
"""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:
						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_						:



							'''simple docstring'''





							_snake_case		     =						None
							_snake_case		     =						()
							_snake_case		     =						()
							_snake_case		     =						True
							_snake_case		     =						True
							_snake_case		     =						True
							_snake_case		     =						False




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_=False							)   ->       dict:
													__lowerCAmelCase        =       copy.deepcopy(snake_case_							)

													if model_class in get_values(snake_case_							):
																			__lowerCAmelCase        =       {
																			    k: tf.tile(tf.expand_dims(snake_case_  ,							1							)  ,							(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)							)
																			    if isinstance(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(snake_case_							):
																									__lowerCAmelCase        =       tf.ones(self.model_tester.batch_size  ,							dtype=tf.intaa							)
																			elif model_class in get_values(snake_case_							):
																									__lowerCAmelCase        =       tf.zeros(self.model_tester.batch_size  ,							dtype=tf.intaa							)
																									__lowerCAmelCase        =       tf.zeros(self.model_tester.batch_size  ,							dtype=tf.intaa							)
																			elif model_class in [
																			    *get_values(snake_case_							),
																			    *get_values(snake_case_							),
																			]:
																									__lowerCAmelCase        =       tf.zeros(self.model_tester.batch_size  ,							dtype=tf.intaa							)
																			elif model_class in get_values(snake_case_							):
																									__lowerCAmelCase        =       tf.zeros(self.model_tester.batch_size  ,							dtype=tf.intaa							)
																			elif model_class in [
																			    *get_values(snake_case_							),
																			    *get_values(snake_case_							),
																			    *get_values(snake_case_							),
																			    *get_values(snake_case_							),
																			    *get_values(snake_case_							),
																			]:
																									__lowerCAmelCase        =       tf.zeros(
																									    (self.model_tester.batch_size, self.model_tester.seq_length)  ,							dtype=tf.intaa							)
													return inputs_dict




							@slow
							def  A__		(				self							)   ->       List[Any]:
													__lowerCAmelCase ,      __lowerCAmelCase        =       self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes[:2]:
																			__lowerCAmelCase        =       self._prepare_for_class(snake_case_  ,							snake_case_							)
																			__lowerCAmelCase        =       model_class(snake_case_							)

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

																			__lowerCAmelCase        =       run_in_graph_mode()
																			self.assertIsNotNone(snake_case_							)




							@slow
							def  A__		(				self							)   ->       int:
													__lowerCAmelCase ,      __lowerCAmelCase        =       self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes[:2]:
																			__lowerCAmelCase        =       self._prepare_for_class(snake_case_  ,							snake_case_							)
																			__lowerCAmelCase        =       model_class(snake_case_							)

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

																			__lowerCAmelCase        =       run_in_graph_mode()
																			self.assertIsNotNone(snake_case_							)




							@slow
							def  A__		(				self							)   ->       List[Any]:
													# This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
													__lowerCAmelCase ,      __lowerCAmelCase        =       self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes[:2]:
																			__lowerCAmelCase        =       model_class(snake_case_							)
																			if getattr(snake_case_  ,							"""hf_compute_loss"""  ,							snake_case_							):
																									# Test that model correctly compute the loss with kwargs
																									__lowerCAmelCase        =       self._prepare_for_class(inputs_dict.copy()  ,							snake_case_  ,							return_labels=snake_case_							)
																									# Is there a better way to remove these decoder inputs?
																									__lowerCAmelCase        =       {
																									    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        =       {
																									    """labels""",
																									    """label""",
																									    """label_ids""",
																									    """start_positions""",
																									    """start_position""",
																									    """end_positions""",
																									    """end_position""",
																									    """next_sentence_label""",
																									}
																									__lowerCAmelCase        =       possible_label_cols.intersection(set(snake_case_							)							)
																									self.assertGreater(len(snake_case_							)  ,							0  ,							msg="""No matching label names found!"""							)
																									__lowerCAmelCase        =       {key: val for key, val in prepared_for_class.items() if key in label_names}
																									__lowerCAmelCase        =       {key: val for key, val in prepared_for_class.items() if key not in label_names}
																									self.assertGreater(len(snake_case_							)  ,							0							)

