



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, 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
 __snake_case		:Any 						=	'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import MarianTokenizer
 from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right





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

class        _A			:



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


           '''simple docstring'''

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



       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

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

           __a					=							shift_tokens_right(__SCREAMING_SNAKE_CASE	,						1	,						2)

           __a					=							MarianConfig(
               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	,						initializer_range=self.initializer_range	,						use_cache=__SCREAMING_SNAKE_CASE	,						)
           __a					=							prepare_marian_inputs_dict(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE)
           return config, inputs_dict



       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

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



       def 						_lowerCamelCase     (						self				:	int	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	str):


           '''simple docstring'''

           __a					=							20
           __a					=							model_class_name(__SCREAMING_SNAKE_CASE)

           __a					=							model.encode(inputs_dict['''input_ids'''])

           __a			,  __a					=							(
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           __a					=							model.init_cache(decoder_input_ids.shape[0]	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE)
           __a					=							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,						dtype='''i4''')

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

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

           __a					=							model.decode(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE)

           __a					=							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 						_lowerCamelCase     (						self				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]):


           '''simple docstring'''

           __a					=							20
           __a					=							model_class_name(__SCREAMING_SNAKE_CASE)

           __a					=							model.encode(inputs_dict['''input_ids'''])

           __a			,  __a					=							(
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

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

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

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

           __a					=							model.decode(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						decoder_attention_mask=__SCREAMING_SNAKE_CASE)

           __a					=							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        _A			(      __UpperCAmelCase ,unittest.TestCase ,__UpperCAmelCase       ):
       UpperCamelCase__    :       Dict		   =		True
       UpperCamelCase__    :       str		   =		(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
       UpperCamelCase__    :       str		   =		(FlaxMarianMTModel,) if is_flax_available() else ()



       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

           __a					=							FlaxMarianModelTester(self)



       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           __a			,  __a					=							self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE)



       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

           __a			,  __a					=							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(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE)



       def 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           __a			,  __a					=							self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                   __a					=							self._prepare_for_class(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE)
                   __a					=							model_class(__SCREAMING_SNAKE_CASE)

                   @jax.jit
                   def encode_jitted(__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	Dict=None	,						**__SCREAMING_SNAKE_CASE				:	Tuple):
                       return model.encode(input_ids=__SCREAMING_SNAKE_CASE	,						attention_mask=__SCREAMING_SNAKE_CASE)

                   with self.subTest('''JIT Enabled'''):
                       __a					=							encode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

                   with self.subTest('''JIT Disabled'''):
                       with jax.disable_jit():
                           __a					=							encode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

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



       def 						_lowerCamelCase     (						self				:	str):


           '''simple docstring'''

           __a			,  __a					=							self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                   __a					=							model_class(__SCREAMING_SNAKE_CASE)
                   __a					=							model.encode(inputs_dict['''input_ids''']	,						inputs_dict['''attention_mask'''])

                   __a					=							{
                       '''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(__SCREAMING_SNAKE_CASE				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	Tuple):
                       return model.decode(
                           decoder_input_ids=__SCREAMING_SNAKE_CASE	,						decoder_attention_mask=__SCREAMING_SNAKE_CASE	,						encoder_outputs=__SCREAMING_SNAKE_CASE	,						)

                   with self.subTest('''JIT Enabled'''):
                       __a					=							decode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

                   with self.subTest('''JIT Disabled'''):
                       with jax.disable_jit():
                           __a					=							decode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

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





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


           '''simple docstring'''

           for model_class_name in self.all_model_classes:
               __a					=							model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de''')
               # FlaxMarianForSequenceClassification expects eos token in input_ids
               __a					=							np.ones((1, 1)) * model.config.eos_token_id
               __a					=							model(__SCREAMING_SNAKE_CASE)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE)


@require_flax
@require_sentencepiece
@require_tokenizers
class        _A			(      unittest.TestCase       ):
       UpperCamelCase__    :       Optional[Any]		   =		None
       UpperCamelCase__    :       Dict		   =		None



       @classmethod
       def 						_lowerCamelCase     (						cls				:	Union[str, Any]):


           '''simple docstring'''

