



"""simple docstring"""
import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase   =	"▁"

_UpperCAmelCase   =	get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class     a		(     UpperCAmelCase__		,					unittest.TestCase				):
  UpperCamelCase		: str     =    BigBirdTokenizer
  UpperCamelCase		: Any     =    BigBirdTokenizerFast
  UpperCamelCase		: List[str]     =    True
  UpperCamelCase		: Optional[Any]     =    True



  def 		lowerCamelCase__				(  self       :    int						)   ->    List[Any]:


     '''simple docstring'''




     super().setUp()

     SCREAMING_SNAKE_CASE_:       List[str]   	=self.tokenizer_class(lowerCamelCase_				,						keep_accents=lowerCamelCase_						)
     tokenizer.save_pretrained(self.tmpdirname						)



  def 		lowerCamelCase__				(  self       :    List[Any]						)   ->    Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Dict   	="""<s>"""
     SCREAMING_SNAKE_CASE_:       Any   	=1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_						)				,						lowerCamelCase_						)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_						)				,						lowerCamelCase_						)



  def 		lowerCamelCase__				(  self       :    Union[str, Any]						)   ->    List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=list(self.get_tokenizer().get_vocab().keys()						)

     self.assertEqual(vocab_keys[0]				,						"""<unk>"""						)
     self.assertEqual(vocab_keys[1]				,						"""<s>"""						)
     self.assertEqual(vocab_keys[-1]				,						"""[MASK]"""						)
     self.assertEqual(len(lowerCamelCase_						)				,						1004						)



  def 		lowerCamelCase__				(  self       :    Tuple						)   ->    List[Any]:


     '''simple docstring'''




     self.assertEqual(self.get_tokenizer().vocab_size				,						1000						)



  def 		lowerCamelCase__				(  self       :    Dict						)   ->    Any:


     '''simple docstring'''




     if not self.test_rust_tokenizer:
        return

     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=self.get_tokenizer()
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=self.get_rust_tokenizer()

     SCREAMING_SNAKE_CASE_:       Optional[int]   	="""I was born in 92000, and this is falsé."""

     SCREAMING_SNAKE_CASE_:       Dict   	=tokenizer.tokenize(lowerCamelCase_						)
     SCREAMING_SNAKE_CASE_:       List[str]   	=rust_tokenizer.tokenize(lowerCamelCase_						)
     self.assertListEqual(lowerCamelCase_				,						lowerCamelCase_						)

     SCREAMING_SNAKE_CASE_:       List[Any]   	=tokenizer.encode(lowerCamelCase_				,						add_special_tokens=lowerCamelCase_						)
     SCREAMING_SNAKE_CASE_:       Tuple   	=rust_tokenizer.encode(lowerCamelCase_				,						add_special_tokens=lowerCamelCase_						)
     self.assertListEqual(lowerCamelCase_				,						lowerCamelCase_						)

     SCREAMING_SNAKE_CASE_:       Any   	=self.get_rust_tokenizer()
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=tokenizer.encode(lowerCamelCase_						)
     SCREAMING_SNAKE_CASE_:       List[str]   	=rust_tokenizer.encode(lowerCamelCase_						)
     self.assertListEqual(lowerCamelCase_				,						lowerCamelCase_						)



  def 		lowerCamelCase__				(  self       :    Tuple						)   ->    int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       int   	=BigBirdTokenizer(lowerCamelCase_				,						keep_accents=lowerCamelCase_						)

     SCREAMING_SNAKE_CASE_:       List[Any]   	=tokenizer.tokenize("""This is a test"""						)
     self.assertListEqual(lowerCamelCase_				,						["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]						)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(lowerCamelCase_						)				,						[285, 46, 10, 170, 382]				,						)

