





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def  __UpperCAmelCase(   lowercase_			,     lowercase_=0.9_9_9			,     lowercase_="cosine"			,     ):
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(lowercase_   ):
                     return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(lowercase_   ):
                     return math.exp(t * -1_2.0   )

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

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







class       __A							(   lowerCamelCase__			,lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   [e.name for e in KarrasDiffusionSchedulers]
      UpperCAmelCase__							=   2


      @register_to_config
      def __init__(		self   ,				a__ = 1000   ,				a__ = 0.0_0085   ,				a__ = 0.012   ,				a__ = "linear"   ,				a__ = None   ,				a__ = "epsilon"   ,				a__ = "linspace"   ,				a__ = 0   ,				):



             """simple docstring"""





             if trained_betas is not None:
                    _lowerCamelCase				:  Dict						=		torch.tensor(a__   ,				dtype=torch.floataa)
             elif beta_schedule == "linear":
                    _lowerCamelCase				:  Optional[int]						=		torch.linspace(a__   ,				a__   ,				a__   ,				dtype=torch.floataa)
             elif beta_schedule == "scaled_linear":
                    # this schedule is very specific to the latent diffusion model.
                    _lowerCamelCase				:  Optional[Any]						=		(
                        torch.linspace(beta_start**0.5   ,				beta_end**0.5   ,				a__   ,				dtype=torch.floataa) ** 2
                    )
             elif beta_schedule == "squaredcos_cap_v2":
                    # Glide cosine schedule
                    _lowerCamelCase				:  Tuple						=		betas_for_alpha_bar(a__)
             else:
                    raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

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

             #  set all values
             self.set_timesteps(a__   ,				a__   ,				a__)


      def 					__snake_case      (		self   ,				a__   ,				a__=None):



             """simple docstring"""





             if schedule_timesteps is None:
                    _lowerCamelCase				:  Union[str, Any]						=		self.timesteps

             _lowerCamelCase				:  Union[str, Any]						=		(schedule_timesteps == timestep).nonzero()

             # The sigma index that is taken for the **very** first `step`
             # is always the second index (or the last index if there is only 1)
             # This way we can ensure we don't accidentally skip a sigma in
             # case we start in the middle of the denoising schedule (e.g. for image-to-image)
             if len(self._index_counter) == 0:
                    _lowerCamelCase				:  List[Any]						=		1 if len(a__) > 1 else 0
             else:
                    _lowerCamelCase				:  List[Any]						=		timestep.cpu().item() if torch.is_tensor(a__) else timestep
                    _lowerCamelCase				:  Tuple						=		self._index_counter[timestep_int]

             return indices[pos].item()


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             if self.config.timestep_spacing in ["linspace", "trailing"]:
                    return self.sigmas.max()

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


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.index_for_timestep(a__)

             if self.state_in_first_order:
                    _lowerCamelCase				:  Optional[Any]						=		self.sigmas[step_index]
             else:
                    _lowerCamelCase				:  List[Any]						=		self.sigmas_interpol[step_index]

             _lowerCamelCase				:  List[Any]						=		sample / ((sigma**2 + 1) ** 0.5)
             return sample


      def 					__snake_case      (		self   ,				a__   ,				a__ = None   ,				a__ = None   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		num_inference_steps

             _lowerCamelCase				:  str						=		num_train_timesteps or self.config.num_train_timesteps

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

             _lowerCamelCase				:  Dict						=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
             _lowerCamelCase				:  List[Any]						=		torch.from_numpy(np.log(a__)).to(a__)

             _lowerCamelCase				:  Tuple						=		np.interp(a__   ,				np.arange(0   ,				len(a__))   ,				a__)
             _lowerCamelCase				:  Any						=		np.concatenate([sigmas, [0.0]]).astype(np.floataa)
             _lowerCamelCase				:  Union[str, Any]						=		torch.from_numpy(a__).to(device=a__)

             # interpolate sigmas
             _lowerCamelCase				:  List[str]						=		sigmas.log().lerp(sigmas.roll(1).log()   ,				0.5).exp()