           __a					=							F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
           return cls



       @cached_property
       def 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           return MarianTokenizer.from_pretrained(self.model_name)



       @property
       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           return self.tokenizer.eos_token_id



       @cached_property
       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

           __a					=							FlaxMarianMTModel.from_pretrained(self.model_name)
           self.assertEqual(model.config.decoder_start_token_id	,						model.config.pad_token_id)
           return model



       def 						_lowerCamelCase     (						self				:	Optional[int]	,						**__SCREAMING_SNAKE_CASE				:	int):


           '''simple docstring'''

           __a					=							self.translate_src_text(**__SCREAMING_SNAKE_CASE)
           self.assertListEqual(self.expected_text	,						__SCREAMING_SNAKE_CASE)





       def 						_lowerCamelCase     (						self				:	int	,						**__SCREAMING_SNAKE_CASE				:	List[str]):


           '''simple docstring'''

           __a					=							self.tokenizer(self.src_text	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''np'''	,						**__SCREAMING_SNAKE_CASE)
           __a					=							self.model.generate(
               model_inputs.input_ids	,						attention_mask=model_inputs.attention_mask	,						num_beams=2	,						max_length=128	,						).sequences
           __a					=							self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	,						skip_special_tokens=__SCREAMING_SNAKE_CASE)
           return generated_words


@require_flax
@require_sentencepiece
@require_tokenizers
class        _A			(      __UpperCAmelCase       ):
       UpperCamelCase__    :       Union[str, Any]		   =		'''en'''
       UpperCamelCase__    :       Any		   =		'''fr'''
       UpperCamelCase__    :       Union[str, Any]		   =		[
           '''I am a small frog.''',
           '''Now I can forget the 100 words of german that I know.''',
       ]
       UpperCamelCase__    :       Dict		   =		[
           '''Je suis une petite grenouille.''',
           '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
       ]



       @slow
       def 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class        _A			(      __UpperCAmelCase       ):
       UpperCamelCase__    :       Any		   =		'''fr'''
       UpperCamelCase__    :       Any		   =		'''en'''
       UpperCamelCase__    :       str		   =		[
           '''Donnez moi le micro.''',
           '''Tom et Mary étaient assis à une table.''',  # Accents
       ]
       UpperCamelCase__    :       Tuple		   =		[
           '''Give me the microphone.''',
           '''Tom and Mary were sitting at a table.''',
       ]



       @slow
       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

           self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class        _A			(      __UpperCAmelCase       ):
       UpperCamelCase__    :       Union[str, Any]		   =		'''mt'''
       UpperCamelCase__    :       Dict		   =		'''en'''
       UpperCamelCase__    :       Tuple		   =		['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
       UpperCamelCase__    :       List[Any]		   =		['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']



       @slow
       def 						_lowerCamelCase     (						self				:	List[Any]):


           '''simple docstring'''

           self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class        _A			(      __UpperCAmelCase       ):
       UpperCamelCase__    :       Any		   =		'''en'''
       UpperCamelCase__    :       List[str]		   =		'''de'''
       UpperCamelCase__    :       Dict		   =		[
           '''I am a small frog.''',
           '''Now I can forget the 100 words of german that I know.''',
           '''Tom asked his teacher for advice.''',
           '''That\'s how I would do it.''',
           '''Tom really admired Mary\'s courage.''',
           '''Turn around and close your eyes.''',
       ]
       UpperCamelCase__    :       Union[str, Any]		   =		[
           '''Ich bin ein kleiner Frosch.''',
           '''Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.''',
           '''Tom bat seinen Lehrer um Rat.''',
           '''So würde ich das machen.''',
           '''Tom bewunderte Marias Mut wirklich.''',
           '''Drehen Sie sich um und schließen Sie die Augen.''',
       ]



       @slow
       def 						_lowerCamelCase     (						self				:	str):


           '''simple docstring'''

           self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class        _A			(      __UpperCAmelCase       ):
       UpperCamelCase__    :       int		   =		'''en'''
       UpperCamelCase__    :       Union[str, Any]		   =		'''zh'''
       UpperCamelCase__    :       int		   =		['''My name is Wolfgang and I live in Berlin''']
       UpperCamelCase__    :       str		   =		['''我叫沃尔夫冈 我住在柏林''']



