



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__						:	Any					=logging.get_logger(__name__)

class   UpperCAmelCase_    (  __snake_case			):

						'''simple docstring'''




						def __init__(       self ,				_A						):







													'''simple docstring'''

													super().__init__()
													__SCREAMING_SNAKE_CASE										=   nn.ModuleList(A__						)






						def 			_A      (       self ,				_A ,				_A ,				_A ,				_A ,				_A ,				_A = None ,				_A = None ,				_A = None ,				_A = None ,				_A = False ,				_A = True ,				):







													'''simple docstring'''

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

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

													return down_block_res_samples, mid_block_res_sample






						def 			_A      (       self ,				_A ,				_A = True ,				_A = None ,				_A = False ,				_A = None ,				):







													'''simple docstring'''

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

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






						@classmethod
						def 			_A      (       cls ,				_A ,				**_A						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   0
													__SCREAMING_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`, ...
													__SCREAMING_SNAKE_CASE										=   pretrained_model_path
													while os.path.isdir(A__						):
																				__SCREAMING_SNAKE_CASE										=   ControlNetModel.from_pretrained(A__ ,				**A__						)
																				controlnets.append(A__						)

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

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

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

													return cls(A__						)














import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase			:	List[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	Dict								={
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Tuple     =   "sew"







		def __init__(					self       ,A__=32       ,A__=768       ,A__=12       ,A__=12       ,A__=3072       ,A__=2       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=0.1       ,A__=0.0       ,A__=0.1       ,A__=0.1       ,A__=0.02       ,A__=1E-5       ,A__="group"       ,A__="gelu"       ,A__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)       ,A__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,A__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,A__=False       ,A__=128       ,A__=16       ,A__=True       ,A__=0.05       ,A__=10       ,A__=2       ,A__=0.0       ,A__=10       ,A__=0       ,A__="mean"       ,A__=False       ,A__=False       ,A__=256       ,A__=0       ,A__=1       ,A__=2       ,**A__       ,):
						super().__init__(**A__       ,pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__		)
						_A	:				str   =      hidden_size
						_A	:				Optional[Any]   =      feat_extract_norm
						_A	:				Any   =      feat_extract_activation
						_A	:				Optional[int]   =      list(A__		)
						_A	:				str   =      list(A__		)
						_A	:				Optional[Any]   =      list(A__		)
						_A	:				List[Any]   =      conv_bias
						_A	:				Optional[int]   =      num_conv_pos_embeddings
						_A	:				Any   =      num_conv_pos_embedding_groups
						_A	:				Union[str, Any]   =      len(self.conv_dim		)
						_A	:				List[Any]   =      num_hidden_layers
						_A	:				str   =      intermediate_size
						_A	:				List[Any]   =      squeeze_factor
						_A	:				Tuple   =      hidden_act
						_A	:				Union[str, Any]   =      num_attention_heads
						_A	:				Optional[int]   =      hidden_dropout
						_A	:				Optional[int]   =      attention_dropout
						_A	:				Optional[int]   =      activation_dropout
						_A	:				Tuple   =      feat_proj_dropout
						_A	:				Optional[Any]   =      final_dropout
						_A	:				str   =      layerdrop
						_A	:				int   =      layer_norm_eps
						_A	:				List[str]   =      initializer_range
						_A	:				List[Any]   =      vocab_size

						if (
						    (len(self.conv_stride		) != self.num_feat_extract_layers)
						    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
						    or (len(self.conv_dim		) != self.num_feat_extract_layers)
						):
										raise ValueError(
										    '''Configuration for convolutional layers is incorrect.'''
										    '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
										    f"""but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"""
										    f"""= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

						# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
						_A	:				Union[str, Any]   =      apply_spec_augment
						_A	:				str   =      mask_time_prob
						_A	:				Any   =      mask_time_length
						_A	:				int   =      mask_time_min_masks
						_A	:				str   =      mask_feature_prob
						_A	:				Optional[Any]   =      mask_feature_length
						_A	:				Dict   =      mask_feature_min_masks

						# ctc loss
						_A	:				Optional[int]   =      ctc_loss_reduction
						_A	:				List[str]   =      ctc_zero_infinity

						# sequence classification
						_A	:				List[Any]   =      use_weighted_layer_sum
						_A	:				Optional[int]   =      classifier_proj_size







		@property
		def  A__      (					self		):
						return functools.reduce(operator.mul       ,self.conv_stride       ,1		)