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

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

																									__lowerCAmelCase        =       history.history["""loss"""][0]
																									self.assertTrue(not isnan(snake_case_							)							)
																									__lowerCAmelCase        =       history.history["""val_loss"""][0]
																									self.assertTrue(not isnan(snake_case_							)							)




							@slow
							def  A__		(				self							)   ->       int:
													__lowerCAmelCase ,      __lowerCAmelCase        =       self.model_tester.prepare_config_and_inputs_for_common()
													__lowerCAmelCase        =       True
													__lowerCAmelCase        =       True

													if hasattr(snake_case_  ,							"""use_cache"""							):
																			__lowerCAmelCase        =       True

													__lowerCAmelCase        =       getattr(self.model_tester  ,							"""encoder_seq_length"""  ,							self.model_tester.seq_length							)
													__lowerCAmelCase        =       getattr(self.model_tester  ,							"""key_length"""  ,							snake_case_							)

													for model_class in self.all_model_classes[:2]:
																			__lowerCAmelCase        =       self._prepare_for_class(snake_case_  ,							snake_case_							)
																			__lowerCAmelCase        =       model_class(snake_case_							)
																			model.build()
																			__lowerCAmelCase        =       len(model(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        =       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(snake_case_  ,							saved_model=snake_case_							)
																									__lowerCAmelCase        =       os.path.join(snake_case_  ,							"""saved_model"""  ,							"""1"""							)
																									__lowerCAmelCase        =       tf.keras.models.load_model(snake_case_							)
																									__lowerCAmelCase        =       model(snake_case_							)

																									if self.is_encoder_decoder:
																															__lowerCAmelCase        =       outputs["""encoder_hidden_states"""]
																															__lowerCAmelCase        =       outputs["""encoder_attentions"""]
																									else:
																															__lowerCAmelCase        =       outputs["""hidden_states"""]
																															__lowerCAmelCase        =       outputs["""attentions"""]

																									self.assertEqual(len(snake_case_							)  ,							snake_case_							)

																									__lowerCAmelCase        =       getattr(
																									    self.model_tester  ,							"""expected_num_hidden_layers"""  ,							self.model_tester.num_hidden_layers + 1							)

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

																									self.assertEqual(len(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  A__		(				self							)   ->       str:
													tf.keras.mixed_precision.set_global_policy("""mixed_float16"""							)

													# try/finally block to ensure subsequent tests run in float32
													try:
																			__lowerCAmelCase ,      __lowerCAmelCase        =       self.model_tester.prepare_config_and_inputs_for_common()
																			for model_class in self.all_model_classes[:2]:
																									__lowerCAmelCase        =       self._prepare_for_class(snake_case_  ,							snake_case_							)
																									__lowerCAmelCase        =       model_class(snake_case_							)
																									__lowerCAmelCase        =       model(snake_case_							)

																									self.assertIsNotNone(snake_case_							)
													finally:
																			tf.keras.mixed_precision.set_global_policy("""float32"""							)




							@slow
							def  A__		(				self							)   ->       Optional[int]:
													__lowerCAmelCase ,      __lowerCAmelCase        =       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        =       {
													    module_member
													    for model_class in self.all_model_classes
													    for module in (import_module(model_class.__module__							),)
													    for module_member_name in dir(snake_case_							)
													    if module_member_name.endswith("""MainLayer"""							)
													    for module_member in (getattr(snake_case_  ,							snake_case_							),)
													    if isinstance(snake_case_  ,							snake_case_							)
													    and tf.keras.layers.Layer in module_member.__bases__
													    and getattr(snake_case_  ,							"""_keras_serializable"""  ,							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        =       TFSharedEmbeddings(self.model_tester.vocab_size  ,							self.model_tester.hidden_size  ,							name="""shared"""							)
																									__lowerCAmelCase        =       False
																									__lowerCAmelCase        =       main_layer_class(snake_case_  ,							embed_tokens=snake_case_							)
																			else:
																									__lowerCAmelCase        =       main_layer_class(snake_case_							)

																			__lowerCAmelCase        =       {
																			    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        =       self.model_tester.num_labels
																			else:
																									__lowerCAmelCase        =       2

