


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, 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
 SCREAMING_SNAKE_CASE			: List[str]				   =					"platform"

 import jax
 import jax.numpy as jnp

 from transformers import BlenderbotTokenizer
 from transformers.models.blenderbot.modeling_flax_blenderbot import (
     FlaxBlenderbotForConditionalGeneration,
     FlaxBlenderbotModel,
     shift_tokens_right,
 )



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=None				,					lowerCamelCase_=None				,					lowerCamelCase_=None				,					lowerCamelCase_=None				,					lowerCamelCase_=None				,					lowerCamelCase_=None				,					)       ->		str:
    if attention_mask is None:
        _lowercase			:				Optional[int] 						=						np.where(input_ids != config.pad_token_id				,					1				,					0					)
    if decoder_attention_mask is None:
        _lowercase			:				Optional[Any] 						=						np.where(decoder_input_ids != config.pad_token_id				,					1				,					0					)
    if head_mask is None:
        _lowercase			:				Tuple 						=						np.ones((config.encoder_layers, config.encoder_attention_heads)					)
    if decoder_head_mask is None:
        _lowercase			:				Union[str, Any] 						=						np.ones((config.decoder_layers, config.decoder_attention_heads)					)
    if cross_attn_head_mask is None:
        _lowercase			:				Tuple 						=						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 _lowerCamelCase:
       def __init__(   self, lowerCamelCase, lowerCamelCase=13, lowerCamelCase=7, lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase=99, lowerCamelCase=16, lowerCamelCase=2, lowerCamelCase=4, lowerCamelCase=4, lowerCamelCase="gelu", lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=32, lowerCamelCase=2, lowerCamelCase=1, lowerCamelCase=0, lowerCamelCase=0.0_2, )			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						parent
           _lowercase			:				Union[str, Any] 						=						batch_size
           _lowercase			:				List[Any] 						=						seq_length
           _lowercase			:				Optional[int] 						=						is_training
           _lowercase			:				List[str] 						=						use_labels
           _lowercase			:				List[str] 						=						vocab_size
           _lowercase			:				Union[str, Any] 						=						hidden_size
           _lowercase			:				Dict 						=						num_hidden_layers
           _lowercase			:				Optional[Any] 						=						num_attention_heads
           _lowercase			:				str 						=						intermediate_size
           _lowercase			:				Optional[Any] 						=						hidden_act
           _lowercase			:				List[Any] 						=						hidden_dropout_prob
           _lowercase			:				Any 						=						attention_probs_dropout_prob
           _lowercase			:				Optional[int] 						=						max_position_embeddings
           _lowercase			:				Union[str, Any] 						=						eos_token_id
           _lowercase			:				int 						=						pad_token_id
           _lowercase			:				Tuple 						=						bos_token_id
           _lowercase			:				Optional[int] 						=						initializer_range
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

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

           _lowercase			:				Union[str, Any] 						=						shift_tokens_right(lowerCamelCase, 1, 2)

           _lowercase			:				Optional[Any] 						=						BlenderbotConfig(
               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=lowerCamelCase, )
           _lowercase			:				Any 						=						prepare_blenderbot_inputs_dict(lowerCamelCase, lowerCamelCase, lowerCamelCase)
           return config, inputs_dict
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Any 						=						self.prepare_config_and_inputs()
           return config, inputs_dict
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						20
           _lowercase			:				Dict 						=						model_class_name(lowerCamelCase)

           _lowercase			:				List[Any] 						=						model.encode(inputs_dict['input_ids'])

           _lowercase						,							_lowercase			:				Optional[Any] 						=						(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

           _lowercase			:				Dict 						=						model.init_cache(decoder_input_ids.shape[0], lowerCamelCase, lowerCamelCase)
           _lowercase			:				int 						=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype='i4')

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

           _lowercase			:				str 						=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype='i4')
           _lowercase			:				Optional[int] 						=						model.decode(
               decoder_input_ids[:, -1:], lowerCamelCase, decoder_attention_mask=lowerCamelCase, past_key_values=outputs_cache.past_key_values, decoder_position_ids=lowerCamelCase, )

           _lowercase			:				int 						=						model.decode(lowerCamelCase, lowerCamelCase)

           _lowercase			:				Optional[Any] 						=						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      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				str 						=						20
           _lowercase			:				Tuple 						=						model_class_name(lowerCamelCase)

           _lowercase			:				Any 						=						model.encode(inputs_dict['input_ids'])

           _lowercase						,							_lowercase			:				List[Any] 						=						(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

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

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

           _lowercase			:				List[Any] 						=						model.decode(
               decoder_input_ids[:, :-1], lowerCamelCase, decoder_attention_mask=lowerCamelCase, past_key_values=lowerCamelCase, decoder_position_ids=lowerCamelCase, )
           _lowercase			:				str 						=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype='i4')
           _lowercase			:				List[str] 						=						model.decode(
               decoder_input_ids[:, -1:], lowerCamelCase, past_key_values=outputs_cache.past_key_values, decoder_attention_mask=lowerCamelCase, decoder_position_ids=lowerCamelCase, )

