





"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     42

 # setable values
 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     None

 @classmethod
 def 					UpperCamelCase__(    cls					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     return cls(common=__lowerCamelCase					,						init_noise_sigma=__lowerCamelCase					,						timesteps=__lowerCamelCase  )






@dataclass
class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     42






class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     [e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCamelCase        =     42

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return True

 @register_to_config
 def __init__(    self					,						__lowerCamelCase = 1000					,						__lowerCamelCase = 0.0_0_0_1					,						__lowerCamelCase = 0.0_2					,						__lowerCamelCase = "linear"					,						__lowerCamelCase = None					,						__lowerCamelCase = "fixed_small"					,						__lowerCamelCase = True					,						__lowerCamelCase = "epsilon"					,						__lowerCamelCase = jnp.floataa					,						):



     '''simple docstring'''




     __A    :						Tuple						 =     dtype

 def 					UpperCamelCase__(    self					,						__lowerCamelCase = None  ):



     '''simple docstring'''




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

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

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

     return DDPMSchedulerState.create(
         common=__lowerCamelCase					,						init_noise_sigma=__lowerCamelCase					,						timesteps=__lowerCamelCase					,						)

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = None  ):



     '''simple docstring'''




     return sample

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = ()  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     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
     __A    :						Optional[Any]						 =     (jnp.arange(0					,						__lowerCamelCase  ) * step_ratio).round()[::-1]

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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase=None					,						__lowerCamelCase=None  ):



     '''simple docstring'''




     __A    :						int						 =     state.common.alphas_cumprod[t]
     __A    :						List[str]						 =     jnp.where(t > 0					,						state.common.alphas_cumprod[t - 1]					,						jnp.array(1.0					,						dtype=self.dtype  )  )

     # 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
     __A    :						str						 =     (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

     if variance_type is None:
         __A    :						Dict						 =     self.config.variance_type

     # hacks - were probably added for training stability
     if variance_type == "fixed_small":
         __A    :						List[Any]						 =     jnp.clip(__lowerCamelCase					,						a_min=1e-2_0  )
     # for rl-diffuser https://arxiv.org/abs/2205.09991
     elif variance_type == "fixed_small_log":
         __A    :						Optional[Any]						 =     jnp.log(jnp.clip(__lowerCamelCase					,						a_min=1e-2_0  )  )
     elif variance_type == "fixed_large":
         __A    :						Tuple						 =     state.common.betas[t]
     elif variance_type == "fixed_large_log":
         # Glide max_log
         __A    :						Union[str, Any]						 =     jnp.log(state.common.betas[t]  )
     elif variance_type == "learned":
         return predicted_variance
     elif variance_type == "learned_range":
         __A    :						Optional[Any]						 =     variance
         __A    :						Optional[Any]						 =     state.common.betas[t]
         __A    :						Any						 =     (predicted_variance + 1) / 2
         __A    :						Union[str, Any]						 =     frac * max_log + (1 - frac) * min_log

     return variance

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = None					,						__lowerCamelCase = True					,						):



     '''simple docstring'''




     __A    :						Optional[int]						 =     timestep

     if key is None:
         __A    :						List[Any]						 =     jax.random.PRNGKey(0  )

     if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
         __A ,       __A    :						Tuple						 =     jnp.split(__lowerCamelCase					,						sample.shape[1]					,						axis=1  )
     else:
         __A    :						List[str]						 =     None

     # 1. compute alphas, betas
     __A    :						Dict						 =     state.common.alphas_cumprod[t]
     __A    :						int						 =     jnp.where(t > 0					,						state.common.alphas_cumprod[t - 1]					,						jnp.array(1.0					,						dtype=self.dtype  )  )
     __A    :						Tuple						 =     1 - alpha_prod_t
     __A    :						Optional[int]						 =     1 - alpha_prod_t_prev

     # 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":
         __A    :						Optional[Any]						 =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif self.config.prediction_type == "sample":
         __A    :						Any						 =     model_output
     elif self.config.prediction_type == "v_prediction":
         __A    :						str						 =     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
     else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
             ''' for the FlaxDDPMScheduler.'''  )

