





"""simple docstring"""





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


__A											=						"""scheduler_config.json"""






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:str						    =   1
       __magic_name__	:Optional[Any]						    =   2
       __magic_name__	:Optional[Any]						    =   3
       __magic_name__	:List[Any]						    =   4
       __magic_name__	:int						    =   5






@dataclass
class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:jnp.ndarray






class    _lowerCAmelCase  :
       """simple docstring"""




       __magic_name__	:Tuple						    =   SCHEDULER_CONFIG_NAME
       __magic_name__	:Dict						    =   ["""dtype"""]
       __magic_name__	:str						    =   []
       __magic_name__	:Tuple						    =   True






       @classmethod
       def 	snake_case		(       cls       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase=False       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :List[str]											=				cls.load_config(
             pretrained_model_name_or_path=__UpperCAmelCase       ,					subfolder=__UpperCAmelCase       ,					return_unused_kwargs=__UpperCAmelCase       ,					**__UpperCAmelCase       ,					)
         lowerCAmelCase__				,							lowerCAmelCase__     :Optional[int]											=				cls.from_config(__UpperCAmelCase       ,					return_unused_kwargs=__UpperCAmelCase       ,					**__UpperCAmelCase )

         if hasattr(__UpperCAmelCase       ,					'create_state' ) and getattr(__UpperCAmelCase       ,					'has_state'       ,					__UpperCAmelCase ):
           lowerCAmelCase__     :Optional[Any]											=				scheduler.create_state()

         if return_unused_kwargs:
           return scheduler, state, unused_kwargs

         return scheduler, state






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = False       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         self.save_config(save_directory=__UpperCAmelCase       ,					push_to_hub=__UpperCAmelCase       ,					**__UpperCAmelCase )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self._get_compatibles()






       @classmethod
       def 	snake_case		(       cls ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				list(set([cls.__name__] + cls._compatibles ) )
         lowerCAmelCase__     :Any											=				importlib.import_module(__name__.split('.' )[0] )
         lowerCAmelCase__     :Union[str, Any]											=				[
             getattr(__UpperCAmelCase       ,					__UpperCAmelCase ) for c in compatible_classes_str if hasattr(__UpperCAmelCase       ,					__UpperCAmelCase )
         ]
         return compatible_classes






def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->jnp.ndarray:





  """simple docstring"""


  assert len(_SCREAMING_SNAKE_CASE     ) >= x.ndim
  return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_SCREAMING_SNAKE_CASE     ) - x.ndim)     )	,  _SCREAMING_SNAKE_CASE     )
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=0.9_9_9	,  _SCREAMING_SNAKE_CASE=jnp.floataa     )   ->jnp.ndarray:





  """simple docstring"""


  def alpha_bar(_SCREAMING_SNAKE_CASE     ):
    return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2     ) ** 2

  lowerCAmelCase__     :Tuple											=				[]
  for i in range(_SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__     :Optional[int]											=				i / num_diffusion_timesteps
    lowerCAmelCase__     :List[Any]											=				(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar(_SCREAMING_SNAKE_CASE     ) / alpha_bar(_SCREAMING_SNAKE_CASE     )	,  _SCREAMING_SNAKE_CASE     )     )
  return jnp.array(_SCREAMING_SNAKE_CASE	,  dtype=_SCREAMING_SNAKE_CASE     )






@flax.struct.dataclass
class    _lowerCAmelCase  :
       """simple docstring"""




       __magic_name__	:jnp.ndarray
       __magic_name__	:jnp.ndarray
       __magic_name__	:jnp.ndarray






       @classmethod
       def 	snake_case		(       cls       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				scheduler.config

         if config.trained_betas is not None:
           lowerCAmelCase__     :Union[str, Any]											=				jnp.asarray(config.trained_betas       ,					dtype=scheduler.dtype )
         elif config.beta_schedule == "linear":
           lowerCAmelCase__     :Tuple											=				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.
           lowerCAmelCase__     :Tuple											=				(
               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
           lowerCAmelCase__     :str											=				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__}" )

         lowerCAmelCase__     :Optional[Any]											=				1.0 - betas

         lowerCAmelCase__     :Union[str, Any]											=				jnp.cumprod(__UpperCAmelCase       ,					axis=0 )

