from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE :	Optional[Any]							     =		{
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		SCREAMING_SNAKE_CASE :	str							     =		[
		    """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:
		SCREAMING_SNAKE_CASE :	Any							     =		[
		    """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:
		SCREAMING_SNAKE_CASE :	List[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

		SCREAMING_SNAKE_CASE :	Any							     =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



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    A_ (       a_			,       a_			,       a_ ):

  @register_to_config
  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      float     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							):
   super().__init__()

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  False

   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

   __a       =  TaConfig(
       vocab_size=__SCREAMING_SNAKE_CASE     ,							d_model=__SCREAMING_SNAKE_CASE     ,							num_heads=__SCREAMING_SNAKE_CASE     ,							d_kv=__SCREAMING_SNAKE_CASE     ,							d_ff=__SCREAMING_SNAKE_CASE     ,							dropout_rate=__SCREAMING_SNAKE_CASE     ,							feed_forward_proj=__SCREAMING_SNAKE_CASE     ,							is_decoder=__SCREAMING_SNAKE_CASE     ,							is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,							)

   __a       =  nn.ModuleList()
   for lyr_num in range(__SCREAMING_SNAKE_CASE       ):
    __a       =  TaBlock(__SCREAMING_SNAKE_CASE       )
    self.encoders.append(__SCREAMING_SNAKE_CASE       )

   __a       =  TaLayerNorm(__SCREAMING_SNAKE_CASE       )
   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __a       =  self.token_embedder(__SCREAMING_SNAKE_CASE       )

   __a       =  encoder_input_tokens.shape[1]
   __a       =  torch.arange(__SCREAMING_SNAKE_CASE     ,							device=encoder_input_tokens.device       )
   x += self.position_encoding(__SCREAMING_SNAKE_CASE       )

   __a       =  self.dropout_pre(__SCREAMING_SNAKE_CASE       )

   # inverted the attention mask
   __a       =  encoder_input_tokens.size()
   __a       =  self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   for lyr in self.encoders:
    __a       =  lyr(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )[0]
   __a       =  self.layer_norm(__SCREAMING_SNAKE_CASE       )

   return self.dropout_post(__SCREAMING_SNAKE_CASE       ), encoder_inputs_mask


