






'''simple docstring'''





import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class   lowerCAmelCase__ ( UpperCamelCase__						,UpperCamelCase__						,UpperCamelCase__  ):

			@register_to_config
			def __init__(					self					:		Optional[int]     ,		UpperCamelCase_					:		int     ,		UpperCamelCase_					:		int     ,		UpperCamelCase_					:		int     ,		UpperCamelCase_					:		float     ,		UpperCamelCase_					:		int     ,		UpperCamelCase_					:		int     ,		UpperCamelCase_					:		int     ,		UpperCamelCase_					:		int     ,		UpperCamelCase_					:		str     ,		UpperCamelCase_					:		bool = False     ,		) ->  Dict:







				"""simple docstring"""






				super().__init__()

				lowerCamelCase_					:  Tuple						=    nn.Embedding(lowercase_     ,		lowercase_	)

				lowerCamelCase_					:  List[str]						=    nn.Embedding(lowercase_     ,		lowercase_	)
				lowerCamelCase_					:  List[Any]						=    False

				lowerCamelCase_					:  List[str]						=    nn.Dropout(p=lowercase_	)

				lowerCamelCase_					:  int						=    TaConfig(
				    vocab_size=lowercase_     ,		d_model=lowercase_     ,		num_heads=lowercase_     ,		d_kv=lowercase_     ,		d_ff=lowercase_     ,		dropout_rate=lowercase_     ,		feed_forward_proj=lowercase_     ,		is_decoder=lowercase_     ,		is_encoder_decoder=lowercase_     ,		)

				lowerCamelCase_					:  List[str]						=    nn.ModuleList()
				for lyr_num in range(lowercase_	):
					lowerCamelCase_					:  List[Any]						=    TaBlock(lowercase_	)
					self.encoders.append(lowercase_	)

				lowerCamelCase_					:  Tuple						=    TaLayerNorm(lowercase_	)
				lowerCamelCase_					:  List[str]						=    nn.Dropout(p=lowercase_	)





			def 						__UpperCamelCase				(					self					:		Union[str, Any]     ,		UpperCamelCase_					:		List[Any]     ,		UpperCamelCase_					:		int	) ->  Optional[Any]:







				"""simple docstring"""






				lowerCamelCase_					:  str						=    self.token_embedder(lowercase_	)

				lowerCamelCase_					:  Tuple						=    encoder_input_tokens.shape[1]
				lowerCamelCase_					:  Dict						=    torch.arange(lowercase_     ,		device=encoder_input_tokens.device	)
				x += self.position_encoding(lowercase_	)

				lowerCamelCase_					:  Optional[Any]						=    self.dropout_pre(lowercase_	)

				# inverted the attention mask
				lowerCamelCase_					:  int						=    encoder_input_tokens.size()
				lowerCamelCase_					:  Any						=    self.get_extended_attention_mask(lowercase_     ,		lowercase_	)

				for lyr in self.encoders:
					lowerCamelCase_					:  List[Any]						=    lyr(lowercase_     ,		lowercase_	)[0]
				lowerCamelCase_					:  int						=    self.layer_norm(lowercase_	)

				return self.dropout_post(lowercase_	), encoder_inputs_mask













'''simple docstring'''

# 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase				:      Union[str, Any]						      ={"configuration_mra": ["MRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MraConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =[
      "MRA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MraForMaskedLM",
      "MraForMultipleChoice",
      "MraForQuestionAnswering",
      "MraForSequenceClassification",
      "MraForTokenClassification",
      "MraLayer",
      "MraModel",
      "MraPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mra import (
        MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraLayer,
        MraModel,
        MraPreTrainedModel,
    )
else:
  import sys

  __lowerCAmelCase				:      Dict						      =_LazyModule(__name__, globals()["__file__"], _import_structure)



