
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        _UpperCamelCase				(					__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase  ):






			'''simple docstring'''

			@register_to_config
			def __init__(       self :       str						,	snake_case_ :       List[Any]						,	snake_case_ :       List[Any]						,	snake_case_ :       Optional[Any]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       str						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       int						,	snake_case_ :       int						,	snake_case_ :       int						,	snake_case_ :       Optional[Any] = False						,	):
							super().__init__()

							UpperCamelCase_:							List[str]	=						nn.Embedding(lowerCAmelCase_						,	lowerCAmelCase_      )

							UpperCamelCase_:							Optional[Any]	=						nn.Embedding(lowerCAmelCase_						,	lowerCAmelCase_      )
							UpperCamelCase_:							str	=						False

							UpperCamelCase_:							List[str]	=						nn.Dropout(p=lowerCAmelCase_      )

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

							UpperCamelCase_:							Dict	=						nn.ModuleList()
							for lyr_num in range(lowerCAmelCase_      ):
											UpperCamelCase_:							Any	=						TaBlock(lowerCAmelCase_      )
											self.encoders.append(lowerCAmelCase_      )

							UpperCamelCase_:							List[Any]	=						TaLayerNorm(lowerCAmelCase_      )
							UpperCamelCase_:							Tuple	=						nn.Dropout(p=lowerCAmelCase_      )




			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Optional[int]						,	snake_case_ :       List[Any]      ):
							UpperCamelCase_:							Tuple	=						self.token_embedder(lowerCAmelCase_      )

							UpperCamelCase_:							List[str]	=						encoder_input_tokens.shape[1]
							UpperCamelCase_:							List[Any]	=						torch.arange(lowerCAmelCase_						,	device=encoder_input_tokens.device      )
							x += self.position_encoding(lowerCAmelCase_      )

							UpperCamelCase_:							Union[str, Any]	=						self.dropout_pre(lowerCAmelCase_      )

							# inverted the attention mask
							UpperCamelCase_:							str	=						encoder_input_tokens.size()
							UpperCamelCase_:							str	=						self.get_extended_attention_mask(lowerCAmelCase_						,	lowerCAmelCase_      )

							for lyr in self.encoders:
											UpperCamelCase_:							str	=						lyr(lowerCAmelCase_						,	lowerCAmelCase_      )[0]
							UpperCamelCase_:							Optional[Any]	=						self.layer_norm(lowerCAmelCase_      )

							return self.dropout_post(lowerCAmelCase_      ), encoder_inputs_mask








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_						: str         =  {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Union[str, Any]         =  ["""RoFormerTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Any         =  [
							    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RoFormerForCausalLM""",
							    """RoFormerForMaskedLM""",
							    """RoFormerForMultipleChoice""",
							    """RoFormerForQuestionAnswering""",
							    """RoFormerForSequenceClassification""",
							    """RoFormerForTokenClassification""",
							    """RoFormerLayer""",
							    """RoFormerModel""",
							    """RoFormerPreTrainedModel""",
							    """load_tf_weights_in_roformer""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Dict         =  [
							    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRoFormerForCausalLM""",
							    """TFRoFormerForMaskedLM""",
							    """TFRoFormerForMultipleChoice""",
							    """TFRoFormerForQuestionAnswering""",
							    """TFRoFormerForSequenceClassification""",
							    """TFRoFormerForTokenClassification""",
							    """TFRoFormerLayer""",
							    """TFRoFormerModel""",
							    """TFRoFormerPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[Any]         =  [
							    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """FlaxRoFormerForMaskedLM""",
							    """FlaxRoFormerForMultipleChoice""",
							    """FlaxRoFormerForQuestionAnswering""",
							    """FlaxRoFormerForSequenceClassification""",
							    """FlaxRoFormerForTokenClassification""",
							    """FlaxRoFormerModel""",
							    """FlaxRoFormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
							from .tokenization_roformer import RoFormerTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_roformer_fast import RoFormerTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roformer import (
														    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RoFormerForCausalLM,
														    RoFormerForMaskedLM,
														    RoFormerForMultipleChoice,
														    RoFormerForQuestionAnswering,
														    RoFormerForSequenceClassification,
														    RoFormerForTokenClassification,
														    RoFormerLayer,
														    RoFormerModel,
														    RoFormerPreTrainedModel,
														    load_tf_weights_in_roformer,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roformer import (
														    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRoFormerForCausalLM,
														    TFRoFormerForMaskedLM,
														    TFRoFormerForMultipleChoice,
														    TFRoFormerForQuestionAnswering,
														    TFRoFormerForSequenceClassification,
														    TFRoFormerForTokenClassification,
														    TFRoFormerLayer,
														    TFRoFormerModel,
														    TFRoFormerPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roformer import (
														    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaxRoFormerForMaskedLM,
														    FlaxRoFormerForMultipleChoice,
														    FlaxRoFormerForQuestionAnswering,
														    FlaxRoFormerForSequenceClassification,
														    FlaxRoFormerForTokenClassification,
														    FlaxRoFormerModel,
														    FlaxRoFormerPreTrainedModel,
														)


else:
							import sys

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






