


'''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


a__    :   Optional[int]								=			logging.get_logger(__name__)

a__    :   Optional[Any]								=			{
    '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    lowercase_	(       __A  ):
    __UpperCAmelCase       =      'mbart'
    __UpperCAmelCase       =      ['past_key_values']
    __UpperCAmelCase       =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


    def __init__(	self    ,   a=5_02_65    ,   a=10_24    ,   a=12    ,   a=40_96    ,   a=16    ,   a=12    ,   a=40_96    ,   a=16    ,   a=0.0    ,   a=0.0    ,   a=True    ,   a=True    ,   a="gelu"    ,   a=10_24    ,   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=__UpperCAmelCase    ,   bos_token_id=__UpperCAmelCase    ,   eos_token_id=__UpperCAmelCase    ,   is_encoder_decoder=__UpperCAmelCase    ,   forced_eos_token_id=__UpperCAmelCase    ,   **__UpperCAmelCase    ,   )

class    lowercase_	(       __A  ):


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


    def   __a				(	self    ,   a    ,   a = -1    ,   a = -1    ,   a = False    ,   a = None    ,   ):
          UpperCamelCase__      =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              __UpperCAmelCase    ,   __UpperCAmelCase    ,   __UpperCAmelCase    ,   __UpperCAmelCase    ,   __UpperCAmelCase       )

          # Generate decoder inputs
          UpperCamelCase__      =							seq_length if not self.use_past else 1
          UpperCamelCase__      =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              __UpperCAmelCase    ,   __UpperCAmelCase    ,   __UpperCAmelCase    ,   __UpperCAmelCase    ,   __UpperCAmelCase       )
          UpperCamelCase__      =							{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
          UpperCamelCase__      =							dict(**__UpperCAmelCase    ,   **__UpperCAmelCase       )

          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(__UpperCAmelCase    ,   __UpperCAmelCase       )]    ,   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(__UpperCAmelCase    ,   __UpperCAmelCase       )
                UpperCamelCase__      =							max(__UpperCAmelCase    ,   __UpperCAmelCase       ) - min_num_layers
                UpperCamelCase__      =							"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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


    def   __a				(	self    ,   a    ,   a = -1    ,   a = -1    ,   a = False    ,   a = None    ,   ):
          UpperCamelCase__      =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              __UpperCAmelCase    ,   __UpperCAmelCase    ,   __UpperCAmelCase    ,   __UpperCAmelCase    ,   __UpperCAmelCase       )

          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(__UpperCAmelCase    ,   __UpperCAmelCase    ,   dtype=__UpperCAmelCase       )]    ,   dim=1       )
                UpperCamelCase__      =							[
                    (torch.zeros(__UpperCAmelCase       ), torch.zeros(__UpperCAmelCase       )) for _ in range(__UpperCAmelCase       )
                ]
          return common_inputs


    def   __a				(	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(
              __UpperCAmelCase    ,   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(__UpperCAmelCase       )
          UpperCamelCase__      =							compute_effective_axis_dimension(
              __UpperCAmelCase    ,   fixed_dimension=OnnxConfig.default_fixed_sequence    ,   num_token_to_add=__UpperCAmelCase       )

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


    def   __a				(	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(
                    __UpperCAmelCase    ,   batch_size=__UpperCAmelCase    ,   seq_length=__UpperCAmelCase    ,   is_pair=__UpperCAmelCase    ,   framework=__UpperCAmelCase       )

          elif self.task == "causal-lm":
                UpperCamelCase__      =							self._generate_dummy_inputs_for_causal_lm(
                    __UpperCAmelCase    ,   batch_size=__UpperCAmelCase    ,   seq_length=__UpperCAmelCase    ,   is_pair=__UpperCAmelCase    ,   framework=__UpperCAmelCase       )
          else:
                UpperCamelCase__      =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                    __UpperCAmelCase    ,   batch_size=__UpperCAmelCase    ,   seq_length=__UpperCAmelCase    ,   is_pair=__UpperCAmelCase    ,   framework=__UpperCAmelCase       )

          return common_inputs


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






"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase									=	logging.get_logger(__name__)

__lowerCamelCase									=	{
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    str    				=		'vilt'





						def __init__(							self		,__UpperCAmelCase=3_05_22		,__UpperCAmelCase=2		,__UpperCAmelCase=2		,__UpperCAmelCase=40		,__UpperCAmelCase=7_68		,__UpperCAmelCase=12		,__UpperCAmelCase=12		,__UpperCAmelCase=30_72		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=1e-12		,__UpperCAmelCase=3_84		,__UpperCAmelCase=32		,__UpperCAmelCase=3		,__UpperCAmelCase=True		,__UpperCAmelCase=-1		,__UpperCAmelCase=False		,__UpperCAmelCase=-1		,**__UpperCAmelCase		,)       ->					Tuple:
													super().__init__(tie_word_embeddings=__UpperCAmelCase		,**__UpperCAmelCase  )

													A__						  =				vocab_size
													A__						  =				type_vocab_size
													A__						  =				modality_type_vocab_size
													A__						  =				max_position_embeddings

													A__						  =				hidden_size
													A__						  =				num_hidden_layers
													A__						  =				num_attention_heads
													A__						  =				intermediate_size
													A__						  =				hidden_act
													A__						  =				hidden_dropout_prob
													A__						  =				attention_probs_dropout_prob
													A__						  =				initializer_range
													A__						  =				layer_norm_eps

													A__						  =				image_size
													A__						  =				patch_size
													A__						  =				num_channels
													A__						  =				qkv_bias
													A__						  =				max_image_length
													A__						  =				num_images