																			__lowerCAmelCase        =       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        =       main_layer(snake_case_							)[0]
																			__lowerCAmelCase        =       tf.keras.layers.Dense(snake_case_  ,							activation="""softmax"""  ,							name="""outputs"""							)(snake_case_							)
																			__lowerCAmelCase        =       tf.keras.models.Model(inputs=snake_case_  ,							outputs=[outputs]							)

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

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									__lowerCAmelCase        =       os.path.join(snake_case_  ,							"""keras_model.h5"""							)
																									model.save(snake_case_							)
																									if "T5" in main_layer_class.__name__:
																															__lowerCAmelCase        =       tf.keras.models.load_model(
																															    snake_case_  ,							custom_objects={
																															        main_layer_class.__name__: main_layer_class,
																															        """TFSharedEmbeddings""": TFSharedEmbeddings,
																															    }  ,							)
																									else:
																															__lowerCAmelCase        =       tf.keras.models.load_model(
																															    snake_case_  ,							custom_objects={main_layer_class.__name__: main_layer_class}							)
																									assert isinstance(snake_case_  ,							tf.keras.Model							)
																									model(snake_case_							)




							@slow
							def  A__		(				self							)   ->       str:
													__lowerCAmelCase ,      __lowerCAmelCase        =       self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes[:2]:
																			__lowerCAmelCase        =       model_class(snake_case_							)

																			__lowerCAmelCase        =       copy.deepcopy(snake_case_							)

																			if not self.is_encoder_decoder:
																									__lowerCAmelCase        =       inputs["""input_ids"""]
																									del inputs["input_ids"]
																			else:
																									__lowerCAmelCase        =       inputs["""input_ids"""]
																									__lowerCAmelCase        =       inputs.get("""decoder_input_ids"""  ,							snake_case_							)
																									del inputs["input_ids"]
																									inputs.pop("""decoder_input_ids"""  ,							snake_case_							)

																			if not self.is_encoder_decoder:
																									__lowerCAmelCase        =       model.get_input_embeddings()(snake_case_							)
																			else:
																									__lowerCAmelCase        =       model.get_input_embeddings()(snake_case_							)
																									__lowerCAmelCase        =       model.get_input_embeddings()(snake_case_							)

																			__lowerCAmelCase        =       self._prepare_for_class(snake_case_  ,							snake_case_							)

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

																			__lowerCAmelCase        =       run_in_graph_mode()
																			self.assertIsNotNone(snake_case_							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       Union[str, Any]:
													# special tokens cannot be bad tokens
													__lowerCAmelCase        =       []
													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        =       []
													while len(snake_case_							) < num_bad_tokens:
																			__lowerCAmelCase        =       tf.squeeze(ids_tensor((1, 1)  ,							self.model_tester.vocab_size							)  ,							0							).numpy()[0]
																			if token not in special_tokens:
																									bad_tokens.append(snake_case_							)
													return bad_tokens




							def  A__		(				self  ,							snake_case_							)   ->       Optional[Any]:
													for token_id in output_ids[0].numpy().tolist():
																			self.assertGreaterEqual(snake_case_  ,							0							)
																			self.assertLess(snake_case_  ,							self.model_tester.vocab_size							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       Any:
													# 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(snake_case_							)  ,							len(snake_case_							)							):
																															# if tokens match
																															if generated_ids_slice[i - len(snake_case_							) : i] == bad_word_ids:
																																					return True
													return False


"""simple docstring"""
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 SchedulerMixin


SCREAMING_SNAKE_CASE_			      =					logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						42
class      lowerCAmelCase_						(							A__							,							A__				):



							'''simple docstring'''





							_snake_case		     =						1




							@register_to_config
							def __init__(				self  ,							snake_case_ = 40  ,							snake_case_ = 0.002  ,							snake_case_ = 80.0  ,							snake_case_ = 0.5  ,							snake_case_ = 1.0  ,							snake_case_ = 7.0  ,							snake_case_ = True  ,							)   ->       List[str]:
													# standard deviation of the initial noise distribution
													__lowerCAmelCase        =       sigma_max