             _lowerCamelCase				:  Optional[Any]						=		torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
             _lowerCamelCase				:  Any						=		torch.cat(
                 [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])

             if str(a__).startswith('''mps'''):
                    # mps does not support float64
                    _lowerCamelCase				:  Dict						=		torch.from_numpy(a__).to(a__   ,				dtype=torch.floataa)
             else:
                    _lowerCamelCase				:  Union[str, Any]						=		torch.from_numpy(a__).to(a__)

             # interpolate timesteps
             _lowerCamelCase				:  Union[str, Any]						=		self.sigma_to_t(a__).to(a__   ,				dtype=timesteps.dtype)
             _lowerCamelCase				:  List[str]						=		torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])   ,				dim=-1).flatten()

             _lowerCamelCase				:  Optional[Any]						=		torch.cat([timesteps[:1], interleaved_timesteps])

             _lowerCamelCase				:  Optional[int]						=		None

             # for exp beta schedules, such as the one for `pipeline_shap_e.py`
             # we need an index counter
             _lowerCamelCase				:  Optional[int]						=		defaultdict(a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		sigma.log()

             # get distribution
             _lowerCamelCase				:  List[Any]						=		log_sigma - self.log_sigmas[:, None]

             # get sigmas range
             _lowerCamelCase				:  str						=		dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
             _lowerCamelCase				:  Union[str, Any]						=		low_idx + 1

             _lowerCamelCase				:  List[Any]						=		self.log_sigmas[low_idx]
             _lowerCamelCase				:  Optional[int]						=		self.log_sigmas[high_idx]

             # interpolate sigmas
             _lowerCamelCase				:  Tuple						=		(low - log_sigma) / (low - high)
             _lowerCamelCase				:  List[str]						=		w.clamp(0   ,				1)

             # transform interpolation to time range
             _lowerCamelCase				:  Dict						=		(1 - w) * low_idx + w * high_idx
             _lowerCamelCase				:  List[str]						=		t.view(sigma.shape)
             return t


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self.sample is None


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__   ,				a__ = True   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		self.index_for_timestep(a__)

             # advance index counter by 1
             _lowerCamelCase				:  Tuple						=		timestep.cpu().item() if torch.is_tensor(a__) else timestep
             self._index_counter[timestep_int] += 1

             if self.state_in_first_order:
                    _lowerCamelCase				:  int						=		self.sigmas[step_index]
                    _lowerCamelCase				:  Optional[int]						=		self.sigmas_interpol[step_index + 1]
                    _lowerCamelCase				:  Optional[int]						=		self.sigmas[step_index + 1]
             else:
                    # 2nd order / KDPM2's method
                    _lowerCamelCase				:  List[str]						=		self.sigmas[step_index - 1]
                    _lowerCamelCase				:  Union[str, Any]						=		self.sigmas_interpol[step_index]
                    _lowerCamelCase				:  str						=		self.sigmas[step_index]

             # currently only gamma=0 is supported. This usually works best anyways.
             # We can support gamma in the future but then need to scale the timestep before
             # passing it to the model which requires a change in API
             _lowerCamelCase				:  Optional[int]						=		0
             _lowerCamelCase				:  List[Any]						=		sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

             # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
             if self.config.prediction_type == "epsilon":
                    _lowerCamelCase				:  int						=		sigma_hat if self.state_in_first_order else sigma_interpol
                    _lowerCamelCase				:  Dict						=		sample - sigma_input * model_output
             elif self.config.prediction_type == "v_prediction":
                    _lowerCamelCase				:  Union[str, Any]						=		sigma_hat if self.state_in_first_order else sigma_interpol
                    _lowerCamelCase				:  Any						=		model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                        sample / (sigma_input**2 + 1)
                    )
             elif self.config.prediction_type == "sample":
                    raise NotImplementedError('''prediction_type not implemented yet: sample''')
             else:
                    raise ValueError(
                        F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""")

             if self.state_in_first_order:
                    # 2. Convert to an ODE derivative for 1st order
                    _lowerCamelCase				:  Union[str, Any]						=		(sample - pred_original_sample) / sigma_hat
                    # 3. delta timestep
                    _lowerCamelCase				:  Optional[Any]						=		sigma_interpol - sigma_hat

