




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	__A						:		Optional[int]          =						"platform"

	import jax
	import jax.numpy as jnp

	from transformers import AutoTokenizer
	from transformers.models.mbart.modeling_flax_mbart import (
	    FlaxMBartForConditionalGeneration,
	    FlaxMBartForQuestionAnswering,
	    FlaxMBartForSequenceClassification,
	    FlaxMBartModel,
	    shift_tokens_right,
	)





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			)     ->     str:







				'''simple docstring'''


				if attention_mask is None:
								__lowerCAmelCase					=  np.where(input_ids != config.pad_token_id						,			1						,			0   )
				if decoder_attention_mask is None:
								__lowerCAmelCase					=  np.where(decoder_input_ids != config.pad_token_id						,			1						,			0   )
				if head_mask is None:
								__lowerCAmelCase					=  np.ones((config.encoder_layers, config.encoder_attention_heads)   )
				if decoder_head_mask is None:
								__lowerCAmelCase					=  np.ones((config.decoder_layers, config.decoder_attention_heads)   )
				if cross_attn_head_mask is None:
								__lowerCAmelCase					=  np.ones((config.decoder_layers, config.decoder_attention_heads)   )
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				}

class 		lowercase_       (	unittest.TestCase				):




					def __init__(   self:		Dict,     _lowercase:		List[Any],     _lowercase:		Any=13,     _lowercase:		int=7,     _lowercase:		str=True,     _lowercase:		List[str]=False,     _lowercase:		Any=99,     _lowercase:		str=16,     _lowercase:		Optional[int]=2,     _lowercase:		List[str]=4,     _lowercase:		List[str]=4,     _lowercase:		Optional[int]="gelu",     _lowercase:		Any=0.1,     _lowercase:		Union[str, Any]=0.1,     _lowercase:		Dict=32,     _lowercase:		int=2,     _lowercase:		Union[str, Any]=1,     _lowercase:		Any=0,     _lowercase:		int=2,     _lowercase:		List[Any]=0.02,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  parent
									__lowerCAmelCase					=  batch_size
									__lowerCAmelCase					=  seq_length
									__lowerCAmelCase					=  is_training
									__lowerCAmelCase					=  use_labels
									__lowerCAmelCase					=  vocab_size
									__lowerCAmelCase					=  hidden_size
									__lowerCAmelCase					=  num_hidden_layers
									__lowerCAmelCase					=  num_attention_heads
									__lowerCAmelCase					=  intermediate_size
									__lowerCAmelCase					=  hidden_act
									__lowerCAmelCase					=  hidden_dropout_prob
									__lowerCAmelCase					=  attention_probs_dropout_prob
									__lowerCAmelCase					=  max_position_embeddings
									__lowerCAmelCase					=  eos_token_id
									__lowerCAmelCase					=  pad_token_id
									__lowerCAmelCase					=  bos_token_id
									__lowerCAmelCase					=  decoder_start_token_id
									__lowerCAmelCase					=  initializer_range




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1],     self.vocab_size),     3,     self.vocab_size)
									__lowerCAmelCase					=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),     dtype=np.intaa)),     -1)

									__lowerCAmelCase					=  shift_tokens_right(_lowercase,     1)

									__lowerCAmelCase					=  MBartConfig(
									    vocab_size=self.vocab_size,     d_model=self.hidden_size,     encoder_layers=self.num_hidden_layers,     decoder_layers=self.num_hidden_layers,     encoder_attention_heads=self.num_attention_heads,     decoder_attention_heads=self.num_attention_heads,     encoder_ffn_dim=self.intermediate_size,     decoder_ffn_dim=self.intermediate_size,     dropout=self.hidden_dropout_prob,     attention_dropout=self.attention_probs_dropout_prob,     max_position_embeddings=self.max_position_embeddings,     eos_token_id=self.eos_token_id,     bos_token_id=self.bos_token_id,     pad_token_id=self.pad_token_id,     decoder_start_token_id=self.decoder_start_token_id,     initializer_range=self.initializer_range,     use_cache=_lowercase,     )
									__lowerCAmelCase					=  prepare_mbart_inputs_dict(_lowercase,     _lowercase,     _lowercase)
									return config, inputs_dict




					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.prepare_config_and_inputs()
									return config, inputs_dict