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
         __A    :						str						 =     jnp.clip(__lowerCamelCase					,						-1					,						1  )

     # 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
     __A    :						Optional[Any]						 =     (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
     __A    :						Union[str, Any]						 =     state.common.alphas[t] ** 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
     __A    :						Optional[Any]						 =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     def random_variance():
         __A    :						List[Any]						 =     jax.random.split(__lowerCamelCase					,						num=1  )
         __A    :						List[str]						 =     jax.random.normal(__lowerCamelCase					,						shape=model_output.shape					,						dtype=self.dtype  )
         return (self._get_variance(__lowerCamelCase					,						__lowerCamelCase					,						predicted_variance=__lowerCamelCase  ) ** 0.5) * noise

     __A    :						Optional[Any]						 =     jnp.where(t > 0					,						random_variance()					,						jnp.zeros(model_output.shape					,						dtype=self.dtype  )  )

     __A    :						Any						 =     pred_prev_sample + variance

     if not return_dict:
         return (pred_prev_sample, state)

     return FlaxDDPMSchedulerOutput(prev_sample=__lowerCamelCase					,						state=__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




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







 def __len__(    self  ):



     '''simple docstring'''




     return self.config.num_train_timesteps











"""simple docstring"""



import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
       import tensorflow as tf

if is_torch_available():
       import torch


a_ =			logging.get_logger(__name__)






class 		__snake_case					:





 """simple docstring"""


 def __init__(    self					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase=None					,						__lowerCamelCase=None  ):



     '''simple docstring'''




     if not conversation_id:
         __A    :						List[Any]						 =     uuid.uuida()
     if past_user_inputs is None:
         __A    :						List[str]						 =     []
     if generated_responses is None:
         __A    :						Tuple						 =     []

     __A    :						uuid.UUID						 =     conversation_id
     __A    :						List[str]						 =     past_user_inputs
     __A    :						List[str]						 =     generated_responses
     __A    :						Optional[str]						 =     text

 def __eq__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     if not isinstance(__lowerCamelCase					,						__lowerCamelCase  ):
         return False
     if self.uuid == other.uuid:
         return True
     return (
         self.new_user_input == other.new_user_input
         and self.past_user_inputs == other.past_user_inputs
         and self.generated_responses == other.generated_responses
     )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase = False  ):



     '''simple docstring'''




     if self.new_user_input:
         if overwrite:
             logger.warning(
                 F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
                 F"""with: \"{text}\"."""  )
             __A    :						str						 =     text
         else:
             logger.warning(
                 F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
                 F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"""  )
     else:
         __A    :						Union[str, Any]						 =     text

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.new_user_input:
         self.past_user_inputs.append(self.new_user_input  )
     __A    :						List[Any]						 =     None

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     self.generated_responses.append(__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     for user_input, generated_response in zip(self.past_user_inputs					,						self.generated_responses  ):
         yield True, user_input
         yield False, generated_response
     if self.new_user_input:
         yield True, self.new_user_input







 def __repr__(    self  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     F"""Conversation id: {self.uuid} \n"""
     for is_user, text in self.iter_texts():
         __A    :						Tuple						 =     '''user''' if is_user else '''bot'''
         output += F"""{name} >> {text} \n"""
     return output






@add_end_docstrings(
    SCREAMING_SNAKE_CASE__							,							R"""
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    """							,							)
class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 def __init__(    self					,						*__lowerCamelCase					,						**__lowerCamelCase  ):



     '''simple docstring'''




     super().__init__(*__lowerCamelCase					,						**__lowerCamelCase  )
     if self.tokenizer.pad_token_id is None:
         __A    :						Union[str, Any]						 =     self.tokenizer.eos_token

 def 					UpperCamelCase__(    self					,						__lowerCamelCase=None					,						__lowerCamelCase=None					,						__lowerCamelCase=None					,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						str						 =     {}
     __A    :						List[str]						 =     {}
     __A    :						Any						 =     {}

     if min_length_for_response is not None:
         __A    :						int						 =     min_length_for_response
     if minimum_tokens is not None:
         __A    :						Any						 =     minimum_tokens

