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)

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

          lowerCAmelCase_  :							Any								=					run_in_graph_mode()
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: Dict       ,     SCREAMING_SNAKE_CASE_							: str			):
        # special tokens cannot be bad tokens
        lowerCAmelCase_  :							Tuple								=					[]
        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
        lowerCAmelCase_  :							Any								=					[]
        while len(SCREAMING_SNAKE_CASE_			) < num_bad_tokens:
          lowerCAmelCase_  :							Tuple								=					tf.squeeze(ids_tensor((1, 1)       ,     self.model_tester.vocab_size			)       ,     0			).numpy()[0]
          if token not in special_tokens:
            bad_tokens.append(SCREAMING_SNAKE_CASE_			)
        return bad_tokens



      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple       ,     SCREAMING_SNAKE_CASE_							: Tuple			):
        for token_id in output_ids[0].numpy().tolist():
          self.assertGreaterEqual(SCREAMING_SNAKE_CASE_       ,     0			)
          self.assertLess(SCREAMING_SNAKE_CASE_       ,     self.model_tester.vocab_size			)





      def 	SCREAMING_SNAKE_CASE__					( self							: int       ,     SCREAMING_SNAKE_CASE_							: str       ,     SCREAMING_SNAKE_CASE_							: List[str]			):
        # 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(SCREAMING_SNAKE_CASE_			)       ,     len(SCREAMING_SNAKE_CASE_			)			):
              # if tokens match
              if generated_ids_slice[i - len(SCREAMING_SNAKE_CASE_			) : i] == bad_word_ids:
                return True
        return False










"""simple docstring"""







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


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







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




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	42
      _SCREAMING_SNAKE_CASE    							=	None




def 							UpperCamelCase_       (       lowerCAmelCase__ :							List[Any]       ,  lowerCAmelCase__ :							Optional[Any]=0.999       ,  lowerCAmelCase__ :							Optional[int]="cosine"       ,  )		->  Optional[Any]:

  """simple docstring"""




  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowerCAmelCase__ :							Optional[Any]		):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(lowerCAmelCase__ :							Union[str, Any]		):
      return math.exp(t * -12.0		)

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

  lowerCAmelCase_  :							List[str]								=					[]
  for i in range(lowerCAmelCase__		):
    lowerCAmelCase_  :							str								=					i / num_diffusion_timesteps
    lowerCAmelCase_  :							Optional[int]								=					(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  UpperCamelCase__  (      lowercase_,   lowercase_    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	[e.name for e in KarrasDiffusionSchedulers]
      _SCREAMING_SNAKE_CASE    							=	1



      @register_to_config
      def __init__( self							: str       ,     SCREAMING_SNAKE_CASE_							: int = 1_0_0_0       ,     SCREAMING_SNAKE_CASE_							: float = 0.00_01       ,     SCREAMING_SNAKE_CASE_							: float = 0.02       ,     SCREAMING_SNAKE_CASE_							: str = "linear"       ,     SCREAMING_SNAKE_CASE_							: Optional[Union[np.ndarray, List[float]]] = None       ,     SCREAMING_SNAKE_CASE_							: str = "epsilon"       ,     SCREAMING_SNAKE_CASE_							: str = "linspace"       ,     SCREAMING_SNAKE_CASE_							: int = 0       ,     ):
        if trained_betas is not None:
          lowerCAmelCase_  :							int								=					torch.tensor(SCREAMING_SNAKE_CASE_       ,     dtype=torch.floataa			)
        elif beta_schedule == "linear":
          lowerCAmelCase_  :							Union[str, Any]								=					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.
          lowerCAmelCase_  :							Dict								=					(
              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
          lowerCAmelCase_  :							Optional[int]								=					betas_for_alpha_bar(SCREAMING_SNAKE_CASE_			)
        else:
          raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"			)

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

        lowerCAmelCase_  :							Dict								=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
        lowerCAmelCase_  :							Optional[int]								=					np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
        lowerCAmelCase_  :							Optional[int]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			)

        # setable values
        lowerCAmelCase_  :							Union[str, Any]								=					None
        lowerCAmelCase_  :							List[Any]								=					np.linspace(0       ,     num_train_timesteps - 1       ,     SCREAMING_SNAKE_CASE_       ,     dtype=SCREAMING_SNAKE_CASE_			)[::-1].copy()
        lowerCAmelCase_  :							int								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							List[str]								=					False



