


"""simple docstring"""


import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
	import torch
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 ="""char"""
							__UpperCAmelCase		:       Optional[int]							 ="""bpe"""
							__UpperCAmelCase		:       List[str]							 ="""wp"""
A	:  int            =     (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Dict							 =["""image_processor""", """char_tokenizer"""]
							__UpperCAmelCase		:       List[str]							 ="""ViTImageProcessor"""
							__UpperCAmelCase		:       List[str]							 ="""MgpstrTokenizer"""

							def __init__(	self       ,  __a=None       ,  __a=None       ,  **__a      ):
										__lowerCAmelCase							       =     None
										if "feature_extractor" in kwargs:
													warnings.warn(
													    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
													    " instead."       ,  __a       ,  )
													__lowerCAmelCase							       =     kwargs.pop("feature_extractor"      )

										__lowerCAmelCase							       =     image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
													raise ValueError("You need to specify an `image_processor`."      )
										if tokenizer is None:
													raise ValueError("You need to specify a `tokenizer`."      )

										__lowerCAmelCase							       =     tokenizer
										__lowerCAmelCase							       =     AutoTokenizer.from_pretrained("gpt2"      )
										__lowerCAmelCase							       =     AutoTokenizer.from_pretrained("bert-base-uncased"      )

										super().__init__(__a       ,  __a      )

							def __call__(	self       ,  __a=None       ,  __a=None       ,  __a=None       ,  **__a      ):
										if images is None and text is None:
													raise ValueError("You need to specify either an `images` or `text` input to process."      )

										if images is not None:
													__lowerCAmelCase							       =     self.image_processor(__a       ,  return_tensors=__a       ,  **__a      )
										if text is not None:
													__lowerCAmelCase							       =     self.char_tokenizer(__a       ,  return_tensors=__a       ,  **__a      )

										if text is None:
													return inputs
										elif images is None:
													return encodings
										else:
													__lowerCAmelCase							       =     encodings["input_ids"]
													return inputs

							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase							       =     sequences
										__lowerCAmelCase							       =     char_preds.size(0      )

										__lowerCAmelCase       ,      __lowerCAmelCase							       =     self._decode_helper(__a       ,  "char"      )
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     self._decode_helper(__a       ,  "bpe"      )
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     self._decode_helper(__a       ,  "wp"      )

										__lowerCAmelCase							       =     []
										__lowerCAmelCase							       =     []
										for i in range(__a      ):
													__lowerCAmelCase							       =     [char_scores[i], bpe_scores[i], wp_scores[i]]
													__lowerCAmelCase							       =     [char_strs[i], bpe_strs[i], wp_strs[i]]
													__lowerCAmelCase							       =     scores.index(max(__a      )      )
													final_strs.append(strs[max_score_index]      )
													final_scores.append(scores[max_score_index]      )

										__lowerCAmelCase							       =     {}
										__lowerCAmelCase							       =     final_strs
										__lowerCAmelCase							       =     final_scores
										__lowerCAmelCase							       =     char_strs
										__lowerCAmelCase							       =     bpe_strs
										__lowerCAmelCase							       =     wp_strs
										return out

							def      snake_case							(	self       ,  __a       ,  __a      ):
										if format == DecodeType.CHARACTER:
													__lowerCAmelCase							       =     self.char_decode
													__lowerCAmelCase							       =     1
													__lowerCAmelCase							       =     "[s]"
										elif format == DecodeType.BPE:
													__lowerCAmelCase							       =     self.bpe_decode
													__lowerCAmelCase							       =     2
													__lowerCAmelCase							       =     "#"
										elif format == DecodeType.WORDPIECE:
													__lowerCAmelCase							       =     self.wp_decode
													__lowerCAmelCase							       =     1_02
													__lowerCAmelCase							       =     "[SEP]"
										else:
													raise ValueError(f"Format {format} is not supported."      )

										__lowerCAmelCase       ,      __lowerCAmelCase							       =     [], []
										__lowerCAmelCase							       =     pred_logits.size(0      )
										__lowerCAmelCase							       =     pred_logits.size(1      )
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     pred_logits.topk(1       ,  dim=-1       ,  largest=__a       ,  sorted=__a      )
										__lowerCAmelCase							       =     preds_index.view(-1       ,  __a      )[:, 1:]
										__lowerCAmelCase							       =     decoder(__a      )
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     torch.nn.functional.softmax(__a       ,  dim=2      ).max(dim=2      )
										__lowerCAmelCase							       =     preds_max_prob[:, 1:]

										for index in range(__a      ):
													__lowerCAmelCase							       =     preds_str[index].find(__a      )
													__lowerCAmelCase							       =     preds_str[index][:pred_eos]
													__lowerCAmelCase							       =     preds_index[index].cpu().tolist()
													__lowerCAmelCase							       =     pred_index.index(__a      ) if eos_token in pred_index else -1
													__lowerCAmelCase							       =     preds_max_prob[index][: pred_eos_index + 1]
													__lowerCAmelCase							       =     pred_max_prob.cumprod(dim=0      )[-1] if pred_max_prob.nelement() != 0 else 0.0
													dec_strs.append(__a      )
													conf_scores.append(__a      )

										return dec_strs, conf_scores

							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     [seq.replace(" "       ,  ""      ) for seq in self.char_tokenizer.batch_decode(__a      )]
										return decode_strs

							def      snake_case							(	self       ,  __a      ):
										return self.bpe_tokenizer.batch_decode(__a      )



							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     [seq.replace(" "       ,  ""      ) for seq in self.wp_tokenizer.batch_decode(__a      )]
										return decode_strs








"""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


A	:  List[Any]            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										super().__init__()
										__lowerCAmelCase							       =     nn.ModuleList(__a      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = False       ,  __a = True       ,  ):
										for i, (image, scale, controlnet) in enumerate(zip(__a       ,  __a       ,  self.nets      )      ):
													__lowerCAmelCase       ,      __lowerCAmelCase							       =     controlnet(
													    __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  )

													# merge samples
													if i == 0:
																__lowerCAmelCase       ,      __lowerCAmelCase							       =     down_samples, mid_sample
													else:
																__lowerCAmelCase							       =     [
																    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      snake_case							(	self       ,  __a       ,  __a = True       ,  __a = None       ,  __a = False       ,  __a = None       ,  ):
										__lowerCAmelCase							       =     0
										__lowerCAmelCase							       =     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
													__lowerCAmelCase							       =     model_path_to_save + f"_{idx}"



							@classmethod
							def      snake_case							(	cls       ,  __a       ,  **__a      ):
										__lowerCAmelCase							       =     0
										__lowerCAmelCase							       =     []

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

													idx += 1
													__lowerCAmelCase							       =     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      )




