


'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


_lowercase    :    int			     =  logging.get_logger(__name__)

_lowercase    :    Optional[Any]			     =  {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowercase    :    Tuple			     =  {
    """vocab_file""": {
        """distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt""",
        """distilbert-base-uncased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"""
        ),
        """distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt""",
        """distilbert-base-cased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"""
        ),
        """distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt""",
        """distilbert-base-multilingual-cased""": (
            """https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json""",
        """distilbert-base-uncased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"""
        ),
        """distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json""",
        """distilbert-base-cased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"""
        ),
        """distilbert-base-german-cased""": (
            """https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"""
        ),
        """distilbert-base-multilingual-cased""": (
            """https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"""
        ),
    },
}

_lowercase    :    List[Any]			     =  {
    """distilbert-base-uncased""": 512,
    """distilbert-base-uncased-distilled-squad""": 512,
    """distilbert-base-cased""": 512,
    """distilbert-base-cased-distilled-squad""": 512,
    """distilbert-base-german-cased""": 512,
    """distilbert-base-multilingual-cased""": 512,
}


_lowercase    :    str			     =  {
    """distilbert-base-uncased""": {"""do_lower_case""": True},
    """distilbert-base-uncased-distilled-squad""": {"""do_lower_case""": True},
    """distilbert-base-cased""": {"""do_lower_case""": False},
    """distilbert-base-cased-distilled-squad""": {"""do_lower_case""": False},
    """distilbert-base-german-cased""": {"""do_lower_case""": False},
    """distilbert-base-multilingual-cased""": {"""do_lower_case""": False},
}






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   str							= VOCAB_FILES_NAMES
				__magic_name__       :   List[str]							= PRETRAINED_VOCAB_FILES_MAP
				__magic_name__       :   Optional[Any]							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__       :   Any							= PRETRAINED_INIT_CONFIGURATION
				__magic_name__       :   Tuple							= ["input_ids", "attention_mask"]
				__magic_name__       :   Any							= DistilBertTokenizer




				def __init__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				int=None   ,					lowerCAmelCase    :				int=True   ,					lowerCAmelCase    :				int="[UNK]"   ,					lowerCAmelCase    :				Union[str, Any]="[SEP]"   ,					lowerCAmelCase    :				Any="[PAD]"   ,					lowerCAmelCase    :				Tuple="[CLS]"   ,					lowerCAmelCase    :				Tuple="[MASK]"   ,					lowerCAmelCase    :				int=True   ,					lowerCAmelCase    :				Dict=None   ,					**lowerCAmelCase    :				List[str]   ,					)->  Dict:

										"""simple docstring"""




										super().__init__(
										    lowerCAmelCase   ,					tokenizer_file=lowerCAmelCase   ,					do_lower_case=lowerCAmelCase   ,					unk_token=lowerCAmelCase   ,					sep_token=lowerCAmelCase   ,					pad_token=lowerCAmelCase   ,					cls_token=lowerCAmelCase   ,					mask_token=lowerCAmelCase   ,					tokenize_chinese_chars=lowerCAmelCase   ,					strip_accents=lowerCAmelCase   ,					**lowerCAmelCase   ,					)

										UpperCAmelCase         =			json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
										if (
										    normalizer_state.get('''lowercase'''   ,					lowerCAmelCase   ) != do_lower_case
										    or normalizer_state.get('''strip_accents'''   ,					lowerCAmelCase   ) != strip_accents
										    or normalizer_state.get('''handle_chinese_chars'''   ,					lowerCAmelCase   ) != tokenize_chinese_chars
										):
																UpperCAmelCase         =			getattr(lowerCAmelCase   ,					normalizer_state.pop('''type'''   )   )
																UpperCAmelCase         =			do_lower_case
																UpperCAmelCase         =			strip_accents
																UpperCAmelCase         =			tokenize_chinese_chars
																UpperCAmelCase         =			normalizer_class(**lowerCAmelCase   )

										UpperCAmelCase         =			do_lower_case




				def   a__(	self    :				Tuple   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				Union[str, Any]=None   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

										if token_ids_a:
																output += token_ids_a + [self.sep_token_id]

										return output




				def   a__(	self    :				int   ,					lowerCAmelCase    :				List[int]   ,					lowerCAmelCase    :				Optional[List[int]] = None   )->  List[int]:

										"""simple docstring"""