           _lowercase			:				Tuple 						=						model.decode(lowerCamelCase, lowerCamelCase, decoder_attention_mask=lowerCamelCase)

           _lowercase			:				str 						=						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(		unittest.TestCase  ):
       lowercase_					:      Optional[int]       					=     99
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						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, )

           _lowercase			:				Optional[Any] 						=						input_ids.shape[0]
           _lowercase			:				int 						=						BlenderbotConfig(
               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      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase						,							_lowercase						,							_lowercase			:				Any 						=						self._get_config_and_data()
           _lowercase			:				Tuple 						=						FlaxBlenderbotForConditionalGeneration(lowerCamelCase)
           _lowercase			:				List[str] 						=						lm_model(input_ids=lowerCamelCase)
           _lowercase			:				Union[str, Any] 						=						(batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs['logits'].shape, lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Dict 						=						BlenderbotConfig(
               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, )
           _lowercase			:				str 						=						FlaxBlenderbotForConditionalGeneration(lowerCamelCase)
           _lowercase			:				List[str] 						=						np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], dtype=np.intaa)
           _lowercase			:				Any 						=						np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], dtype=np.intaa)
           _lowercase			:				Tuple 						=						lm_model(input_ids=lowerCamelCase, decoder_input_ids=lowerCamelCase)
           _lowercase			:				List[Any] 						=						(*summary.shape, config.vocab_size)
           self.assertEqual(outputs['logits'].shape, lowerCamelCase)




       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

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






@require_flax
class _lowerCamelCase(		_a,					unittest.TestCase,					_a  ):
       lowercase_					:      Any       					=     True
       lowercase_					:      Optional[int]       					=     (
           (
               FlaxBlenderbotModel,
               FlaxBlenderbotForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       lowercase_					:      Dict       					=     (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						FlaxBlenderbotModelTester(self)
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase						,							_lowercase			:				int 						=						self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(lowerCamelCase, lowerCamelCase, lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				List[str] 						=						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(lowerCamelCase, lowerCamelCase, lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Any 						=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                   _lowercase			:				Optional[Any] 						=						self._prepare_for_class(lowerCamelCase, lowerCamelCase)
                   _lowercase			:				Any 						=						model_class(lowerCamelCase)

                   @jax.jit
                   def encode_jitted(lowerCamelCase, lowerCamelCase=None, **lowerCamelCase):
                       return model.encode(input_ids=lowerCamelCase, attention_mask=lowerCamelCase)

                   with self.subTest('JIT Enabled'):
                       _lowercase			:				List[str] 						=						encode_jitted(**lowerCamelCase).to_tuple()

                   with self.subTest('JIT Disabled'):
                       with jax.disable_jit():
                           _lowercase			:				Union[str, Any] 						=						encode_jitted(**lowerCamelCase).to_tuple()

                   self.assertEqual(len(lowerCamelCase), len(lowerCamelCase))
                   for jitted_output, output in zip(lowerCamelCase, lowerCamelCase):
                       self.assertEqual(jitted_output.shape, output.shape)
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				List[str] 						=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                   _lowercase			:				int 						=						model_class(lowerCamelCase)
                   _lowercase			:				Union[str, Any] 						=						model.encode(inputs_dict['input_ids'], inputs_dict['attention_mask'])

                   _lowercase			:				Optional[int] 						=						{
                       '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(lowerCamelCase, lowerCamelCase, lowerCamelCase):
                       return model.decode(
                           decoder_input_ids=lowerCamelCase, decoder_attention_mask=lowerCamelCase, encoder_outputs=lowerCamelCase, )

                   with self.subTest('JIT Enabled'):
                       _lowercase			:				Union[str, Any] 						=						decode_jitted(**lowerCamelCase).to_tuple()

                   with self.subTest('JIT Disabled'):
                       with jax.disable_jit():
                           _lowercase			:				Optional[int] 						=						decode_jitted(**lowerCamelCase).to_tuple()

                   self.assertEqual(len(lowerCamelCase), len(lowerCamelCase))
                   for jitted_output, output in zip(lowerCamelCase, lowerCamelCase):
                       self.assertEqual(jitted_output.shape, output.shape)
       @slow
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           for model_class_name in self.all_model_classes:
               _lowercase			:				Optional[Any] 						=						model_class_name.from_pretrained('facebook/blenderbot-400M-distill')
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               _lowercase			:				Any 						=						np.ones((1, 1)) * model.config.eos_token_id
               _lowercase			:				Optional[Any] 						=						model(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)




       @unittest.skipUnless(jax_device != 'cpu', '3B test too slow on CPU.')
       @slow
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						{'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
           _lowercase			:				Any 						=						{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

           _lowercase			:				Any 						=						FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B', from_pt=lowerCamelCase)
           _lowercase			:				Dict 						=						BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B')

           _lowercase			:				int 						=						['Sam']
           _lowercase			:				str 						=						tokenizer(lowerCamelCase, return_tensors='jax')

           _lowercase			:				Optional[Any] 						=						model.generate(**lowerCamelCase, **lowerCamelCase)
           _lowercase			:				Any 						=						'Sam is a great name. It means "sun" in Gaelic.'

