


import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_lowerCAmelCase:						Optional[Any]   				=       logging.getLogger(__name__)




@dataclass
class       lowercase_					:
				snake_case	    =42
				snake_case	    =42
				snake_case	    =42







@dataclass
class       lowercase_					:
				snake_case	    =42
				snake_case	    =42
				snake_case	    =None
				snake_case	    =None







class       lowercase_					(lowercase__   ):
				snake_case	    ='train'
				snake_case	    ='dev'
				snake_case	    ='test'







class       lowercase_					:





				@staticmethod
				def 						__UpperCamelCase    (				lowercase_       ,  lowercase_)  -> List[InputExample]:
											raise NotImplementedError





				@staticmethod
				def 						__UpperCamelCase    (				lowercase_)  -> List[str]:
											raise NotImplementedError





				@staticmethod
				def 						__UpperCamelCase    (				lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_=False       ,  lowercase_="[CLS]"       ,  lowercase_=1       ,  lowercase_="[SEP]"       ,  lowercase_=False       ,  lowercase_=False       ,  lowercase_=0       ,  lowercase_=0       ,  lowercase_=-100       ,  lowercase_=0       ,  lowercase_=True       ,  )  -> List[InputFeatures]:
											a__      ={label: i for i, label in enumerate(lowercase_)}

											a__      =[]
											for ex_index, example in enumerate(lowercase_):
																		if ex_index % 10000 == 0:
																									logger.info('Writing example %d of %d'       ,  lowercase_       ,  len(lowercase_))

																		a__      =[]
																		a__      =[]
																		for word, label in zip(example.words       ,  example.labels):
																									a__      =tokenizer.tokenize(lowercase_)

																									# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																									if len(lowercase_) > 0:
																																tokens.extend(lowercase_)
																																# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																																label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(lowercase_) - 1))

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																		a__      =tokenizer.num_special_tokens_to_add()
																		if len(lowercase_) > max_seq_length - special_tokens_count:
																									a__      =tokens[: (max_seq_length - special_tokens_count)]
																									a__      =label_ids[: (max_seq_length - special_tokens_count)]

																		# The convention in BERT is:
																		# (a) For sequence pairs:
																		#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
																		#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
																		# (b) For single sequences:
																		#  tokens:   [CLS] the dog is hairy . [SEP]
																		#  type_ids:   0   0   0   0  0     0   0
																		#
																		# Where "type_ids" are used to indicate whether this is the first
																		# sequence or the second sequence. The embedding vectors for `type=0` and
																		# `type=1` were learned during pre-training and are added to the wordpiece
																		# embedding vector (and position vector). This is not *strictly* necessary
																		# since the [SEP] token unambiguously separates the sequences, but it makes
																		# it easier for the model to learn the concept of sequences.
																		#
																		# For classification tasks, the first vector (corresponding to [CLS]) is
																		# used as the "sentence vector". Note that this only makes sense because
																		# the entire model is fine-tuned.
																		tokens += [sep_token]
																		label_ids += [pad_token_label_id]
																		if sep_token_extra:
																									# roberta uses an extra separator b/w pairs of sentences
																									tokens += [sep_token]
																									label_ids += [pad_token_label_id]
																		a__      =[sequence_a_segment_id] * len(lowercase_)

																		if cls_token_at_end:
																									tokens += [cls_token]
																									label_ids += [pad_token_label_id]
																									segment_ids += [cls_token_segment_id]
																		else:
																									a__      =[cls_token] + tokens
																									a__      =[pad_token_label_id] + label_ids
																									a__      =[cls_token_segment_id] + segment_ids

																		a__      =tokenizer.convert_tokens_to_ids(lowercase_)

																		# The mask has 1 for real tokens and 0 for padding tokens. Only real
																		# tokens are attended to.
																		a__      =[1 if mask_padding_with_zero else 0] * len(lowercase_)

