
"""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


SCREAMING_SNAKE_CASE_         =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_         =  {
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							Tuple							 =		"mbart"
      __SCREAMING_SNAKE_CASE				:							Union[str, Any]							 =		["past_key_values"]
      __SCREAMING_SNAKE_CASE				:							List[str]							 =		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




      def __init__(							self				,    lowercase_=5_0_2_6_5				,    lowercase_=1_0_2_4				,    lowercase_=1_2				,    lowercase_=4_0_9_6				,    lowercase_=1_6				,    lowercase_=1_2				,    lowercase_=4_0_9_6				,    lowercase_=1_6				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=True				,    lowercase_=True				,    lowercase_="gelu"				,    lowercase_=1_0_2_4				,    lowercase_=0.1				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=0.0_2				,    lowercase_=0.0				,    lowercase_=False				,    lowercase_=1				,    lowercase_=0				,    lowercase_=2				,    lowercase_=2				,    **lowercase_				,    )  ->   Union[str, Any]:
          UpperCAmelCase       =   vocab_size
          UpperCAmelCase       =   max_position_embeddings
          UpperCAmelCase       =   d_model
          UpperCAmelCase       =   encoder_ffn_dim
          UpperCAmelCase       =   encoder_layers
          UpperCAmelCase       =   encoder_attention_heads
          UpperCAmelCase       =   decoder_ffn_dim
          UpperCAmelCase       =   decoder_layers
          UpperCAmelCase       =   decoder_attention_heads
          UpperCAmelCase       =   dropout
          UpperCAmelCase       =   attention_dropout
          UpperCAmelCase       =   activation_dropout
          UpperCAmelCase       =   activation_function
          UpperCAmelCase       =   init_std
          UpperCAmelCase       =   encoder_layerdrop
          UpperCAmelCase       =   decoder_layerdrop
          UpperCAmelCase       =   classifier_dropout
          UpperCAmelCase       =   use_cache
          UpperCAmelCase       =   encoder_layers
          UpperCAmelCase       =   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_				,    forced_eos_token_id=lowercase_				,    **lowercase_				,    )

class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):




      @property
      def       a_ (							self							)  ->   Mapping[str, Mapping[int, str]]:
          if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase       =   OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ]							)

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

              if self.use_past:
                  self.fill_with_past_key_values_(lowercase_				,    direction='inputs'							)
          elif self.task == "causal-lm":
              # TODO: figure this case out.
              UpperCAmelCase       =   OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ]							)
              if self.use_past:
                  UpperCAmelCase     ,							UpperCAmelCase       =   self.num_layers
                  for i in range(lowercase_							):
                      UpperCAmelCase       =   {0: 'batch', 2: 'past_sequence + sequence'}
                      UpperCAmelCase       =   {0: 'batch', 2: 'past_sequence + sequence'}
          else:
              UpperCAmelCase       =   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       a_ (							self							)  ->   Mapping[str, Mapping[int, str]]:
          if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase       =   super().outputs
          else:
              UpperCAmelCase       =   super(lowercase_				,    self							).outputs
              if self.use_past:
                  UpperCAmelCase     ,							UpperCAmelCase       =   self.num_layers
                  for i in range(lowercase_							):
                      UpperCAmelCase       =   {0: 'batch', 2: 'past_sequence + sequence'}
                      UpperCAmelCase       =   {0: 'batch', 2: 'past_sequence + sequence'}
          return common_outputs




      def       a_ (							self				,    lowercase_				,    lowercase_ = -1				,    lowercase_ = -1				,    lowercase_ = False				,    lowercase_ = None				,    )  ->   Mapping[str, Any]:
          UpperCAmelCase       =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)

          # Generate decoder inputs
          UpperCAmelCase       =   seq_length if not self.use_past else 1
          UpperCAmelCase       =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)
          UpperCAmelCase       =   {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
          UpperCAmelCase       =   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
              UpperCAmelCase     ,							UpperCAmelCase       =   common_inputs['input_ids'].shape
              UpperCAmelCase       =   common_inputs['decoder_input_ids'].shape[1]
              UpperCAmelCase     ,							UpperCAmelCase       =   self.num_attention_heads
              UpperCAmelCase       =   (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              UpperCAmelCase       =   decoder_seq_length + 3
              UpperCAmelCase       =   (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              UpperCAmelCase       =   torch.cat(
                  [common_inputs['decoder_attention_mask'], torch.ones(lowercase_				,    lowercase_							)]				,    dim=1							)

