
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							_snake_case  		=	42
							_snake_case  		=	42

							# setable values
							_snake_case  		=	42
							_snake_case  		=	42
							_snake_case  		=	None



							@classmethod
							def      A__		(	cls  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> Union[str, Any]:





													'''simple docstring'''






													return cls(
													    common=SCREAMING_SNAKE_CASE_  ,						final_alpha_cumprod=SCREAMING_SNAKE_CASE_  ,						init_noise_sigma=SCREAMING_SNAKE_CASE_  ,						timesteps=SCREAMING_SNAKE_CASE_  ,						)






@dataclass
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	42






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	[e.name for e in FlaxKarrasDiffusionSchedulers]

							_snake_case  		=	42



							@property
							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													return True



							@register_to_config
							def __init__(	self  ,						SCREAMING_SNAKE_CASE_ = 1000  ,						SCREAMING_SNAKE_CASE_ = 0.0_0_0_1  ,						SCREAMING_SNAKE_CASE_ = 0.0_2  ,						SCREAMING_SNAKE_CASE_ = "linear"  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = 0  ,						SCREAMING_SNAKE_CASE_ = "epsilon"  ,						SCREAMING_SNAKE_CASE_ = jnp.floataa  ,						)-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      dtype



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_ = None      )-> DDIMSchedulerState:





													'''simple docstring'''






													if common is None:
																			__UpperCamelCase  	=      CommonSchedulerState.create(self      )

													# At every step in ddim, we are looking into the previous alphas_cumprod
													# For the final step, there is no previous alphas_cumprod because we are already at 0
													# `set_alpha_to_one` decides whether we set this parameter simply to one or
													# whether we use the final alpha of the "non-previous" one.
													__UpperCamelCase  	=      (
													    jnp.array(1.0  ,						dtype=self.dtype      ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
													)

													# standard deviation of the initial noise distribution
													__UpperCamelCase  	=      jnp.array(1.0  ,						dtype=self.dtype      )

													__UpperCamelCase  	=      jnp.arange(0  ,						self.config.num_train_timesteps      ).round()[::-1]

													return DDIMSchedulerState.create(
													    common=SCREAMING_SNAKE_CASE_  ,						final_alpha_cumprod=SCREAMING_SNAKE_CASE_  ,						init_noise_sigma=SCREAMING_SNAKE_CASE_  ,						timesteps=SCREAMING_SNAKE_CASE_  ,						)



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None      )-> jnp.ndarray:





													'''simple docstring'''






													return sample



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = ()      )-> DDIMSchedulerState:





													'''simple docstring'''






													__UpperCamelCase  	=      self.config.num_train_timesteps // num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# rounding to avoid issues when num_inference_step is power of 3
													__UpperCamelCase  	=      (jnp.arange(0  ,						SCREAMING_SNAKE_CASE_      ) * step_ratio).round()[::-1] + self.config.steps_offset

													return state.replace(
													    num_inference_steps=SCREAMING_SNAKE_CASE_  ,						timesteps=SCREAMING_SNAKE_CASE_  ,						)



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      state.common.alphas_cumprod[timestep]
													__UpperCamelCase  	=      jnp.where(
													    prev_timestep >= 0  ,						state.common.alphas_cumprod[prev_timestep]  ,						state.final_alpha_cumprod      )
													__UpperCamelCase  	=      1 - alpha_prod_t
													__UpperCamelCase  	=      1 - alpha_prod_t_prev

													__UpperCamelCase  	=      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

													return variance



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = 0.0  ,						SCREAMING_SNAKE_CASE_ = True  ,						)-> Union[FlaxDDIMSchedulerOutput, Tuple]:





													'''simple docstring'''






													if state.num_inference_steps is None:
																			raise ValueError(
																			    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''      )

													# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
													# Ideally, read DDIM paper in-detail understanding

													# Notation (<variable name> -> <name in paper>
													# - pred_noise_t -> e_theta(x_t, t)
													# - pred_original_sample -> f_theta(x_t, t) or x_0
													# - std_dev_t -> sigma_t
													# - eta -> η
													# - pred_sample_direction -> "direction pointing to x_t"
													# - pred_prev_sample -> "x_t-1"

													# 1. get previous step value (=t-1)
													__UpperCamelCase  	=      timestep - self.config.num_train_timesteps // state.num_inference_steps

													__UpperCamelCase  	=      state.common.alphas_cumprod
													__UpperCamelCase  	=      state.final_alpha_cumprod

													# 2. compute alphas, betas
													__UpperCamelCase  	=      alphas_cumprod[timestep]
													__UpperCamelCase  	=      jnp.where(prev_timestep >= 0  ,						alphas_cumprod[prev_timestep]  ,						SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      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":
																			__UpperCamelCase  	=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																			__UpperCamelCase  	=      model_output
													elif self.config.prediction_type == "sample":
																			__UpperCamelCase  	=      model_output
																			__UpperCamelCase  	=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
													elif self.config.prediction_type == "v_prediction":
																			__UpperCamelCase  	=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																			__UpperCamelCase  	=      (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. compute variance: "sigma_t(η)" -> see formula (16)
													# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
													__UpperCamelCase  	=      self._get_variance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      eta * variance ** 0.5