													__lowerCAmelCase        =       np.linspace(0  ,							1  ,							snake_case_							)
													__lowerCAmelCase        =       self._convert_to_karras(snake_case_							)
													__lowerCAmelCase        =       self.sigma_to_t(snake_case_							)

													# setable values
													__lowerCAmelCase        =       None
													__lowerCAmelCase        =       torch.from_numpy(snake_case_							)
													__lowerCAmelCase        =       torch.from_numpy(snake_case_							)
													__lowerCAmelCase        =       False
													__lowerCAmelCase        =       False




							def  A__		(				self  ,							snake_case_  ,							snake_case_=None							)   ->       str:
													if schedule_timesteps is None:
																			__lowerCAmelCase        =       self.timesteps

													__lowerCAmelCase        =       (schedule_timesteps == timestep).nonzero()
													return indices.item()




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       torch.FloatTensor:
													# Get sigma corresponding to timestep
													if isinstance(snake_case_  ,							torch.Tensor							):
																			__lowerCAmelCase        =       timestep.to(self.timesteps.device							)
													__lowerCAmelCase        =       self.index_for_timestep(snake_case_							)
													__lowerCAmelCase        =       self.sigmas[step_idx]

													__lowerCAmelCase        =       sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

													__lowerCAmelCase        =       True
													return sample




							def  A__		(				self  ,							snake_case_							)   ->       Union[str, Any]:
													if not isinstance(snake_case_  ,							np.ndarray							):
																			__lowerCAmelCase        =       np.array(snake_case_  ,							dtype=np.floataa							)

													__lowerCAmelCase        =       1_000 * 0.25 * np.log(sigmas + 1e-4_4							)

													return timesteps




							def  A__		(				self  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							)   ->       Any:
													if num_inference_steps is None and timesteps is None:
																			raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""							)

													if num_inference_steps is not None and timesteps is not None:
																			raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""							)

													# Follow DDPMScheduler custom timesteps logic
													if timesteps is not None:
																			for i in range(1  ,							len(snake_case_							)							):
																									if timesteps[i] >= timesteps[i - 1]:
																															raise ValueError("""`timesteps` must be in descending order."""							)

																			if timesteps[0] >= self.config.num_train_timesteps:
																									raise ValueError(
																									    f"""`timesteps` must start before `self.config.train_timesteps`:"""
																									    f""" {self.config.num_train_timesteps}."""							)

																			__lowerCAmelCase        =       np.array(snake_case_  ,							dtype=np.intaa							)
																			__lowerCAmelCase        =       True
													else:
																			if num_inference_steps > self.config.num_train_timesteps:
																									raise ValueError(
																									    f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
																									    f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
																									    f""" maximal {self.config.num_train_timesteps} timesteps."""							)

																			__lowerCAmelCase        =       num_inference_steps

																			__lowerCAmelCase        =       self.config.num_train_timesteps // self.num_inference_steps
																			__lowerCAmelCase        =       (np.arange(0  ,							snake_case_							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
																			__lowerCAmelCase        =       False

													# Map timesteps to Karras sigmas directly for multistep sampling
													# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
													__lowerCAmelCase        =       self.config.num_train_timesteps
													__lowerCAmelCase        =       timesteps[::-1].copy()
													__lowerCAmelCase        =       ramp / (num_train_timesteps - 1)
													__lowerCAmelCase        =       self._convert_to_karras(snake_case_							)
													__lowerCAmelCase        =       self.sigma_to_t(snake_case_							)

													__lowerCAmelCase        =       np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
													__lowerCAmelCase        =       torch.from_numpy(snake_case_							).to(device=snake_case_							)

													if str(snake_case_							).startswith("""mps"""							):
																			# mps does not support float64
																			__lowerCAmelCase        =       torch.from_numpy(snake_case_							).to(snake_case_  ,							dtype=torch.floataa							)
													else:
																			__lowerCAmelCase        =       torch.from_numpy(snake_case_							).to(device=snake_case_							)




							def  A__		(				self  ,							snake_case_							)   ->       str:
													__lowerCAmelCase        =       self.config.sigma_min
													__lowerCAmelCase        =       self.config.sigma_max