																		# Zero-pad up to the sequence length.
																		a__      =max_seq_length - len(lowercase_)
																		if pad_on_left:
																									a__      =([pad_token] * padding_length) + input_ids
																									a__      =([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																									a__      =([pad_token_segment_id] * padding_length) + segment_ids
																									a__      =([pad_token_label_id] * padding_length) + label_ids
																		else:
																									input_ids += [pad_token] * padding_length
																									input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																									segment_ids += [pad_token_segment_id] * padding_length
																									label_ids += [pad_token_label_id] * padding_length

																		assert len(lowercase_) == max_seq_length
																		assert len(lowercase_) == max_seq_length
																		assert len(lowercase_) == max_seq_length
																		assert len(lowercase_) == max_seq_length

																		if ex_index < 5:
																									logger.info('*** Example ***')
																									logger.info('guid: %s'       ,  example.guid)
																									logger.info('tokens: %s'       ,  ' '.join([str(lowercase_) for x in tokens]))
																									logger.info('input_ids: %s'       ,  ' '.join([str(lowercase_) for x in input_ids]))
																									logger.info('input_mask: %s'       ,  ' '.join([str(lowercase_) for x in input_mask]))
																									logger.info('segment_ids: %s'       ,  ' '.join([str(lowercase_) for x in segment_ids]))
																									logger.info('label_ids: %s'       ,  ' '.join([str(lowercase_) for x in label_ids]))

																		if "token_type_ids" not in tokenizer.model_input_names:
																									a__      =None

																		features.append(
																		    InputFeatures(
																		        input_ids=lowercase_       ,  attention_mask=lowercase_       ,  token_type_ids=lowercase_       ,  label_ids=lowercase_))
											return features


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset




	class       lowercase_					(lowercase__   ):
					snake_case	    =42
					snake_case	    =nn.CrossEntropyLoss().ignore_index





					def __init__(				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  lowercase_=False       ,  lowercase_ = Split.train       ,  )  -> str:
												# Load data features from cache or dataset file
												a__      =os.path.join(
												    lowercase_       ,  'cached_{}_{}_{}'.format(mode.value       ,  tokenizer.__class__.__name__       ,  str(lowercase_))       ,  )

												# Make sure only the first process in distributed training processes the dataset,
												# and the others will use the cache.
												a__      =cached_features_file + '.lock'
												with FileLock(lowercase_):
																			if os.path.exists(lowercase_) and not overwrite_cache:
																										logger.info(F"""Loading features from cached file {cached_features_file}""")
																										a__      =torch.load(lowercase_)
																			else:
																										logger.info(F"""Creating features from dataset file at {data_dir}""")
																										a__      =token_classification_task.read_examples_from_file(lowercase_       ,  lowercase_)
																										# TODO clean up all this to leverage built-in features of tokenizers
																										a__      =token_classification_task.convert_examples_to_features(
																										    lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  cls_token_at_end=bool(model_type in ['xlnet'])       ,  cls_token=tokenizer.cls_token       ,  cls_token_segment_id=2 if model_type in ['xlnet'] else 0       ,  sep_token=tokenizer.sep_token       ,  sep_token_extra=lowercase_       ,  pad_on_left=bool(tokenizer.padding_side == 'left')       ,  pad_token=tokenizer.pad_token_id       ,  pad_token_segment_id=tokenizer.pad_token_type_id       ,  pad_token_label_id=self.pad_token_label_id       ,  )
																										logger.info(F"""Saving features into cached file {cached_features_file}""")
																										torch.save(self.features       ,  lowercase_)





					def __len__(				self)  -> Optional[int]:
												return len(self.features)





					def __getitem__(				self       ,  lowercase_)  -> InputFeatures:
												return self.features[i]