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

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




      def       a_ (							self				,    lowercase_				,    lowercase_ = -1				,    lowercase_ = -1				,    lowercase_ = False				,    lowercase_ = None				,    )  ->   Mapping[str, Any]:
          UpperCAmelCase       =   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
              UpperCAmelCase     ,							UpperCAmelCase       =   common_inputs['input_ids'].shape
              # Not using the same length for past_key_values
              UpperCAmelCase       =   seqlen + 2
              UpperCAmelCase     ,							UpperCAmelCase       =   self.num_layers
              UpperCAmelCase     ,							UpperCAmelCase       =   self.num_attention_heads
              UpperCAmelCase       =   (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

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




      def       a_ (							self				,    lowercase_				,    lowercase_ = -1				,    lowercase_ = -1				,    lowercase_ = False				,    lowercase_ = 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
          UpperCAmelCase       =   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
          UpperCAmelCase       =   tokenizer.num_special_tokens_to_add(lowercase_							)
          UpperCAmelCase       =   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
          UpperCAmelCase       =   [' '.join([tokenizer.unk_token]							) * seq_length] * batch_size
          UpperCAmelCase       =   dict(tokenizer(lowercase_				,    return_tensors=lowercase_							)							)
          return common_inputs




      def       a_ (							self				,    lowercase_				,    lowercase_ = -1				,    lowercase_ = -1				,    lowercase_ = False				,    lowercase_ = None				,    )  ->   Mapping[str, Any]:
          if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase       =   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":
              UpperCAmelCase       =   self._generate_dummy_inputs_for_causal_lm(
                  lowercase_				,    batch_size=lowercase_				,    seq_length=lowercase_				,    is_pair=lowercase_				,    framework=lowercase_							)
          else:
              UpperCAmelCase       =   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       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Union[str, Any]:
          if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase       =   super()._flatten_past_key_values_(lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)
          else:
              UpperCAmelCase       =   super(lowercase_				,    self							)._flatten_past_key_values_(
                  lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)








"""simple docstring"""

import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_							):




      @register_to_config
      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = False				,    )  ->   List[Any]:
          super().__init__()

          UpperCAmelCase       =   nn.Linear(lowercase_				,    lowercase_				,    bias=lowercase_							)

          UpperCAmelCase       =   nn.Embedding(lowercase_				,    lowercase_							)
          UpperCAmelCase       =   False

          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)

          UpperCAmelCase       =   TaConfig(
              d_model=lowercase_				,    num_heads=lowercase_				,    d_kv=lowercase_				,    d_ff=lowercase_				,    feed_forward_proj=lowercase_				,    dropout_rate=lowercase_				,    is_decoder=lowercase_				,    is_encoder_decoder=lowercase_				,    )
          UpperCAmelCase       =   nn.ModuleList()
          for lyr_num in range(lowercase_							):
              UpperCAmelCase       =   TaBlock(lowercase_							)
              self.encoders.append(lowercase_							)

          UpperCAmelCase       =   TaLayerNorm(lowercase_							)
          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   self.input_proj(lowercase_							)

          # terminal relative positional encodings
          UpperCAmelCase       =   encoder_inputs.shape[1]
          UpperCAmelCase       =   torch.arange(lowercase_				,    device=encoder_inputs.device							)

          UpperCAmelCase       =   encoder_inputs_mask.sum(-1							)
          UpperCAmelCase       =   torch.roll(input_positions.unsqueeze(0							)				,    tuple(seq_lens.tolist()							)				,    dims=0							)
          x += self.position_encoding(lowercase_							)

          UpperCAmelCase       =   self.dropout_pre(lowercase_							)

          # inverted the attention mask
          UpperCAmelCase       =   encoder_inputs.size()
          UpperCAmelCase       =   self.get_extended_attention_mask(lowercase_				,    lowercase_							)

          for lyr in self.encoders:
              UpperCAmelCase       =   lyr(lowercase_				,    lowercase_							)[0]
          UpperCAmelCase       =   self.layer_norm(lowercase_							)

          return self.dropout_post(lowercase_							), encoder_inputs_mask