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



      def 	SCREAMING_SNAKE_CASE__					( self							: str       ,     SCREAMING_SNAKE_CASE_							: int       ,     SCREAMING_SNAKE_CASE_							: Union[str, torch.device] = None			):
        lowerCAmelCase_  :							Optional[int]								=					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":
          lowerCAmelCase_  :							Any								=					np.linspace(0       ,     self.config.num_train_timesteps - 1       ,     SCREAMING_SNAKE_CASE_       ,     dtype=SCREAMING_SNAKE_CASE_			)[
              ::-1
          ].copy()
        elif self.config.timestep_spacing == "leading":
          lowerCAmelCase_  :							int								=					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
          lowerCAmelCase_  :							List[Any]								=					(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":
          lowerCAmelCase_  :							List[Any]								=					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
          lowerCAmelCase_  :							int								=					(np.arange(self.config.num_train_timesteps       ,     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'."			)

        lowerCAmelCase_  :							Optional[int]								=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
        lowerCAmelCase_  :							List[str]								=					np.interp(SCREAMING_SNAKE_CASE_       ,     np.arange(0       ,     len(SCREAMING_SNAKE_CASE_			)			)       ,     SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Union[str, Any]								=					np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
        lowerCAmelCase_  :							List[str]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_			)
        if str(SCREAMING_SNAKE_CASE_			).startswith('mps'			):
          # mps does not support float64
          lowerCAmelCase_  :							List[str]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_       ,     dtype=torch.floataa			)
        else:
          lowerCAmelCase_  :							List[Any]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: Union[float, torch.FloatTensor]       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: Optional[torch.Generator] = None       ,     SCREAMING_SNAKE_CASE_							: bool = True       ,     ):

        if (
            isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
            or isinstance(SCREAMING_SNAKE_CASE_       ,     torch.IntTensor			)
            or isinstance(SCREAMING_SNAKE_CASE_       ,     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(SCREAMING_SNAKE_CASE_       ,     torch.Tensor			):
          lowerCAmelCase_  :							Union[str, Any]								=					timestep.to(self.timesteps.device			)

        lowerCAmelCase_  :							Optional[int]								=					(self.timesteps == timestep).nonzero().item()
        lowerCAmelCase_  :							Dict								=					self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          lowerCAmelCase_  :							List[str]								=					sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          lowerCAmelCase_  :							Tuple								=					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`"			)

        lowerCAmelCase_  :							Any								=					self.sigmas[step_index]
        lowerCAmelCase_  :							Union[str, Any]								=					self.sigmas[step_index + 1]
        lowerCAmelCase_  :							List[str]								=					(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
        lowerCAmelCase_  :							Dict								=					(sigma_to**2 - sigma_up**2) ** 0.5

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

        lowerCAmelCase_  :							Tuple								=					sigma_down - sigma

        lowerCAmelCase_  :							List[str]								=					sample + derivative * dt

        lowerCAmelCase_  :							Any								=					model_output.device
        lowerCAmelCase_  :							Optional[int]								=					randn_tensor(model_output.shape       ,     dtype=model_output.dtype       ,     device=SCREAMING_SNAKE_CASE_       ,     generator=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Optional[Any]								=					prev_sample + noise * sigma_up

        if not return_dict:
          return (prev_sample,)

        return EulerAncestralDiscreteSchedulerOutput(
            prev_sample=SCREAMING_SNAKE_CASE_       ,     pred_original_sample=SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Dict       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     ):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowerCAmelCase_  :							int								=					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
          lowerCAmelCase_  :							Any								=					self.timesteps.to(original_samples.device       ,     dtype=torch.floataa			)
          lowerCAmelCase_  :							str								=					timesteps.to(original_samples.device       ,     dtype=torch.floataa			)
        else:
          lowerCAmelCase_  :							Dict								=					self.timesteps.to(original_samples.device			)
          lowerCAmelCase_  :							List[str]								=					timesteps.to(original_samples.device			)

        lowerCAmelCase_  :							Any								=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

        lowerCAmelCase_  :							Dict								=					sigmas[step_indices].flatten()
        while len(sigma.shape			) < len(original_samples.shape			):
          lowerCAmelCase_  :							Optional[int]								=					sigma.unsqueeze(-1			)

        lowerCAmelCase_  :							List[str]								=					original_samples + noise * sigma
        return noisy_samples





      def __len__( self							: List[Any]			):
        return self.config.num_train_timesteps





