





"""simple docstring"""






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)




@flax.struct.dataclass
class 				__lowercase			:







     '''simple docstring'''




     __lowerCAmelCase  =  42

     # setable values
     __lowerCAmelCase  =  42
     __lowerCAmelCase  =  42
     __lowerCAmelCase  =  None







     @classmethod
     def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
         return cls(common=_lowercase	,			init_noise_sigma=_lowercase	,			timesteps=_lowercase     )






@dataclass
class 				__lowercase			(			UpperCAmelCase_							):







     '''simple docstring'''




     __lowerCAmelCase  =  42






class 				__lowercase			(			UpperCAmelCase_   ,      UpperCAmelCase_							):







     '''simple docstring'''




     __lowerCAmelCase  =  [e.name for e in FlaxKarrasDiffusionSchedulers]

     __lowerCAmelCase  =  42







     @property
     def  _lowerCamelCase		(						self     ):
         return True







     @register_to_config
     def __init__(						self	,			_UpperCAmelCase = 1000	,			_UpperCAmelCase = 0.0_0_0_1	,			_UpperCAmelCase = 0.0_2	,			_UpperCAmelCase = "linear"	,			_UpperCAmelCase = None	,			_UpperCAmelCase = "fixed_small"	,			_UpperCAmelCase = True	,			_UpperCAmelCase = "epsilon"	,			_UpperCAmelCase = jnp.floataa	,			):
         __a :						Any			       =  dtype







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase = None     ):
         if common is None:
             __a :						Dict			       =  CommonSchedulerState.create(self     )

         # standard deviation of the initial noise distribution
         __a :						Optional[Any]			       =  jnp.array(1.0	,			dtype=self.dtype     )

         __a :						List[Any]			       =  jnp.arange(0	,			self.config.num_train_timesteps     ).round()[::-1]

         return DDPMSchedulerState.create(
             common=_lowercase	,			init_noise_sigma=_lowercase	,			timesteps=_lowercase	,			)







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = None     ):
         return sample







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = ()     ):
         __a :						Union[str, Any]			       =  self.config.num_train_timesteps // num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # rounding to avoid issues when num_inference_step is power of 3
         __a :						Tuple			       =  (jnp.arange(0	,			_lowercase     ) * step_ratio).round()[::-1]

         return state.replace(
             num_inference_steps=_lowercase	,			timesteps=_lowercase	,			)







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None     ):
         __a :						int			       =  state.common.alphas_cumprod[t]
         __a :						Optional[int]			       =  jnp.where(t > 0	,			state.common.alphas_cumprod[t - 1]	,			jnp.array(1.0	,			dtype=self.dtype     )     )

         # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
         # and sample from it to get previous sample
         # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
         __a :						List[Any]			       =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

         if variance_type is None:
             __a :						Tuple			       =  self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small":
             __a :						int			       =  jnp.clip(_lowercase	,			a_min=1e-2_0     )
         # for rl-diffuser https://arxiv.org/abs/2205.09991
         elif variance_type == "fixed_small_log":
             __a :						Optional[int]			       =  jnp.log(jnp.clip(_lowercase	,			a_min=1e-2_0     )     )
         elif variance_type == "fixed_large":
             __a :						int			       =  state.common.betas[t]
         elif variance_type == "fixed_large_log":
             # Glide max_log
             __a :						int			       =  jnp.log(state.common.betas[t]     )
         elif variance_type == "learned":
             return predicted_variance
         elif variance_type == "learned_range":
             __a :						int			       =  variance
             __a :						List[str]			       =  state.common.betas[t]
             __a :						Optional[Any]			       =  (predicted_variance + 1) / 2
             __a :						Tuple			       =  frac * max_log + (1 - frac) * min_log

         return variance







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			_UpperCAmelCase = True	,			):
         __a :						Dict			       =  timestep

         if key is None:
             __a :						Optional[Any]			       =  jax.random.PRNGKey(0     )

         if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
             __a       ,						__a :						Any			       =  jnp.split(_lowercase	,			sample.shape[1]	,			axis=1     )
         else:
             __a :						Dict			       =  None

         # 1. compute alphas, betas
         __a :						List[str]			       =  state.common.alphas_cumprod[t]
         __a :						List[Any]			       =  jnp.where(t > 0	,			state.common.alphas_cumprod[t - 1]	,			jnp.array(1.0	,			dtype=self.dtype     )     )
         __a :						int			       =  1 - alpha_prod_t
         __a :						int			       =  1 - alpha_prod_t_prev