       @slow
       def 						_lowerCamelCase     (						self				:	Optional[Any]):


           '''simple docstring'''

           self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class        _A			(      __UpperCAmelCase       ):
       UpperCamelCase__    :       Any		   =		'''ru'''
       UpperCamelCase__    :       Any		   =		'''fr'''
       UpperCamelCase__    :       Optional[int]		   =		['''Он показал мне рукопись своей новой пьесы.''']
       UpperCamelCase__    :       Dict		   =		['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']



       @slow
       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

           self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class        _A			(      __UpperCAmelCase       ):
       UpperCamelCase__    :       Optional[int]		   =		'''en'''
       UpperCamelCase__    :       List[Any]		   =		'''ROMANCE'''
       UpperCamelCase__    :       Any		   =		[
           '''>>fr<< Don\'t spend so much time watching TV.''',
           '''>>pt<< Your message has been sent.''',
           '''>>es<< He\'s two years older than me.''',
       ]
       UpperCamelCase__    :       List[str]		   =		[
           '''Ne passez pas autant de temps à regarder la télé.''',
           '''A sua mensagem foi enviada.''',
           '''Es dos años más viejo que yo.''',
       ]



       @slow
       def 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           self._assert_generated_batch_equal_expected()










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

import numpy as np
import torch
from scipy import integrate

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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class        _A			(      __UpperCAmelCase       ):
       UpperCamelCase__    :       torch.FloatTensor
       UpperCamelCase__    :       Optional[torch.FloatTensor]		   =		None





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase=0.9_99  ,					_UpperCAmelCase="cosine"  ,					):
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(_UpperCAmelCase							):
            return math.cos((t + 0.0_08) / 1.0_08 * 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}'							)

    __a					=							[]
    for i in range(_UpperCAmelCase							):
        __a					=							i / num_diffusion_timesteps
        __a					=							(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        _A			(      __UpperCAmelCase ,__UpperCAmelCase       ):
       UpperCamelCase__    :       List[Any]		   =		[e.name for e in KarrasDiffusionSchedulers]
       UpperCamelCase__    :       Optional[Any]		   =		1



       @register_to_config
       def __init__(						self				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	int = 1_000	,						__SCREAMING_SNAKE_CASE				:	float = 0.00_01	,						__SCREAMING_SNAKE_CASE				:	float = 0.02	,						__SCREAMING_SNAKE_CASE				:	str = "linear"	,						__SCREAMING_SNAKE_CASE				:	Optional[Union[np.ndarray, List[float]]] = None	,						__SCREAMING_SNAKE_CASE				:	Optional[bool] = False	,						__SCREAMING_SNAKE_CASE				:	str = "epsilon"	,						__SCREAMING_SNAKE_CASE				:	str = "linspace"	,						__SCREAMING_SNAKE_CASE				:	int = 0	,						):


           '''simple docstring'''

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

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

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

           # setable values
           __a					=							None
           __a					=							use_karras_sigmas
           self.set_timesteps(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE)
           __a					=							[]
           __a					=							False



       @property
       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

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

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



       def 						_lowerCamelCase     (						self				:	Any	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	Union[float, torch.FloatTensor]):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE	,						torch.Tensor):
               __a					=							timestep.to(self.timesteps.device)
           __a					=							(self.timesteps == timestep).nonzero().item()
           __a					=							self.sigmas[step_index]
           __a					=							sample / ((sigma**2 + 1) ** 0.5)
           __a					=							True
           return sample



       def 						_lowerCamelCase     (						self				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	int	,						__SCREAMING_SNAKE_CASE				:	Optional[int]):


           '''simple docstring'''

           def lms_derivative(__SCREAMING_SNAKE_CASE				:	str):
               __a					=							1.0
               for k in range(__SCREAMING_SNAKE_CASE):
                   if current_order == k:
                       continue
                   prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
               return prod

           __a					=							integrate.quad(__SCREAMING_SNAKE_CASE	,						self.sigmas[t]	,						self.sigmas[t + 1]	,						epsrel=1E-4)[0]

           return integrated_coeff



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


           '''simple docstring'''

