
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
class 							A__		(     __SCREAMING_SNAKE_CASE):
      _UpperCAmelCase    :   torch.FloatTensor
      _UpperCAmelCase    :   torch.FloatTensor





def    _a (						lowerCamelCase,     lowerCamelCase=0.9_9_9,     lowerCamelCase="cosine",     ):
 if alpha_transform_type == "cosine":

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

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

 lowerCamelCase		:     Tuple          =				[]
 for i in range(lowerCamelCase							):
  lowerCamelCase		:     Optional[int]          =				i / num_diffusion_timesteps
  lowerCamelCase		:     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 							A__		(     __SCREAMING_SNAKE_CASE  ,       __SCREAMING_SNAKE_CASE):
      _UpperCAmelCase    :   Union[str, Any]   	=      1
      @register_to_config
      def __init__(				self   ,  __magic_name__ = 1_0_0_0   ,  __magic_name__ = 0.0_001   ,  __magic_name__ = 0.02   ,  __magic_name__ = "linear"   ,  __magic_name__ = 0.0   ,  __magic_name__ = None   ,  __magic_name__ = True   ,  ):
       if trained_betas is not None:
        lowerCamelCase		:     List[Any]          =				torch.from_numpy(__magic_name__				)
       elif beta_schedule == "linear":
        lowerCamelCase		:     str          =				torch.linspace(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  dtype=torch.floataa				)
       elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        lowerCamelCase		:     Tuple          =				(
            torch.linspace(beta_start**0.5   ,  beta_end**0.5   ,  __magic_name__   ,  dtype=torch.floataa				) ** 2
        )
       elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        lowerCamelCase		:     Optional[Any]          =				betas_for_alpha_bar(__magic_name__				)
       elif beta_schedule == "sigmoid":
        # GeoDiff sigmoid schedule
        lowerCamelCase		:     Optional[Any]          =				torch.linspace(-6   ,  6   ,  __magic_name__				)
        lowerCamelCase		:     Optional[int]          =				torch.sigmoid(__magic_name__				) * (beta_end - beta_start) + beta_start
       else:
        raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''				)

       lowerCamelCase		:     int          =				1.0 - self.betas
       lowerCamelCase		:     Tuple          =				torch.cumprod(self.alphas   ,  dim=0				)
       lowerCamelCase		:     int          =				torch.tensor(1.0				)

       lowerCamelCase		:     Any          =				torch.tensor(1.0				)

       # standard deviation of the initial noise distribution
       lowerCamelCase		:     Union[str, Any]          =				1.0

       # setable values
       lowerCamelCase		:     Optional[Any]          =				None
       lowerCamelCase		:     Optional[Any]          =				torch.from_numpy(np.arange(0   ,  __magic_name__				)[::-1].copy()				)

       lowerCamelCase		:     List[str]          =				eta
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = None				):
       return sample
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = 1_0   ,  __magic_name__ = 1_0   ,  __magic_name__ = None   ,  ):
       lowerCamelCase		:     int          =				min(self.config.num_train_timesteps   ,  __magic_name__				)
       lowerCamelCase		:     str          =				num_inference_steps

       lowerCamelCase		:     List[Any]          =				[]

       lowerCamelCase		:     Union[str, Any]          =				{}
       for j in range(0   ,  num_inference_steps - jump_length   ,  __magic_name__				):
        lowerCamelCase		:     Tuple          =				jump_n_sample - 1

       lowerCamelCase		:     List[Any]          =				num_inference_steps
       while t >= 1:
        lowerCamelCase		:     Tuple          =				t - 1
        timesteps.append(__magic_name__				)

        if jumps.get(__magic_name__   ,  0				) > 0:
         lowerCamelCase		:     Optional[int]          =				jumps[t] - 1
         for _ in range(__magic_name__				):
          lowerCamelCase		:     List[str]          =				t + 1
          timesteps.append(__magic_name__				)

       lowerCamelCase		:     Dict          =				np.array(__magic_name__				) * (self.config.num_train_timesteps // self.num_inference_steps)
       lowerCamelCase		:     int          =				torch.from_numpy(__magic_name__				).to(__magic_name__				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       lowerCamelCase		:     Union[str, Any]          =				t - self.config.num_train_timesteps // self.num_inference_steps

       lowerCamelCase		:     Union[str, Any]          =				self.alphas_cumprod[t]
       lowerCamelCase		:     List[str]          =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
       lowerCamelCase		:     Union[str, Any]          =				1 - alpha_prod_t
       lowerCamelCase		:     Optional[Any]          =				1 - 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
       # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
       # without eta.
       # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
       lowerCamelCase		:     List[str]          =				(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

       return variance
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__ = None   ,  __magic_name__ = True   ,  ):
       lowerCamelCase		:     Optional[Any]          =				timestep
       lowerCamelCase		:     List[Any]          =				timestep - self.config.num_train_timesteps // self.num_inference_steps

       # 1. compute alphas, betas
       lowerCamelCase		:     List[Any]          =				self.alphas_cumprod[t]
       lowerCamelCase		:     List[Any]          =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
       lowerCamelCase		:     List[Any]          =				1 - alpha_prod_t

       # 2. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
       lowerCamelCase		:     List[Any]          =				(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

       # 3. Clip "predicted x_0"
       if self.config.clip_sample:
        lowerCamelCase		:     str          =				torch.clamp(__magic_name__   ,  -1   ,  1				)

       # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
       # substitute formula (7) in the algorithm coming from DDPM paper
       # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
       # DDIM schedule gives the same results as DDPM with eta = 1.0
       # Noise is being reused in 7. and 8., but no impact on quality has
       # been observed.

