
'''simple docstring'''

import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 	__lowerCAmelCase    (  __a				):
     snake_case   :    torch.FloatTensor
     snake_case   :    Optional[torch.FloatTensor]							      =      None







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_=0.999 ,       lowerCAmelCase_="cosine" ,       ):
     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

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

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

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







class 	__lowerCAmelCase    (  __a       ,					__a				):
     snake_case   :    Any							      =      [e.name for e in KarrasDiffusionSchedulers]
     snake_case   :    Any							      =      1



     @register_to_config
     def __init__(self						,	lowerCAmelCase__ = 1_0_0_0						,	lowerCAmelCase__ = 0.0_0_0_1						,	lowerCAmelCase__ = 0.0_2						,	lowerCAmelCase__ = "linear"						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = False						,	lowerCAmelCase__ = "epsilon"						,	lowerCAmelCase__ = "linspace"						,	lowerCAmelCase__ = 0						,	):
          if trained_betas is not None:
               _UpperCAmelCase					:  Tuple         =    torch.tensor(lowerCAmelCase__						,	dtype=torch.floataa	)
          elif beta_schedule == "linear":
               _UpperCAmelCase					:  Dict         =    torch.linspace(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	dtype=torch.floataa	)
          elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _UpperCAmelCase					:  Union[str, Any]         =    (
                   torch.linspace(beta_start**0.5						,	beta_end**0.5						,	lowerCAmelCase__						,	dtype=torch.floataa	) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _UpperCAmelCase					:  Dict         =    betas_for_alpha_bar(lowerCAmelCase__	)
          else:
               raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"	)

          _UpperCAmelCase					:  List[Any]         =    1.0 - self.betas
          _UpperCAmelCase					:  str         =    torch.cumprod(self.alphas						,	dim=0	)

          _UpperCAmelCase					:  Tuple         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
          _UpperCAmelCase					:  Optional[Any]         =    np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
          _UpperCAmelCase					:  str         =    torch.from_numpy(lowerCAmelCase__	)

          # setable values
          _UpperCAmelCase					:  List[Any]         =    None
          _UpperCAmelCase					:  List[str]         =    use_karras_sigmas
          self.set_timesteps(lowerCAmelCase__						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  str         =    []
          _UpperCAmelCase					:  int         =    False



     @property
     def     snake_case_ (self	):
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          if isinstance(lowerCAmelCase__						,	torch.Tensor	):
               _UpperCAmelCase					:  List[Any]         =    timestep.to(self.timesteps.device	)
          _UpperCAmelCase					:  Union[str, Any]         =    (self.timesteps == timestep).nonzero().item()
          _UpperCAmelCase					:  Dict         =    self.sigmas[step_index]
          _UpperCAmelCase					:  Union[str, Any]         =    sample / ((sigma**2 + 1) ** 0.5)
          _UpperCAmelCase					:  Any         =    True
          return sample



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):

          def lms_derivative(lowerCAmelCase__	):
               _UpperCAmelCase					:  Any         =    1.0
               for k in range(lowerCAmelCase__	):
                    if current_order == k:
                         continue
                    prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
               return prod

          _UpperCAmelCase					:  Any         =    integrate.quad(lowerCAmelCase__						,	self.sigmas[t]						,	self.sigmas[t + 1]						,	epsrel=1e-4	)[0]

          return integrated_coeff



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__ = None	):
          _UpperCAmelCase					:  Tuple         =    num_inference_steps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
               _UpperCAmelCase					:  Union[str, Any]         =    np.linspace(0						,	self.config.num_train_timesteps - 1						,	lowerCAmelCase__						,	dtype=lowerCAmelCase__	)[
                   ::-1
               ].copy()
          elif self.config.timestep_spacing == "leading":
               _UpperCAmelCase					:  Dict         =    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
               _UpperCAmelCase					:  str         =    (np.arange(0						,	lowerCAmelCase__	) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__	)
               timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
               _UpperCAmelCase					:  List[str]         =    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
               _UpperCAmelCase					:  int         =    (np.arange(self.config.num_train_timesteps						,	0						,	-step_ratio	)).round().copy().astype(lowerCAmelCase__	)
               timesteps -= 1
          else:
               raise ValueError(
                   F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."	)

