
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        ={
    """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 							A__		(     __SCREAMING_SNAKE_CASE):
      _UpperCAmelCase    :   Optional[int]   	=      """mbart"""
      _UpperCAmelCase    :   str   	=      ["""past_key_values"""]
      _UpperCAmelCase    :   Tuple   	=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
      def __init__(				self   ,  __magic_name__=5_0_2_6_5   ,  __magic_name__=1_0_2_4   ,  __magic_name__=1_2   ,  __magic_name__=4_0_9_6   ,  __magic_name__=1_6   ,  __magic_name__=1_2   ,  __magic_name__=4_0_9_6   ,  __magic_name__=1_6   ,  __magic_name__=0.0   ,  __magic_name__=0.0   ,  __magic_name__=True   ,  __magic_name__=True   ,  __magic_name__="gelu"   ,  __magic_name__=1_0_2_4   ,  __magic_name__=0.1   ,  __magic_name__=0.0   ,  __magic_name__=0.0   ,  __magic_name__=0.02   ,  __magic_name__=0.0   ,  __magic_name__=False   ,  __magic_name__=1   ,  __magic_name__=0   ,  __magic_name__=2   ,  __magic_name__=2   ,  **__magic_name__   ,  ):
       lowerCamelCase		:     Any          =				vocab_size
       lowerCamelCase		:     str          =				max_position_embeddings
       lowerCamelCase		:     Tuple          =				d_model
       lowerCamelCase		:     Optional[int]          =				encoder_ffn_dim
       lowerCamelCase		:     Union[str, Any]          =				encoder_layers
       lowerCamelCase		:     Tuple          =				encoder_attention_heads
       lowerCamelCase		:     int          =				decoder_ffn_dim
       lowerCamelCase		:     Optional[int]          =				decoder_layers
       lowerCamelCase		:     str          =				decoder_attention_heads
       lowerCamelCase		:     Dict          =				dropout
       lowerCamelCase		:     Tuple          =				attention_dropout
       lowerCamelCase		:     List[Any]          =				activation_dropout
       lowerCamelCase		:     Optional[Any]          =				activation_function
       lowerCamelCase		:     Union[str, Any]          =				init_std
       lowerCamelCase		:     Any          =				encoder_layerdrop
       lowerCamelCase		:     Optional[int]          =				decoder_layerdrop
       lowerCamelCase		:     int          =				classifier_dropout
       lowerCamelCase		:     str          =				use_cache
       lowerCamelCase		:     Any          =				encoder_layers
       lowerCamelCase		:     Optional[int]          =				scale_embedding  # scale factor will be sqrt(d_model) if True
       super().__init__(
           pad_token_id=__magic_name__   ,  bos_token_id=__magic_name__   ,  eos_token_id=__magic_name__   ,  is_encoder_decoder=__magic_name__   ,  forced_eos_token_id=__magic_name__   ,  **__magic_name__   ,  )





class 							A__		(     __SCREAMING_SNAKE_CASE):
      @property
      def 						UpperCamelCase__							(				self				):
       if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase		:     List[str]          =				OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
            ]				)

        if self.use_past:
         lowerCamelCase		:     int          =				{0: """batch"""}
         lowerCamelCase		:     str          =				{0: """batch""", 1: """past_decoder_sequence + sequence"""}
        else:
         lowerCamelCase		:     Optional[Any]          =				{0: """batch""", 1: """decoder_sequence"""}
         lowerCamelCase		:     Optional[int]          =				{0: """batch""", 1: """decoder_sequence"""}

        if self.use_past:
         self.fill_with_past_key_values_(__magic_name__   ,  direction="""inputs"""				)
       elif self.task == "causal-lm":
        # TODO: figure this case out.
        lowerCamelCase		:     Tuple          =				OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
            ]				)
        if self.use_past:
         lowerCamelCase      ,   lowerCamelCase		:     int          =				self.num_layers
         for i in range(__magic_name__				):
          lowerCamelCase		:     Dict          =				{0: """batch""", 2: """past_sequence + sequence"""}
          lowerCamelCase		:     Union[str, Any]          =				{0: """batch""", 2: """past_sequence + sequence"""}
       else:
        lowerCamelCase		:     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				):
       if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase		:     str          =				super().outputs
       else:
        lowerCamelCase		:     Optional[Any]          =				super(__magic_name__   ,  self				).outputs
        if self.use_past:
         lowerCamelCase      ,   lowerCamelCase		:     Tuple          =				self.num_layers
         for i in range(__magic_name__				):
          lowerCamelCase		:     Tuple          =				{0: """batch""", 2: """past_sequence + sequence"""}
          lowerCamelCase		:     int          =				{0: """batch""", 2: """past_sequence + sequence"""}
       return common_outputs
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = -1   ,  __magic_name__ = -1   ,  __magic_name__ = False   ,  __magic_name__ = None   ,  ):
       lowerCamelCase		:     List[str]          =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)

       # Generate decoder inputs
       lowerCamelCase		:     List[Any]          =				seq_length if not self.use_past else 1
       lowerCamelCase		:     Tuple          =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)
       lowerCamelCase		:     Union[str, Any]          =				{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       lowerCamelCase		:     int          =				dict(**__magic_name__   ,  **__magic_name__				)