										UpperCAmelCase         =			[self.sep_token_id]
										UpperCAmelCase         =			[self.cls_token_id]
										if token_ids_a is None:
																return len(cls + token_ids_a + sep   ) * [0]
										return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]





				def   a__(	self    :				Any   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Optional[str] = None   )->  Tuple[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self._tokenizer.model.save(lowerCAmelCase   ,					name=lowerCAmelCase   )
										return tuple(lowerCAmelCase   )









'''simple docstring'''



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    Tuple=1  ):




						'''simple docstring'''






						if n_shave_prefix_segments >= 0:
												return ".".join(path.split('''.'''  )[n_shave_prefix_segments:]  )
						else:
												return ".".join(path.split('''.'''  )[:n_shave_prefix_segments]  )


def    lowerCamelCase__   ( A     :    int   ,     A     :    Optional[Any]=0  ):




						'''simple docstring'''






						UpperCAmelCase         =			[]
						for old_item in old_list:
												UpperCAmelCase         =			old_item.replace('''in_layers.0'''   ,     '''norm1'''  )
												UpperCAmelCase         =			new_item.replace('''in_layers.2'''   ,     '''conv1'''  )

												UpperCAmelCase         =			new_item.replace('''out_layers.0'''   ,     '''norm2'''  )
												UpperCAmelCase         =			new_item.replace('''out_layers.3'''   ,     '''conv2'''  )

												UpperCAmelCase         =			new_item.replace('''emb_layers.1'''   ,     '''time_emb_proj'''  )
												UpperCAmelCase         =			new_item.replace('''skip_connection'''   ,     '''conv_shortcut'''  )

												UpperCAmelCase         =			shave_segments(A   ,     n_shave_prefix_segments=A  )

												mapping.append({'''old''': old_item, '''new''': new_item}  )

						return mapping


def    lowerCamelCase__   ( A     :    Any   ,     A     :    int=0  ):




						'''simple docstring'''






						UpperCAmelCase         =			[]
						for old_item in old_list:
												UpperCAmelCase         =			old_item

												UpperCAmelCase         =			new_item.replace('''norm.weight'''   ,     '''group_norm.weight'''  )
												UpperCAmelCase         =			new_item.replace('''norm.bias'''   ,     '''group_norm.bias'''  )

												UpperCAmelCase         =			new_item.replace('''proj_out.weight'''   ,     '''proj_attn.weight'''  )
												UpperCAmelCase         =			new_item.replace('''proj_out.bias'''   ,     '''proj_attn.bias'''  )

												UpperCAmelCase         =			shave_segments(A   ,     n_shave_prefix_segments=A  )

												mapping.append({'''old''': old_item, '''new''': new_item}  )

						return mapping


def    lowerCamelCase__   ( A     :    Tuple   ,     A     :    Union[str, Any]   ,     A     :    int   ,     A     :    Dict=None   ,     A     :    Optional[int]=None   ,     A     :    Optional[Any]=None  ):




						'''simple docstring'''






						assert isinstance(A   ,     A  ), "Paths should be a list of dicts containing 'old' and 'new' keys."

						# Splits the attention layers into three variables.
						if attention_paths_to_split is not None:
												for path, path_map in attention_paths_to_split.items():
																		UpperCAmelCase         =			old_checkpoint[path]
																		UpperCAmelCase         =			old_tensor.shape[0] // 3

																		UpperCAmelCase         =			(-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

																		UpperCAmelCase         =			old_tensor.shape[0] // config['''num_head_channels'''] // 3

																		UpperCAmelCase         =			old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]  )
																		UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			old_tensor.split(channels // num_heads   ,     dim=1  )

																		UpperCAmelCase         =			query.reshape(A  )
																		UpperCAmelCase         =			key.reshape(A  )
																		UpperCAmelCase         =			value.reshape(A  )

						for path in paths:
												UpperCAmelCase         =			path['''new''']

												# These have already been assigned
												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												# Global renaming happens here
												UpperCAmelCase         =			new_path.replace('''middle_block.0'''   ,     '''mid_block.resnets.0'''  )
												UpperCAmelCase         =			new_path.replace('''middle_block.1'''   ,     '''mid_block.attentions.0'''  )
												UpperCAmelCase         =			new_path.replace('''middle_block.2'''   ,     '''mid_block.resnets.1'''  )

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								UpperCAmelCase         =			new_path.replace(replacement['''old''']   ,     replacement['''new''']  )

