






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 				lowerCAmelCase_	(		__snake_case			):
				_UpperCamelCase					:       torch.FloatTensor
				_UpperCamelCase					:       Optional[torch.FloatTensor]							       = None






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=0.999     ,     SCREAMING_SNAKE_CASE="cosine"     ,     )     ->    Tuple:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(SCREAMING_SNAKE_CASE				):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(SCREAMING_SNAKE_CASE				):
																					return math.exp(t * -12.0				)

							else:
														raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""				)

							_lowercase							:   Dict 							=   []
							for i in range(SCREAMING_SNAKE_CASE				):
														_lowercase							:   str 							=   i / num_diffusion_timesteps
														_lowercase							:   int 							=   (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE				) / alpha_bar_fn(SCREAMING_SNAKE_CASE				)     ,     SCREAMING_SNAKE_CASE				)				)
							return torch.tensor(SCREAMING_SNAKE_CASE     ,     dtype=torch.floataa				)






class 				lowerCAmelCase_	(		__snake_case   ,		__snake_case			):
				_UpperCamelCase					:       Optional[int]							       = 1


				@register_to_config
				def __init__(					self ,						_lowerCAmelCase = 1_0_0_0 ,						_lowerCAmelCase = 0.00_01 ,						_lowerCAmelCase = 0.02 ,						_lowerCAmelCase = "linear" ,						_lowerCAmelCase = None ,						_lowerCAmelCase = True ,						_lowerCAmelCase = True ,						_lowerCAmelCase = 0 ,						_lowerCAmelCase = "epsilon" ,						_lowerCAmelCase = 1.0 ,						**_lowerCAmelCase ,						):
											if kwargs.get('set_alpha_to_one' ,						_lowerCAmelCase      ) is not None:
																		_lowercase							:   Optional[int] 							=   (
																		    'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
																		)
																		deprecate('set_alpha_to_one' ,						'1.0.0' ,						_lowerCAmelCase ,						standard_warn=_lowerCAmelCase      )
																		_lowercase							:   str 							=   kwargs['set_alpha_to_one']
											if trained_betas is not None:
																		_lowercase							:   List[str] 							=   torch.tensor(_lowerCAmelCase ,						dtype=torch.floataa      )
											elif beta_schedule == "linear":
																		_lowercase							:   str 							=   torch.linspace(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						dtype=torch.floataa      )
											elif beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		_lowercase							:   Optional[int] 							=   (
																		    torch.linspace(beta_start**0.5 ,						beta_end**0.5 ,						_lowerCAmelCase ,						dtype=torch.floataa      ) ** 2
																		)
											elif beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		_lowercase							:   str 							=   betas_for_alpha_bar(_lowerCAmelCase      )
											else:
																		raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""      )

											_lowercase							:   Dict 							=   1.0 - self.betas
											_lowercase							:   Tuple 							=   torch.cumprod(self.alphas ,						dim=0      )

											# At every step in inverted ddim, we are looking into the next alphas_cumprod
											# For the final step, there is no next alphas_cumprod, and the index is out of bounds
											# `set_alpha_to_zero` decides whether we set this parameter simply to zero
											# in this case, self.step() just output the predicted noise
											# or whether we use the final alpha of the "non-previous" one.
											_lowercase							:   Union[str, Any] 							=   torch.tensor(0.0      ) if set_alpha_to_zero else self.alphas_cumprod[-1]

											# standard deviation of the initial noise distribution
											_lowercase							:   Tuple 							=   1.0

											# setable values
											_lowercase							:   List[str] 							=   None
											_lowercase							:   int 							=   torch.from_numpy(np.arange(0 ,						_lowerCAmelCase      ).copy().astype(np.intaa      )      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase = None      ):
											return sample


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase = None      ):

											if num_inference_steps > self.config.num_train_timesteps:
																		raise ValueError(
																		    F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
																		    F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
																		    F""" maximal {self.config.num_train_timesteps} timesteps."""      )

											_lowercase							:   int 							=   num_inference_steps
											_lowercase							:   int 							=   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
											_lowercase							:   Optional[int] 							=   (np.arange(0 ,						_lowerCAmelCase      ) * step_ratio).round().copy().astype(np.intaa      )
											_lowercase							:   Optional[int] 							=   torch.from_numpy(_lowerCAmelCase      ).to(_lowerCAmelCase      )
											self.timesteps += self.config.steps_offset