													__lowerCAmelCase        =       self.config.rho
													__lowerCAmelCase        =       sigma_min ** (1 / rho)
													__lowerCAmelCase        =       sigma_max ** (1 / rho)
													__lowerCAmelCase        =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
													return sigmas




							def  A__		(				self  ,							snake_case_							)   ->       Optional[int]:
													__lowerCAmelCase        =       self.config.sigma_data

													__lowerCAmelCase        =       sigma_data**2 / (sigma**2 + sigma_data**2)
													__lowerCAmelCase        =       sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
													return c_skip, c_out




							def  A__		(				self  ,							snake_case_							)   ->       Any:
													__lowerCAmelCase        =       self.config.sigma_min
													__lowerCAmelCase        =       self.config.sigma_data

													__lowerCAmelCase        =       sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
													__lowerCAmelCase        =       (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
													return c_skip, c_out




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_ = None  ,							snake_case_ = True  ,							)   ->       Union[CMStochasticIterativeSchedulerOutput, Tuple]:

													if (
													    isinstance(snake_case_  ,							snake_case_							)
													    or isinstance(snake_case_  ,							torch.IntTensor							)
													    or isinstance(snake_case_  ,							torch.LongTensor							)
													):
																			raise ValueError(
																			    (
																			        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
																			        f""" `{self.__class__}.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(snake_case_  ,							torch.Tensor							):
																			__lowerCAmelCase        =       timestep.to(self.timesteps.device							)

													__lowerCAmelCase        =       self.config.sigma_min
													__lowerCAmelCase        =       self.config.sigma_max

													__lowerCAmelCase        =       self.index_for_timestep(snake_case_							)

													# sigma_next corresponds to next_t in original implementation
													__lowerCAmelCase        =       self.sigmas[step_index]
													if step_index + 1 < self.config.num_train_timesteps:
																			__lowerCAmelCase        =       self.sigmas[step_index + 1]
													else:
																			# Set sigma_next to sigma_min
																			__lowerCAmelCase        =       self.sigmas[-1]

													# Get scalings for boundary conditions
													__lowerCAmelCase ,      __lowerCAmelCase        =       self.get_scalings_for_boundary_condition(snake_case_							)

													# 1. Denoise model output using boundary conditions
													__lowerCAmelCase        =       c_out * model_output + c_skip * sample
													if self.config.clip_denoised:
																			__lowerCAmelCase        =       denoised.clamp(-1  ,							1							)

													# 2. Sample z ~ N(0, s_noise^2 * I)
													# Noise is not used for onestep sampling.
													if len(self.timesteps							) > 1:
																			__lowerCAmelCase        =       randn_tensor(
																			    model_output.shape  ,							dtype=model_output.dtype  ,							device=model_output.device  ,							generator=snake_case_							)
													else:
																			__lowerCAmelCase        =       torch.zeros_like(snake_case_							)
													__lowerCAmelCase        =       noise * self.config.s_noise

													__lowerCAmelCase        =       sigma_next.clamp(min=snake_case_  ,							max=snake_case_							)

													# 3. Return noisy sample
													# tau = sigma_hat, eps = sigma_min
													__lowerCAmelCase        =       denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

													if not return_dict:
																			return (prev_sample,)

													return CMStochasticIterativeSchedulerOutput(prev_sample=snake_case_							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							)   ->       torch.FloatTensor:
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													__lowerCAmelCase        =       self.sigmas.to(device=original_samples.device  ,							dtype=original_samples.dtype							)
													if original_samples.device.type == "mps" and torch.is_floating_point(snake_case_							):
																			# mps does not support float64
																			__lowerCAmelCase        =       self.timesteps.to(original_samples.device  ,							dtype=torch.floataa							)
																			__lowerCAmelCase        =       timesteps.to(original_samples.device  ,							dtype=torch.floataa							)
													else:
																			__lowerCAmelCase        =       self.timesteps.to(original_samples.device							)
																			__lowerCAmelCase        =       timesteps.to(original_samples.device							)

													__lowerCAmelCase        =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

													__lowerCAmelCase        =       original_samples + noise * sigma
													return noisy_samples




							def __len__(				self							)   ->       Optional[int]:
													return self.config.num_train_timesteps
