
import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD

class        _UpperCamelCase				(					nn.Module  ):






			'''simple docstring'''

			__UpperCamelCase      :						Dict					      = 42
			__UpperCamelCase      :						List[str]					      = 42
			__UpperCamelCase      :						List[Any]					      = 0.0
			__UpperCamelCase      :						int					      = 1
			__UpperCamelCase      :						Dict					      = 1
			__UpperCamelCase      :						Tuple					      = True
			__UpperCamelCase      :						Tuple					      = False
			__UpperCamelCase      :						Union[str, Any]					      = False
			__UpperCamelCase      :						str					      = False
			__UpperCamelCase      :						Optional[Any]					      = jnp.floataa

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							str	=						[]
							UpperCamelCase_:							Tuple	=						[]

							for i in range(self.num_layers      ):
											UpperCamelCase_:							Optional[int]	=						self.in_channels if i == 0 else self.out_channels

											UpperCamelCase_:							List[str]	=						FlaxResnetBlockaD(
											    in_channels=snake_case_						,	out_channels=self.out_channels						,	dropout_prob=self.dropout						,	dtype=self.dtype						,	)
											resnets.append(snake_case_      )

											UpperCamelCase_:							str	=						FlaxTransformeraDModel(
											    in_channels=self.out_channels						,	n_heads=self.num_attention_heads						,	d_head=self.out_channels // self.num_attention_heads						,	depth=1						,	use_linear_projection=self.use_linear_projection						,	only_cross_attention=self.only_cross_attention						,	use_memory_efficient_attention=self.use_memory_efficient_attention						,	dtype=self.dtype						,	)
											attentions.append(snake_case_      )

							UpperCamelCase_:							Tuple	=						resnets
							UpperCamelCase_:							Tuple	=						attentions

							if self.add_downsample:
											UpperCamelCase_:							Any	=						FlaxDownsampleaD(self.out_channels						,	dtype=self.dtype      )




			def __call__(       self :       int						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       List[Any]						,	snake_case_ :       str						,	snake_case_ :       Dict=True      ):
							UpperCamelCase_:							Union[str, Any]	=						()

							for resnet, attn in zip(self.resnets						,	self.attentions      ):
											UpperCamelCase_:							str	=						resnet(snake_case_						,	snake_case_						,	deterministic=snake_case_      )
											UpperCamelCase_:							Tuple	=						attn(snake_case_						,	snake_case_						,	deterministic=snake_case_      )
											output_states += (hidden_states,)

							if self.add_downsample:
											UpperCamelCase_:							Union[str, Any]	=						self.downsamplers_a(snake_case_      )
											output_states += (hidden_states,)

							return hidden_states, output_states




class        _UpperCamelCase				(					nn.Module  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[Any]					      = 42
			__UpperCamelCase      :						Optional[int]					      = 42
			__UpperCamelCase      :						int					      = 0.0
			__UpperCamelCase      :						Optional[int]					      = 1
			__UpperCamelCase      :						Dict					      = True
			__UpperCamelCase      :						Union[str, Any]					      = jnp.floataa

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Dict	=						[]

							for i in range(self.num_layers      ):
											UpperCamelCase_:							Optional[int]	=						self.in_channels if i == 0 else self.out_channels

											UpperCamelCase_:							List[Any]	=						FlaxResnetBlockaD(
											    in_channels=snake_case_						,	out_channels=self.out_channels						,	dropout_prob=self.dropout						,	dtype=self.dtype						,	)
											resnets.append(snake_case_      )
							UpperCamelCase_:							Dict	=						resnets

							if self.add_downsample:
											UpperCamelCase_:							int	=						FlaxDownsampleaD(self.out_channels						,	dtype=self.dtype      )




			def __call__(       self :       Union[str, Any]						,	snake_case_ :       List[Any]						,	snake_case_ :       List[Any]						,	snake_case_ :       Dict=True      ):
							UpperCamelCase_:							Optional[Any]	=						()

							for resnet in self.resnets:
											UpperCamelCase_:							Union[str, Any]	=						resnet(snake_case_						,	snake_case_						,	deterministic=snake_case_      )
											output_states += (hidden_states,)

							if self.add_downsample:
											UpperCamelCase_:							Optional[int]	=						self.downsamplers_a(snake_case_      )
											output_states += (hidden_states,)

							return hidden_states, output_states




class        _UpperCamelCase				(					nn.Module  ):






			'''simple docstring'''

			__UpperCamelCase      :						Dict					      = 42
			__UpperCamelCase      :						Any					      = 42
			__UpperCamelCase      :						str					      = 42
			__UpperCamelCase      :						int					      = 0.0
			__UpperCamelCase      :						Union[str, Any]					      = 1
			__UpperCamelCase      :						List[str]					      = 1
			__UpperCamelCase      :						Optional[Any]					      = True
			__UpperCamelCase      :						Any					      = False
			__UpperCamelCase      :						Dict					      = False
			__UpperCamelCase      :						Optional[int]					      = False
			__UpperCamelCase      :						List[str]					      = jnp.floataa

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							Any	=						[]
							UpperCamelCase_:							Tuple	=						[]

							for i in range(self.num_layers      ):
											UpperCamelCase_:							Optional[Any]	=						self.in_channels if (i == self.num_layers - 1) else self.out_channels
											UpperCamelCase_:							Dict	=						self.prev_output_channel if i == 0 else self.out_channels

											UpperCamelCase_:							Dict	=						FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels						,	out_channels=self.out_channels						,	dropout_prob=self.dropout						,	dtype=self.dtype						,	)
											resnets.append(snake_case_      )