     SCREAMING_SNAKE_CASE_:       Tuple   	=tokenizer.tokenize("""I was born in 92000, and this is falsé."""						)
     self.assertListEqual(
         lowerCamelCase_				,						[
             SPIECE_UNDERLINE + """I""",
             SPIECE_UNDERLINE + """was""",
             SPIECE_UNDERLINE + """b""",
             """or""",
             """n""",
             SPIECE_UNDERLINE + """in""",
             SPIECE_UNDERLINE + """""",
             """9""",
             """2""",
             """0""",
             """0""",
             """0""",
             """,""",
             SPIECE_UNDERLINE + """and""",
             SPIECE_UNDERLINE + """this""",
             SPIECE_UNDERLINE + """is""",
             SPIECE_UNDERLINE + """f""",
             """al""",
             """s""",
             """é""",
             """.""",
         ]				,						)
     SCREAMING_SNAKE_CASE_:       Dict   	=tokenizer.convert_tokens_to_ids(lowerCamelCase_						)
     self.assertListEqual(
         lowerCamelCase_				,						[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]				,						)

     SCREAMING_SNAKE_CASE_:       str   	=tokenizer.convert_ids_to_tokens(lowerCamelCase_						)
     self.assertListEqual(
         lowerCamelCase_				,						[
             SPIECE_UNDERLINE + """I""",
             SPIECE_UNDERLINE + """was""",
             SPIECE_UNDERLINE + """b""",
             """or""",
             """n""",
             SPIECE_UNDERLINE + """in""",
             SPIECE_UNDERLINE + """""",
             """<unk>""",
             """2""",
             """0""",
             """0""",
             """0""",
             """,""",
             SPIECE_UNDERLINE + """and""",
             SPIECE_UNDERLINE + """this""",
             SPIECE_UNDERLINE + """is""",
             SPIECE_UNDERLINE + """f""",
             """al""",
             """s""",
             """<unk>""",
             """.""",
         ]				,						)



  @cached_property
  def 		lowerCamelCase__				(  self       :    Tuple						)   ->    Optional[Any]:


     '''simple docstring'''




     return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""						)



  @slow
  def 		lowerCamelCase__				(  self       :    Tuple						)   ->    Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[Any]   	="""Hello World!"""
     SCREAMING_SNAKE_CASE_:       Tuple   	=[65, 1_8536, 2260, 101, 66]

     self.assertListEqual(lowerCamelCase_				,						self.big_tokenizer.encode(lowerCamelCase_						)						)



  @slow
  def 		lowerCamelCase__				(  self       :    Optional[int]						)   ->    Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Tuple   	=(
         """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
         """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
     )
     # fmt: off
     SCREAMING_SNAKE_CASE_:       int   	=[65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 3_4324, 497, 391, 408, 1_1342, 1244, 385, 100, 938, 985, 456, 574, 362, 1_2597, 3200, 3129, 1172, 66]  # noqa: E231
     # fmt: on
     self.assertListEqual(lowerCamelCase_				,						self.big_tokenizer.encode(lowerCamelCase_						)						)



  @require_torch
  @slow
  def 		lowerCamelCase__				(  self       :    List[Any]						)   ->    Tuple:


     '''simple docstring'''




     import torch

     from transformers import BigBirdConfig, BigBirdModel

     # Build sequence
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=list(self.big_tokenizer.get_vocab().keys()						)[:10]
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=""" """.join(lowerCamelCase_						)
     SCREAMING_SNAKE_CASE_:       Tuple   	=self.big_tokenizer.encode_plus(lowerCamelCase_				,						return_tensors="""pt"""				,						return_token_type_ids=lowerCamelCase_						)
     SCREAMING_SNAKE_CASE_:       Dict   	=self.big_tokenizer.batch_encode_plus(
         [sequence + """ """ + sequence]				,						return_tensors="""pt"""				,						return_token_type_ids=lowerCamelCase_						)

     SCREAMING_SNAKE_CASE_:       List[Any]   	=BigBirdConfig(attention_type="""original_full"""						)
     SCREAMING_SNAKE_CASE_:       Tuple   	=BigBirdModel(lowerCamelCase_						)

     assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

     with torch.no_grad():
        model(**lowerCamelCase_						)
        model(**lowerCamelCase_						)