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase = 0.0 ,						_lowerCAmelCase = False ,						_lowerCAmelCase = None ,						_lowerCAmelCase = True ,						):
											# 1. get previous step value (=t+1)
											_lowercase							:   str 							=   timestep + self.config.num_train_timesteps // self.num_inference_steps

											# 2. compute alphas, betas
											# change original implementation to exactly match noise levels for analogous forward process
											_lowercase							:   List[str] 							=   self.alphas_cumprod[timestep]
											_lowercase							:   Tuple 							=   (
											    self.alphas_cumprod[prev_timestep]
											    if prev_timestep < self.config.num_train_timesteps
											    else self.final_alpha_cumprod
											)

											_lowercase							:   Any 							=   1 - alpha_prod_t

											# 3. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											if self.config.prediction_type == "epsilon":
																		_lowercase							:   Optional[Any] 							=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																		_lowercase							:   List[Any] 							=   model_output
											elif self.config.prediction_type == "sample":
																		_lowercase							:   Optional[int] 							=   model_output
																		_lowercase							:   Any 							=   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																		_lowercase							:   Any 							=   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																		_lowercase							:   List[Any] 							=   (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																		raise ValueError(
																		    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
																		    ' `v_prediction`'      )

											# 4. Clip or threshold "predicted x_0"
											if self.config.clip_sample:
																		_lowercase							:   Optional[Any] 							=   pred_original_sample.clamp(
																		    -self.config.clip_sample_range ,						self.config.clip_sample_range      )

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											_lowercase							:   str 							=   (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											_lowercase							:   Optional[int] 							=   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																		return (prev_sample, pred_original_sample)
											return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase ,						pred_original_sample=_lowerCAmelCase      )


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









'''simple docstring'''





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 					snake_case			(UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:str     =       MgpstrTokenizer
       lowerCAmelCase__	:Tuple     =       False
       lowerCAmelCase__	:Any     =       {}
       lowerCAmelCase__	:List[Any]     =       False



       def 				_a   (					self   )	->				Union[str, Any]:
           super().setUp()

           # fmt: off
           lowercase__									=    ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
           # fmt: on
           lowercase__									=    dict(zip(UpperCAmelCase_  ,range(len(UpperCAmelCase_   )   )   )   )

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



       def 				_a   (					self  ,**UpperCAmelCase_   )	->				Any:
           return MgpstrTokenizer.from_pretrained(self.tmpdirname  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Dict:
           lowercase__									=    "tester"
           lowercase__									=    "tester"
           return input_text, output_text



       @unittest.skip("MGP-STR always lower cases letters."   )
       def 				_a   (					self   )	->				Dict:
           pass



       def 				_a   (					self   )	->				Optional[Any]:
           lowercase__									=    self.get_tokenizers(do_lower_case=UpperCAmelCase_   )
           for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
                   lowercase__									=    "[SPECIAL_TOKEN]"

                   tokenizer.add_special_tokens({"cls_token": special_token}   )
                   lowercase__									=    tokenizer.encode([special_token]  ,add_special_tokens=UpperCAmelCase_   )
                   self.assertEqual(len(UpperCAmelCase_   )  ,1   )

                   lowercase__									=    tokenizer.decode(UpperCAmelCase_  ,skip_special_tokens=UpperCAmelCase_   )
                   self.assertTrue(special_token not in decoded   )



       def 				_a   (					self   )	->				str:
           lowercase__									=    self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
                   lowercase__							,							lowercase__									=    self.get_input_output_texts(UpperCAmelCase_   )

                   lowercase__									=    tokenizer.tokenize(UpperCAmelCase_   )
                   lowercase__									=    tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )
                   lowercase__									=    tokenizer.encode(UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_   )
                   self.assertListEqual(UpperCAmelCase_  ,UpperCAmelCase_   )

                   lowercase__									=    tokenizer.convert_ids_to_tokens(UpperCAmelCase_   )
                   self.assertNotEqual(len(UpperCAmelCase_   )  ,0   )
                   lowercase__									=    tokenizer.decode(UpperCAmelCase_   )
                   self.assertIsInstance(UpperCAmelCase_  ,UpperCAmelCase_   )

                   self.assertEqual(text_a.replace(" "  ,""   )  ,UpperCAmelCase_   )



       @unittest.skip("MGP-STR tokenizer only handles one sequence."   )
       def 				_a   (					self   )	->				Optional[int]:
           pass







       @unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"   )
       def 				_a   (					self   )	->				Any:
           pass