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		Union[str, Any],     _lowercase:		Dict,     _lowercase:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  20
									__lowerCAmelCase					=  model_class_name(_lowercase)

									__lowerCAmelCase					=  model.encode(inputs_dict["""input_ids"""])

									__lowerCAmelCase	,				__lowerCAmelCase					=  (
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									__lowerCAmelCase					=  model.init_cache(decoder_input_ids.shape[0],     _lowercase,     _lowercase)
									__lowerCAmelCase					=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length),     dtype="""i4""")

									__lowerCAmelCase					=  jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),     )
									__lowerCAmelCase					=  model.decode(
									    decoder_input_ids[:, :-1],     _lowercase,     decoder_attention_mask=_lowercase,     past_key_values=_lowercase,     decoder_position_ids=_lowercase,     )

									__lowerCAmelCase					=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],     dtype="""i4""")
									__lowerCAmelCase					=  model.decode(
									    decoder_input_ids[:, -1:],     _lowercase,     decoder_attention_mask=_lowercase,     past_key_values=outputs_cache.past_key_values,     decoder_position_ids=_lowercase,     )

									__lowerCAmelCase					=  model.decode(_lowercase,     _lowercase)

									__lowerCAmelCase					=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
									self.parent.assertTrue(diff < 1e-3,     msg=f'''Max diff is {diff}''')







					def     _lowercase						(   self:		List[str],     _lowercase:		int,     _lowercase:		str,     _lowercase:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  20
									__lowerCAmelCase					=  model_class_name(_lowercase)

									__lowerCAmelCase					=  model.encode(inputs_dict["""input_ids"""])

									__lowerCAmelCase	,				__lowerCAmelCase					=  (
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									__lowerCAmelCase					=  jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
									    ],     axis=-1,     )

									__lowerCAmelCase					=  model.init_cache(decoder_input_ids.shape[0],     _lowercase,     _lowercase)
									__lowerCAmelCase					=  jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),     )

									__lowerCAmelCase					=  model.decode(
									    decoder_input_ids[:, :-1],     _lowercase,     decoder_attention_mask=_lowercase,     past_key_values=_lowercase,     decoder_position_ids=_lowercase,     )
									__lowerCAmelCase					=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],     dtype="""i4""")
									__lowerCAmelCase					=  model.decode(
									    decoder_input_ids[:, -1:],     _lowercase,     past_key_values=outputs_cache.past_key_values,     decoder_attention_mask=_lowercase,     decoder_position_ids=_lowercase,     )

									__lowerCAmelCase					=  model.decode(_lowercase,     _lowercase,     decoder_attention_mask=_lowercase)

									__lowerCAmelCase					=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
									self.parent.assertTrue(diff < 1e-3,     msg=f'''Max diff is {diff}''')





@require_flax
class 		lowercase_       (	unittest.TestCase				):
					__UpperCamelCase     	= 99




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  np.array(
									    [
									        [71, 82, 18, 33, 46, 91, 2],
									        [68, 34, 26, 58, 30, 82, 2],
									        [5, 97, 17, 39, 94, 40, 2],
									        [76, 83, 94, 25, 70, 78, 2],
									        [87, 59, 41, 35, 48, 66, 2],
									        [55, 13, 16, 58, 5, 2, 1],  # note padding
									        [64, 27, 31, 51, 12, 75, 2],
									        [52, 64, 86, 17, 83, 39, 2],
									        [48, 61, 9, 24, 71, 82, 2],
									        [26, 1, 60, 48, 22, 13, 2],
									        [21, 5, 62, 28, 14, 76, 2],
									        [45, 98, 37, 86, 59, 48, 2],
									        [70, 70, 50, 9, 28, 0, 2],
									    ],     dtype=np.intaa,     )

									__lowerCAmelCase					=  input_ids.shape[0]
									__lowerCAmelCase					=  MBartConfig(
									    vocab_size=self.vocab_size,     d_model=24,     encoder_layers=2,     decoder_layers=2,     encoder_attention_heads=2,     decoder_attention_heads=2,     encoder_ffn_dim=32,     decoder_ffn_dim=32,     max_position_embeddings=48,     eos_token_id=2,     pad_token_id=1,     bos_token_id=0,     )
									return config, input_ids, batch_size