  @slow
  def 		lowerCamelCase__				(  self       :    int						)   ->    Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""						)
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=tokenizer.decode(tokenizer("""Paris is the [MASK]."""						).input_ids						)

     self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]"""						)





  @slow
  def 		lowerCamelCase__				(  self       :    int						)   ->    List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[int]   	={"""input_ids""": [[65, 3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114, 66], [65, 448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowerCamelCase_				,						model_name="""google/bigbird-roberta-base"""				,						revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""				,						)








# 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 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							42
	__A							=							None






def     a(      A       :    Optional[Any] ,     A       :    Any=0.999 ,     A       :    Dict="cosine" ,     )		->       Optional[int]:
					"""simple docstring"""





					if alpha_transform_type == "cosine":

										def alpha_bar_fn(A       :    Optional[int]       ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

					elif alpha_transform_type == "exp":

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

					else:
										raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''       )

					a					     =   []
					for i in range(A       ):
										a					     =   i / num_diffusion_timesteps
										a					     =   (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(A       ) / alpha_bar_fn(A       ) ,     A       )       )
					return torch.tensor(A ,     dtype=torch.floataa       )







class 	_lowercase							(   lowerCAmelCase,      lowerCAmelCase	):





	"""simple docstring"""
	__A							=							1




	@register_to_config
	def __init__(self		,       lowerCamelCase_ = 1000		,       lowerCamelCase_ = 0.0001		,       lowerCamelCase_ = 0.02		,       lowerCamelCase_ = "linear"		,       lowerCamelCase_ = None		,       lowerCamelCase_ = True		,       lowerCamelCase_ = True		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = "epsilon"		,       lowerCamelCase_ = 1.0		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						if kwargs.get("set_alpha_to_one"		,       lowerCamelCase_		) is not None:
											a					     =   (
											    "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_		)
											a					     =   kwargs["set_alpha_to_one"]
						if trained_betas is not None:
											a					     =   torch.tensor(lowerCamelCase_		,       dtype=torch.floataa		)
						elif beta_schedule == "linear":
											a					     =   torch.linspace(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       dtype=torch.floataa		)
						elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											a					     =   (
											    torch.linspace(beta_start**0.5		,       beta_end**0.5		,       lowerCamelCase_		,       dtype=torch.floataa		) ** 2
											)
						elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											a					     =   betas_for_alpha_bar(lowerCamelCase_		)
						else:
											raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''		)

						a					     =   1.0 - self.betas
						a					     =   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.
						a					     =   torch.tensor(0.0		) if set_alpha_to_zero else self.alphas_cumprod[-1]

						# standard deviation of the initial noise distribution
						a					     =   1.0

						# setable values
						a					     =   None
						a					     =   torch.from_numpy(np.arange(0		,       lowerCamelCase_		).copy().astype(np.intaa		)		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						return sample




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						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.'''		)

						a					     =   num_inference_steps
						a					     =   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
						a					     =   (np.arange(0		,       lowerCamelCase_		) * step_ratio).round().copy().astype(np.intaa		)
						a					     =   torch.from_numpy(lowerCamelCase_		).to(lowerCamelCase_		)
						self.timesteps += self.config.steps_offset




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = 0.0		,       lowerCamelCase_ = False		,       lowerCamelCase_ = None		,       lowerCamelCase_ = True		,       ):
						"""simple docstring"""

						a					     =   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
						a					     =   self.alphas_cumprod[timestep]
						a					     =   (
						    self.alphas_cumprod[prev_timestep]
						    if prev_timestep < self.config.num_train_timesteps
						    else self.final_alpha_cumprod
						)

						a					     =   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":
											a					     =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											a					     =   model_output
						elif self.config.prediction_type == "sample":
											a					     =   model_output
											a					     =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
						elif self.config.prediction_type == "v_prediction":
											a					     =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											a					     =   (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:
											a					     =   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
						a					     =   (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
						a					     =   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		):
						"""simple docstring"""

						return self.config.num_train_timesteps