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






def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Dict:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				state.alphas_cumprod

  lowerCAmelCase__     :Union[str, Any]											=				alphas_cumprod[timesteps] ** 0.5
  lowerCAmelCase__     :Dict											=				sqrt_alpha_prod.flatten()
  lowerCAmelCase__     :Any											=				broadcast_to_shape_from_left(_SCREAMING_SNAKE_CASE	,  original_samples.shape     )

  lowerCAmelCase__     :Optional[Any]											=				(1 - alphas_cumprod[timesteps]) ** 0.5
  lowerCAmelCase__     :Optional[int]											=				sqrt_one_minus_alpha_prod.flatten()
  lowerCAmelCase__     :Optional[int]											=				broadcast_to_shape_from_left(_SCREAMING_SNAKE_CASE	,  original_samples.shape     )

  return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__				,							lowerCAmelCase__     :str											=				get_sqrt_alpha_prod(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Optional[int]											=				sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
  return noisy_samples




def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__				,							lowerCAmelCase__     :Tuple											=				get_sqrt_alpha_prod(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Tuple											=				sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
  return velocity







"""simple docstring"""





import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
							from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
							from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
							from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


_UpperCamelCase					      =   DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

_UpperCamelCase					      =   'main'
# Default branch name
_UpperCamelCase					      =   'f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'
# One particular commit (not the top of `main`)
_UpperCamelCase					      =   'aaaaaaa'
# This commit does not exist, so we should 404.

_UpperCamelCase					      =   'd9e9f15bc825e4b2c9249e9578f884bbcb5e3684'
# Sha-1 of config.json on the top of `main`, for checking purposes
_UpperCamelCase					      =   '4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'



@contextlib.contextmanager
def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							print('''Welcome!''' )
							yield
							print('''Bye!''' )



@contextlib.contextmanager
def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							print('''Bonjour!''' )
							yield
							print('''Au revoir!''' )
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				def        __lowercase	(  self	:str      ):
											# If the spec is missing, importlib would not be able to import the module dynamically.
											assert transformers.__spec__ is not None
											assert importlib.util.find_spec('''transformers'''      ) is not None

class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				@unittest.mock.patch('''sys.stdout'''    ,     new_callable=io.StringIO      )
				def        __lowercase	(  self	:str    ,     __lowercase	:str      ):
											with ContextManagers([]      ):
																		print('''Transformers are awesome!'''      )
											# The print statement adds a new line at the end of the output
											self.assertEqual(mock_stdout.getvalue()    ,     '''Transformers are awesome!\n'''      )




				@unittest.mock.patch('''sys.stdout'''    ,     new_callable=io.StringIO      )
				def        __lowercase	(  self	:Any    ,     __lowercase	:Tuple      ):
											with ContextManagers([context_en()]      ):
																		print('''Transformers are awesome!'''      )
											# The output should be wrapped with an English welcome and goodbye
											self.assertEqual(mock_stdout.getvalue()    ,     '''Welcome!\nTransformers are awesome!\nBye!\n'''      )




				@unittest.mock.patch('''sys.stdout'''    ,     new_callable=io.StringIO      )
				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:Optional[Any]      ):
											with ContextManagers([context_fr(), context_en()]      ):
																		print('''Transformers are awesome!'''      )
											# The output should be wrapped with an English and French welcome and goodbye
											self.assertEqual(mock_stdout.getvalue()    ,     '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n'''      )




				@require_torch
				def        __lowercase	(  self	:Tuple      ):
											self.assertEqual(find_labels(__lowercase      )    ,     ['''labels''']      )
											self.assertEqual(find_labels(__lowercase      )    ,     ['''labels''', '''next_sentence_label''']      )
											self.assertEqual(find_labels(__lowercase      )    ,     ['''start_positions''', '''end_positions''']      )
											class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
															"""simple docstring"""
															pass

											self.assertEqual(find_labels(__lowercase      )    ,     ['''labels''']      )




				@require_tf
				def        __lowercase	(  self	:str      ):
											self.assertEqual(find_labels(__lowercase      )    ,     ['''labels''']      )
											self.assertEqual(find_labels(__lowercase      )    ,     ['''labels''', '''next_sentence_label''']      )
											self.assertEqual(find_labels(__lowercase      )    ,     ['''start_positions''', '''end_positions''']      )
											class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
															"""simple docstring"""
															pass

											self.assertEqual(find_labels(__lowercase      )    ,     ['''labels''']      )




				@require_flax
				def        __lowercase	(  self	:str      ):
											# Flax models don't have labels
											self.assertEqual(find_labels(__lowercase      )    ,     []      )
											self.assertEqual(find_labels(__lowercase      )    ,     []      )
											self.assertEqual(find_labels(__lowercase      )    ,     []      )
											class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
															"""simple docstring"""
															pass

											self.assertEqual(find_labels(__lowercase      )    ,     []      )




