




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


_snake_case   :   List[str]     							=	logging.get_logger(__name__)

class     A					(	__lowercase					):







							def __init__(						self :						Dict							,    lowerCAmelCase_ :						Union[List[ControlNetModel], Tuple[ControlNetModel]]      )    ->							Any:


														"""simple docstring"""


														super().__init__()
														_a			  =       nn.ModuleList(lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Dict							,    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							,    )    ->							Union[ControlNetOutput, Tuple]:


														"""simple docstring"""


														for i, (image, scale, controlnet) in enumerate(zip(lowerCAmelCase_							,    lowerCAmelCase_							,    self.nets      )      ):
																					_a			  =       controlnet(
																					    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    )

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

														return down_block_res_samples, mid_block_res_sample







							def      __lowerCAmelCase    (						self :						Dict							,    lowerCAmelCase_ :						Union[str, os.PathLike]							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Callable = None							,    lowerCAmelCase_ :						bool = False							,    lowerCAmelCase_ :						Optional[str] = None							,    )    ->							List[Any]:


														"""simple docstring"""


														_a			  =       0
														_a			  =       save_directory
														for controlnet in self.nets:
																					controlnet.save_pretrained(
																					    lowerCAmelCase_							,    is_main_process=lowerCAmelCase_							,    save_function=lowerCAmelCase_							,    safe_serialization=lowerCAmelCase_							,    variant=lowerCAmelCase_							,    )

																					idx += 1
																					_a			  =       model_path_to_save + F'_{idx}'







							@classmethod
							def      __lowerCAmelCase    (						cls :						Union[str, Any]							,    lowerCAmelCase_ :						Optional[Union[str, os.PathLike]]							,    **lowerCAmelCase_ :						List[Any]      )    ->							Any:


														"""simple docstring"""


														_a			  =       0
														_a			  =       []

														# 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`, ...
														_a			  =       pretrained_model_path
														while os.path.isdir(lowerCAmelCase_      ):
																					_a			  =       ControlNetModel.from_pretrained(lowerCAmelCase_							,    **lowerCAmelCase_      )
																					controlnets.append(lowerCAmelCase_      )

																					idx += 1
																					_a			  =       pretrained_model_path + F'_{idx}'

														logger.info(F'{len(lowerCAmelCase_      )} controlnets loaded from {pretrained_model_path}.'      )

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

														return cls(lowerCAmelCase_      )







'''simple docstring'''


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 	snake_case_					(UpperCamelCase :						BertModel				,		UpperCamelCase :						str				,		UpperCamelCase :						str   ):





							'''simple docstring'''


							_a			  =       ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

							_a			  =       (
							    ('''layer.''', '''layer_'''),
							    ('''word_embeddings.weight''', '''word_embeddings'''),
							    ('''position_embeddings.weight''', '''position_embeddings'''),
							    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
							    ('''.''', '''/'''),
							    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
							    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
							    ('''weight''', '''kernel'''),
							)

							if not os.path.isdir(UpperCamelCase   ):
														os.makedirs(UpperCamelCase   )

							_a			  =       model.state_dict()

							def to_tf_var_name(UpperCamelCase :						str   ):
														for patt, repl in iter(UpperCamelCase   ):
																					_a			  =       name.replace(UpperCamelCase				,		UpperCamelCase   )
														return f'bert/{name}'

							def create_tf_var(UpperCamelCase :						np.ndarray				,		UpperCamelCase :						str				,		UpperCamelCase :						tf.Session   ):
														_a			  =       tf.dtypes.as_dtype(tensor.dtype   )
														_a			  =       tf.get_variable(dtype=UpperCamelCase				,		shape=tensor.shape				,		name=UpperCamelCase				,		initializer=tf.zeros_initializer()   )
														session.run(tf.variables_initializer([tf_var]   )   )
														session.run(UpperCamelCase   )
														return tf_var

							tf.reset_default_graph()
							with tf.Session() as session:
														for var_name in state_dict:
																					_a			  =       to_tf_var_name(UpperCamelCase   )
																					_a			  =       state_dict[var_name].numpy()
																					if any(x in var_name for x in tensors_to_transpose   ):
																												_a			  =       torch_tensor.T
																					_a			  =       create_tf_var(tensor=UpperCamelCase				,		name=UpperCamelCase				,		session=UpperCamelCase   )
																					tf.keras.backend.set_value(UpperCamelCase				,		UpperCamelCase   )
																					_a			  =       session.run(UpperCamelCase   )
																					print(f'Successfully created {tf_name}: {np.allclose(UpperCamelCase				,		UpperCamelCase   )}'   )

														_a			  =       tf.train.Saver(tf.trainable_variables()   )
														saver.save(UpperCamelCase				,		os.path.join(UpperCamelCase				,		model_name.replace('''-'''				,		'''_'''   ) + '''.ckpt'''   )   )
def 	snake_case_					(UpperCamelCase :						Tuple=None   ):





							'''simple docstring'''


							_a			  =       argparse.ArgumentParser()
							parser.add_argument('''--model_name'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''model name e.g. bert-base-uncased'''   )
							parser.add_argument(
							    '''--cache_dir'''				,		type=UpperCamelCase				,		default=UpperCamelCase				,		required=UpperCamelCase				,		help='''Directory containing pytorch model'''   )
							parser.add_argument('''--pytorch_model_path'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''/path/to/<pytorch-model-name>.bin'''   )
							parser.add_argument('''--tf_cache_dir'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''Directory in which to save tensorflow model'''   )
							_a			  =       parser.parse_args(UpperCamelCase   )

							_a			  =       BertModel.from_pretrained(
							    pretrained_model_name_or_path=args.model_name				,		state_dict=torch.load(args.pytorch_model_path   )				,		cache_dir=args.cache_dir				,		)

							convert_pytorch_checkpoint_to_tf(model=UpperCamelCase				,		ckpt_dir=args.tf_cache_dir				,		model_name=args.model_name   )


if __name__ == "__main__":
				main()

