




'''simple docstring'''





import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


UpperCamelCase_		:     str          =		logging.get_logger(__name__)

class   _a							(		__lowerCAmelCase				):






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     Optional[int]:
          super().__init__()
          _snake_case				= nn.ModuleList(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = False   ,_SCREAMING_SNAKE_CASE = True   ,)				->     Union[ControlNetOutput, Tuple]:
          for i, (image, scale, controlnet) in enumerate(zip(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,self.nets       )       ):
               _snake_case				,   _snake_case				= controlnet(
                   _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   ,_SCREAMING_SNAKE_CASE   ,)

               # merge samples
               if i == 0:
                    _snake_case				,   _snake_case				= down_samples, mid_sample
               else:
                    _snake_case				= [
                        samples_prev + samples_curr
                        for samples_prev, samples_curr in zip(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
                    ]
                    mid_block_res_sample += mid_sample

          return down_block_res_samples, mid_block_res_sample






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = True   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = False   ,_SCREAMING_SNAKE_CASE = None   ,)				->     Union[str, Any]:
          _snake_case				= 0
          _snake_case				= save_directory
          for controlnet in self.nets:
               controlnet.save_pretrained(
                   _SCREAMING_SNAKE_CASE   ,is_main_process=_SCREAMING_SNAKE_CASE   ,save_function=_SCREAMING_SNAKE_CASE   ,safe_serialization=_SCREAMING_SNAKE_CASE   ,variant=_SCREAMING_SNAKE_CASE   ,)

               idx += 1
               _snake_case				= model_path_to_save + f"""_{idx}"""






     @classmethod
     def 							_lowercase      (					cls   ,_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     Optional[int]:
          _snake_case				= 0
          _snake_case				= []

          # load controlnet and append to list until no controlnet directory exists anymore
          # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
          # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
          _snake_case				= pretrained_model_path
          while os.path.isdir(_SCREAMING_SNAKE_CASE       ):
               _snake_case				= ControlNetModel.from_pretrained(_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )
               controlnets.append(_SCREAMING_SNAKE_CASE       )

               idx += 1
               _snake_case				= pretrained_model_path + f"""_{idx}"""

          logger.info(f"""{len(_SCREAMING_SNAKE_CASE       )} controlnets loaded from {pretrained_model_path}."""       )

          if len(_SCREAMING_SNAKE_CASE       ) == 0:
               raise ValueError(
                   f"""No ControlNets found under {os.path.dirname(_SCREAMING_SNAKE_CASE       )}. Expected at least {pretrained_model_path + '_0'}."""       )

          return cls(_SCREAMING_SNAKE_CASE       )








'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase_		:     Dict          =		{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Union[str, Any]          =		['''RoFormerTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Tuple          =		[
       '''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:
   UpperCamelCase_		:     str          =		[
       '''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:
   UpperCamelCase_		:     Tuple          =		[
       '''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

   UpperCamelCase_		:     Optional[int]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


