code: 
  
  
  
  
  
  '''simple docstring'''
  
  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
  
  
  lowercase_			       =				logging.get_logger(__name__)
  
  lowercase_			       =				{
      """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       a_  (       snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				'''blenderbot'''
      UpperCamelCase									=				['''past_key_values''']
      UpperCamelCase									=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
      def __init__(	self				,	A=8008				,	A=128				,	A=2				,	A=1_0240				,	A=32				,	A=24				,	A=1_0240				,	A=32				,	A=0.0				,	A=0.0				,	A=True				,	A=True				,	A="gelu"				,	A=2560				,	A=0.1				,	A=0.0				,	A=0.0				,	A=0.02				,	A=1				,	A=False				,	A=0				,	A=1				,	A=2				,	A=3				,	A=2				,	**A				,	)	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    vocab_size
          _SCREAMING_SNAKE_CASE             =    max_position_embeddings
          _SCREAMING_SNAKE_CASE             =    d_model
          _SCREAMING_SNAKE_CASE             =    encoder_ffn_dim
          _SCREAMING_SNAKE_CASE             =    encoder_layers
          _SCREAMING_SNAKE_CASE             =    encoder_attention_heads
          _SCREAMING_SNAKE_CASE             =    decoder_ffn_dim
          _SCREAMING_SNAKE_CASE             =    decoder_layers
          _SCREAMING_SNAKE_CASE             =    decoder_attention_heads
          _SCREAMING_SNAKE_CASE             =    dropout
          _SCREAMING_SNAKE_CASE             =    attention_dropout
          _SCREAMING_SNAKE_CASE             =    activation_dropout
          _SCREAMING_SNAKE_CASE             =    activation_function
          _SCREAMING_SNAKE_CASE             =    init_std
          _SCREAMING_SNAKE_CASE             =    encoder_layerdrop
          _SCREAMING_SNAKE_CASE             =    decoder_layerdrop
          _SCREAMING_SNAKE_CASE             =    use_cache
          _SCREAMING_SNAKE_CASE             =    encoder_layers
          _SCREAMING_SNAKE_CASE             =    scale_embedding  # scale factor will be sqrt(d_model) if True
  
          super().__init__(
              pad_token_id=A				,	bos_token_id=A				,	eos_token_id=A				,	is_encoder_decoder=A				,	decoder_start_token_id=A				,	encoder_no_repeat_ngram_size=A				,	forced_eos_token_id=A				,	**A				,	)
  
  
  
  
  class       a_  (       snake_case_ ):
  
      '''simple docstring'''
  
  
      @property
      def     snake_case_(	self )	->     Mapping[str, Mapping[int, str]]:
          if self.task in ["default", "seq2seq-lm"]:
              _SCREAMING_SNAKE_CASE             =    OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ] )
              if self.use_past:
                  _SCREAMING_SNAKE_CASE             =    {0: """batch"""}
                  _SCREAMING_SNAKE_CASE             =    {0: """batch""", 1: """past_decoder_sequence + sequence"""}
              else:
                  _SCREAMING_SNAKE_CASE             =    {0: """batch""", 1: """decoder_sequence"""}
                  _SCREAMING_SNAKE_CASE             =    {0: """batch""", 1: """decoder_sequence"""}
              if self.use_past:
                  self.fill_with_past_key_values_(A				,	direction="""inputs""" )
          elif self.task == "causal-lm":
              _SCREAMING_SNAKE_CASE             =    OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ] )
              if self.use_past:
                  _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_layers
                  for i in range(A ):
                      _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: """past_sequence + sequence"""}
                      _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: """past_sequence + sequence"""}
          else:
              _SCREAMING_SNAKE_CASE             =    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     snake_case_(	self )	->     Mapping[str, Mapping[int, str]]:
          if self.task in ["default", "seq2seq-lm"]:
              _SCREAMING_SNAKE_CASE             =    super().outputs
          else:
              _SCREAMING_SNAKE_CASE             =    super(A				,	self ).outputs
              if self.use_past:
                  _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_layers
                  for i in range(A ):
                      _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: """past_sequence + sequence"""}
                      _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: """past_sequence + sequence"""}
          return common_outputs
  
  
      def     snake_case_(	self				,	A				,	A = -1				,	A = -1				,	A = False				,	A = None				,	)	->     Mapping[str, Any]:
          _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A				,	A				,	A				,	A				,	A )
          # Generate decoder inputs
          _SCREAMING_SNAKE_CASE             =    seq_length if not self.use_past else 1
          _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A				,	A				,	A				,	A				,	A )
          _SCREAMING_SNAKE_CASE             =    {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
          _SCREAMING_SNAKE_CASE             =    dict(**A				,	**A )
  
