

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

import numpy as np
import torch

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


lowerCamelCase									= logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__			:     torch.FloatTensor







class 						__magic_name__      (							lowerCamelCase__		,	lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__			:     Optional[Any]    =       1



   @register_to_config
   def __init__(    self,   lowercase_ = 40,   lowercase_ = 0.002,   lowercase_ = 80.0,   lowercase_ = 0.5,   lowercase_ = 1.0,   lowercase_ = 7.0,   lowercase_ = True,   )							->   Optional[int]:






     """simple docstring"""






     a__  =sigma_max

     a__  =np.linspace(0,   1,   lowercase_	)
     a__  =self._convert_to_karras(lowercase_	)
     a__  =self.sigma_to_t(lowercase_	)

     # setable values
     a__  =None
     a__  =torch.from_numpy(lowercase_	)
     a__  =torch.from_numpy(lowercase_	)
     a__  =False
     a__  =False



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_=None	)							->   Union[str, Any]:






     """simple docstring"""






     if schedule_timesteps is None:
       a__  =self.timesteps

     a__  =(schedule_timesteps == timestep).nonzero()
     return indices.item()



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   torch.FloatTensor:






     """simple docstring"""






     if isinstance(lowercase_,   torch.Tensor	):
       a__  =timestep.to(self.timesteps.device	)
     a__  =self.index_for_timestep(lowercase_	)
     a__  =self.sigmas[step_idx]

     a__  =sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

     a__  =True
     return sample



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Optional[Any]:






     """simple docstring"""






     if not isinstance(lowercase_,   np.ndarray	):
       a__  =np.array(lowercase_,   dtype=np.floataa	)

     a__  =1000 * 0.25 * np.log(sigmas + 1E-44	)

     return timesteps



   def 			_UpperCAmelCase			(    self,   lowercase_ = None,   lowercase_ = None,   lowercase_ = None,   )							->   Tuple:






     """simple docstring"""






     if num_inference_steps is None and timesteps is None:
       raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''	)

     if num_inference_steps is not None and timesteps is not None:
       raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''	)

     # Follow DDPMScheduler custom timesteps logic
     if timesteps is not None:
       for i in range(1,   len(lowercase_	)	):
         if timesteps[i] >= timesteps[i - 1]:
           raise ValueError('''`timesteps` must be in descending order.'''	)

       if timesteps[0] >= self.config.num_train_timesteps:
         raise ValueError(
             F"""`timesteps` must start before `self.config.train_timesteps`:"""
             F""" {self.config.num_train_timesteps}."""	)

       a__  =np.array(lowercase_,   dtype=np.intaa	)
       a__  =True
     else:
       if num_inference_steps > self.config.num_train_timesteps:
         raise ValueError(
             F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
             F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
             F""" maximal {self.config.num_train_timesteps} timesteps."""	)

       a__  =num_inference_steps

       a__  =self.config.num_train_timesteps // self.num_inference_steps
       a__  =(np.arange(0,   lowercase_	) * step_ratio).round()[::-1].copy().astype(np.intaa	)
       a__  =False

     # Map timesteps to Karras sigmas directly for multistep sampling
     # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
     a__  =self.config.num_train_timesteps
     a__  =timesteps[::-1].copy()
     a__  =ramp / (num_train_timesteps - 1)
     a__  =self._convert_to_karras(lowercase_	)
     a__  =self.sigma_to_t(lowercase_	)

     a__  =np.concatenate([sigmas, [self.sigma_min]]	).astype(np.floataa	)
     a__  =torch.from_numpy(lowercase_	).to(device=lowercase_	)

     if str(lowercase_	).startswith('''mps'''	):
       # mps does not support float64
       a__  =torch.from_numpy(lowercase_	).to(lowercase_,   dtype=torch.floataa	)
     else:
       a__  =torch.from_numpy(lowercase_	).to(device=lowercase_	)



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =self.config.sigma_min
     a__  =self.config.sigma_max

     a__  =self.config.rho
     a__  =sigma_min ** (1 / rho)
     a__  =sigma_max ** (1 / rho)
     a__  =(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Dict:






     """simple docstring"""






     a__  =self.config.sigma_data

     a__  =sigma_data**2 / (sigma**2 + sigma_data**2)
     a__  =sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
     return c_skip, c_out



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Optional[int]:






     """simple docstring"""






     a__  =self.config.sigma_min
     a__  =self.config.sigma_data

     a__  =sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
     a__  =(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
     return c_skip, c_out



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = None,   lowercase_ = True,   )							->   Union[CMStochasticIterativeSchedulerOutput, Tuple]:






     """simple docstring"""






     if (
         isinstance(lowercase_,   lowercase_	)
         or isinstance(lowercase_,   torch.IntTensor	)
         or isinstance(lowercase_,   torch.LongTensor	)
     ):
       raise ValueError(
           (
               '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
               F""" `{self.__class__}.step()` is not supported. Make sure to pass"""
               ''' one of the `scheduler.timesteps` as a timestep.'''
           ),   )

     if not self.is_scale_input_called:
       logger.warning(
           '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
           '''See `StableDiffusionPipeline` for a usage example.'''	)

     if isinstance(lowercase_,   torch.Tensor	):
       a__  =timestep.to(self.timesteps.device	)

     a__  =self.config.sigma_min
     a__  =self.config.sigma_max

     a__  =self.index_for_timestep(lowercase_	)

     # sigma_next corresponds to next_t in original implementation
     a__  =self.sigmas[step_index]
     if step_index + 1 < self.config.num_train_timesteps:
       a__  =self.sigmas[step_index + 1]
     else:
       # Set sigma_next to sigma_min
       a__  =self.sigmas[-1]

     # Get scalings for boundary conditions
     a__,				a__  =self.get_scalings_for_boundary_condition(lowercase_	)

     # 1. Denoise model output using boundary conditions
     a__  =c_out * model_output + c_skip * sample
     if self.config.clip_denoised:
       a__  =denoised.clamp(-1,   1	)

     # 2. Sample z ~ N(0, s_noise^2 * I)
     # Noise is not used for onestep sampling.
     if len(self.timesteps	) > 1:
       a__  =randn_tensor(
           model_output.shape,   dtype=model_output.dtype,   device=model_output.device,   generator=lowercase_	)
     else:
       a__  =torch.zeros_like(lowercase_	)
     a__  =noise * self.config.s_noise

     a__  =sigma_next.clamp(min=lowercase_,   max=lowercase_	)

     # 3. Return noisy sample
     # tau = sigma_hat, eps = sigma_min
     a__  =denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

     if not return_dict:
       return (prev_sample,)

     return CMStochasticIterativeSchedulerOutput(prev_sample=lowercase_	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   )							->   torch.FloatTensor:






     """simple docstring"""






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

     a__  =[(schedule_timesteps == t).nonzero().item() for t in timesteps]

     a__  =sigmas[step_indices].flatten()
     while len(sigma.shape	) < len(original_samples.shape	):
       a__  =sigma.unsqueeze(-1	)

     a__  =original_samples + noise * sigma
     return noisy_samples



   def __len__(    self	)							->   int:






     """simple docstring"""






     return self.config.num_train_timesteps



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


lowerCamelCase									= logging.get_logger(__name__)

lowerCamelCase									= {
    '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json''',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__			:     List[Any]    =       'blenderbot'
   lowerCamelCase__			:     int    =       ['past_key_values']
   lowerCamelCase__			:     Optional[Any]    =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



   def __init__(    self,   lowercase_=8008,   lowercase_=128,   lowercase_=2,   lowercase_=10240,   lowercase_=32,   lowercase_=24,   lowercase_=10240,   lowercase_=32,   lowercase_=0.0,   lowercase_=0.0,   lowercase_=True,   lowercase_=True,   lowercase_="gelu",   lowercase_=2560,   lowercase_=0.1,   lowercase_=0.0,   lowercase_=0.0,   lowercase_=0.02,   lowercase_=1,   lowercase_=False,   lowercase_=0,   lowercase_=1,   lowercase_=2,   lowercase_=3,   lowercase_=2,   **lowercase_,   )							->   Any:






     """simple docstring"""






     a__  =vocab_size
     a__  =max_position_embeddings
     a__  =d_model
     a__  =encoder_ffn_dim
     a__  =encoder_layers
     a__  =encoder_attention_heads
     a__  =decoder_ffn_dim
     a__  =decoder_layers
     a__  =decoder_attention_heads
     a__  =dropout
     a__  =attention_dropout
     a__  =activation_dropout
     a__  =activation_function
     a__  =init_std
     a__  =encoder_layerdrop
     a__  =decoder_layerdrop
     a__  =use_cache
     a__  =encoder_layers
     a__  =scale_embedding  # scale factor will be sqrt(d_model) if True

     super().__init__(
         pad_token_id=lowercase_,   bos_token_id=lowercase_,   eos_token_id=lowercase_,   is_encoder_decoder=lowercase_,   decoder_start_token_id=lowercase_,   encoder_no_repeat_ngram_size=lowercase_,   forced_eos_token_id=lowercase_,   **lowercase_,   )