          _UpperCAmelCase					:  Tuple         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
          _UpperCAmelCase					:  Any         =    np.log(lowerCAmelCase__	)
          _UpperCAmelCase					:  Tuple         =    np.interp(lowerCAmelCase__						,	np.arange(0						,	len(lowerCAmelCase__	)	)						,	lowerCAmelCase__	)

          if self.use_karras_sigmas:
               _UpperCAmelCase					:  List[Any]         =    self._convert_to_karras(in_sigmas=lowerCAmelCase__	)
               _UpperCAmelCase					:  str         =    np.array([self._sigma_to_t(lowerCAmelCase__						,	lowerCAmelCase__	) for sigma in sigmas]	)

          _UpperCAmelCase					:  str         =    np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)

          _UpperCAmelCase					:  Optional[int]         =    torch.from_numpy(lowerCAmelCase__	).to(device=lowerCAmelCase__	)
          if str(lowerCAmelCase__	).startswith("""mps"""	):
               # mps does not support float64
               _UpperCAmelCase					:  Tuple         =    torch.from_numpy(lowerCAmelCase__	).to(lowerCAmelCase__						,	dtype=torch.floataa	)
          else:
               _UpperCAmelCase					:  List[Any]         =    torch.from_numpy(lowerCAmelCase__	).to(device=lowerCAmelCase__	)

          _UpperCAmelCase					:  Any         =    []



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          # get log sigma
          _UpperCAmelCase					:  List[str]         =    np.log(lowerCAmelCase__	)

          # get distribution
          _UpperCAmelCase					:  List[str]         =    log_sigma - log_sigmas[:, np.newaxis]

          # get sigmas range
          _UpperCAmelCase					:  str         =    np.cumsum((dists >= 0)						,	axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
          _UpperCAmelCase					:  str         =    low_idx + 1

          _UpperCAmelCase					:  List[str]         =    log_sigmas[low_idx]
          _UpperCAmelCase					:  List[Any]         =    log_sigmas[high_idx]

          # interpolate sigmas
          _UpperCAmelCase					:  Optional[int]         =    (low - log_sigma) / (low - high)
          _UpperCAmelCase					:  Tuple         =    np.clip(lowerCAmelCase__						,	0						,	1	)

          # transform interpolation to time range
          _UpperCAmelCase					:  Optional[Any]         =    (1 - w) * low_idx + w * high_idx
          _UpperCAmelCase					:  str         =    t.reshape(sigma.shape	)
          return t



     def     snake_case_ (self						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  float         =    in_sigmas[-1].item()
          _UpperCAmelCase					:  float         =    in_sigmas[0].item()

          _UpperCAmelCase					:  Optional[int]         =    7.0  # 7.0 is the value used in the paper
          _UpperCAmelCase					:  List[Any]         =    np.linspace(0						,	1						,	self.num_inference_steps	)
          _UpperCAmelCase					:  Any         =    sigma_min ** (1 / rho)
          _UpperCAmelCase					:  Optional[Any]         =    sigma_max ** (1 / rho)
          _UpperCAmelCase					:  List[Any]         =    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
          return sigmas



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = 4						,	lowerCAmelCase__ = True						,	):
          if not self.is_scale_input_called:
               warnings.warn(
                   """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                   """See `StableDiffusionPipeline` for a usage example."""	)

          if isinstance(lowerCAmelCase__						,	torch.Tensor	):
               _UpperCAmelCase					:  List[str]         =    timestep.to(self.timesteps.device	)
          _UpperCAmelCase					:  Any         =    (self.timesteps == timestep).nonzero().item()
          _UpperCAmelCase					:  Optional[int]         =    self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
               _UpperCAmelCase					:  Optional[Any]         =    sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               _UpperCAmelCase					:  int         =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          elif self.config.prediction_type == "sample":
               _UpperCAmelCase					:  List[str]         =    model_output
          else:
               raise ValueError(
                   F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"	)

          # 2. Convert to an ODE derivative
          _UpperCAmelCase					:  Any         =    (sample - pred_original_sample) / sigma
          self.derivatives.append(lowerCAmelCase__	)
          if len(self.derivatives	) > order:
               self.derivatives.pop(0	)

          # 3. Compute linear multistep coefficients
          _UpperCAmelCase					:  List[Any]         =    min(step_index + 1						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  Any         =    [self.get_lms_coefficient(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	) for curr_order in range(lowerCAmelCase__	)]