											UpperCamelCase_:							str	=						FlaxTransformeraDModel(
											    in_channels=self.out_channels						,	n_heads=self.num_attention_heads						,	d_head=self.out_channels // self.num_attention_heads						,	depth=1						,	use_linear_projection=self.use_linear_projection						,	only_cross_attention=self.only_cross_attention						,	use_memory_efficient_attention=self.use_memory_efficient_attention						,	dtype=self.dtype						,	)
											attentions.append(snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						resnets
							UpperCamelCase_:							Optional[int]	=						attentions

							if self.add_upsample:
											UpperCamelCase_:							str	=						FlaxUpsampleaD(self.out_channels						,	dtype=self.dtype      )




			def __call__(       self :       Any						,	snake_case_ :       str						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       List[Any]						,	snake_case_ :       Optional[int]=True      ):

							for resnet, attn in zip(self.resnets						,	self.attentions      ):
											# pop res hidden states
											UpperCamelCase_:							str	=						res_hidden_states_tuple[-1]
											UpperCamelCase_:							Any	=						res_hidden_states_tuple[:-1]
											UpperCamelCase_:							Optional[int]	=						jnp.concatenate((hidden_states, res_hidden_states)						,	axis=-1      )

											UpperCamelCase_:							Optional[int]	=						resnet(snake_case_						,	snake_case_						,	deterministic=snake_case_      )
											UpperCamelCase_:							int	=						attn(snake_case_						,	snake_case_						,	deterministic=snake_case_      )

							if self.add_upsample:
											UpperCamelCase_:							Optional[int]	=						self.upsamplers_a(snake_case_      )

							return hidden_states




class        _UpperCamelCase				(					nn.Module  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[Any]					      = 42
			__UpperCamelCase      :						str					      = 42
			__UpperCamelCase      :						Optional[Any]					      = 42
			__UpperCamelCase      :						List[Any]					      = 0.0
			__UpperCamelCase      :						str					      = 1
			__UpperCamelCase      :						Dict					      = True
			__UpperCamelCase      :						Any					      = jnp.floataa

			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							str	=						[]

							for i in range(self.num_layers      ):
											UpperCamelCase_:							Dict	=						self.in_channels if (i == self.num_layers - 1) else self.out_channels
											UpperCamelCase_:							List[Any]	=						self.prev_output_channel if i == 0 else self.out_channels

											UpperCamelCase_:							Tuple	=						FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels						,	out_channels=self.out_channels						,	dropout_prob=self.dropout						,	dtype=self.dtype						,	)
											resnets.append(snake_case_      )

							UpperCamelCase_:							List[Any]	=						resnets

							if self.add_upsample:
											UpperCamelCase_:							int	=						FlaxUpsampleaD(self.out_channels						,	dtype=self.dtype      )




			def __call__(       self :       List[str]						,	snake_case_ :       Optional[int]						,	snake_case_ :       List[Any]						,	snake_case_ :       Any						,	snake_case_ :       Union[str, Any]=True      ):

							for resnet in self.resnets:
											# pop res hidden states
											UpperCamelCase_:							str	=						res_hidden_states_tuple[-1]
											UpperCamelCase_:							Any	=						res_hidden_states_tuple[:-1]
											UpperCamelCase_:							str	=						jnp.concatenate((hidden_states, res_hidden_states)						,	axis=-1      )

											UpperCamelCase_:							List[str]	=						resnet(snake_case_						,	snake_case_						,	deterministic=snake_case_      )

							if self.add_upsample:
											UpperCamelCase_:							Optional[int]	=						self.upsamplers_a(snake_case_      )

							return hidden_states




class        _UpperCamelCase				(					nn.Module  ):






			'''simple docstring'''

			__UpperCamelCase      :						str					      = 42
			__UpperCamelCase      :						Dict					      = 0.0
			__UpperCamelCase      :						Optional[Any]					      = 1
			__UpperCamelCase      :						Dict					      = 1
			__UpperCamelCase      :						Optional[Any]					      = False
			__UpperCamelCase      :						List[str]					      = False
			__UpperCamelCase      :						Optional[int]					      = jnp.floataa

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Tuple	=						[
							    FlaxResnetBlockaD(
							        in_channels=self.in_channels						,	out_channels=self.in_channels						,	dropout_prob=self.dropout						,	dtype=self.dtype						,	)
							]

							UpperCamelCase_:							List[Any]	=						[]

							for _ in range(self.num_layers      ):
											UpperCamelCase_:							str	=						FlaxTransformeraDModel(
											    in_channels=self.in_channels						,	n_heads=self.num_attention_heads						,	d_head=self.in_channels // self.num_attention_heads						,	depth=1						,	use_linear_projection=self.use_linear_projection						,	use_memory_efficient_attention=self.use_memory_efficient_attention						,	dtype=self.dtype						,	)
											attentions.append(snake_case_      )

											UpperCamelCase_:							Optional[Any]	=						FlaxResnetBlockaD(
											    in_channels=self.in_channels						,	out_channels=self.in_channels						,	dropout_prob=self.dropout						,	dtype=self.dtype						,	)
											resnets.append(snake_case_      )

							UpperCamelCase_:							Tuple	=						resnets
							UpperCamelCase_:							str	=						attentions




			def __call__(       self :       str						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       Optional[Any]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       Tuple=True      ):
							UpperCamelCase_:							List[str]	=						self.resnets[0](snake_case_						,	snake_case_      )
							for attn, resnet in zip(self.attentions						,	self.resnets[1:]      ):
											UpperCamelCase_:							Optional[Any]	=						attn(snake_case_						,	snake_case_						,	deterministic=snake_case_      )
											UpperCamelCase_:							int	=						resnet(snake_case_						,	snake_case_						,	deterministic=snake_case_      )

							return hidden_states








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__)






