




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

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_ 		=  "platform"

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=None   )							->						Tuple:

    """simple docstring"""


    if attention_mask is None:
        __UpperCamelCase			=    np.where(input_ids != config.pad_token_id       ,  1       ,  0   )
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
    }

@require_flax
class 			_lowerCamelCase    :

      """simple docstring"""

      def __init__(  self       :			Tuple			,							snake_case       :			Optional[Any]			,							snake_case       :			Union[str, Any]=13			,							snake_case       :			int=7			,							snake_case       :			Union[str, Any]=True			,							snake_case       :			Any=False			,							snake_case       :			List[str]=99			,							snake_case       :			Union[str, Any]=16			,							snake_case       :			Union[str, Any]=2			,							snake_case       :			Any=4			,							snake_case       :			int=4			,							snake_case       :			str="gelu"			,							snake_case       :			Optional[int]=0.1			,							snake_case       :			int=0.1			,							snake_case       :			Any=20			,							snake_case       :			int=2			,							snake_case       :			Optional[Any]=1			,							snake_case       :			int=0			,							snake_case       :			str=16			,							snake_case       :			Any=16			,							snake_case       :			Optional[Any]=0.02			,							):
          __UpperCamelCase			=    parent
          __UpperCamelCase			=    batch_size
          __UpperCamelCase			=    seq_length
          __UpperCamelCase			=    is_training
          __UpperCamelCase			=    use_labels
          __UpperCamelCase			=    vocab_size
          __UpperCamelCase			=    hidden_size
          __UpperCamelCase			=    num_hidden_layers
          __UpperCamelCase			=    num_attention_heads
          __UpperCamelCase			=    intermediate_size
          __UpperCamelCase			=    hidden_act
          __UpperCamelCase			=    hidden_dropout_prob
          __UpperCamelCase			=    attention_probs_dropout_prob
          __UpperCamelCase			=    max_position_embeddings
          __UpperCamelCase			=    eos_token_id
          __UpperCamelCase			=    pad_token_id
          __UpperCamelCase			=    bos_token_id
          __UpperCamelCase			=    embed_dim
          __UpperCamelCase			=    word_embed_proj_dim
          __UpperCamelCase			=    initializer_range
          __UpperCamelCase			=    False







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

          __UpperCamelCase			=    OPTConfig(
              vocab_size=self.vocab_size			,							hidden_size=self.hidden_size			,							num_hidden_layers=self.num_hidden_layers			,							num_attention_heads=self.num_attention_heads			,							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			,							embed_dim=self.embed_dim			,							is_encoder_decoder=snake_case			,							word_embed_proj_dim=self.word_embed_proj_dim			,							initializer_range=self.initializer_range			,							use_cache=snake_case			,							)
          __UpperCamelCase			=    prepare_opt_inputs_dict(snake_case			,							snake_case					)
          return config, inputs_dict







      def    snake_case						(  self       :			str					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_config_and_inputs()
          return config, inputs_dict







      def    snake_case						(  self       :			Tuple			,							snake_case       :			int			,							snake_case       :			int			,							snake_case       :			Any					):
          __UpperCamelCase			=    20
          __UpperCamelCase			=    model_class_name(snake_case					)

          __UpperCamelCase			=    inputs_dict['''input_ids''']
          __UpperCamelCase			=    inputs_dict['''attention_mask''']

          __UpperCamelCase			=    model.init_cache(input_ids.shape[0]			,							snake_case					)
          __UpperCamelCase			=    jnp.ones((input_ids.shape[0], max_length)			,							dtype='''i4'''					)

          __UpperCamelCase			=    jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1					)[None, :]			,							(input_ids.shape[0], input_ids.shape[-1] - 1)			,							)
          __UpperCamelCase			=    model(
              input_ids[:, :-1]			,							attention_mask=snake_case			,							past_key_values=snake_case			,							position_ids=snake_case			,							)

          __UpperCamelCase			=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,							dtype='''i4'''					)
          __UpperCamelCase			=    model(
              input_ids[:, -1:]			,							attention_mask=snake_case			,							past_key_values=outputs_cache.past_key_values			,							position_ids=snake_case			,							)