     if "max_length" in generate_kwargs:
         __A    :						List[Any]						 =     generate_kwargs['''max_length''']
         # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
     if clean_up_tokenization_spaces is not None:
         __A    :						str						 =     clean_up_tokenization_spaces

     if generate_kwargs:
         forward_params.update(__lowerCamelCase  )
     return preprocess_params, forward_params, postprocess_params

 def __call__(    self					,						__lowerCamelCase					,						__lowerCamelCase=0					,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Any						 =     super().__call__(__lowerCamelCase					,						num_workers=__lowerCamelCase					,						**__lowerCamelCase  )
     if isinstance(__lowerCamelCase					,						__lowerCamelCase  ) and len(__lowerCamelCase  ) == 1:
         return outputs[0]
     return outputs

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase=32  ):



     '''simple docstring'''




     if not isinstance(__lowerCamelCase					,						__lowerCamelCase  ):
         raise ValueError('''ConversationalPipeline, expects Conversation as inputs'''  )
     if conversation.new_user_input is None:
         raise ValueError(
             F"""Conversation with UUID {type(conversation.uuid  )} does not contain new user input to process. """
             '''Add user inputs with the conversation\'s `add_user_input` method'''  )
     if hasattr(self.tokenizer					,						'''_build_conversation_input_ids'''  ):
         __A    :						List[Any]						 =     self.tokenizer._build_conversation_input_ids(__lowerCamelCase  )
     else:
         # If the tokenizer cannot handle conversations, we default to only the old version
         __A    :						int						 =     self._legacy_parse_and_tokenize(__lowerCamelCase  )

     if self.framework == "pt":
         __A    :						Union[str, Any]						 =     torch.LongTensor([input_ids]  )
     elif self.framework == "tf":
         __A    :						int						 =     tf.constant([input_ids]  )
     return {"input_ids": input_ids, "conversation": conversation}

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase=10					,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Tuple						 =     generate_kwargs.get('''max_length'''					,						self.model.config.max_length  )

     __A    :						str						 =     model_inputs['''input_ids'''].shape[1]
     if max_length - minimum_tokens < n:
         logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"""  )
         __A    :						str						 =     max_length - minimum_tokens
         __A    :						Any						 =     model_inputs['''input_ids'''][:, -trim:]
         if "attention_mask" in model_inputs:
             __A    :						Union[str, Any]						 =     model_inputs['''attention_mask'''][:, -trim:]
     __A    :						Dict						 =     model_inputs.pop('''conversation'''  )
     __A    :						List[str]						 =     max_length
     __A    :						Dict						 =     self.model.generate(**__lowerCamelCase					,						**__lowerCamelCase  )
     if self.model.config.is_encoder_decoder:
         __A    :						Any						 =     1
     else:
         __A    :						List[Any]						 =     n
     return {"output_ids": output_ids[:, start_position:], "conversation": conversation}

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase=True  ):



     '''simple docstring'''




     __A    :						int						 =     model_outputs['''output_ids''']
     __A    :						Optional[int]						 =     self.tokenizer.decode(
         output_ids[0]					,						skip_special_tokens=__lowerCamelCase					,						clean_up_tokenization_spaces=__lowerCamelCase					,						)
     __A    :						Dict						 =     model_outputs['''conversation''']
     conversation.mark_processed()
     conversation.append_response(__lowerCamelCase  )
     return conversation







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Tuple						 =     self.tokenizer.eos_token_id
     __A    :						List[str]						 =     []
     for is_user, text in conversation.iter_texts():
         if eos_token_id is not None:
             input_ids.extend(self.tokenizer.encode(__lowerCamelCase					,						add_special_tokens=__lowerCamelCase  ) + [eos_token_id]  )
         else:
             input_ids.extend(self.tokenizer.encode(__lowerCamelCase					,						add_special_tokens=__lowerCamelCase  )  )

     if len(__lowerCamelCase  ) > self.tokenizer.model_max_length:
         __A    :						List[str]						 =     input_ids[-self.tokenizer.model_max_length :]
     return input_ids




