





from typing import TYPE_CHECKING

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


snake_case_           =     {
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =     [
        'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ResNetForImageClassification',
        'ResNetModel',
        'ResNetPreTrainedModel',
        'ResNetBackbone',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =     [
        'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFResNetForImageClassification',
        'TFResNetModel',
        'TFResNetPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =     [
        'FlaxResNetForImageClassification',
        'FlaxResNetModel',
        'FlaxResNetPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_resnet import (
            RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            ResNetBackbone,
            ResNetForImageClassification,
            ResNetModel,
            ResNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_resnet import (
            TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFResNetForImageClassification,
            TFResNetModel,
            TFResNetPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
    import sys

    snake_case_           =     _LazyModule(__name__, globals()['__file__'], _import_structure)






"""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       UpperCamelCase__			(   _lowerCAmelCase       ,		_lowerCAmelCase       ,		_lowerCAmelCase      ):

			"""simple docstring"""


			@register_to_config
			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = False						,	)      ->	Union[str, Any]:
				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   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	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