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  self._get_config_and_data()
									__lowerCAmelCase					=  FlaxMBartForSequenceClassification(_lowercase)
									__lowerCAmelCase					=  model(input_ids=_lowercase,     decoder_input_ids=_lowercase)
									__lowerCAmelCase					=  (batch_size, config.num_labels)
									self.assertEqual(outputs["""logits"""].shape,     _lowercase)




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  self._get_config_and_data()
									__lowerCAmelCase					=  FlaxMBartForQuestionAnswering(_lowercase)
									__lowerCAmelCase					=  model(input_ids=_lowercase)

									self.assertEqual(outputs["""start_logits"""].shape,     input_ids.shape)
									self.assertEqual(outputs["""end_logits"""].shape,     input_ids.shape)




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  self._get_config_and_data()
									__lowerCAmelCase					=  FlaxMBartForConditionalGeneration(_lowercase)
									__lowerCAmelCase					=  lm_model(input_ids=_lowercase)
									__lowerCAmelCase					=  (batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs["""logits"""].shape,     _lowercase)




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  MBartConfig(
									    vocab_size=self.vocab_size,     d_model=14,     encoder_layers=2,     decoder_layers=2,     encoder_attention_heads=2,     decoder_attention_heads=2,     encoder_ffn_dim=8,     decoder_ffn_dim=8,     max_position_embeddings=48,     )
									__lowerCAmelCase					=  FlaxMBartForConditionalGeneration(_lowercase)
									__lowerCAmelCase					=  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],     dtype=np.intaa)
									__lowerCAmelCase					=  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],     dtype=np.intaa)
									__lowerCAmelCase					=  lm_model(input_ids=_lowercase,     decoder_input_ids=_lowercase)
									__lowerCAmelCase					=  (*summary.shape, config.vocab_size)
									self.assertEqual(outputs["""logits"""].shape,     _lowercase)







					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									__lowerCAmelCase					=  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],     dtype=np.intaa)
									__lowerCAmelCase					=  shift_tokens_right(_lowercase,     1)
									__lowerCAmelCase					=  np.equal(_lowercase,     1).astype(np.floataa).sum()
									__lowerCAmelCase					=  np.equal(_lowercase,     1).astype(np.floataa).sum()
									self.assertEqual(shifted.shape,     input_ids.shape)
									self.assertEqual(_lowercase,     n_pad_before - 1)
									self.assertTrue(np.equal(shifted[:, 0],     2).all())





@require_flax
class 		lowercase_       (	lowerCAmelCase__						,							unittest.TestCase						,							lowerCAmelCase__				):
					__UpperCamelCase     	= True
					__UpperCamelCase     	= (
					    (
					        FlaxMBartModel,
					        FlaxMBartForConditionalGeneration,
					        FlaxMBartForSequenceClassification,
					        FlaxMBartForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)
					__UpperCamelCase     	= (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  FlaxMBartModelTester(self)




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(_lowercase,     _lowercase,     _lowercase)




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward_with_attn_mask(_lowercase,     _lowercase,     _lowercase)




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__):
																	__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
																	__lowerCAmelCase					=  model_class(_lowercase)

																	@jax.jit
																	def encode_jitted(_lowercase:		List[Any],     _lowercase:		Tuple=None,     **_lowercase:		Optional[Any]):
																					return model.encode(input_ids=_lowercase,     attention_mask=_lowercase)

																	with self.subTest("""JIT Enabled"""):
																					__lowerCAmelCase					=  encode_jitted(**_lowercase).to_tuple()

																	with self.subTest("""JIT Disabled"""):
																					with jax.disable_jit():
																									__lowerCAmelCase					=  encode_jitted(**_lowercase).to_tuple()

																	self.assertEqual(len(_lowercase),     len(_lowercase))
																	for jitted_output, output in zip(_lowercase,     _lowercase):
																					self.assertEqual(jitted_output.shape,     output.shape)




					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__):
																	__lowerCAmelCase					=  model_class(_lowercase)
																	__lowerCAmelCase					=  model.encode(inputs_dict["""input_ids"""],     inputs_dict["""attention_mask"""])

																	__lowerCAmelCase					=  {
																	    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
																	    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
																	    """encoder_outputs""": encoder_outputs,
																	}

