



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


__lowerCamelCase								=   logging.get_logger(__name__)

__lowerCamelCase								=   {
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     '''bigbird_pegasus'''
 lowerCamelCase_       =     ['''past_key_values''']
 lowerCamelCase_       =     {
     '''num_attention_heads''': '''encoder_attention_heads''',
     '''hidden_size''': '''d_model''',
     '''attention_probs_dropout_prob''': '''attention_dropout''',
 }
 def __init__(  self		,      a=9_6_1_0_3		,      a=4_0_9_6		,      a=1_6		,      a=4_0_9_6		,      a=1_6		,      a=1_6		,      a=4_0_9_6		,      a=1_6		,      a=0.0		,      a=0.0		,      a=True		,      a=True		,      a="gelu_new"		,      a=1_0_2_4		,      a=0.1		,      a=0.0		,      a=0.0		,      a=0.02		,      a=2		,      a=0.0		,      a=True		,      a=0		,      a=2		,      a=1		,      a="block_sparse"		,      a=6_4		,      a=3		,      a=False		,      **a		,      )		->							List[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          =      classifier_dropout
   _A          =      use_cache
   _A          =      encoder_layers
   _A          =      scale_embedding  # scale factor will be sqrt(d_model) if True

   # extra config
   _A          =      attention_type
   _A          =      block_size
   _A          =      num_random_blocks
   _A          =      use_bias

   super().__init__(
       pad_token_id=a		,      bos_token_id=a		,      eos_token_id=a		,      is_encoder_decoder=a		,      decoder_start_token_id=a		,      **a		,      )







class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 @property
 def lowercase_						(  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_(a		,      direction='''inputs'''   )
   elif self.task == "causal-lm":
     # TODO: figure this case out.
     _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(a   ):
         _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
 def lowercase_						(  self   )		->							Mapping[str, Mapping[int, str]]:




   """simple docstring"""







   if self.task in ["default", "seq2seq-lm"]:
     _A          =      super().outputs
   else:
     _A          =      super(a		,      self   ).outputs
     if self.use_past:
       _A      ,				_A          =      self.num_layers
       for i in range(a   ):
         _A          =      {0: '''batch''', 2: '''past_sequence + sequence'''}
         _A          =      {0: '''batch''', 2: '''past_sequence + sequence'''}
   return common_outputs
 def lowercase_						(  self		,      a		,      a = -1		,      a = -1		,      a = False		,      a = None		,      )		->							Mapping[str, Any]:




   """simple docstring"""







   _A          =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
       a		,      a		,      a		,      a		,      a   )

   # Generate decoder inputs
   _A          =      seq_length if not self.use_past else 1
   _A          =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
       a		,      a		,      a		,      a		,      a   )
   _A          =      {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
   _A          =      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
     _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 + 3
     _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(a		,      a   )]		,      dim=1   )

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

     for _ in range(a   ):
       common_inputs["past_key_values"].append(
           (
               torch.zeros(a   ),
               torch.zeros(a   ),
               torch.zeros(a   ),
               torch.zeros(a   ),
           )   )
     # TODO: test this.
     _A          =      encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
     for _ in range(a		,      a   ):
       common_inputs["past_key_values"].append((torch.zeros(a   ), torch.zeros(a   ))   )
   return common_inputs
 def lowercase_						(  self		,      a		,      a = -1		,      a = -1		,      a = False		,      a = None		,      )		->							Mapping[str, Any]:




   """simple docstring"""







   _A          =      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
     _A      ,				_A          =      common_inputs['''input_ids'''].shape
     # Not using the same length for past_key_values
     _A          =      seqlen + 2
     _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(a		,      a		,      dtype=a   )]		,      dim=1   )
     _A          =      [
         (torch.zeros(a   ), torch.zeros(a   )) for _ in range(a   )
     ]
   return common_inputs
 def lowercase_						(  self		,      a		,      a = -1		,      a = -1		,      a = False		,      a = None		,      )		->							Mapping[str, Any]:




   """simple docstring"""







   _A          =      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
   _A          =      tokenizer.num_special_tokens_to_add(a   )
   _A          =      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
   _A          =      [''' '''.join([tokenizer.unk_token]   ) * seq_length] * batch_size
   _A          =      dict(tokenizer(a		,      return_tensors=a   )   )
   return common_inputs
 def lowercase_						(  self		,      a		,      a = -1		,      a = -1		,      a = False		,      a = None		,      )		->							Mapping[str, Any]:




   """simple docstring"""







   if self.task in ["default", "seq2seq-lm"]:
     _A          =      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":
     _A          =      self._generate_dummy_inputs_for_causal_lm(
         a		,      batch_size=a		,      seq_length=a		,      is_pair=a		,      framework=a   )
   else:
     _A          =      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 lowercase_						(  self		,      a		,      a		,      a		,      a   )		->							List[str]:




   """simple docstring"""







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




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     (EulerAncestralDiscreteScheduler,)
 lowerCamelCase_       =     1_0
 def lowercase_						(  self		,      **a   )		->							Tuple:




   """simple docstring"""







   _A          =      {
       '''num_train_timesteps''': 1_1_0_0,
       '''beta_start''': 0.0_001,
       '''beta_end''': 0.02,
       '''beta_schedule''': '''linear''',
   }

   config.update(**a   )
   return config
 def lowercase_						(  self   )		->							Dict:




   """simple docstring"""







   for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
     self.check_over_configs(num_train_timesteps=a   )
 def lowercase_						(  self   )		->							List[str]:




   """simple docstring"""







   for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,      [0.0_002, 0.002, 0.02]   ):
     self.check_over_configs(beta_start=a		,      beta_end=a   )
 def lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







   for schedule in ["linear", "scaled_linear"]:
     self.check_over_configs(beta_schedule=a   )
 def lowercase_						(  self   )		->							List[Any]:




   """simple docstring"""







   for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=a   )
 def lowercase_						(  self   )		->							Union[str, Any]:




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config()
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps   )

   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   _A          =      sample.to(a   )

   for i, t in enumerate(scheduler.timesteps   ):
     _A          =      scheduler.scale_model_input(a		,      a   )

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 152.3_192   ) < 1e-2
   assert abs(result_mean.item() - 0.1_983   ) < 1e-3
 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config(prediction_type='''v_prediction'''   )
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps   )

   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma
   _A          =      sample.to(a   )

   for i, t in enumerate(scheduler.timesteps   ):
     _A          =      scheduler.scale_model_input(a		,      a   )

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 108.4_439   ) < 1e-2
   assert abs(result_mean.item() - 0.1_412   ) < 1e-3



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




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config()
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps		,      device=a   )
   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   _A          =      sample.to(a   )

   for t in scheduler.timesteps:
     _A          =      scheduler.scale_model_input(a		,      a   )

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 152.3_192   ) < 1e-2
   assert abs(result_mean.item() - 0.1_983   ) < 1e-3