       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
        else:
         import torch
        lowerCamelCase      ,   lowerCamelCase		:     Tuple          =				common_inputs["""input_ids"""].shape
        lowerCamelCase		:     Tuple          =				common_inputs["""decoder_input_ids"""].shape[1]
        lowerCamelCase      ,   lowerCamelCase		:     Optional[Any]          =				self.num_attention_heads
        lowerCamelCase		:     Optional[int]          =				(
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        lowerCamelCase		:     Dict          =				decoder_seq_length + 3
        lowerCamelCase		:     List[Any]          =				(
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        lowerCamelCase		:     str          =				torch.cat(
            [common_inputs["""decoder_attention_mask"""], torch.ones(__magic_name__   ,  __magic_name__				)]   ,  dim=1				)

        lowerCamelCase		:     Dict          =				[]
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        lowerCamelCase      ,   lowerCamelCase		:     Dict          =				self.num_layers
        lowerCamelCase		:     Optional[int]          =				min(__magic_name__   ,  __magic_name__				)
        lowerCamelCase		:     Dict          =				max(__magic_name__   ,  __magic_name__				) - min_num_layers
        lowerCamelCase		:     List[Any]          =				"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

        for _ in range(__magic_name__				):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(__magic_name__				),
                 torch.zeros(__magic_name__				),
                 torch.zeros(__magic_name__				),
                 torch.zeros(__magic_name__				),
             )				)
        # TODO: test this.
        lowerCamelCase		:     Any          =				encoder_shape if remaining_side_name == """encoder""" else decoder_shape
        for _ in range(__magic_name__   ,  __magic_name__				):
         common_inputs["past_key_values"].append((torch.zeros(__magic_name__				), torch.zeros(__magic_name__				))				)
       return common_inputs
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = -1   ,  __magic_name__ = -1   ,  __magic_name__ = False   ,  __magic_name__ = None   ,  ):
       lowerCamelCase		:     Optional[Any]          =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)

       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
        else:
         import torch
        lowerCamelCase      ,   lowerCamelCase		:     Dict          =				common_inputs["""input_ids"""].shape
        # Not using the same length for past_key_values
        lowerCamelCase		:     Optional[Any]          =				seqlen + 2
        lowerCamelCase      ,   lowerCamelCase		:     List[str]          =				self.num_layers
        lowerCamelCase      ,   lowerCamelCase		:     int          =				self.num_attention_heads
        lowerCamelCase		:     Optional[int]          =				(
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )

        lowerCamelCase		:     int          =				common_inputs["""attention_mask"""].dtype
        lowerCamelCase		:     str          =				torch.cat(
            [common_inputs["""attention_mask"""], torch.ones(__magic_name__   ,  __magic_name__   ,  dtype=__magic_name__				)]   ,  dim=1				)
        lowerCamelCase		:     Union[str, Any]          =				[
            (torch.zeros(__magic_name__				), torch.zeros(__magic_name__				)) for _ in range(__magic_name__				)
        ]
       return common_inputs
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = -1   ,  __magic_name__ = -1   ,  __magic_name__ = False   ,  __magic_name__ = 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
       lowerCamelCase		:     List[str]          =				compute_effective_axis_dimension(
           __magic_name__   ,  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
       lowerCamelCase		:     str          =				tokenizer.num_special_tokens_to_add(__magic_name__				)
       lowerCamelCase		:     List[str]          =				compute_effective_axis_dimension(
           __magic_name__   ,  fixed_dimension=OnnxConfig.default_fixed_sequence   ,  num_token_to_add=__magic_name__				)

       # Generate dummy inputs according to compute batch and sequence
       lowerCamelCase		:     str          =				[""" """.join([tokenizer.unk_token]				) * seq_length] * batch_size
       lowerCamelCase		:     List[Any]          =				dict(tokenizer(__magic_name__   ,  return_tensors=__magic_name__				)				)
       return common_inputs
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = -1   ,  __magic_name__ = -1   ,  __magic_name__ = False   ,  __magic_name__ = None   ,  ):
       if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase		:     Tuple          =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            __magic_name__   ,  batch_size=__magic_name__   ,  seq_length=__magic_name__   ,  is_pair=__magic_name__   ,  framework=__magic_name__				)

       elif self.task == "causal-lm":
        lowerCamelCase		:     Dict          =				self._generate_dummy_inputs_for_causal_lm(
            __magic_name__   ,  batch_size=__magic_name__   ,  seq_length=__magic_name__   ,  is_pair=__magic_name__   ,  framework=__magic_name__				)
       else:
        lowerCamelCase		:     Optional[int]          =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __magic_name__   ,  batch_size=__magic_name__   ,  seq_length=__magic_name__   ,  is_pair=__magic_name__   ,  framework=__magic_name__				)

       return common_inputs






      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase		:     Optional[Any]          =				super()._flatten_past_key_values_(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)
       else:
        lowerCamelCase		:     Optional[Any]          =				super(__magic_name__   ,  self				)._flatten_past_key_values_(
            __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase        ={
    """configuration_tvlt""": ["""TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TvltConfig"""],
    """feature_extraction_tvlt""": ["""TvltFeatureExtractor"""],
    """processing_tvlt""": ["""TvltProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =[
          """TVLT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TvltModel""",
          """TvltForPreTraining""",
          """TvltForAudioVisualClassification""",
          """TvltPreTrainedModel""",
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =["""TvltImageProcessor"""]


if TYPE_CHECKING:
      from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
      from .processing_tvlt import TvltProcessor
      from .feature_extraction_tvlt import TvltFeatureExtractor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tvlt import (
                TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TvltForAudioVisualClassification,
                TvltForPreTraining,
                TvltModel,
                TvltPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_tvlt import TvltImageProcessor


else:
      import sys

      _lowerCamelCase        =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