          __UpperCamelCase			=    model(snake_case					)

          __UpperCamelCase			=    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    snake_case						(  self       :			Optional[Any]			,							snake_case       :			Optional[Any]			,							snake_case       :			Any			,							snake_case       :			int					):
          __UpperCamelCase			=    20
          __UpperCamelCase			=    model_class_name(snake_case					)

          __UpperCamelCase		,   __UpperCamelCase			=    (
              inputs_dict['''input_ids'''],
              inputs_dict['''attention_mask'''],
          )

          __UpperCamelCase			=    jnp.concatenate(
              [
                  attention_mask,
                  jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])					),
              ]			,							axis=-1			,							)

          __UpperCamelCase			=    model.init_cache(input_ids.shape[0]			,							snake_case					)
          __UpperCamelCase			=    jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1					)[None, :]			,							(input_ids.shape[0], input_ids.shape[-1] - 1)			,							)

          __UpperCamelCase			=    model(
              input_ids[:, :-1]			,							attention_mask=snake_case			,							past_key_values=snake_case			,							position_ids=snake_case			,							)
          __UpperCamelCase			=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,							dtype='''i4'''					)
          __UpperCamelCase			=    model(
              input_ids[:, -1:]			,							past_key_values=outputs_cache.past_key_values			,							attention_mask=snake_case			,							position_ids=snake_case			,							)

          __UpperCamelCase			=    model(snake_case			,							attention_mask=snake_case					)

          __UpperCamelCase			=    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 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      unittest.TestCase				,      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Any     =       (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      lowerCAmelCase__      :			Optional[int]     =       () if is_flax_available() else ()







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    FlaxOPTModelTester(self					)







      def    snake_case						(  self       :			str					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(snake_case			,							snake_case			,							snake_case					)







      def    snake_case						(  self       :			int					):
          __UpperCamelCase		,   __UpperCamelCase			=    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(snake_case			,							snake_case			,							snake_case					)







      @slow
      def    snake_case						(  self       :			Union[str, Any]					):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase			=    model_class_name.from_pretrained('''facebook/opt-125m'''					)
              __UpperCamelCase			=    np.ones((1, 1)					) * model.config.eos_token_id
              __UpperCamelCase			=    model(snake_case					)
              self.assertIsNotNone(snake_case					)







@require_sentencepiece
@require_flax
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      @slow
      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    FlaxOPTModel.from_pretrained('''facebook/opt-350m'''					)
          __UpperCamelCase			=    jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]					)
          __UpperCamelCase			=    model(input_ids=snake_case					).last_hidden_state
          __UpperCamelCase			=    (1, 11, 512)
          self.assertEqual(output.shape			,							snake_case					)
          __UpperCamelCase			=    jnp.array(
              [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]					)
          self.assertTrue(jnp.allclose(output[:, :3, :3]			,							snake_case			,							atol=4E-2					)					)







@require_flax
@slow
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			Optional[Any]					):
          super().setUp()
          __UpperCamelCase			=    '''facebook/opt-350m'''







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(self.path_model					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(self.path_model					)

          __UpperCamelCase			=    [
              '''Today is a beautiful day and I want to''',
              '''In the city of''',
              '''Paris is the capital of France and''',
              '''Computers and mobile phones have taken''',
          ]
          # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
          __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''			,							padding=snake_case			,							add_special_tokens=snake_case					)
          __UpperCamelCase			=    model(inputs.input_ids			,							attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
          __UpperCamelCase			=    jnp.array(
              [
                  [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
                  [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
                  [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
                  [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
              ]					)
          self.assertTrue(jnp.allclose(snake_case			,							snake_case			,							atol=4E-2					)					)

          __UpperCamelCase			=    jax.jit(snake_case					)
          __UpperCamelCase			=    model(inputs.input_ids			,							attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
          self.assertTrue(jnp.allclose(snake_case			,							snake_case			,							atol=4E-2					)					)