                    # store for 2nd order step
                    _lowerCamelCase				:  Any						=		sample
             else:
                    # DPM-Solver-2
                    # 2. Convert to an ODE derivative for 2nd order
                    _lowerCamelCase				:  Union[str, Any]						=		(sample - pred_original_sample) / sigma_interpol

                    # 3. delta timestep
                    _lowerCamelCase				:  Any						=		sigma_next - sigma_hat

                    _lowerCamelCase				:  Optional[Any]						=		self.sample
                    _lowerCamelCase				:  Union[str, Any]						=		None

             _lowerCamelCase				:  Optional[Any]						=		sample + derivative * dt

             if not return_dict:
                    return (prev_sample,)

             return SchedulerOutput(prev_sample=a__)


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		self.sigmas.to(device=original_samples.device   ,				dtype=original_samples.dtype)
             if original_samples.device.type == "mps" and torch.is_floating_point(a__):
                    # mps does not support float64
                    _lowerCamelCase				:  str						=		self.timesteps.to(original_samples.device   ,				dtype=torch.floataa)
                    _lowerCamelCase				:  str						=		timesteps.to(original_samples.device   ,				dtype=torch.floataa)
             else:
                    _lowerCamelCase				:  str						=		self.timesteps.to(original_samples.device)
                    _lowerCamelCase				:  int						=		timesteps.to(original_samples.device)

             _lowerCamelCase				:  int						=		[self.index_for_timestep(a__   ,				a__) for t in timesteps]

             _lowerCamelCase				:  List[Any]						=		sigmas[step_indices].flatten()
             while len(sigma.shape) < len(original_samples.shape):
                    _lowerCamelCase				:  List[str]						=		sigma.unsqueeze(-1)

             _lowerCamelCase				:  Any						=		original_samples + noise * sigma
             return noisy_samples



      def __len__(		self):



             """simple docstring"""





             return self.config.num_train_timesteps








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       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   OpenAIGPTTokenizer
      UpperCAmelCase__							=   OpenAIGPTTokenizerFast
      UpperCAmelCase__							=   True
      UpperCAmelCase__							=   False


      def 					__snake_case      (		self):



             """simple docstring"""





             super().setUp()

             # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
             _lowerCamelCase				:  Tuple						=		[
                 '''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>''',
             ]
             _lowerCamelCase				:  Any						=		dict(zip(a__   ,				range(len(a__))))
             _lowerCamelCase				:  List[str]						=		['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

             _lowerCamelCase				:  str						=		os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file'''])
             _lowerCamelCase				:  int						=		os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''merges_file'''])
             with open(self.vocab_file   ,				'''w''') as fp:
                    fp.write(json.dumps(a__))
             with open(self.merges_file   ,				'''w''') as fp:
                    fp.write('''\n'''.join(a__))


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return "lower newer", "lower newer"


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		OpenAIGPTTokenizer(self.vocab_file   ,				self.merges_file)

             _lowerCamelCase				:  Dict						=		'''lower'''
             _lowerCamelCase				:  Optional[Any]						=		['''low''', '''er</w>''']
             _lowerCamelCase				:  List[Any]						=		tokenizer.tokenize(a__)
             self.assertListEqual(a__   ,				a__)

             _lowerCamelCase				:  Any						=		tokens + ['''<unk>''']
             _lowerCamelCase				:  Optional[int]						=		[14, 15, 20]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(a__)   ,				a__)


      def 					__snake_case      (		self   ,				a__=15):



             """simple docstring"""





             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                           _lowerCamelCase				:  Dict						=		self.rust_tokenizer_class.from_pretrained(a__   ,				**a__)

                           # Simple input
                           _lowerCamelCase				:  Optional[int]						=		'''This is a simple input'''
                           _lowerCamelCase				:  Optional[Any]						=		['''This is a simple input 1''', '''This is a simple input 2''']
                           _lowerCamelCase				:  Optional[int]						=		('''This is a simple input''', '''This is a pair''')
                           _lowerCamelCase				:  List[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(a__   ,				tokenizer_r.encode   ,				a__   ,				max_length=a__   ,				padding='''max_length''')

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

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

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

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

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



      def 					__snake_case      (		self):



             """simple docstring"""





             pass







@require_ftfy
@require_spacy
@require_tokenizers
class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      pass