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

													# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													__UpperCamelCase  	=      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

													if not return_dict:
																			return (prev_sample, state)

													return FlaxDDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_  ,						state=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> jnp.ndarray:





													'''simple docstring'''






													return add_noise_common(state.common  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> jnp.ndarray:





													'''simple docstring'''






													return get_velocity_common(state.common  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



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





													'''simple docstring'''






													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  lowerCamelCase__  (			unittest.TestCase):
     SCREAMING_SNAKE_CASE__      =     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     SCREAMING_SNAKE_CASE__      =     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      if isinstance(model.config						,	UpperCAmelCase			):
       _lowercase ,       _lowercase				 =list(tokenizer.lang_code_to_id.keys()			)[:2]
       _lowercase				 =TranslationPipeline(model=UpperCAmelCase						,	tokenizer=UpperCAmelCase						,	src_lang=UpperCAmelCase						,	tgt_lang=UpperCAmelCase			)
      else:
       _lowercase				 =TranslationPipeline(model=UpperCAmelCase						,	tokenizer=UpperCAmelCase			)
      return translator, ["Some string", "Some other text"]





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  List[Any]:
      _lowercase				 =translator('''Some string'''			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': ANY(UpperCAmelCase			)}]			)

      _lowercase				 =translator(['''Some string''']			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': ANY(UpperCAmelCase			)}]			)

      _lowercase				 =translator(['''Some string''', '''other string''']			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': ANY(UpperCAmelCase			)}, {'''translation_text''': ANY(UpperCAmelCase			)}]			)





     @require_torch
     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =pipeline('''translation_en_to_ro'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''pt'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
                      ''' Beide Beide'''
                  )
              }
          ]						,	)





     @require_tf
     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =pipeline('''translation_en_to_ro'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''tf'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
                      ''' Beide Beide'''
                  )
              }
          ]						,	)





     @require_torch
     def 	__A   (self			)	->  str:
      _lowercase				 =pipeline('''translation_en_to_de'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''pt'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
                      ''' urine urine urine urine urine urine urine'''
                  )
              }
          ]						,	)





     @require_tf
     def 	__A   (self			)	->  Optional[int]:
      _lowercase				 =pipeline('''translation_en_to_de'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''tf'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
                      ''' urine urine urine urine urine urine urine'''
                  )
              }
          ]						,	)







class  lowerCamelCase__  (			unittest.TestCase):





     @require_torch
     @slow
     def 	__A   (self			)	->  Optional[Any]:
      # We don't provide a default for this pair
      with self.assertRaises(UpperCAmelCase			):
       pipeline(task='''translation_cn_to_ar'''			)

      # but we do for this one
      _lowercase				 =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 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =MBartForConditionalGeneration.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''			)
      _lowercase				 =MBartaaTokenizerFast.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''			)

      _lowercase				 =pipeline(task='''translation'''						,	model=UpperCAmelCase						,	tokenizer=UpperCAmelCase			)
      # Missing src_lang, tgt_lang
      with self.assertRaises(UpperCAmelCase			):
       translator('''This is a test'''			)

      _lowercase				 =translator('''This is a test'''						,	src_lang='''en_XX'''						,	tgt_lang='''ar_AR'''			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': '''هذا إختبار'''}]			)

      _lowercase				 =translator('''This is a test'''						,	src_lang='''en_XX'''						,	tgt_lang='''hi_IN'''			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': '''यह एक परीक्षण है'''}]			)

      # src_lang, tgt_lang can be defined at pipeline call time
      _lowercase				 =pipeline(task='''translation'''						,	model=UpperCAmelCase						,	tokenizer=UpperCAmelCase						,	src_lang='''en_XX'''						,	tgt_lang='''ar_AR'''			)
      _lowercase				 =translator('''This is a test'''			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': '''هذا إختبار'''}]			)





     @require_torch
     def 	__A   (self			)	->  Tuple:
      _lowercase				 ='''patrickvonplaten/t5-tiny-random'''
      _lowercase				 =pipeline(task='''translation_cn_to_ar'''						,	model=UpperCAmelCase			)
      self.assertEqual(translator._preprocess_params['''src_lang''']						,	'''cn'''			)
      self.assertEqual(translator._preprocess_params['''tgt_lang''']						,	'''ar'''			)





     @require_torch
     def 	__A   (self			)	->  str:
      _lowercase				 ='''patrickvonplaten/t5-tiny-random'''
      with pytest.warns(UpperCAmelCase						,	match=R'''.*translation_en_to_de.*'''			):
       _lowercase				 =pipeline(task='''translation'''						,	model=UpperCAmelCase			)
      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 	__A   (self			)	->  List[str]:
      with self.assertRaises(UpperCAmelCase			):
       pipeline(task='''translation'''			)