           __a					=							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":
               __a					=							np.linspace(0	,						self.config.num_train_timesteps - 1	,						__SCREAMING_SNAKE_CASE	,						dtype=__SCREAMING_SNAKE_CASE)[
                   ::-1
               ].copy()
           elif self.config.timestep_spacing == "leading":
               __a					=							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
               __a					=							(np.arange(0	,						__SCREAMING_SNAKE_CASE) * step_ratio).round()[::-1].copy().astype(__SCREAMING_SNAKE_CASE)
               timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
               __a					=							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
               __a					=							(np.arange(self.config.num_train_timesteps	,						0	,						-step_ratio)).round().copy().astype(__SCREAMING_SNAKE_CASE)
               timesteps -= 1
           else:
               raise ValueError(
                   F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.')

           __a					=							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
           __a					=							np.log(__SCREAMING_SNAKE_CASE)
           __a					=							np.interp(__SCREAMING_SNAKE_CASE	,						np.arange(0	,						len(__SCREAMING_SNAKE_CASE))	,						__SCREAMING_SNAKE_CASE)

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

           __a					=							np.concatenate([sigmas, [0.0]]).astype(np.floataa)

           __a					=							torch.from_numpy(__SCREAMING_SNAKE_CASE).to(device=__SCREAMING_SNAKE_CASE)
           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               # mps does not support float64
               __a					=							torch.from_numpy(__SCREAMING_SNAKE_CASE).to(__SCREAMING_SNAKE_CASE	,						dtype=torch.floataa)
           else:
               __a					=							torch.from_numpy(__SCREAMING_SNAKE_CASE).to(device=__SCREAMING_SNAKE_CASE)

           __a					=							[]



       def 						_lowerCamelCase     (						self				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	Any):


           '''simple docstring'''

           __a					=							np.log(__SCREAMING_SNAKE_CASE)

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

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

           __a					=							log_sigmas[low_idx]
           __a					=							log_sigmas[high_idx]

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

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



       def 						_lowerCamelCase     (						self				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor):


           '''simple docstring'''

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

           __a					=							7.0  # 7.0 is the value used in the paper
           __a					=							np.linspace(0	,						1	,						self.num_inference_steps)
           __a					=							sigma_min ** (1 / rho)
           __a					=							sigma_max ** (1 / rho)
           __a					=							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas



       def 						_lowerCamelCase     (						self				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	Union[float, torch.FloatTensor]	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	int = 4	,						__SCREAMING_SNAKE_CASE				:	bool = True	,						):


           '''simple docstring'''

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

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

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
               __a					=							sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               __a					=							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
           elif self.config.prediction_type == "sample":
               __a					=							model_output
           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
           __a					=							(sample - pred_original_sample) / sigma
           self.derivatives.append(__SCREAMING_SNAKE_CASE)
           if len(self.derivatives) > order:
               self.derivatives.pop(0)

           # 3. Compute linear multistep coefficients
           __a					=							min(step_index + 1	,						__SCREAMING_SNAKE_CASE)
           __a					=							[self.get_lms_coefficient(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE) for curr_order in range(__SCREAMING_SNAKE_CASE)]

           # 4. Compute previous sample based on the derivatives path
           __a					=							sample + sum(
               coeff * derivative for coeff, derivative in zip(__SCREAMING_SNAKE_CASE	,						reversed(self.derivatives)))

           if not return_dict:
               return (prev_sample,)

           return LMSDiscreteSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE	,						pred_original_sample=__SCREAMING_SNAKE_CASE)



       def 						_lowerCamelCase     (						self				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						):


           '''simple docstring'''

           __a					=							self.sigmas.to(device=original_samples.device	,						dtype=original_samples.dtype)
           if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE):
               # mps does not support float64
               __a					=							self.timesteps.to(original_samples.device	,						dtype=torch.floataa)
               __a					=							timesteps.to(original_samples.device	,						dtype=torch.floataa)
           else:
               __a					=							self.timesteps.to(original_samples.device)
               __a					=							timesteps.to(original_samples.device)

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

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

           __a					=							original_samples + noise * sigma
           return noisy_samples





       def __len__(						self				:	List[str]):


           '''simple docstring'''

           return self.config.num_train_timesteps