																	@jax.jit
																	def decode_jitted(_lowercase:		Union[str, Any],     _lowercase:		str,     _lowercase:		Optional[Any]):
																					return model.decode(
																					    decoder_input_ids=_lowercase,     decoder_attention_mask=_lowercase,     encoder_outputs=_lowercase,     )

																	with self.subTest("""JIT Enabled"""):
																					__lowerCAmelCase					=  decode_jitted(**_lowercase).to_tuple()

																	with self.subTest("""JIT Disabled"""):
																					with jax.disable_jit():
																									__lowerCAmelCase					=  decode_jitted(**_lowercase).to_tuple()

																	self.assertEqual(len(_lowercase),     len(_lowercase))
																	for jitted_output, output in zip(_lowercase,     _lowercase):
																					self.assertEqual(jitted_output.shape,     output.shape)







					@slow
					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									for model_class_name in self.all_model_classes:
													__lowerCAmelCase					=  model_class_name.from_pretrained("""facebook/mbart-large-cc25""",     from_pt=_lowercase)
													# FlaxMBartForSequenceClassification expects eos token in input_ids
													__lowerCAmelCase					=  np.ones((1, 1)) * model.config.eos_token_id
													__lowerCAmelCase					=  model(_lowercase)
													self.assertIsNotNone(_lowercase)





@require_flax
@require_sentencepiece
@require_tokenizers
class 		lowercase_       (	unittest.TestCase				):
					__UpperCamelCase     	= [
					    " UN Chief Says There Is No Military Solution in Syria",
					]
					__UpperCamelCase     	= [
					    "Şeful ONU declară că nu există o soluţie militară în Siria",
					]
					__UpperCamelCase     	= "facebook/mbart-large-en-ro"




					@cached_property
					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									return AutoTokenizer.from_pretrained(self.model_name)




					@cached_property
					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name,     from_pt=_lowercase)
									return model




					def     _lowercase						(   self:		Tuple,     **_lowercase:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  self.translate_src_text(**_lowercase)
									self.assertListEqual(self.expected_text,     _lowercase)




					def     _lowercase						(   self:		Optional[Any],     **_lowercase:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.tokenizer(self.src_text,     **_lowercase,     return_tensors="""np""")
									__lowerCAmelCase					=  self.model.generate(
									    model_inputs.input_ids,     attention_mask=model_inputs.attention_mask,     decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""],     early_stopping=_lowercase,     num_beams=2,     ).sequences
									__lowerCAmelCase					=  self.tokenizer.batch_decode(_lowercase,     skip_special_tokens=_lowercase)
									return generated_words







					@slow
					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									self._assert_generated_batch_equal_expected()











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						:		Union[str, Any]          =						logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= 42
					__UpperCamelCase     	= None





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__=0.999						,			UpperCamelCase__="cosine"						,			)     ->     Optional[int]:







				'''simple docstring'''


				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

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

class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__				):
					__UpperCamelCase     	= [e.name for e in KarrasDiffusionSchedulers]
					__UpperCamelCase     	= 1




					@register_to_config
					def __init__(   self:		Any,     _lowercase:		int = 1000,     _lowercase:		float = 0.0_001,     _lowercase:		float = 0.02,     _lowercase:		str = "linear",     _lowercase:		Optional[Union[np.ndarray, List[float]]] = None,     _lowercase:		str = "epsilon",     _lowercase:		str = "linear",     _lowercase:		Optional[bool] = False,     _lowercase:		str = "linspace",     _lowercase:		int = 0,     ):


									'''simple docstring'''


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

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

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

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




					@property
					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


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

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




					def     _lowercase						(   self:		Tuple,     _lowercase:		torch.FloatTensor,     _lowercase:		Union[float, torch.FloatTensor]):


									'''simple docstring'''


									if isinstance(_lowercase,     torch.Tensor):
													__lowerCAmelCase					=  timestep.to(self.timesteps.device)
									__lowerCAmelCase					=  (self.timesteps == timestep).nonzero().item()
									__lowerCAmelCase					=  self.sigmas[step_index]

									__lowerCAmelCase					=  sample / ((sigma**2 + 1) ** 0.5)

									__lowerCAmelCase					=  True
									return sample




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		int,     _lowercase:		Union[str, torch.device] = None):


									'''simple docstring'''


