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

import numpy as np
import torch

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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class   UpperCAmelCase		(						A_ ):
   A__					:		torch.FloatTensor
   A__					:		Optional[torch.FloatTensor]						     =     None



def 			UpperCamelCase					(    __lowerCamelCase     : Tuple				,					__lowerCamelCase     : Tuple=0.999				,					__lowerCamelCase     : Optional[Any]="cosine"				,					):
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(__lowerCamelCase     : List[Any]    ):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

       elif alpha_transform_type == "exp":

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

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

       snake_case       :   Optional[Any]							=      []
       for i in range(__lowerCamelCase    ):
              snake_case       :   List[str]							=      i / num_diffusion_timesteps
              snake_case       :   List[Any]							=      (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   UpperCAmelCase		(						A_       ,A_ ):



   @register_to_config
   def __init__(self		:					str    ,						snake_case__		:					int = 10_00    ,						snake_case__		:					str = "fixed_small_log"    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[float] = 1.0    ,						snake_case__		:					str = "epsilon"    ,						snake_case__		:					str = "squaredcos_cap_v2"    ,						)		->	List[str]:


          '''simple docstring'''


          if beta_schedule != "squaredcos_cap_v2":
                 raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'" )

          snake_case       :   Union[str, Any]							=      betas_for_alpha_bar(snake_case__ )

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

          # standard deviation of the initial noise distribution
          snake_case       :   Optional[Any]							=      1.0

          # setable values
          snake_case       :   Optional[Any]							=      None
          snake_case       :   List[str]							=      torch.from_numpy(np.arange(0    ,						snake_case__ )[::-1].copy() )

          snake_case       :   str							=      variance_type



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					torch.FloatTensor    ,						snake_case__		:					Optional[int] = None )		->	torch.FloatTensor:


          '''simple docstring'''


          return sample



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					int    ,						snake_case__		:					Union[str, torch.device] = None )		->	int:


          '''simple docstring'''


          snake_case       :   int							=      num_inference_steps
          snake_case       :   Tuple							=      (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
          snake_case       :   Union[str, Any]							=      (np.arange(0    ,						snake_case__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
          snake_case       :   List[str]							=      torch.from_numpy(snake_case__ ).to(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Dict    ,						snake_case__		:					Union[str, Any]=None    ,						snake_case__		:					Optional[Any]=None    ,						snake_case__		:					Tuple=None )		->	Union[str, Any]:


          '''simple docstring'''


          if prev_timestep is None:
                 snake_case       :   Optional[int]							=      t - 1

          snake_case       :   Union[str, Any]							=      self.alphas_cumprod[t]
          snake_case       :   int							=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
          snake_case       :   Union[str, Any]							=      1 - alpha_prod_t
          snake_case       :   int							=      1 - alpha_prod_t_prev

          if prev_timestep == t - 1:
                 snake_case       :   List[str]							=      self.betas[t]
          else:
                 snake_case       :   Any							=      1 - alpha_prod_t / alpha_prod_t_prev

          # 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
          snake_case       :   str							=      beta_prod_t_prev / beta_prod_t * beta

          if variance_type is None:
                 snake_case       :   int							=      self.config.variance_type

          # hacks - were probably added for training stability
          if variance_type == "fixed_small_log":
                 snake_case       :   Union[str, Any]							=      torch.log(torch.clamp(snake_case__    ,						min=1e-20 ) )
                 snake_case       :   Any							=      torch.exp(0.5 * variance )
          elif variance_type == "learned_range":
                 # NOTE difference with DDPM scheduler
                 snake_case       :   Union[str, Any]							=      variance.log()
                 snake_case       :   Union[str, Any]							=      beta.log()

                 snake_case       :   Optional[Any]							=      (predicted_variance + 1) / 2
                 snake_case       :   Dict							=      frac * max_log + (1 - frac) * min_log

          return variance



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					torch.FloatTensor    ,						snake_case__		:					int    ,						snake_case__		:					torch.FloatTensor    ,						snake_case__		:					Optional[int] = None    ,						snake_case__		:					int=None    ,						snake_case__		:					bool = True    ,						)		->	Union[UnCLIPSchedulerOutput, Tuple]:


          '''simple docstring'''


          snake_case       :   Tuple							=      timestep

          if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                 snake_case				,					snake_case       :   int							=      torch.split(snake_case__    ,						sample.shape[1]    ,						dim=1 )
          else:
                 snake_case       :   List[Any]							=      None

          # 1. compute alphas, betas
          if prev_timestep is None:
                 snake_case       :   Any							=      t - 1

          snake_case       :   int							=      self.alphas_cumprod[t]
          snake_case       :   Tuple							=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
          snake_case       :   str							=      1 - alpha_prod_t
          snake_case       :   Tuple							=      1 - alpha_prod_t_prev

          if prev_timestep == t - 1:
                 snake_case       :   int							=      self.betas[t]
                 snake_case       :   Union[str, Any]							=      self.alphas[t]
          else:
                 snake_case       :   str							=      1 - alpha_prod_t / alpha_prod_t_prev
                 snake_case       :   List[str]							=      1 - beta

          # 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":
                 snake_case       :   Optional[int]							=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          elif self.config.prediction_type == "sample":
                 snake_case       :   Any							=      model_output
          else:
                 raise ValueError(
                     f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
                     " for the UnCLIPScheduler." )

          # 3. Clip "predicted x_0"
          if self.config.clip_sample:
                 snake_case       :   List[Any]							=      torch.clamp(
                     snake_case__    ,						-self.config.clip_sample_range    ,						self.config.clip_sample_range )