          # 4. Compute previous sample based on the derivatives path
          _UpperCAmelCase					:  Optional[Any]         =    sample + sum(
              coeff * derivative for coeff, derivative in zip(lowerCAmelCase__						,	reversed(self.derivatives	)	)	)

          if not return_dict:
               return (prev_sample,)

          return LMSDiscreteSchedulerOutput(prev_sample=lowerCAmelCase__						,	pred_original_sample=lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          _UpperCAmelCase					:  List[str]         =    self.sigmas.to(device=original_samples.device						,	dtype=original_samples.dtype	)
          if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__	):
               # mps does not support float64
               _UpperCAmelCase					:  Any         =    self.timesteps.to(original_samples.device						,	dtype=torch.floataa	)
               _UpperCAmelCase					:  Any         =    timesteps.to(original_samples.device						,	dtype=torch.floataa	)
          else:
               _UpperCAmelCase					:  Any         =    self.timesteps.to(original_samples.device	)
               _UpperCAmelCase					:  List[Any]         =    timesteps.to(original_samples.device	)

          _UpperCAmelCase					:  Union[str, Any]         =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

          _UpperCAmelCase					:  Dict         =    sigmas[step_indices].flatten()
          while len(sigma.shape	) < len(original_samples.shape	):
               _UpperCAmelCase					:  Optional[int]         =    sigma.unsqueeze(-1	)

          _UpperCAmelCase					:  List[Any]         =    original_samples + noise * sigma
          return noisy_samples



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




'''simple docstring'''

import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 	__lowerCAmelCase    (  __a       ,					unittest.TestCase				):
     snake_case   :    Dict							      =      PreTrainedTokenizerFast
     snake_case   :    Optional[Any]							      =      False
     snake_case   :    List[str]							      =      True
     snake_case   :    Any							      =      """tokenizer_file"""



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Dict         =    False  # because we don't have pretrained_vocab_files_map
          super().setUp()
          _UpperCAmelCase					:  Optional[Any]         =    True

          _UpperCAmelCase					:  int         =    ["""robot-test/dummy-tokenizer-fast""", """robot-test/dummy-tokenizer-wordlevel"""]
          _UpperCAmelCase					:  Union[str, Any]         =    """SaulLu/dummy-tokenizer-bytelevel-bpe"""

          # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
          _UpperCAmelCase					:  List[str]         =    [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

          _UpperCAmelCase					:  Optional[Any]         =    PreTrainedTokenizerFast.from_pretrained(model_paths[0]	)
          tokenizer.save_pretrained(self.tmpdirname	)



     def     snake_case_ (self	):
          # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
          # model
          pass



     def     snake_case_ (self	):
          # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
          # model
          pass



     def     snake_case_ (self	):
          # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
          # model
          pass



     def     snake_case_ (self	):
          # PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
          pass



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Optional[Any]         =    self.tmpdirname
          # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
                    try:
                         _UpperCAmelCase					:  Optional[int]         =    tempfile.mkdtemp()
                         _UpperCAmelCase					:  Tuple         =    self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__						,	**lowerCAmelCase__	)

                         tokenizer.save_pretrained(self.tmpdirname	)
                         super().test_training_new_tokenizer()
                    finally:
                         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                         # is restored
                         shutil.rmtree(self.tmpdirname	)
                         _UpperCAmelCase					:  Optional[int]         =    tmpdirname_orig



     def     snake_case_ (self	):
          _UpperCAmelCase					:  int         =    self.tmpdirname
          # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
                    try:
                         _UpperCAmelCase					:  Any         =    tempfile.mkdtemp()
                         _UpperCAmelCase					:  Union[str, Any]         =    self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__						,	**lowerCAmelCase__	)

                         tokenizer.save_pretrained(self.tmpdirname	)
                         super().test_training_new_tokenizer_with_special_tokens_change()
                    finally:
                         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                         # is restored
                         shutil.rmtree(self.tmpdirname	)
                         _UpperCAmelCase					:  Dict         =    tmpdirname_orig



     def     snake_case_ (self	):
          _UpperCAmelCase					:  List[Any]         =    self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name	)

          _UpperCAmelCase					:  Dict         =    ("""a""" for _ in range(1_0_0_0	))
          _UpperCAmelCase					:  Optional[int]         =    tokenizer.train_new_from_iterator(text_iterator=lowerCAmelCase__						,	length=1_0_0_0						,	vocab_size=5_0	)

          _UpperCAmelCase					:  int         =    new_tokenizer.encode("""a🤗"""	)
          self.assertEqual(lowerCAmelCase__						,	[6_4, 1_7_2, 2_5_3, 9_7, 2_4_5]	)







@require_tokenizers
class 	__lowerCAmelCase    (  unittest.TestCase				):



