





"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional

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


__UpperCamelCase     :						Union[str, Any]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						List[str]						       =  {
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}







class       a							(   a__  ):
 snake_case__             =       '''marian'''
 snake_case__             =       ['''past_key_values''']
 snake_case__             =       {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



 def __init__(     self				,       _snake_case=5_81_01				,       _snake_case=None				,       _snake_case=10_24				,       _snake_case=12				,       _snake_case=40_96				,       _snake_case=16				,       _snake_case=12				,       _snake_case=40_96				,       _snake_case=16				,       _snake_case=0.0				,       _snake_case=0.0				,       _snake_case=True				,       _snake_case=True				,       _snake_case="gelu"				,       _snake_case=10_24				,       _snake_case=0.1				,       _snake_case=0.0				,       _snake_case=0.0				,       _snake_case=0.02				,       _snake_case=5_81_00				,       _snake_case=False				,       _snake_case=5_81_00				,       _snake_case=0				,       _snake_case=0				,       _snake_case=True				,       **_snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= vocab_size
  lowerCAmelCase    				= decoder_vocab_size or vocab_size
  lowerCAmelCase    				= max_position_embeddings
  lowerCAmelCase    				= d_model
  lowerCAmelCase    				= encoder_ffn_dim
  lowerCAmelCase    				= encoder_layers
  lowerCAmelCase    				= encoder_attention_heads
  lowerCAmelCase    				= decoder_ffn_dim
  lowerCAmelCase    				= decoder_layers
  lowerCAmelCase    				= decoder_attention_heads
  lowerCAmelCase    				= dropout
  lowerCAmelCase    				= attention_dropout
  lowerCAmelCase    				= activation_dropout
  lowerCAmelCase    				= activation_function
  lowerCAmelCase    				= init_std
  lowerCAmelCase    				= encoder_layerdrop
  lowerCAmelCase    				= decoder_layerdrop
  lowerCAmelCase    				= use_cache
  lowerCAmelCase    				= encoder_layers
  lowerCAmelCase    				= scale_embedding  # scale factor will be sqrt(d_model) if True
  lowerCAmelCase    				= share_encoder_decoder_embeddings
  super().__init__(
      pad_token_id=_snake_case				,       eos_token_id=_snake_case				,       is_encoder_decoder=_snake_case				,       decoder_start_token_id=_snake_case				,       forced_eos_token_id=_snake_case				,       **_snake_case				,       )







class       a							(   a__  ):



 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  if self.task in ["default", "seq2seq-lm"]:
   lowerCAmelCase    				= OrderedDict(
       [
           ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
           ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
       ]     )

   if self.use_past:
    lowerCAmelCase    				= {0: 'batch'}
    lowerCAmelCase    				= {0: 'batch', 1: 'past_decoder_sequence + sequence'}
   else:
    lowerCAmelCase    				= {0: 'batch', 1: 'decoder_sequence'}
    lowerCAmelCase    				= {0: 'batch', 1: 'decoder_sequence'}

   if self.use_past:
    self.fill_with_past_key_values_(_snake_case				,       direction='inputs'     )
  elif self.task == "causal-lm":
   # TODO: figure this case out.
   lowerCAmelCase    				= OrderedDict(
       [
           ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
           ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
       ]     )
   if self.use_past:
    lowerCAmelCase				,lowerCAmelCase    				= self.num_layers
    for i in range(_snake_case     ):
     lowerCAmelCase    				= {0: 'batch', 2: 'past_sequence + sequence'}
     lowerCAmelCase    				= {0: 'batch', 2: 'past_sequence + sequence'}
  else:
   lowerCAmelCase    				= 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     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = -1				,       _snake_case = -1				,       _snake_case = False				,       _snake_case = None				,       ):







  """simple docstring"""






  lowerCAmelCase    				= self._generate_dummy_inputs_for_encoder_and_decoder(
      _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     )