          if self.use_past:
              if not is_torch_available():
                  raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
              else:
                  import torch
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    common_inputs["""input_ids"""].shape
              _SCREAMING_SNAKE_CASE             =    common_inputs["""decoder_input_ids"""].shape[1]
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_attention_heads
              _SCREAMING_SNAKE_CASE             =    (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              _SCREAMING_SNAKE_CASE             =    decoder_seq_length
              _SCREAMING_SNAKE_CASE             =    (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )
              _SCREAMING_SNAKE_CASE             =    torch.cat(
                  [common_inputs["""decoder_attention_mask"""], torch.ones(A				,	A )]				,	dim=1 )
              _SCREAMING_SNAKE_CASE             =    []
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_layers
  
              for _ in range(A ):
                  common_inputs["past_key_values"].append(
                      (
                          torch.zeros(A ),
                          torch.zeros(A ),
                          torch.zeros(A ),
                          torch.zeros(A ),
                      ) )
          return common_inputs
  
  
      def     snake_case_(	self				,	A				,	A = -1				,	A = -1				,	A = False				,	A = None				,	)	->     Mapping[str, Any]:
          _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A				,	A				,	A				,	A				,	A )
  
          if self.use_past:
              if not is_torch_available():
                  raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
              else:
                  import torch
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    common_inputs["""input_ids"""].shape
              _SCREAMING_SNAKE_CASE             =    seqlen
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_layers
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_attention_heads
              _SCREAMING_SNAKE_CASE             =    (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              _SCREAMING_SNAKE_CASE             =    common_inputs["""attention_mask"""].dtype
              _SCREAMING_SNAKE_CASE             =    torch.cat(
                  [common_inputs["""attention_mask"""], torch.ones(A				,	A				,	dtype=A )]				,	dim=1 )
              _SCREAMING_SNAKE_CASE             =    [
                  (torch.zeros(A ), torch.zeros(A )) for _ in range(A )
              ]
          return common_inputs
  
  
      def     snake_case_(	self				,	A				,	A = -1				,	A = -1				,	A = False				,	A = None				,	)	->     Mapping[str, Any]:
          # Copied from OnnxConfig.generate_dummy_inputs
          # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          _SCREAMING_SNAKE_CASE             =    compute_effective_axis_dimension(
              A				,	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
          _SCREAMING_SNAKE_CASE             =    tokenizer.num_special_tokens_to_add(A )
          _SCREAMING_SNAKE_CASE             =    compute_effective_axis_dimension(
              A				,	fixed_dimension=OnnxConfig.default_fixed_sequence				,	num_token_to_add=A )
  
          # Generate dummy inputs according to compute batch and sequence
          _SCREAMING_SNAKE_CASE             =    [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
          _SCREAMING_SNAKE_CASE             =    dict(tokenizer(A				,	return_tensors=A ) )
          return common_inputs
  
  
      def     snake_case_(	self				,	A				,	A = -1				,	A = -1				,	A = False				,	A = None				,	)	->     Mapping[str, Any]:
          if self.task in ["default", "seq2seq-lm"]:
              _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  A				,	batch_size=A				,	seq_length=A				,	is_pair=A				,	framework=A )
  
          elif self.task == "causal-lm":
              _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_causal_lm(
                  A				,	batch_size=A				,	seq_length=A				,	is_pair=A				,	framework=A )
          else:
              _SCREAMING_SNAKE_CASE             =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  A				,	batch_size=A				,	seq_length=A				,	is_pair=A				,	framework=A )
  
          return common_inputs
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A )	->     Any:
          if self.task in ["default", "seq2seq-lm"]:
              _SCREAMING_SNAKE_CASE             =    super()._flatten_past_key_values_(A				,	A				,	A				,	A )
          else:
              _SCREAMING_SNAKE_CASE             =    super(A				,	self )._flatten_past_key_values_(
                  A				,	A				,	A				,	A )
  
  
  
  
  
      def     snake_case_(	self				,	A				,	A )	->     List[str]:
          if direction not in ["inputs", "outputs"]:
              raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given' )
  
          _SCREAMING_SNAKE_CASE             =    """past_key_values""" if direction == """inputs""" else """present"""
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.num_layers
  
          _SCREAMING_SNAKE_CASE             =    """past_encoder_sequence"""
          _SCREAMING_SNAKE_CASE             =    """past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""
  
          for i in range(A ):
              _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: decoder_sequence}
              _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: decoder_sequence}
              _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: encoder_sequence}
              _SCREAMING_SNAKE_CASE             =    {0: """batch""", 2: encoder_sequence}
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  # 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       a_  :
  
