






"""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 						A_							(      snake_case_					,							snake_case_					,							snake_case_  ):







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

										__magic_name__            =     nn.Embedding(__lowerCamelCase						,    __lowerCamelCase	)

										__magic_name__            =     nn.Embedding(__lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     False

										__magic_name__            =     nn.Dropout(p=__lowerCamelCase	)

										__magic_name__            =     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						,    )

										__magic_name__            =     nn.ModuleList()
										for lyr_num in range(__lowerCamelCase	):
																__magic_name__            =     TaBlock(__lowerCamelCase	)
																self.encoders.append(__lowerCamelCase	)

										__magic_name__            =     TaLayerNorm(__lowerCamelCase	)
										__magic_name__            =     nn.Dropout(p=__lowerCamelCase	)







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						Dict	)				->					Any:
										__magic_name__            =     self.token_embedder(__lowerCamelCase	)

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

										__magic_name__            =     self.dropout_pre(__lowerCamelCase	)

										# inverted the attention mask
										__magic_name__            =     encoder_input_tokens.size()
										__magic_name__            =     self.get_extended_attention_mask(__lowerCamelCase						,    __lowerCamelCase	)

										for lyr in self.encoders:
																__magic_name__            =     lyr(__lowerCamelCase						,    __lowerCamelCase	)[0]
										__magic_name__            =     self.layer_norm(__lowerCamelCase	)

										return self.dropout_post(__lowerCamelCase	), encoder_inputs_mask











"""simple docstring"""

from typing import TYPE_CHECKING

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


lowercase    			=       {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       ['''XLMRobertaTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       ['''XLMRobertaTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMRobertaForCausalLM''',
			    '''XLMRobertaForMaskedLM''',
			    '''XLMRobertaForMultipleChoice''',
			    '''XLMRobertaForQuestionAnswering''',
			    '''XLMRobertaForSequenceClassification''',
			    '''XLMRobertaForTokenClassification''',
			    '''XLMRobertaModel''',
			    '''XLMRobertaPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMRobertaForCausalLM''',
			    '''TFXLMRobertaForMaskedLM''',
			    '''TFXLMRobertaForMultipleChoice''',
			    '''TFXLMRobertaForQuestionAnswering''',
			    '''TFXLMRobertaForSequenceClassification''',
			    '''TFXLMRobertaForTokenClassification''',
			    '''TFXLMRobertaModel''',
			    '''TFXLMRobertaPreTrainedModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FlaxXLMRobertaForMaskedLM''',
			    '''FlaxXLMRobertaForCausalLM''',
			    '''FlaxXLMRobertaForMultipleChoice''',
			    '''FlaxXLMRobertaForQuestionAnswering''',
			    '''FlaxXLMRobertaForSequenceClassification''',
			    '''FlaxXLMRobertaForTokenClassification''',
			    '''FlaxXLMRobertaModel''',
			    '''FlaxXLMRobertaPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta import (
			    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaConfig,
			    XLMRobertaOnnxConfig,
			)

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta import XLMRobertaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta import (
						    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaForCausalLM,
						    XLMRobertaForMaskedLM,
						    XLMRobertaForMultipleChoice,
						    XLMRobertaForQuestionAnswering,
						    XLMRobertaForSequenceClassification,
						    XLMRobertaForTokenClassification,
						    XLMRobertaModel,
						    XLMRobertaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm_roberta import (
						    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMRobertaForCausalLM,
						    TFXLMRobertaForMaskedLM,
						    TFXLMRobertaForMultipleChoice,
						    TFXLMRobertaForQuestionAnswering,
						    TFXLMRobertaForSequenceClassification,
						    TFXLMRobertaForTokenClassification,
						    TFXLMRobertaModel,
						    TFXLMRobertaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xlm_roberta import (
						    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxXLMRobertaForCausalLM,
						    FlaxXLMRobertaForMaskedLM,
						    FlaxXLMRobertaForMultipleChoice,
						    FlaxXLMRobertaForQuestionAnswering,
						    FlaxXLMRobertaForSequenceClassification,
						    FlaxXLMRobertaForTokenClassification,
						    FlaxXLMRobertaModel,
						    FlaxXLMRobertaPreTrainedModel,
						)

else:
			import sys

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



