






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 						SCREAMING_SNAKE_CASE				( _UpperCAmelCase					):





							"""simple docstring"""

							A_						  =					1
							A_						  =					2
							A_						  =					3
							A_						  =					4
							A_						  =					5





@dataclass
class 						SCREAMING_SNAKE_CASE				( _UpperCAmelCase					):





							"""simple docstring"""

							A_						  =					42





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							A_						  =					SCHEDULER_CONFIG_NAME
							A_						  =					['''dtype''']
							A_						  =					[]
							A_						  =					True

							@classmethod
							def       __A (  cls:   List[Any]		,     __A:   Dict[str, Any] = None		,     __A:   Optional[str] = None		,     __A:   str=False		,     **__A:   Union[str, Any]		,     )    ->				Any:
												_A							,_A								= cls.load_config(
												    pretrained_model_name_or_path=A_		,     subfolder=A_		,     return_unused_kwargs=A_		,     **A_		,     )
												_A							,_A								= cls.from_config(A_		,     return_unused_kwargs=A_		,     **A_   )

												if hasattr(A_		,     '''create_state'''   ) and getattr(A_		,     '''has_state'''		,     A_   ):
																	_A								= scheduler.create_state()

												if return_unused_kwargs:
																	return scheduler, state, unused_kwargs

												return scheduler, state

							def       __A (  self:   List[Any]		,     __A:   Union[str, os.PathLike]		,     __A:   bool = False		,     **__A:   Dict   )    ->				int:
												self.save_config(save_directory=A_		,     push_to_hub=A_		,     **A_   )

							@property
							def       __A (  self:   Dict   )    ->				Union[str, Any]:
												return self._get_compatibles()



							@classmethod
							def       __A (  cls:   Any   )    ->				Union[str, Any]:
												_A								= list(set([cls.__name__] + cls._compatibles   )   )
												_A								= importlib.import_module(__name__.split('''.'''   )[0]   )
												_A								= [
												    getattr(A_		,     A_   ) for c in compatible_classes_str if hasattr(A_		,     A_   )
												]
												return compatible_classes




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					assert len(_lowercase ) >= x.ndim
					return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_lowercase ) - x.ndim) )					,			_lowercase )




def      __A					(     _lowercase					,			_lowercase=0.9_99					,			_lowercase=jnp.floataa ):

					'''simple docstring'''







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

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





@flax.struct.dataclass
class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							A_						  =					42
							A_						  =					42
							A_						  =					42

							@classmethod
							def       __A (  cls:   List[str]		,     __A:   Union[str, Any]   )    ->				str:
												_A								= scheduler.config

												if config.trained_betas is not None:
																	_A								= jnp.asarray(config.trained_betas		,     dtype=scheduler.dtype   )
												elif config.beta_schedule == "linear":
																	_A								= 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.
																	_A								= (
																	    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
																	_A								= 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__}"""   )

												_A								= 1.0 - betas

												_A								= jnp.cumprod(A_		,     axis=0   )

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




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= state.alphas_cumprod

					_A								= alphas_cumprod[timesteps] ** 0.5
					_A								= sqrt_alpha_prod.flatten()
					_A								= broadcast_to_shape_from_left(_lowercase					,			original_samples.shape )

					_A								= (1 - alphas_cumprod[timesteps]) ** 0.5
					_A								= sqrt_one_minus_alpha_prod.flatten()
					_A								= broadcast_to_shape_from_left(_lowercase					,			original_samples.shape )

					return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A							,_A								= get_sqrt_alpha_prod(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )
					_A								= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
					return noisy_samples




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A							,_A								= get_sqrt_alpha_prod(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )
					_A								= sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
					return velocity











import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					BlenderbotSmallTokenizer
							A_						  =					False

							def       __A (  self:   List[str]   )    ->				int:
												super().setUp()

												_A								= ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
												_A								= dict(zip(__A		,     range(len(__A   )   )   )   )

												_A								= ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
												_A								= {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

												_A								= os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''vocab_file''']   )
												_A								= os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''merges_file''']   )
												with open(self.vocab_file		,     '''w'''		,     encoding='''utf-8'''   ) as fp:
																	fp.write(json.dumps(__A   ) + '''\n'''   )
												with open(self.merges_file		,     '''w'''		,     encoding='''utf-8'''   ) as fp:
																	fp.write('''\n'''.join(__A   )   )

							def       __A (  self:   str		,     **__A:   Optional[Any]   )    ->				Dict:
												kwargs.update(self.special_tokens_map   )
												return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname		,     **__A   )

							def       __A (  self:   str		,     __A:   List[str]   )    ->				int:
												_A								= '''adapt act apte'''
												_A								= '''adapt act apte'''
												return input_text, output_text

							def       __A (  self:   Union[str, Any]   )    ->				Any:
												_A								= BlenderbotSmallTokenizer(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map   )
												_A								= '''adapt act apte'''
												_A								= ['''adapt''', '''act''', '''ap@@''', '''te''']
												_A								= tokenizer.tokenize(__A   )
												self.assertListEqual(__A		,     __A   )

												_A								= [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

												_A								= [0, 1, 2, 3, 4, 5]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__A   )		,     __A   )

							def       __A (  self:   Any   )    ->				List[str]:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												assert tok('''sam'''   ).input_ids == [13_84]
												_A								= '''I am a small frog.'''
												_A								= tok([src_text]		,     padding=__A		,     truncation=__A   )['''input_ids''']
												_A								= tok.batch_decode(__A		,     skip_special_tokens=__A		,     clean_up_tokenization_spaces=__A   )[0]
												assert src_text != decoded  # I wish it did!
												assert decoded == "i am a small frog ."



							def       __A (  self:   Any   )    ->				int:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												_A								= '''I am a small frog .'''
												_A								= '''.'''
												_A								= tok(__A   )['''input_ids''']
												_A								= tok(__A   )['''input_ids''']

												assert encoded[-1] == encoded_dot[0]