          # 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
          snake_case       :   Union[str, Any]							=      (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
          snake_case       :   str							=      alpha ** 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
          snake_case       :   int							=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

          # 6. Add noise
          snake_case       :   Tuple							=      0
          if t > 0:
                 snake_case       :   Optional[int]							=      randn_tensor(
                     model_output.shape    ,						dtype=model_output.dtype    ,						generator=snake_case__    ,						device=model_output.device )

                 snake_case       :   str							=      self._get_variance(
                     snake_case__    ,						predicted_variance=snake_case__    ,						prev_timestep=snake_case__    ,						)

                 if self.variance_type == "fixed_small_log":
                        snake_case       :   Optional[Any]							=      variance
                 elif self.variance_type == "learned_range":
                        snake_case       :   Optional[Any]							=      (0.5 * variance).exp()
                 else:
                        raise ValueError(
                            f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
                            " for the UnCLIPScheduler." )

                 snake_case       :   Any							=      variance * variance_noise

          snake_case       :   Tuple							=      pred_prev_sample + variance

          if not return_dict:
                 return (pred_prev_sample,)

          return UnCLIPSchedulerOutput(prev_sample=snake_case__    ,						pred_original_sample=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					torch.FloatTensor    ,						snake_case__		:					torch.FloatTensor    ,						snake_case__		:					torch.IntTensor    ,						)		->	torch.FloatTensor:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      self.alphas_cumprod.to(device=original_samples.device    ,						dtype=original_samples.dtype )
          snake_case       :   List[Any]							=      timesteps.to(original_samples.device )

          snake_case       :   Optional[int]							=      alphas_cumprod[timesteps] ** 0.5
          snake_case       :   List[Any]							=      sqrt_alpha_prod.flatten()
          while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
                 snake_case       :   Tuple							=      sqrt_alpha_prod.unsqueeze(-1 )

          snake_case       :   List[Any]							=      (1 - alphas_cumprod[timesteps]) ** 0.5
          snake_case       :   str							=      sqrt_one_minus_alpha_prod.flatten()
          while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
                 snake_case       :   Optional[Any]							=      sqrt_one_minus_alpha_prod.unsqueeze(-1 )

          snake_case       :   List[Any]							=      sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
          return noisy_samples







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


__lowerCamelCase							=       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase							=       {
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

__lowerCamelCase							=       {
    """unc-nlp/lxmert-base-uncased""": 5_12,
}

__lowerCamelCase							=       {
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}






class   UpperCAmelCase		(						A_ ):
   A__					:		Any						     =     VOCAB_FILES_NAMES
   A__					:		Optional[int]						     =     PRETRAINED_VOCAB_FILES_MAP
   A__					:		Tuple						     =     PRETRAINED_INIT_CONFIGURATION
   A__					:		Tuple						     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__					:		List[Any]						     =     LxmertTokenizer



   def __init__(self		:					Dict    ,						snake_case__		:					Tuple=None    ,						snake_case__		:					Optional[Any]=None    ,						snake_case__		:					Optional[Any]=True    ,						snake_case__		:					Tuple="[UNK]"    ,						snake_case__		:					Optional[Any]="[SEP]"    ,						snake_case__		:					Optional[Any]="[PAD]"    ,						snake_case__		:					List[Any]="[CLS]"    ,						snake_case__		:					Tuple="[MASK]"    ,						snake_case__		:					Dict=True    ,						snake_case__		:					Union[str, Any]=None    ,						**snake_case__		:					Dict    ,						)		->	Optional[int]:


          '''simple docstring'''


          super().__init__(
              snake_case__    ,						tokenizer_file=snake_case__    ,						do_lower_case=snake_case__    ,						unk_token=snake_case__    ,						sep_token=snake_case__    ,						pad_token=snake_case__    ,						cls_token=snake_case__    ,						mask_token=snake_case__    ,						tokenize_chinese_chars=snake_case__    ,						strip_accents=snake_case__    ,						**snake_case__    ,						)

          snake_case       :   int							=      json.loads(self.backend_tokenizer.normalizer.__getstate__() )
          if (
              normalizer_state.get("lowercase"    ,						snake_case__ ) != do_lower_case
              or normalizer_state.get("strip_accents"    ,						snake_case__ ) != strip_accents
              or normalizer_state.get("handle_chinese_chars"    ,						snake_case__ ) != tokenize_chinese_chars
          ):
                 snake_case       :   Union[str, Any]							=      getattr(snake_case__    ,						normalizer_state.pop("type" ) )
                 snake_case       :   str							=      do_lower_case
                 snake_case       :   List[Any]							=      strip_accents
                 snake_case       :   Optional[int]							=      tokenize_chinese_chars
                 snake_case       :   int							=      normalizer_class(**snake_case__ )

          snake_case       :   Optional[Any]							=      do_lower_case



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Dict=None )		->	Any:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
                 output += token_ids_a + [self.sep_token_id]

          return output



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					List[int]    ,						snake_case__		:					Optional[List[int]] = None )		->	List[int]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      [self.sep_token_id]
          snake_case       :   Optional[Any]							=      [self.cls_token_id]
          if token_ids_a is None:
                 return len(cls + token_ids_a + sep ) * [0]
          return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					str    ,						snake_case__		:					Optional[str] = None )		->	Tuple[str]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self._tokenizer.model.save(snake_case__    ,						name=snake_case__ )
          return tuple(snake_case__ )





