

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_a	 =     logging.get_logger(__name__)






class 							A_				(	snake_case__      ):
     _lowercase    :  Any          =			'mt5'
     _lowercase    :  List[str]          =			['past_key_values']


     def __init__(							self    :	Dict    ,						UpperCAmelCase    :	str=2_5_0_1_1_2    ,						UpperCAmelCase    :	Optional[Any]=5_1_2    ,						UpperCAmelCase    :	Optional[int]=6_4    ,						UpperCAmelCase    :	Optional[Any]=1_0_2_4    ,						UpperCAmelCase    :	int=8    ,						UpperCAmelCase    :	int=None    ,						UpperCAmelCase    :	int=6    ,						UpperCAmelCase    :	Optional[Any]=3_2    ,						UpperCAmelCase    :	Dict=1_2_8    ,						UpperCAmelCase    :	List[Any]=0.1    ,						UpperCAmelCase    :	Union[str, Any]=1E-6    ,						UpperCAmelCase    :	Union[str, Any]=1.0    ,						UpperCAmelCase    :	Tuple="gated-gelu"    ,						UpperCAmelCase    :	str=True    ,						UpperCAmelCase    :	Optional[int]=True    ,						UpperCAmelCase    :	List[Any]="T5Tokenizer"    ,						UpperCAmelCase    :	List[str]=False    ,						UpperCAmelCase    :	Any=0    ,						UpperCAmelCase    :	Dict=1    ,						UpperCAmelCase    :	Tuple=0    ,						**UpperCAmelCase    :	List[str]    ,						)							->			Dict:
      super().__init__(
          is_encoder_decoder=UpperCAmelCase    ,						tokenizer_class=UpperCAmelCase    ,						tie_word_embeddings=UpperCAmelCase    ,						pad_token_id=UpperCAmelCase    ,						eos_token_id=UpperCAmelCase    ,						decoder_start_token_id=UpperCAmelCase    ,						**UpperCAmelCase    ,						)
      __lowerCAmelCase:     Tuple     			=   vocab_size
      __lowerCAmelCase:     Optional[int]     			=   d_model
      __lowerCAmelCase:     Tuple     			=   d_kv
      __lowerCAmelCase:     Optional[Any]     			=   d_ff
      __lowerCAmelCase:     Dict     			=   num_layers
      __lowerCAmelCase:     Any     			=   (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      __lowerCAmelCase:     str     			=   num_heads
      __lowerCAmelCase:     int     			=   relative_attention_num_buckets
      __lowerCAmelCase:     Optional[int]     			=   relative_attention_max_distance
      __lowerCAmelCase:     Optional[Any]     			=   dropout_rate
      __lowerCAmelCase:     str     			=   layer_norm_epsilon
      __lowerCAmelCase:     str     			=   initializer_factor
      __lowerCAmelCase:     Any     			=   feed_forward_proj
      __lowerCAmelCase:     int     			=   use_cache

      __lowerCAmelCase:     Dict     			=   self.feed_forward_proj.split('-'      )
      __lowerCAmelCase:     Any     			=   act_info[-1]
      __lowerCAmelCase:     int     			=   act_info[0] == 'gated'

      if len(UpperCAmelCase      ) > 1 and act_info[0] != "gated" or len(UpperCAmelCase      ) > 2:
       raise ValueError(
           F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
           'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
           '\'gated-gelu\' or \'relu\''      )

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
       __lowerCAmelCase:     str     			=   'gelu_new'


     @property
     def 						UpperCAmelCase						(							self    :	int      )							->			Tuple:
      return self.d_model


     @property
     def 						UpperCAmelCase						(							self    :	int      )							->			int:
      return self.num_heads


     @property
     def 						UpperCAmelCase						(							self    :	Optional[Any]      )							->			List[str]:
      return self.num_layers







class 							A_				(	snake_case__      ):


     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			Mapping[str, Mapping[int, str]]:
      __lowerCAmelCase:     Dict     			=   {
          'input_ids': {0: 'batch', 1: 'encoder_sequence'},
          'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
      }
      if self.use_past:
       __lowerCAmelCase:     Tuple     			=   'past_encoder_sequence + sequence'
       __lowerCAmelCase:     Optional[Any]     			=   {0: 'batch'}
       __lowerCAmelCase:     Tuple     			=   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
      else:
       __lowerCAmelCase:     List[str]     			=   {0: 'batch', 1: 'decoder_sequence'}
       __lowerCAmelCase:     int     			=   {0: 'batch', 1: 'decoder_sequence'}

      if self.use_past:
       self.fill_with_past_key_values_(UpperCAmelCase    ,						direction='inputs'      )

      return common_inputs


     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def 						UpperCAmelCase						(							self    :	int      )							->			int:
      return 1_3


     @property
     def 						UpperCAmelCase						(							self    :	str      )							->			float:
      return 5E-4








# 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,
)


_a	 =     {
    '''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:
  _a	 =     [
      '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TvltModel''',
      '''TvltForPreTraining''',
      '''TvltForAudioVisualClassification''',
      '''TvltPreTrainedModel''',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''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

  _a	 =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