if is_tf_available():
	import tensorflow as tf




	class       lowercase_					:
					snake_case	    =42
					snake_case	    =-100





					def __init__(				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  lowercase_=False       ,  lowercase_ = Split.train       ,  )  -> Union[str, Any]:
												a__      =token_classification_task.read_examples_from_file(lowercase_       ,  lowercase_)
												# TODO clean up all this to leverage built-in features of tokenizers
												a__      =token_classification_task.convert_examples_to_features(
												    lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  cls_token_at_end=bool(model_type in ['xlnet'])       ,  cls_token=tokenizer.cls_token       ,  cls_token_segment_id=2 if model_type in ['xlnet'] else 0       ,  sep_token=tokenizer.sep_token       ,  sep_token_extra=lowercase_       ,  pad_on_left=bool(tokenizer.padding_side == 'left')       ,  pad_token=tokenizer.pad_token_id       ,  pad_token_segment_id=tokenizer.pad_token_type_id       ,  pad_token_label_id=self.pad_token_label_id       ,  )

												def gen():
																			for ex in self.features:
																										if ex.token_type_ids is None:
																																	yield (
																																	    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																																	    ex.label_ids,
																																	)
																										else:
																																	yield (
																																	    {
																																	        "input_ids": ex.input_ids,
																																	        "attention_mask": ex.attention_mask,
																																	        "token_type_ids": ex.token_type_ids,
																																	    },
																																	    ex.label_ids,
																																	)

												if "token_type_ids" not in tokenizer.model_input_names:
																			a__      =tf.data.Dataset.from_generator(
																			    lowercase_       ,  ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)       ,  (
																			        {'input_ids': tf.TensorShape([None]), 'attention_mask': tf.TensorShape([None])},
																			        tf.TensorShape([None]),
																			    )       ,  )
												else:
																			a__      =tf.data.Dataset.from_generator(
																			    lowercase_       ,  ({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa)       ,  (
																			        {
																			            'input_ids': tf.TensorShape([None]),
																			            'attention_mask': tf.TensorShape([None]),
																			            'token_type_ids': tf.TensorShape([None]),
																			        },
																			        tf.TensorShape([None]),
																			    )       ,  )





					def 						__UpperCamelCase    (				self)  -> int:
												a__      =self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))

												return self.dataset





					def __len__(				self)  -> Any:
												return len(self.features)





					def __getitem__(				self       ,  lowercase_)  -> InputFeatures:
												return self.features[i]





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	_lowerCAmelCase:						int   				=       argparse.ArgumentParser()

	parser.add_argument(
	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
	)
	parser.add_argument(
	    '--original_config_file',
	    type=str,
	    required=True,
	    help='The YAML config file corresponding to the original architecture.',
	)
	parser.add_argument(
	    '--num_in_channels',
	    default=None,
	    type=int,
	    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
	)
	parser.add_argument(
	    '--image_size',
	    default=512,
	    type=int,
	    help=(
	        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
	        ' Base. Use 768 for Stable Diffusion v2.'
	    ),
	)
	parser.add_argument(
	    '--extract_ema',
	    action='store_true',
	    help=(
	        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
	        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
	        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
	    ),
	)
	parser.add_argument(
	    '--upcast_attention',
	    action='store_true',
	    help=(
	        'Whether the attention computation should always be upcasted. This is necessary when running stable'
	        ' diffusion 2.1.'
	    ),
	)
	parser.add_argument(
	    '--from_safetensors',
	    action='store_true',
	    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
	)
	parser.add_argument(
	    '--to_safetensors',
	    action='store_true',
	    help='Whether to store pipeline in safetensors format or not.',
	)
	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
	parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')






	def   _lowercase(					__a				:						Optional[Any]     ):
								if string == "True":
															return True
								elif string == "False":
															return False
								else:
															raise ValueError(f"""could not parse string as bool {string}"""     )

	parser.add_argument(
	    '--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
	)

	parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)

	_lowerCAmelCase:						str   				=       parser.parse_args()

	_lowerCAmelCase:						Tuple   				=       download_controlnet_from_original_ckpt(
	    checkpoint_path=args.checkpoint_path,
	    original_config_file=args.original_config_file,
	    image_size=args.image_size,
	    extract_ema=args.extract_ema,
	    num_in_channels=args.num_in_channels,
	    upcast_attention=args.upcast_attention,
	    from_safetensors=args.from_safetensors,
	    device=args.device,
	    use_linear_projection=args.use_linear_projection,
	    cross_attention_dim=args.cross_attention_dim,
	)







	controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