       # 5. Add noise
       lowerCamelCase		:     int          =				model_output.device
       lowerCamelCase		:     str          =				randn_tensor(model_output.shape   ,  generator=__magic_name__   ,  device=__magic_name__   ,  dtype=model_output.dtype				)
       lowerCamelCase		:     Optional[Any]          =				self.eta * self._get_variance(__magic_name__				) ** 0.5

       lowerCamelCase		:     str          =				0
       if t > 0 and self.eta > 0:
        lowerCamelCase		:     Union[str, Any]          =				std_dev_t * noise

       # 6. compute "direction pointing to x_t" of formula (12)
       # from https://arxiv.org/pdf/2010.02502.pdf
       lowerCamelCase		:     Any          =				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

       # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       lowerCamelCase		:     Optional[int]          =				alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

       # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
       lowerCamelCase		:     Optional[int]          =				(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

       # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
       lowerCamelCase		:     Tuple          =				mask * prev_known_part + (1.0 - mask) * prev_unknown_part

       if not return_dict:
        return (
            pred_prev_sample,
            pred_original_sample,
        )

       return RePaintSchedulerOutput(prev_sample=__magic_name__   ,  pred_original_sample=__magic_name__				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__=None				):
       lowerCamelCase		:     Any          =				self.config.num_train_timesteps // self.num_inference_steps

       for i in range(__magic_name__				):
        lowerCamelCase		:     Optional[int]          =				self.betas[timestep + i]
        if sample.device.type == "mps":
         # randn does not work reproducibly on mps
         lowerCamelCase		:     List[Any]          =				randn_tensor(sample.shape   ,  dtype=sample.dtype   ,  generator=__magic_name__				)
         lowerCamelCase		:     List[str]          =				noise.to(sample.device				)
        else:
         lowerCamelCase		:     List[Any]          =				randn_tensor(sample.shape   ,  generator=__magic_name__   ,  device=sample.device   ,  dtype=sample.dtype				)

        # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
        lowerCamelCase		:     str          =				(1 - beta) ** 0.5 * sample + beta**0.5 * noise

       return sample
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  ):
       raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""				)






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






import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY





@is_pipeline_test
class 							A__		(     unittest.TestCase):
      _UpperCAmelCase    :   Tuple   	=      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      _UpperCAmelCase    :   Optional[Any]   	=      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       if isinstance(model.config   ,  __magic_name__				):
        lowerCamelCase      ,   lowerCamelCase		:     Union[str, Any]          =				list(tokenizer.lang_code_to_id.keys()				)[:2]
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang=__magic_name__   ,  tgt_lang=__magic_name__				)
       else:
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__				)
       return translator, ["Some string", "Some other text"]
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Optional[Any]          =				translator("""Some string"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Dict          =				translator(["""Some string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Any          =				translator(["""Some string""", """other string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}, {"""translation_text""": ANY(__magic_name__				)}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Any          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     str          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Tuple          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     int          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     List[Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )






      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Union[str, Any]          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )





class 							A__		(     unittest.TestCase):
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       # We don't provide a default for this pair
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation_cn_to_ar"""				)

       # but we do for this one
       lowerCamelCase		:     Optional[Any]          =				pipeline(task="""translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)
       lowerCamelCase		:     List[Any]          =				MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)

       lowerCamelCase		:     Any          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__				)
       # Missing src_lang, tgt_lang
       with self.assertRaises(__magic_name__				):
        translator("""This is a test"""				)

       lowerCamelCase		:     Optional[int]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)

       lowerCamelCase		:     Optional[Any]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""hi_IN"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """यह एक परीक्षण है"""}]				)

       # src_lang, tgt_lang can be defined at pipeline call time
       lowerCamelCase		:     str          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[str]          =				"""patrickvonplaten/t5-tiny-random"""
       lowerCamelCase		:     Tuple          =				pipeline(task="""translation_cn_to_ar"""   ,  model=__magic_name__				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """cn"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """ar"""				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Dict          =				"""patrickvonplaten/t5-tiny-random"""
       with pytest.warns(__magic_name__   ,  match=r""".*translation_en_to_de.*"""				):
        lowerCamelCase		:     Optional[int]          =				pipeline(task="""translation"""   ,  model=__magic_name__				)
       self.assertEqual(translator.task   ,  """translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)






      @require_torch
      def 						UpperCamelCase__							(				self				):
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation"""				)