        # proj_attn.weight has to be converted from conv 1D to linear
												if "proj_attn.weight" in new_path:
																		UpperCAmelCase         =			old_checkpoint[path['''old''']][:, :, 0]
												else:
																		UpperCAmelCase         =			old_checkpoint[path['''old''']]


def    lowerCamelCase__   ( A     :    Union[str, Any]   ,     A     :    Dict  ):




						'''simple docstring'''






						UpperCAmelCase         =			{}

						UpperCAmelCase         =			checkpoint['''time_embed.0.weight''']
						UpperCAmelCase         =			checkpoint['''time_embed.0.bias''']
						UpperCAmelCase         =			checkpoint['''time_embed.2.weight''']
						UpperCAmelCase         =			checkpoint['''time_embed.2.bias''']

						UpperCAmelCase         =			checkpoint['''input_blocks.0.0.weight''']
						UpperCAmelCase         =			checkpoint['''input_blocks.0.0.bias''']

						UpperCAmelCase         =			checkpoint['''out.0.weight''']
						UpperCAmelCase         =			checkpoint['''out.0.bias''']
						UpperCAmelCase         =			checkpoint['''out.2.weight''']
						UpperCAmelCase         =			checkpoint['''out.2.bias''']

						# Retrieves the keys for the input blocks only
						UpperCAmelCase         =			len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in checkpoint if '''input_blocks''' in layer}  )
						UpperCAmelCase         =			{
						    layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
						    for layer_id in range(A  )
						}

						# Retrieves the keys for the middle blocks only
						UpperCAmelCase         =			len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in checkpoint if '''middle_block''' in layer}  )
						UpperCAmelCase         =			{
						    layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
						    for layer_id in range(A  )
						}

						# Retrieves the keys for the output blocks only
						UpperCAmelCase         =			len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in checkpoint if '''output_blocks''' in layer}  )
						UpperCAmelCase         =			{
						    layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
						    for layer_id in range(A  )
						}

						for i in range(1   ,     A  ):
												UpperCAmelCase         =			(i - 1) // (config['''num_res_blocks'''] + 1)
												UpperCAmelCase         =			(i - 1) % (config['''num_res_blocks'''] + 1)

												UpperCAmelCase         =			[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
												UpperCAmelCase         =			[key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

												if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
																		UpperCAmelCase         =			checkpoint[
																		    f"""input_blocks.{i}.0.op.weight"""
																		]
																		UpperCAmelCase         =			checkpoint[
																		    f"""input_blocks.{i}.0.op.bias"""
																		]
																		continue

												UpperCAmelCase         =			renew_resnet_paths(A  )
												UpperCAmelCase         =			{'''old''': f"""input_blocks.{i}.0""", '''new''': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
												UpperCAmelCase         =			{'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
												assign_to_checkpoint(
												    A   ,     A   ,     A   ,     additional_replacements=[meta_path, resnet_op]   ,     config=A  )

												if len(A  ):
																		UpperCAmelCase         =			renew_attention_paths(A  )
																		UpperCAmelCase         =			{
																		    '''old''': f"""input_blocks.{i}.1""",
																		    '''new''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
																		}
																		UpperCAmelCase         =			{
																		    f"""input_blocks.{i}.1.qkv.bias""": {
																		        '''key''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
																		        '''query''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
																		        '''value''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
																		    },
																		    f"""input_blocks.{i}.1.qkv.weight""": {
																		        '''key''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
																		        '''query''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
																		        '''value''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
																		    },
																		}
																		assign_to_checkpoint(
																		    A   ,     A   ,     A   ,     additional_replacements=[meta_path]   ,     attention_paths_to_split=A   ,     config=A   ,     )

						UpperCAmelCase         =			middle_blocks[0]
						UpperCAmelCase         =			middle_blocks[1]
						UpperCAmelCase         =			middle_blocks[2]

						UpperCAmelCase         =			renew_resnet_paths(A  )
						assign_to_checkpoint(A   ,     A   ,     A   ,     config=A  )

						UpperCAmelCase         =			renew_resnet_paths(A  )
						assign_to_checkpoint(A   ,     A   ,     A   ,     config=A  )