@require_flax
@slow
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      @property
      def    snake_case						(  self       :			Any					):
          return [
              "Today is a beautiful day and I want",
              "In the city of",
              "Paris is the capital of France and",
              "Computers and mobile phones have taken",
          ]







      def    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    '''facebook/opt-125m'''

          __UpperCamelCase			=    [
              '''Today is a beautiful day and I want to''',
              '''In the city of New York, the city''',
              '''Paris is the capital of France and the capital''',
              '''Computers and mobile phones have taken over the''',
          ]

          __UpperCamelCase			=    []

          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)

          for prompt in self.prompts:
              __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''					).input_ids

              __UpperCamelCase			=    model.generate(snake_case			,							max_length=10					)
              __UpperCamelCase			=    generated_ids[0]

              __UpperCamelCase			=    tokenizer.batch_decode(snake_case			,							skip_special_tokens=snake_case					)
              predicted_outputs += generated_string

          self.assertListEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Any					):
          __UpperCamelCase			=    '''facebook/opt-350m'''

          __UpperCamelCase			=    [
              '''Today is a beautiful day and I want to''',
              '''In the city of San Francisco, the city''',
              '''Paris is the capital of France and the capital''',
              '''Computers and mobile phones have taken over the''',
          ]

          __UpperCamelCase			=    []
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)

          for prompt in self.prompts:
              __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''					).input_ids

              __UpperCamelCase			=    model.generate(snake_case			,							max_length=10					)
              __UpperCamelCase			=    generated_ids[0]

              __UpperCamelCase			=    tokenizer.batch_decode(snake_case			,							skip_special_tokens=snake_case					)
              predicted_outputs += generated_string

          self.assertListEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    '''facebook/opt-125m'''
          __UpperCamelCase			=    [
              '''Today is a beautiful day and I want to thank''',
              '''In the city of Rome Canaver Canaver Canaver Canaver''',
          ]
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)
          __UpperCamelCase			=    tokenizer(
              [
                  '''Today is a beautiful day and I want to''',
                  '''In the city of''',
              ]			,							return_tensors='''jax'''			,							padding=snake_case			,							)

          __UpperCamelCase			=    jax.jit(model.generate					)

          __UpperCamelCase			=    jit_generate(inputs['''input_ids''']			,							attention_mask=inputs['''attention_mask''']					).sequences

          __UpperCamelCase			=    tokenizer.batch_decode(snake_case			,							skip_special_tokens=snake_case					)

          self.assertIsNotNone(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    '''facebook/opt-350m'''

          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)

          __UpperCamelCase			=    '''left'''

          # use different length sentences to test batching
          __UpperCamelCase			=    [
              '''Hello, my dog is a little''',
              '''Today, I''',
          ]

          __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''			,							padding=snake_case					)
          __UpperCamelCase			=    inputs['''input_ids''']

          __UpperCamelCase			=    model.generate(input_ids=snake_case			,							attention_mask=inputs['''attention_mask''']			,							trace=snake_case					)

          __UpperCamelCase			=    tokenizer(sentences[0]			,							return_tensors='''jax'''					).input_ids
          __UpperCamelCase			=    model.generate(input_ids=snake_case					)

          __UpperCamelCase			=    inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
          __UpperCamelCase			=    tokenizer(sentences[1]			,							return_tensors='''jax'''					).input_ids
          __UpperCamelCase			=    model.generate(input_ids=snake_case			,							max_length=model.config.max_length - num_paddings					)

          __UpperCamelCase			=    tokenizer.batch_decode(outputs[0]			,							skip_special_tokens=snake_case					)
          __UpperCamelCase			=    tokenizer.decode(output_non_padded[0][0]			,							skip_special_tokens=snake_case					)
          __UpperCamelCase			=    tokenizer.decode(output_padded[0][0]			,							skip_special_tokens=snake_case					)

          __UpperCamelCase			=    [
              '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
              '''Today, I was in the middle of a conversation with a friend about the''',
          ]
          self.assertListEqual(snake_case			,							snake_case					)
          self.assertListEqual(snake_case			,							[non_padded_sentence, padded_sentence]					)












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





