



'''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


lowerCAmelCase_     :       Dict								=						logging.get_logger(__name__)
class 	lowerCamelCase_							(					__lowerCamelCase			):







      def __init__(  self   :      Any				,    lowerCAmelCase__   :      Union[List[ControlNetModel], Tuple[ControlNetModel]]				):

             """simple docstring"""
             super().__init__()
             SCREAMING_SNAKE_CASE					:      Tuple     					=			nn.ModuleList(UpperCamelCase_				)







      def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      torch.FloatTensor				,    lowerCAmelCase__   :      Union[torch.Tensor, float, int]				,    lowerCAmelCase__   :      torch.Tensor				,    lowerCAmelCase__   :      List[torch.tensor]				,    lowerCAmelCase__   :      List[float]				,    lowerCAmelCase__   :      Optional[torch.Tensor] = None				,    lowerCAmelCase__   :      Optional[torch.Tensor] = None				,    lowerCAmelCase__   :      Optional[torch.Tensor] = None				,    lowerCAmelCase__   :      Optional[Dict[str, Any]] = None				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = True				,    ):

             """simple docstring"""
             for i, (image, scale, controlnet) in enumerate(zip(UpperCamelCase_				,    UpperCamelCase_				,    self.nets				)				):
                    SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[int]     					=			controlnet(
                        UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    )

                    # merge samples
                    if i == 0:
                           SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[Any]     					=			down_samples, mid_sample
                    else:
                           SCREAMING_SNAKE_CASE					:      int     					=			[
                               samples_prev + samples_curr
                               for samples_prev, samples_curr in zip(UpperCamelCase_				,    UpperCamelCase_				)
                           ]
                           mid_block_res_sample += mid_sample

             return down_block_res_samples, mid_block_res_sample







      def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Union[str, os.PathLike]				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Callable = None				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      Optional[str] = None				,    ):

             """simple docstring"""
             SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			0
             SCREAMING_SNAKE_CASE					:      Optional[int]     					=			save_directory
             for controlnet in self.nets:
                    controlnet.save_pretrained(
                        UpperCamelCase_				,    is_main_process=UpperCamelCase_				,    save_function=UpperCamelCase_				,    safe_serialization=UpperCamelCase_				,    variant=UpperCamelCase_				,    )

                    idx += 1
                    SCREAMING_SNAKE_CASE					:      Dict     					=			model_path_to_save + F"""_{idx}"""







      @classmethod
      def 					__lowercase    (  cls   :      Optional[int]				,    lowerCAmelCase__   :      Optional[Union[str, os.PathLike]]				,    **lowerCAmelCase__   :      Any				):

             """simple docstring"""
             SCREAMING_SNAKE_CASE					:      Dict     					=			0
             SCREAMING_SNAKE_CASE					:      Tuple     					=			[]

             # 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`, ...
             SCREAMING_SNAKE_CASE					:      Dict     					=			pretrained_model_path
             while os.path.isdir(UpperCamelCase_				):
                    SCREAMING_SNAKE_CASE					:      Tuple     					=			ControlNetModel.from_pretrained(UpperCamelCase_				,    **UpperCamelCase_				)
                    controlnets.append(UpperCamelCase_				)

                    idx += 1
                    SCREAMING_SNAKE_CASE					:      Optional[int]     					=			pretrained_model_path + F"""_{idx}"""

             logger.info(F"""{len(UpperCamelCase_				)} controlnets loaded from {pretrained_model_path}."""				)

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

             return cls(UpperCamelCase_				)









'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCAmelCase_     :       str								=						{
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Dict								=						['LlamaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       str								=						['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Optional[int]								=						[
							    'LlamaForCausalLM',
							    'LlamaModel',
							    'LlamaPreTrainedModel',
							    'LlamaForSequenceClassification',
							]


if TYPE_CHECKING:
							from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama import LlamaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama_fast import LlamaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
							import sys

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