      '''simple docstring'''
      UpperCamelCase									=				42
      UpperCamelCase									=				42
  
      # setable values
      UpperCamelCase									=				42
      UpperCamelCase									=				42
      UpperCamelCase									=				None
  
  
      @classmethod
      def     snake_case_(	cls				,	A				,	A				,	A				,	A				,	)	->     List[Any]:
          return cls(
              common=A				,	final_alpha_cumprod=A				,	init_noise_sigma=A				,	timesteps=A				,	)
  
  
  
  
  @dataclass
  class       a_  (       snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				42
  
  
  
  
  class       a_  (       snake_case_   ,					snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				[e.name for e in FlaxKarrasDiffusionSchedulers]
  
      UpperCamelCase									=				42
  
  
      @property
      def     snake_case_(	self )	->     List[Any]:
          return True
  
  
      @register_to_config
      def __init__(	self				,	A = 1000				,	A = 0.0001				,	A = 0.02				,	A = "linear"				,	A = None				,	A = True				,	A = 0				,	A = "epsilon"				,	A = jnp.floataa				,	)	->     int:
          _SCREAMING_SNAKE_CASE             =    dtype
  
  
      def     snake_case_(	self				,	A = None )	->     DDIMSchedulerState:
          if common is None:
              _SCREAMING_SNAKE_CASE             =    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.
          _SCREAMING_SNAKE_CASE             =    (
              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
          _SCREAMING_SNAKE_CASE             =    jnp.array(1.0				,	dtype=self.dtype )
  
          _SCREAMING_SNAKE_CASE             =    jnp.arange(0				,	self.config.num_train_timesteps ).round()[::-1]
  
          return DDIMSchedulerState.create(
              common=A				,	final_alpha_cumprod=A				,	init_noise_sigma=A				,	timesteps=A				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A = None )	->     jnp.ndarray:
          return sample
  
  
      def     snake_case_(	self				,	A				,	A				,	A = () )	->     DDIMSchedulerState:
          _SCREAMING_SNAKE_CASE             =    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
          _SCREAMING_SNAKE_CASE             =    (jnp.arange(0				,	A ) * step_ratio).round()[::-1] + self.config.steps_offset
  
          return state.replace(
              num_inference_steps=A				,	timesteps=A				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A )	->     int:
          _SCREAMING_SNAKE_CASE             =    state.common.alphas_cumprod[timestep]
          _SCREAMING_SNAKE_CASE             =    jnp.where(
              prev_timestep >= 0				,	state.common.alphas_cumprod[prev_timestep]				,	state.final_alpha_cumprod )
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t_prev
  
          _SCREAMING_SNAKE_CASE             =    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
          return variance
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A = 0.0				,	A = True				,	)	->     Union[FlaxDDIMSchedulerOutput, Tuple]:
          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)
          _SCREAMING_SNAKE_CASE             =    timestep - self.config.num_train_timesteps // state.num_inference_steps
  
          _SCREAMING_SNAKE_CASE             =    state.common.alphas_cumprod
          _SCREAMING_SNAKE_CASE             =    state.final_alpha_cumprod
  
          # 2. compute alphas, betas
          _SCREAMING_SNAKE_CASE             =    alphas_cumprod[timestep]
          _SCREAMING_SNAKE_CASE             =    jnp.where(prev_timestep >= 0				,	alphas_cumprod[prev_timestep]				,	A )
  
          _SCREAMING_SNAKE_CASE             =    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":
              _SCREAMING_SNAKE_CASE             =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              _SCREAMING_SNAKE_CASE             =    model_output
          elif self.config.prediction_type == "sample":
              _SCREAMING_SNAKE_CASE             =    model_output
              _SCREAMING_SNAKE_CASE             =    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
              _SCREAMING_SNAKE_CASE             =    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
              _SCREAMING_SNAKE_CASE             =    (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)
          _SCREAMING_SNAKE_CASE             =    self._get_variance(A				,	A				,	A )
          _SCREAMING_SNAKE_CASE             =    eta * variance ** 0.5
  
          # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _SCREAMING_SNAKE_CASE             =    (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
          _SCREAMING_SNAKE_CASE             =    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
          if not return_dict:
              return (prev_sample, state)
  
          return FlaxDDIMSchedulerOutput(prev_sample=A				,	state=A )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	)	->     jnp.ndarray:
          return add_noise_common(state.common				,	A				,	A				,	A )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	)	->     jnp.ndarray:
          return get_velocity_common(state.common				,	A				,	A				,	A )
  
  
  
  
  
      def __len__(	self )	->     Any:
          return self.config.num_train_timesteps
  
  
  
  