           _lowercase			:				Tuple 						=						tokenizer.batch_decode(lowerCamelCase, **lowerCamelCase)
           assert generated_txt[0].strip() == tgt_text






import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE			: str				   =					"scheduler_config.json"






class _lowerCamelCase(		_a  ):
       lowercase_					:      Any       					=     1
       lowercase_					:      Dict       					=     2
       lowercase_					:      Union[str, Any]       					=     3
       lowercase_					:      Tuple       					=     4
       lowercase_					:      Optional[Any]       					=     5






@dataclass
class _lowerCamelCase(		_a  ):
       lowercase_					:      jnp.ndarray






class _lowerCamelCase:
       lowercase_					:      Union[str, Any]       					=     SCHEDULER_CONFIG_NAME
       lowercase_					:      str       					=     ["""dtype"""]
       lowercase_					:      Dict       					=     []
       lowercase_					:      int       					=     True
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase=False, **lowerCamelCase, )			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[int] 						=						cls.load_config(
               pretrained_model_name_or_path=lowerCamelCase, subfolder=lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase, )
           _lowercase						,							_lowercase			:				Tuple 						=						cls.from_config(lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase)

           if hasattr(lowerCamelCase, 'create_state') and getattr(lowerCamelCase, 'has_state', lowerCamelCase):
               _lowercase			:				List[Any] 						=						scheduler.create_state()

           if return_unused_kwargs:
               return scheduler, state, unused_kwargs

           return scheduler, state
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = False, **lowerCamelCase)			->  Any:




           """simple docstring"""

           self.save_config(save_directory=lowerCamelCase, push_to_hub=lowerCamelCase, **lowerCamelCase)
       @property
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return self._get_compatibles()




       @classmethod
       def      UpperCamelCase	(   cls)			->  Any:




           """simple docstring"""

           _lowercase			:				Any 						=						list(set([cls.__name__] + cls._compatibles))
           _lowercase			:				Dict 						=						importlib.import_module(__name__.split('.')[0])
           _lowercase			:				Any 						=						[
               getattr(lowerCamelCase, lowerCamelCase) for c in compatible_classes_str if hasattr(lowerCamelCase, lowerCamelCase)
           ]
           return compatible_classes



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		jnp.ndarray:
    assert len(lowerCamelCase_					) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_					) - x.ndim)					)				,					lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=0.9_99				,					lowerCamelCase_=jnp.floataa					)       ->		jnp.ndarray:

    def alpha_bar(lowerCamelCase_					):
        return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

    _lowercase			:				List[Any] 						=						[]
    for i in range(lowerCamelCase_					):
        _lowercase			:				Any 						=						i / num_diffusion_timesteps
        _lowercase			:				Any 						=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(lowerCamelCase_					) / alpha_bar(lowerCamelCase_					)				,					lowerCamelCase_					)					)
    return jnp.array(lowerCamelCase_				,					dtype=lowerCamelCase_					)






@flax.struct.dataclass
class _lowerCamelCase:
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				int 						=						scheduler.config

           if config.trained_betas is not None:
               _lowercase			:				str 						=						jnp.asarray(config.trained_betas, dtype=scheduler.dtype)
           elif config.beta_schedule == "linear":
               _lowercase			:				List[Any] 						=						jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype)
           elif config.beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _lowercase			:				Dict 						=						(
                   jnp.linspace(
                       config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype)
                   ** 2
               )
           elif config.beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _lowercase			:				Optional[int] 						=						betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype)
           else:
               raise NotImplementedError(
                   F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''')

           _lowercase			:				List[str] 						=						1.0 - betas

           _lowercase			:				Union[str, Any] 						=						jnp.cumprod(lowerCamelCase, axis=0)

           return cls(
               alphas=lowerCamelCase, betas=lowerCamelCase, alphas_cumprod=lowerCamelCase, )



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    _lowercase			:				str 						=						state.alphas_cumprod

    _lowercase			:				str 						=						alphas_cumprod[timesteps] ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_alpha_prod.flatten()
    _lowercase			:				Tuple 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    _lowercase			:				Union[str, Any] 						=						(1 - alphas_cumprod[timesteps]) ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_one_minus_alpha_prod.flatten()
    _lowercase			:				int 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		List[str]:
    _lowercase						,							_lowercase			:				Optional[int] 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				str 						=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Tuple:
    _lowercase						,							_lowercase			:				Tuple 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				List[Any] 						=						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity