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   @property
   def 			_UpperCAmelCase			(    self	)							->   Mapping[str, Mapping[int, str]]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__  =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]	)
       if self.use_past:
         a__  ={0: '''batch'''}
         a__  ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
         a__  ={0: '''batch''', 1: '''decoder_sequence'''}
         a__  ={0: '''batch''', 1: '''decoder_sequence'''}
       if self.use_past:
         self.fill_with_past_key_values_(lowercase_,   direction='''inputs'''	)
     elif self.task == "causal-lm":
       a__  =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]	)
       if self.use_past:
         a__,				a__  =self.num_layers
         for i in range(lowercase_	):
           a__  ={0: '''batch''', 2: '''past_sequence + sequence'''}
           a__  ={0: '''batch''', 2: '''past_sequence + sequence'''}
     else:
       a__  =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
               ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
           ]	)

     return common_inputs



   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def 			_UpperCAmelCase			(    self	)							->   Mapping[str, Mapping[int, str]]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__  =super().outputs
     else:
       a__  =super(lowercase_,   self	).outputs
       if self.use_past:
         a__,				a__  =self.num_layers
         for i in range(lowercase_	):
           a__  ={0: '''batch''', 2: '''past_sequence + sequence'''}
           a__  ={0: '''batch''', 2: '''past_sequence + sequence'''}
     return common_outputs



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






     a__  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)
     # Generate decoder inputs
     a__  =seq_length if not self.use_past else 1
     a__  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)
     a__  ={F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
     a__  =dict(**lowercase_,   **lowercase_	)

     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
       else:
         import torch
       a__,				a__  =common_inputs['''input_ids'''].shape
       a__  =common_inputs['''decoder_input_ids'''].shape[1]
       a__,				a__  =self.num_attention_heads
       a__  =(
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       a__  =decoder_seq_length
       a__  =(
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )
       a__  =torch.cat(
           [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_,   lowercase_	)],   dim=1	)
       a__  =[]
       a__,				a__  =self.num_layers

       for _ in range(lowercase_	):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(lowercase_	),
                 torch.zeros(lowercase_	),
                 torch.zeros(lowercase_	),
                 torch.zeros(lowercase_	),
             )	)
     return common_inputs



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






     a__  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)

     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
       else:
         import torch
       a__,				a__  =common_inputs['''input_ids'''].shape
       a__  =seqlen
       a__,				a__  =self.num_layers
       a__,				a__  =self.num_attention_heads
       a__  =(
           batch,
           num_encoder_attention_heads,
           past_key_values_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       a__  =common_inputs['''attention_mask'''].dtype
       a__  =torch.cat(
           [common_inputs['''attention_mask'''], torch.ones(lowercase_,   lowercase_,   dtype=lowercase_	)],   dim=1	)
       a__  =[
           (torch.zeros(lowercase_	), torch.zeros(lowercase_	)) for _ in range(lowercase_	)
       ]
     return common_inputs



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






     a__  =compute_effective_axis_dimension(
         lowercase_,   fixed_dimension=OnnxConfig.default_fixed_batch,   num_token_to_add=0	)

     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     a__  =tokenizer.num_special_tokens_to_add(lowercase_	)
     a__  =compute_effective_axis_dimension(
         lowercase_,   fixed_dimension=OnnxConfig.default_fixed_sequence,   num_token_to_add=lowercase_	)

     # Generate dummy inputs according to compute batch and sequence
     a__  =[''' '''.join([tokenizer.unk_token]	) * seq_length] * batch_size
     a__  =dict(tokenizer(lowercase_,   return_tensors=lowercase_	)	)
     return common_inputs



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__  =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
           lowercase_,   batch_size=lowercase_,   seq_length=lowercase_,   is_pair=lowercase_,   framework=lowercase_	)

     elif self.task == "causal-lm":
       a__  =self._generate_dummy_inputs_for_causal_lm(
           lowercase_,   batch_size=lowercase_,   seq_length=lowercase_,   is_pair=lowercase_,   framework=lowercase_	)
     else:
       a__  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_,   batch_size=lowercase_,   seq_length=lowercase_,   is_pair=lowercase_,   framework=lowercase_	)

     return common_inputs



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   Optional[int]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__  =super()._flatten_past_key_values_(lowercase_,   lowercase_,   lowercase_,   lowercase_	)
     else:
       a__  =super(lowercase_,   self	)._flatten_past_key_values_(
           lowercase_,   lowercase_,   lowercase_,   lowercase_	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Tuple:






     """simple docstring"""






     if direction not in ["inputs", "outputs"]:
       raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""	)

     a__  ='''past_key_values''' if direction == '''inputs''' else '''present'''
     a__,				a__  =self.num_layers

     a__  ='''past_encoder_sequence'''
     a__  ='''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

     for i in range(lowercase_	):
       a__  ={0: '''batch''', 2: decoder_sequence}
       a__  ={0: '''batch''', 2: decoder_sequence}
       a__  ={0: '''batch''', 2: encoder_sequence}
       a__  ={0: '''batch''', 2: encoder_sequence}
