






"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_UpperCamelCase     			=  logging.get_logger(__name__)







class 					lowerCamelCase__					(       snake_case ):






    def __init__( self	,*A	,**A					):
          warnings.warn(
              """The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
              """ Please use LayoutLMv3ImageProcessor instead."""	,A	,)
          super().__init__(*A	,**A					)











"""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     			=  logging.get_logger(__name__)

_UpperCamelCase     			=  {
    """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 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			'''mbart'''
    SCREAMING_SNAKE_CASE		=			['''past_key_values''']
    SCREAMING_SNAKE_CASE		=			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






    def __init__( self	,A=50_265	,A=1_024	,A=12	,A=4_096	,A=16	,A=12	,A=4_096	,A=16	,A=0.0	,A=0.0	,A=True	,A=True	,A="gelu"	,A=1_024	,A=0.1	,A=0.0	,A=0.0	,A=0.02	,A=0.0	,A=False	,A=1	,A=0	,A=2	,A=2	,**A	,):
          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=A	,bos_token_id=A	,eos_token_id=A	,is_encoder_decoder=A	,forced_eos_token_id=A	,**A	,)







class 					lowerCamelCase__					(       snake_case ):






    @property
    def 				_UpperCamelCase							( self					):
          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_(A	,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(A					):
                            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 				_UpperCamelCase							( self					):
          if self.task in ["default", "seq2seq-lm"]:
                UpperCAmelCase							=       super().outputs
          else:
                UpperCAmelCase							=       super(A	,self					).outputs
                if self.use_past:
                      UpperCAmelCase			,					UpperCAmelCase							=       self.num_layers
                      for i in range(A					):
                            UpperCAmelCase							=       {0: """batch""", 2: """past_sequence + sequence"""}
                            UpperCAmelCase							=       {0: """batch""", 2: """past_sequence + sequence"""}
          return common_outputs






    def 				_UpperCamelCase							( self	,A	,A = -1	,A = -1	,A = False	,A = None	,):
          UpperCAmelCase							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A	,A	,A	,A	,A					)

          # Generate decoder inputs
          UpperCAmelCase							=       seq_length if not self.use_past else 1
          UpperCAmelCase							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A	,A	,A	,A	,A					)
          UpperCAmelCase							=       {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
          UpperCAmelCase							=       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
                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(A	,A					)]	,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(A	,A					)
                UpperCAmelCase							=       max(A	,A					) - min_num_layers
                UpperCAmelCase							=       """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.
                UpperCAmelCase							=       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 				_UpperCamelCase							( self	,A	,A = -1	,A = -1	,A = False	,A = None	,):
          UpperCAmelCase							=       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
                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(A	,A	,dtype=A					)]	,dim=1					)
                UpperCAmelCase							=       [
                    (torch.zeros(A					), torch.zeros(A					)) for _ in range(A					)
                ]
          return common_inputs






    def 				_UpperCamelCase							( self	,A	,A = -1	,A = -1	,A = False	,A = None	,):
          # 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(
              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
          UpperCAmelCase							=       tokenizer.num_special_tokens_to_add(A					)
          UpperCAmelCase							=       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
          UpperCAmelCase							=       [""" """.join([tokenizer.unk_token]					) * seq_length] * batch_size
          UpperCAmelCase							=       dict(tokenizer(A	,return_tensors=A					)					)
          return common_inputs






    def 				_UpperCamelCase							( self	,A	,A = -1	,A = -1	,A = False	,A = None	,):
          if self.task in ["default", "seq2seq-lm"]:
                UpperCAmelCase							=       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":
                UpperCAmelCase							=       self._generate_dummy_inputs_for_causal_lm(
                    A	,batch_size=A	,seq_length=A	,is_pair=A	,framework=A					)
          else:
                UpperCAmelCase							=       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 				_UpperCamelCase							( self	,A	,A	,A	,A					):
          if self.task in ["default", "seq2seq-lm"]:
                UpperCAmelCase							=       super()._flatten_past_key_values_(A	,A	,A	,A					)
          else:
                UpperCAmelCase							=       super(A	,self					)._flatten_past_key_values_(
                    A	,A	,A	,A					)