  # Generate decoder inputs
  lowerCAmelCase    				= seq_length if not self.use_past else 1
  lowerCAmelCase    				= self._generate_dummy_inputs_for_encoder_and_decoder(
      _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     )
  lowerCAmelCase    				= {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
  lowerCAmelCase    				= dict(**_snake_case				,       **_snake_case     )

  if self.use_past:
   if not is_torch_available():
    raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
   else:
    import torch
   lowerCAmelCase				,lowerCAmelCase    				= common_inputs['input_ids'].shape
   lowerCAmelCase    				= common_inputs['decoder_input_ids'].shape[1]
   lowerCAmelCase				,lowerCAmelCase    				= self.num_attention_heads
   lowerCAmelCase    				= (
       batch,
       num_encoder_attention_heads,
       encoder_seq_length,
       self._config.hidden_size // num_encoder_attention_heads,
   )
   lowerCAmelCase    				= decoder_seq_length + 3
   lowerCAmelCase    				= (
       batch,
       num_decoder_attention_heads,
       decoder_past_length,
       self._config.hidden_size // num_decoder_attention_heads,
   )

   lowerCAmelCase    				= torch.cat(
       [common_inputs['decoder_attention_mask'], torch.ones(_snake_case				,       _snake_case     )]				,       dim=1     )

   lowerCAmelCase    				= []
   # If the number of encoder and decoder layers are present in the model configuration, both are considered
   lowerCAmelCase				,lowerCAmelCase    				= self.num_layers
   lowerCAmelCase    				= min(_snake_case				,       _snake_case     )
   lowerCAmelCase    				= max(_snake_case				,       _snake_case     ) - min_num_layers
   lowerCAmelCase    				= 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

   for _ in range(_snake_case     ):
    common_inputs["past_key_values"].append(
        (
            torch.zeros(_snake_case     ),
            torch.zeros(_snake_case     ),
            torch.zeros(_snake_case     ),
            torch.zeros(_snake_case     ),
        )     )
   # TODO: test this.
   lowerCAmelCase    				= encoder_shape if remaining_side_name == 'encoder' else decoder_shape
   for _ in range(_snake_case				,       _snake_case     ):
    common_inputs["past_key_values"].append((torch.zeros(_snake_case     ), torch.zeros(_snake_case     ))     )
  return common_inputs



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = -1				,       _snake_case = -1				,       _snake_case = False				,       _snake_case = None				,       ):







  """simple docstring"""






  lowerCAmelCase    				= self._generate_dummy_inputs_for_encoder_and_decoder(
      _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     )

  if self.use_past:
   if not is_torch_available():
    raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
   else:
    import torch
   lowerCAmelCase				,lowerCAmelCase    				= common_inputs['input_ids'].shape
   # Not using the same length for past_key_values
   lowerCAmelCase    				= seqlen + 2
   lowerCAmelCase				,lowerCAmelCase    				= self.num_layers
   lowerCAmelCase				,lowerCAmelCase    				= self.num_attention_heads
   lowerCAmelCase    				= (
       batch,
       num_encoder_attention_heads,
       past_key_values_length,
       self._config.hidden_size // num_encoder_attention_heads,
   )

   lowerCAmelCase    				= common_inputs['attention_mask'].dtype
   lowerCAmelCase    				= torch.cat(
       [common_inputs['attention_mask'], torch.ones(_snake_case				,       _snake_case				,       dtype=_snake_case     )]				,       dim=1     )
   lowerCAmelCase    				= [
       (torch.zeros(_snake_case     ), torch.zeros(_snake_case     )) for _ in range(_snake_case     )
   ]
  return common_inputs



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = -1				,       _snake_case = -1				,       _snake_case = False				,       _snake_case = None				,       ):







  """simple docstring"""






  lowerCAmelCase    				= compute_effective_axis_dimension(
      _snake_case				,       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
  lowerCAmelCase    				= tokenizer.num_special_tokens_to_add(_snake_case     )
  lowerCAmelCase    				= compute_effective_axis_dimension(
      _snake_case				,       fixed_dimension=OnnxConfig.default_fixed_sequence				,       num_token_to_add=_snake_case     )

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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = -1				,       _snake_case = -1				,       _snake_case = False				,       _snake_case = None				,       ):







  """simple docstring"""






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

  else:
   lowerCAmelCase    				= self._generate_dummy_inputs_for_causal_lm(
       _snake_case				,       batch_size=_snake_case				,       seq_length=_snake_case				,       is_pair=_snake_case				,       framework=_snake_case     )

  return common_inputs



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






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



 @property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return 1E-4









import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE			: str				   =					"scheduler_config.json"






class _lowerCamelCase(		_a  ):
       lowercase_					:      Any       					=     1
       lowercase_					:      Dict       					=     2
       lowercase_					:      Union[str, Any]       					=     3
       lowercase_					:      Tuple       					=     4
       lowercase_					:      Optional[Any]       					=     5