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Any         =    AutoTokenizer.from_pretrained("""bert-base-cased"""	)
          _UpperCAmelCase					:  Dict         =    json.loads(tokenizer._tokenizer.to_str()	)
          _UpperCAmelCase					:  List[Any]         =    len(lowerCAmelCase__	)

          with tempfile.TemporaryDirectory() as tmp_dir:
               # Hack to save this in the tokenizer_config.json
               _UpperCAmelCase					:  Union[str, Any]         =    ["""tokenizer.4.0.0.json"""]
               tokenizer.save_pretrained(lowerCAmelCase__	)
               json.dump(lowerCAmelCase__						,	open(os.path.join(lowerCAmelCase__						,	"""tokenizer.4.0.0.json"""	)						,	"""w"""	)	)

               # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
               _UpperCAmelCase					:  Optional[int]         =    AutoTokenizer.from_pretrained(lowerCAmelCase__	)
               self.assertEqual(len(lowerCAmelCase__	)						,	len(lowerCAmelCase__	) + 1	)
               _UpperCAmelCase					:  Tuple         =    json.loads(new_tokenizer._tokenizer.to_str()	)
               self.assertIn("""huggingface"""						,	json_tokenizer["""model"""]["""vocab"""]	)

               # Will need to be adjusted if we reach v42 and this test is still here.
               # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
               shutil.move(os.path.join(lowerCAmelCase__						,	"""tokenizer.4.0.0.json"""	)						,	os.path.join(lowerCAmelCase__						,	"""tokenizer.42.0.0.json"""	)	)
               _UpperCAmelCase					:  Tuple         =    ["""tokenizer.42.0.0.json"""]
               tokenizer.save_pretrained(lowerCAmelCase__	)
               _UpperCAmelCase					:  str         =    AutoTokenizer.from_pretrained(lowerCAmelCase__	)
               self.assertEqual(len(lowerCAmelCase__	)						,	len(lowerCAmelCase__	)	)
               _UpperCAmelCase					:  Union[str, Any]         =    json.loads(new_tokenizer._tokenizer.to_str()	)
               self.assertNotIn("""huggingface"""						,	json_tokenizer["""model"""]["""vocab"""]	)



     def     snake_case_ (self	):
          # This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
          _UpperCAmelCase					:  List[Any]         =    """hf-internal-testing/test-two-tokenizers"""

          # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
          _UpperCAmelCase					:  Dict         =    AutoTokenizer.from_pretrained(lowerCAmelCase__	)
          self.assertEqual(len(lowerCAmelCase__	)						,	2_8_9_9_7	)
          _UpperCAmelCase					:  List[str]         =    json.loads(tokenizer._tokenizer.to_str()	)
          self.assertIn("""huggingface"""						,	json_tokenizer["""model"""]["""vocab"""]	)

          # Testing an older version by monkey-patching the version in the module it's used.
          import transformers as old_transformers

          _UpperCAmelCase					:  Optional[Any]         =    """3.0.0"""
          _UpperCAmelCase					:  str         =    old_transformers.models.auto.AutoTokenizer.from_pretrained(lowerCAmelCase__	)
          self.assertEqual(len(lowerCAmelCase__	)						,	2_8_9_9_6	)
          _UpperCAmelCase					:  Optional[int]         =    json.loads(old_tokenizer._tokenizer.to_str()	)
          self.assertNotIn("""huggingface"""						,	json_tokenizer["""model"""]["""vocab"""]	)







@require_tokenizers
class 	__lowerCAmelCase    (  unittest.TestCase				):



     def     snake_case_ (self	):
          # See https://github.com/huggingface/transformers/pull/12550
          # and https://github.com/huggingface/tokenizers/issues/537
          _UpperCAmelCase					:  int         =    PreTrainedTokenizerFast.from_pretrained("""robot-test/dummy-tokenizer-wordlevel"""	)
          _UpperCAmelCase					:  str         =    """The Matrix is a 1999 science fiction action film."""

          with concurrent.futures.ThreadPoolExecutor() as executor:
               _UpperCAmelCase					:  List[Any]         =    [executor.submit(self.fetch						,	lowerCAmelCase__						,	lowerCAmelCase__	) for i in range(1_0	)]
               _UpperCAmelCase					:  Any         =    [future.result() for future in futures]
               self.assertEqual(lowerCAmelCase__						,	[[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0	)]	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          return tokenizer.encode(lowerCAmelCase__						,	truncation="""longest_first"""						,	padding="""longest"""	)