         # 2. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
         if self.config.prediction_type == "epsilon":
             __a :						int			       =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
             __a :						int			       =  model_output
         elif self.config.prediction_type == "v_prediction":
             __a :						int			       =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         else:
             raise ValueError(
                 f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
                 ''' for the FlaxDDPMScheduler.'''     )

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
             __a :						str			       =  jnp.clip(_lowercase	,			-1	,			1     )

         # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         __a :						List[Any]			       =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
         __a :						Dict			       =  state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

         # 5. Compute predicted previous sample µ_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         __a :						List[str]			       =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         def random_variance():
             __a :						Any			       =  jax.random.split(_lowercase	,			num=1     )
             __a :						str			       =  jax.random.normal(_lowercase	,			shape=model_output.shape	,			dtype=self.dtype     )
             return (self._get_variance(_lowercase	,			_lowercase	,			predicted_variance=_lowercase     ) ** 0.5) * noise

         __a :						Optional[Any]			       =  jnp.where(t > 0	,			random_variance()	,			jnp.zeros(model_output.shape	,			dtype=self.dtype     )     )

         __a :						Optional[Any]			       =  pred_prev_sample + variance

         if not return_dict:
             return (pred_prev_sample, state)

         return FlaxDDPMSchedulerOutput(prev_sample=_lowercase	,			state=_lowercase     )







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			):
         return add_noise_common(state.common	,			_lowercase	,			_lowercase	,			_lowercase     )







     def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			):
         return get_velocity_common(state.common	,			_lowercase	,			_lowercase	,			_lowercase     )







     def __len__(						self     ):
         return self.config.num_train_timesteps






"""simple docstring"""






import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 				__lowercase			(			_UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  OpenAIGPTTokenizer
					__lowerCAmelCase  =  OpenAIGPTTokenizerFast
					__lowerCAmelCase  =  True
					__lowerCAmelCase  =  False







					def  _lowerCamelCase		(						self     ):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									__a :						List[str]			       =  [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''w</w>''',
									    '''r</w>''',
									    '''t</w>''',
									    '''lo''',
									    '''low''',
									    '''er</w>''',
									    '''low</w>''',
									    '''lowest</w>''',
									    '''newer</w>''',
									    '''wider</w>''',
									    '''<unk>''',
									]
									__a :						str			       =  dict(zip(_UpperCAmelCase	,			range(len(_UpperCAmelCase     )     )     )     )
									__a :						List[Any]			       =  ['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

									__a :						Union[str, Any]			       =  os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''vocab_file''']     )
									__a :						Dict			       =  os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''merges_file''']     )
									with open(self.vocab_file	,			'''w'''     ) as fp:
													fp.write(json.dumps(_UpperCAmelCase     )     )
									with open(self.merges_file	,			'''w'''     ) as fp:
													fp.write('''\n'''.join(_UpperCAmelCase     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									return "lower newer", "lower newer"







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  OpenAIGPTTokenizer(self.vocab_file	,			self.merges_file     )

									__a :						Tuple			       =  '''lower'''
									__a :						Union[str, Any]			       =  ['''low''', '''er</w>''']
									__a :						str			       =  tokenizer.tokenize(_UpperCAmelCase     )
									self.assertListEqual(_UpperCAmelCase	,			_UpperCAmelCase     )

									__a :						List[Any]			       =  tokens + ['''<unk>''']
									__a :						Tuple			       =  [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase=15     ):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
																	__a :						Any			       =  self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,			**_UpperCAmelCase     )

																	# Simple input
																	__a :						str			       =  '''This is a simple input'''
																	__a :						Dict			       =  ['''This is a simple input 1''', '''This is a simple input 2''']
																	__a :						List[Any]			       =  ('''This is a simple input''', '''This is a pair''')
																	__a :						Optional[Any]			       =  [
																	    ('''This is a simple input 1''', '''This is a simple input 2'''),
																	    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																	]

																	# Simple input tests
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Simple input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Simple input
																	self.assertRaises(
																	    _UpperCAmelCase	,			tokenizer_r.batch_encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''	,			)

																	# Pair input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Pair input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Pair input
																	self.assertRaises(
																	    _UpperCAmelCase	,			tokenizer_r.batch_encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''	,			)







					def  _lowerCamelCase		(						self     ):
									pass






@require_ftfy
@require_spacy
@require_tokenizers
class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					pass