						UpperCAmelCase         =			renew_attention_paths(A  )
						UpperCAmelCase         =			{
						    '''middle_block.1.qkv.bias''': {
						        '''key''': '''mid_block.attentions.0.key.bias''',
						        '''query''': '''mid_block.attentions.0.query.bias''',
						        '''value''': '''mid_block.attentions.0.value.bias''',
						    },
						    '''middle_block.1.qkv.weight''': {
						        '''key''': '''mid_block.attentions.0.key.weight''',
						        '''query''': '''mid_block.attentions.0.query.weight''',
						        '''value''': '''mid_block.attentions.0.value.weight''',
						    },
						}
						assign_to_checkpoint(
						    A   ,     A   ,     A   ,     attention_paths_to_split=A   ,     config=A  )

						for i in range(A  ):
												UpperCAmelCase         =			i // (config['''num_res_blocks'''] + 1)
												UpperCAmelCase         =			i % (config['''num_res_blocks'''] + 1)
												UpperCAmelCase         =			[shave_segments(A   ,     2  ) for name in output_blocks[i]]
												UpperCAmelCase         =			{}

												for layer in output_block_layers:
																		UpperCAmelCase       ,       UpperCAmelCase         =			layer.split('''.'''  )[0], shave_segments(A   ,     1  )
																		if layer_id in output_block_list:
																								output_block_list[layer_id].append(A  )
																		else:
																								UpperCAmelCase         =			[layer_name]

												if len(A  ) > 1:
																		UpperCAmelCase         =			[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
																		UpperCAmelCase         =			[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

																		UpperCAmelCase         =			renew_resnet_paths(A  )
																		UpperCAmelCase         =			renew_resnet_paths(A  )

																		UpperCAmelCase         =			{'''old''': f"""output_blocks.{i}.0""", '''new''': f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
																		assign_to_checkpoint(A   ,     A   ,     A   ,     additional_replacements=[meta_path]   ,     config=A  )

																		if ["conv.weight", "conv.bias"] in output_block_list.values():
																								UpperCAmelCase         =			list(output_block_list.values()  ).index(['''conv.weight''', '''conv.bias''']  )
																								UpperCAmelCase         =			checkpoint[
																								    f"""output_blocks.{i}.{index}.conv.weight"""
																								]
																								UpperCAmelCase         =			checkpoint[
																								    f"""output_blocks.{i}.{index}.conv.bias"""
																								]

																								# Clear attentions as they have been attributed above.
																								if len(A  ) == 2:
																														UpperCAmelCase         =			[]

																		if len(A  ):
																								UpperCAmelCase         =			renew_attention_paths(A  )
																								UpperCAmelCase         =			{
																								    '''old''': f"""output_blocks.{i}.1""",
																								    '''new''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																								}
																								UpperCAmelCase         =			{
																								    f"""output_blocks.{i}.1.qkv.bias""": {
																								        '''key''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
																								        '''query''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
																								        '''value''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
																								    },
																								    f"""output_blocks.{i}.1.qkv.weight""": {
																								        '''key''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
																								        '''query''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
																								        '''value''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
																								    },
																								}
																								assign_to_checkpoint(
																								    A   ,     A   ,     A   ,     additional_replacements=[meta_path]   ,     attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions  ) else None   ,     config=A   ,     )
												else:
																		UpperCAmelCase         =			renew_resnet_paths(A   ,     n_shave_prefix_segments=1  )
																		for path in resnet_0_paths:
																								UpperCAmelCase         =			'''.'''.join(['''output_blocks''', str(A  ), path['''old''']]  )
																								UpperCAmelCase         =			'''.'''.join(['''up_blocks''', str(A  ), '''resnets''', str(A  ), path['''new''']]  )

																								UpperCAmelCase         =			checkpoint[old_path]

						return new_checkpoint


if __name__ == "__main__":
	_lowercase    :    Union[str, Any]			     =  argparse.ArgumentParser()

	parser.add_argument(
	    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
	)

	parser.add_argument(
	    """--config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help="""The config json file corresponding to the architecture.""",
	)

	parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

	_lowercase    :    Dict			     =  parser.parse_args()

	_lowercase    :    List[Any]			     =  torch.load(args.checkpoint_path)

	with open(args.config_file) as f:
		_lowercase    :    List[str]			     =  json.loads(f.read())

	_lowercase    :    Union[str, Any]			     =  convert_ldm_checkpoint(checkpoint, config)

	if "ldm" in config:
		del config["ldm"]

	_lowercase    :    Any			     =  UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		_lowercase    :    Tuple			     =  DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
		_lowercase    :    Optional[Any]			     =  VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

		_lowercase    :    Optional[Any]			     =  LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)