									__lowerCAmelCase					=  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					=  np.linspace(0,     self.config.num_train_timesteps - 1,     _lowercase,     dtype=_lowercase)[
													    ::-1
													].copy()
									elif self.config.timestep_spacing == "leading":
													__lowerCAmelCase					=  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					=  (np.arange(0,     _lowercase) * step_ratio).round()[::-1].copy().astype(_lowercase)
													timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
													__lowerCAmelCase					=  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					=  (np.arange(self.config.num_train_timesteps,     0,     -step_ratio)).round().copy().astype(_lowercase)
													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					=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									__lowerCAmelCase					=  np.log(_lowercase)

									if self.config.interpolation_type == "linear":
													__lowerCAmelCase					=  np.interp(_lowercase,     np.arange(0,     len(_lowercase)),     _lowercase)
									elif self.config.interpolation_type == "log_linear":
													__lowerCAmelCase					=  torch.linspace(np.log(sigmas[-1]),     np.log(sigmas[0]),     num_inference_steps + 1).exp()
									else:
													raise ValueError(
													    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
													    """ 'linear' or 'log_linear'""")

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

									__lowerCAmelCase					=  np.concatenate([sigmas, [0.0]]).astype(np.floataa)
									__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(device=_lowercase)
									if str(_lowercase).startswith("""mps"""):
													# mps does not support float64
													__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(_lowercase,     dtype=torch.floataa)
									else:
													__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(device=_lowercase)




					def     _lowercase						(   self:		List[Any],     _lowercase:		Optional[int],     _lowercase:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  np.log(_lowercase)

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

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

									__lowerCAmelCase					=  log_sigmas[low_idx]
									__lowerCAmelCase					=  log_sigmas[high_idx]

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

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




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		torch.FloatTensor,     _lowercase:		Union[str, Any]):


									'''simple docstring'''


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

									__lowerCAmelCase					=  7.0  # 7.0 is the value used in the paper
									__lowerCAmelCase					=  np.linspace(0,     1,     _lowercase)
									__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     _lowercase						(   self:		Optional[int],     _lowercase:		torch.FloatTensor,     _lowercase:		Union[float, torch.FloatTensor],     _lowercase:		torch.FloatTensor,     _lowercase:		float = 0.0,     _lowercase:		float = 0.0,     _lowercase:		float = float("""inf"""),     _lowercase:		float = 1.0,     _lowercase:		Optional[torch.Generator] = None,     _lowercase:		bool = True,     ):


									'''simple docstring'''


									if (
									    isinstance(_lowercase,     _lowercase)
									    or isinstance(_lowercase,     torch.IntTensor)
									    or isinstance(_lowercase,     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(_lowercase,     torch.Tensor):
													__lowerCAmelCase					=  timestep.to(self.timesteps.device)

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

									__lowerCAmelCase					=  min(s_churn / (len(self.sigmas) - 1),     2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0

									__lowerCAmelCase					=  randn_tensor(
									    model_output.shape,     dtype=model_output.dtype,     device=model_output.device,     generator=_lowercase)

									__lowerCAmelCase					=  noise * s_noise
									__lowerCAmelCase					=  sigma * (gamma + 1)

									if gamma > 0:
													__lowerCAmelCase					=  sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									# NOTE: "original_sample" should not be an expected prediction_type but is left in for
									# backwards compatibility
									if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
													__lowerCAmelCase					=  model_output
									elif self.config.prediction_type == "epsilon":
													__lowerCAmelCase					=  sample - sigma_hat * model_output
									elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													__lowerCAmelCase					=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									else:
													raise ValueError(
													    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''')

									# 2. Convert to an ODE derivative
									__lowerCAmelCase					=  (sample - pred_original_sample) / sigma_hat

									__lowerCAmelCase					=  self.sigmas[step_index + 1] - sigma_hat

									__lowerCAmelCase					=  sample + derivative * dt

									if not return_dict:
													return (prev_sample,)

									return EulerDiscreteSchedulerOutput(prev_sample=_lowercase,     pred_original_sample=_lowercase)




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		torch.FloatTensor,     _lowercase:		torch.FloatTensor,     _lowercase:		torch.FloatTensor,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  self.sigmas.to(device=original_samples.device,     dtype=original_samples.dtype)
									if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase):
													# 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:		str):


									'''simple docstring'''


									return self.config.num_train_timesteps





