
"""simple docstring"""


from typing import TYPE_CHECKING

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


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

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """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:
						A_    							=					[
						    """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:
						A_    							=					[
						    """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

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block







@dataclass
class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			42







class        __lowerCAmelCase					(			__magic_name__     ,							__magic_name__					):

			"""simple docstring"""







			@register_to_config
			def __init__( self     ,		lowerCamelCase__ = 65_536     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = 2     ,		lowerCamelCase__ = 2     ,		lowerCamelCase__ = 0     ,		lowerCamelCase__ = "fourier"     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = False     ,		lowerCamelCase__ = 0.0     ,		lowerCamelCase__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")     ,		lowerCamelCase__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")     ,		lowerCamelCase__ = "UNetMidBlock1D"     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = (32, 32, 64)     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = 8     ,		lowerCamelCase__ = 1     ,		lowerCamelCase__ = False     ,		)       -> Dict:



							'''simple docstring'''
							super().__init__()
							__lowerCamelCase									=       sample_size

							# time
							if time_embedding_type == "fourier":
											__lowerCamelCase									=       GaussianFourierProjection(
											    embedding_size=8     ,		set_W_to_weight=lowerCamelCase__     ,		log=lowerCamelCase__     ,		flip_sin_to_cos=lowerCamelCase__				)
											__lowerCamelCase									=       2 * block_out_channels[0]
							elif time_embedding_type == "positional":
											__lowerCamelCase									=       Timesteps(
											    block_out_channels[0]     ,		flip_sin_to_cos=lowerCamelCase__     ,		downscale_freq_shift=lowerCamelCase__				)
											__lowerCamelCase									=       block_out_channels[0]

							if use_timestep_embedding:
											__lowerCamelCase									=       block_out_channels[0] * 4
											__lowerCamelCase									=       TimestepEmbedding(
											    in_channels=lowerCamelCase__     ,		time_embed_dim=lowerCamelCase__     ,		act_fn=lowerCamelCase__     ,		out_dim=block_out_channels[0]     ,		)

							__lowerCamelCase									=       nn.ModuleList([]				)
							__lowerCamelCase									=       None
							__lowerCamelCase									=       nn.ModuleList([]				)
							__lowerCamelCase									=       None

							# down
							__lowerCamelCase									=       in_channels
							for i, down_block_type in enumerate(lowerCamelCase__				):
											__lowerCamelCase									=       output_channel
											__lowerCamelCase									=       block_out_channels[i]

											if i == 0:
															input_channel += extra_in_channels

											__lowerCamelCase									=       i == len(lowerCamelCase__				) - 1

											__lowerCamelCase									=       get_down_block(
											    lowerCamelCase__     ,		num_layers=lowerCamelCase__     ,		in_channels=lowerCamelCase__     ,		out_channels=lowerCamelCase__     ,		temb_channels=block_out_channels[0]     ,		add_downsample=not is_final_block or downsample_each_block     ,		)
											self.down_blocks.append(lowerCamelCase__				)

							# mid
							__lowerCamelCase									=       get_mid_block(
							    lowerCamelCase__     ,		in_channels=block_out_channels[-1]     ,		mid_channels=block_out_channels[-1]     ,		out_channels=block_out_channels[-1]     ,		embed_dim=block_out_channels[0]     ,		num_layers=lowerCamelCase__     ,		add_downsample=lowerCamelCase__     ,		)

							# up
							__lowerCamelCase									=       list(reversed(lowerCamelCase__				)				)
							__lowerCamelCase									=       reversed_block_out_channels[0]
							if out_block_type is None:
											__lowerCamelCase									=       out_channels
							else:
											__lowerCamelCase									=       block_out_channels[0]

							for i, up_block_type in enumerate(lowerCamelCase__				):
											__lowerCamelCase									=       output_channel
											__lowerCamelCase									=       (
											    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase__				) - 1 else final_upsample_channels
											)

											__lowerCamelCase									=       i == len(lowerCamelCase__				) - 1

											__lowerCamelCase									=       get_up_block(
											    lowerCamelCase__     ,		num_layers=lowerCamelCase__     ,		in_channels=lowerCamelCase__     ,		out_channels=lowerCamelCase__     ,		temb_channels=block_out_channels[0]     ,		add_upsample=not is_final_block     ,		)
											self.up_blocks.append(lowerCamelCase__				)
											__lowerCamelCase									=       output_channel

							# out
							__lowerCamelCase									=       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     ,		32				)
							__lowerCamelCase									=       get_out_block(
							    out_block_type=lowerCamelCase__     ,		num_groups_out=lowerCamelCase__     ,		embed_dim=block_out_channels[0]     ,		out_channels=lowerCamelCase__     ,		act_fn=lowerCamelCase__     ,		fc_dim=block_out_channels[-1] // 4     ,		)






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = True     ,		)       -> Union[UNetaDOutput, Tuple]:



							'''simple docstring'''
							__lowerCamelCase									=       timestep
							if not torch.is_tensor(lowerCamelCase__				):
											__lowerCamelCase									=       torch.tensor([timesteps]     ,		dtype=torch.long     ,		device=sample.device				)
							elif torch.is_tensor(lowerCamelCase__				) and len(timesteps.shape				) == 0:
											__lowerCamelCase									=       timesteps[None].to(sample.device				)

							__lowerCamelCase									=       self.time_proj(lowerCamelCase__				)
							if self.config.use_timestep_embedding:
											__lowerCamelCase									=       self.time_mlp(lowerCamelCase__				)
							else:
											__lowerCamelCase									=       timestep_embed[..., None]
											__lowerCamelCase									=       timestep_embed.repeat([1, 1, sample.shape[2]]				).to(sample.dtype				)
											__lowerCamelCase									=       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])				)

							# 2. down
							__lowerCamelCase									=       ()
							for downsample_block in self.down_blocks:
											__lowerCamelCase			,		__lowerCamelCase									=       downsample_block(hidden_states=lowerCamelCase__     ,		temb=lowerCamelCase__				)
											down_block_res_samples += res_samples

							# 3. mid
							if self.mid_block:
											__lowerCamelCase									=       self.mid_block(lowerCamelCase__     ,		lowerCamelCase__				)

							# 4. up
							for i, upsample_block in enumerate(self.up_blocks				):
											__lowerCamelCase									=       down_block_res_samples[-1:]
											__lowerCamelCase									=       down_block_res_samples[:-1]
											__lowerCamelCase									=       upsample_block(lowerCamelCase__     ,		res_hidden_states_tuple=lowerCamelCase__     ,		temb=lowerCamelCase__				)

							# 5. post-process
							if self.out_block:
											__lowerCamelCase									=       self.out_block(lowerCamelCase__     ,		lowerCamelCase__				)

							if not return_dict:
											return (sample,)

							return UNetaDOutput(sample=lowerCamelCase__				)


