"""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       :						Dict     =   logging.get_logger(__name__)

_UpperCamelCase       :						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 					UpperCAmelCase_     (   SCREAMING_SNAKE_CASE_):
      lowerCamelCase__		:   Tuple			  =  "mbart"
      lowerCamelCase__		:   Union[str, Any]			  =  ["past_key_values"]
      lowerCamelCase__		:   List[str]			  =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


      def __init__(       self		,      a=5_0_2_6_5		,      a=1_0_2_4		,      a=1_2		,      a=4_0_9_6		,      a=1_6		,      a=1_2		,      a=4_0_9_6		,      a=1_6		,      a=0.0		,      a=0.0		,      a=True		,      a=True		,      a="gelu"		,      a=1_0_2_4		,      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		,      )	-> Union[str, Any]:
             lowercase__      :		str          =     vocab_size
             lowercase__      :		Dict          =     max_position_embeddings
             lowercase__      :		str          =     d_model
             lowercase__      :		Optional[Any]          =     encoder_ffn_dim
             lowercase__      :		Optional[int]          =     encoder_layers
             lowercase__      :		Optional[Any]          =     encoder_attention_heads
             lowercase__      :		str          =     decoder_ffn_dim
             lowercase__      :		Optional[int]          =     decoder_layers
             lowercase__      :		Any          =     decoder_attention_heads
             lowercase__      :		List[str]          =     dropout
             lowercase__      :		List[str]          =     attention_dropout
             lowercase__      :		List[Any]          =     activation_dropout
             lowercase__      :		List[Any]          =     activation_function
             lowercase__      :		Tuple          =     init_std
             lowercase__      :		Tuple          =     encoder_layerdrop
             lowercase__      :		List[Any]          =     decoder_layerdrop
             lowercase__      :		List[Any]          =     classifier_dropout
             lowercase__      :		List[Any]          =     use_cache
             lowercase__      :		Any          =     encoder_layers
             lowercase__      :		Any          =     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 		_UpperCAmelCase	(       self							)	-> Mapping[str, Mapping[int, str]]:
             if self.task in ["default", "seq2seq-lm"]:
                    lowercase__      :		Tuple          =     OrderedDict(
                        [
                            ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                            ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                        ]							)

                    if self.use_past:
                           lowercase__      :		Union[str, Any]          =     {0: 'batch'}
                           lowercase__      :		List[str]          =     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
                    else:
                           lowercase__      :		Any          =     {0: 'batch', 1: 'decoder_sequence'}
                           lowercase__      :		Tuple          =     {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.
                    lowercase__      :		Optional[Any]          =     OrderedDict(
                        [
                            ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                            ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                        ]							)
                    if self.use_past:
                           lowercase__   ,				lowercase__      :		Dict          =     self.num_layers
                           for i in range(lowercase_							):
                                  lowercase__      :		List[Any]          =     {0: 'batch', 2: 'past_sequence + sequence'}
                                  lowercase__      :		Any          =     {0: 'batch', 2: 'past_sequence + sequence'}
             else:
                    lowercase__      :		Any          =     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							)	-> Mapping[str, Mapping[int, str]]:
             if self.task in ["default", "seq2seq-lm"]:
                    lowercase__      :		Optional[int]          =     super().outputs
             else:
                    lowercase__      :		Tuple          =     super(lowercase_		,      self							).outputs
                    if self.use_past:
                           lowercase__   ,				lowercase__      :		List[str]          =     self.num_layers
                           for i in range(lowercase_							):
                                  lowercase__      :		int          =     {0: 'batch', 2: 'past_sequence + sequence'}
                                  lowercase__      :		Optional[Any]          =     {0: 'batch', 2: 'past_sequence + sequence'}
             return common_outputs


      def 		_UpperCAmelCase	(       self		,      a		,      a = -1		,      a = -1		,      a = False		,      a = None		,      )	-> Mapping[str, Any]:
             lowercase__      :		int          =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_		,      lowercase_		,      lowercase_		,      lowercase_		,      lowercase_							)

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

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

                    lowercase__      :		Tuple          =     []
                    # If the number of encoder and decoder layers are present in the model configuration, both are considered
                    lowercase__   ,				lowercase__      :		List[str]          =     self.num_layers
                    lowercase__      :		Tuple          =     min(lowercase_		,      lowercase_							)
                    lowercase__      :		Union[str, Any]          =     max(lowercase_		,      lowercase_							) - min_num_layers
                    lowercase__      :		List[str]          =     '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.
                    lowercase__      :		Tuple          =     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 		_UpperCAmelCase	(       self		,      a		,      a = -1		,      a = -1		,      a = False		,      a = None		,      )	-> Mapping[str, Any]:
             lowercase__      :		Any          =     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
                    lowercase__   ,				lowercase__      :		int          =     common_inputs['input_ids'].shape
                    # Not using the same length for past_key_values
                    lowercase__      :		Optional[Any]          =     seqlen + 2
                    lowercase__   ,				lowercase__      :		Any          =     self.num_layers
                    lowercase__   ,				lowercase__      :		int          =     self.num_attention_heads
                    lowercase__      :		str          =     (
                        batch,
                        num_encoder_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )

                    lowercase__      :		Tuple          =     common_inputs['attention_mask'].dtype
                    lowercase__      :		int          =     torch.cat(
                        [common_inputs['attention_mask'], torch.ones(lowercase_		,      lowercase_		,      dtype=lowercase_							)]		,      dim=1							)
                    lowercase__      :		Optional[int]          =     [
                        (torch.zeros(lowercase_							), torch.zeros(lowercase_							)) for _ in range(lowercase_							)
                    ]
             return common_inputs


      def 		_UpperCAmelCase	(       self		,      a		,      a = -1		,      a = -1		,      a = False		,      a = 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
             lowercase__      :		Any          =     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
             lowercase__      :		Any          =     tokenizer.num_special_tokens_to_add(lowercase_							)
             lowercase__      :		str          =     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
             lowercase__      :		Any          =     [' '.join([tokenizer.unk_token]							) * seq_length] * batch_size
             lowercase__      :		str          =     dict(tokenizer(lowercase_		,      return_tensors=lowercase_							)							)
             return common_inputs


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


"""simple docstring"""





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCamelCase       :						int     =   {
    "configuration_retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig"],
    "tokenization_retribert": ["RetriBertTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase       :						Tuple     =   ["RetriBertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase       :						int     =   [
           "RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RetriBertModel",
           "RetriBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
       from .tokenization_retribert import RetriBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_retribert_fast import RetriBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_retribert import (
                  RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RetriBertModel,
                  RetriBertPreTrainedModel,
              )

else:
       import sys

       _UpperCamelCase       :						Tuple     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