@dataclass
class _lowerCamelCase(		_a  ):
       lowercase_					:      jnp.ndarray






class _lowerCamelCase:
       lowercase_					:      Union[str, Any]       					=     SCHEDULER_CONFIG_NAME
       lowercase_					:      str       					=     ["""dtype"""]
       lowercase_					:      Dict       					=     []
       lowercase_					:      int       					=     True
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase=False, **lowerCamelCase, )			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[int] 						=						cls.load_config(
               pretrained_model_name_or_path=lowerCamelCase, subfolder=lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase, )
           _lowercase						,							_lowercase			:				Tuple 						=						cls.from_config(lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase)

           if hasattr(lowerCamelCase, 'create_state') and getattr(lowerCamelCase, 'has_state', lowerCamelCase):
               _lowercase			:				List[Any] 						=						scheduler.create_state()

           if return_unused_kwargs:
               return scheduler, state, unused_kwargs

           return scheduler, state
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = False, **lowerCamelCase)			->  Any:




           """simple docstring"""

           self.save_config(save_directory=lowerCamelCase, push_to_hub=lowerCamelCase, **lowerCamelCase)
       @property
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return self._get_compatibles()




       @classmethod
       def      UpperCamelCase	(   cls)			->  Any:




           """simple docstring"""

           _lowercase			:				Any 						=						list(set([cls.__name__] + cls._compatibles))
           _lowercase			:				Dict 						=						importlib.import_module(__name__.split('.')[0])
           _lowercase			:				Any 						=						[
               getattr(lowerCamelCase, lowerCamelCase) for c in compatible_classes_str if hasattr(lowerCamelCase, lowerCamelCase)
           ]
           return compatible_classes



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		jnp.ndarray:
    assert len(lowerCamelCase_					) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_					) - x.ndim)					)				,					lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=0.9_99				,					lowerCamelCase_=jnp.floataa					)       ->		jnp.ndarray:

    def alpha_bar(lowerCamelCase_					):
        return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

    _lowercase			:				List[Any] 						=						[]
    for i in range(lowerCamelCase_					):
        _lowercase			:				Any 						=						i / num_diffusion_timesteps
        _lowercase			:				Any 						=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(lowerCamelCase_					) / alpha_bar(lowerCamelCase_					)				,					lowerCamelCase_					)					)
    return jnp.array(lowerCamelCase_				,					dtype=lowerCamelCase_					)






@flax.struct.dataclass
class _lowerCamelCase:
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				int 						=						scheduler.config

           if config.trained_betas is not None:
               _lowercase			:				str 						=						jnp.asarray(config.trained_betas, dtype=scheduler.dtype)
           elif config.beta_schedule == "linear":
               _lowercase			:				List[Any] 						=						jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype)
           elif config.beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _lowercase			:				Dict 						=						(
                   jnp.linspace(
                       config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype)
                   ** 2
               )
           elif config.beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _lowercase			:				Optional[int] 						=						betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype)
           else:
               raise NotImplementedError(
                   F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''')

           _lowercase			:				List[str] 						=						1.0 - betas

           _lowercase			:				Union[str, Any] 						=						jnp.cumprod(lowerCamelCase, axis=0)

           return cls(
               alphas=lowerCamelCase, betas=lowerCamelCase, alphas_cumprod=lowerCamelCase, )



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    _lowercase			:				str 						=						state.alphas_cumprod

    _lowercase			:				str 						=						alphas_cumprod[timesteps] ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_alpha_prod.flatten()
    _lowercase			:				Tuple 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    _lowercase			:				Union[str, Any] 						=						(1 - alphas_cumprod[timesteps]) ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_one_minus_alpha_prod.flatten()
    _lowercase			:				int 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		List[str]:
    _lowercase						,							_lowercase			:				Optional[int] 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				str 						=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Tuple:
    _lowercase						,							_lowercase			:				Tuple 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				List[Any] 						=						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity


