


'''simple docstring'''







import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowerCAmelCase    :     Optional[Any]					=						logging.get_logger(__name__)


lowerCAmelCase    :     Dict					=						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
lowerCAmelCase    :     List[Any]					=						{
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

lowerCAmelCase    :     Tuple					=						{
    """allenai/led-base-16384""": 1_63_84,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Union[str, Any]   			=       (
           list(range(ord('!'					)   ,						ord('~'					) + 1					)					) + list(range(ord('¡'					)   ,						ord('¬'					) + 1					)					) + list(range(ord('®'					)   ,						ord('ÿ'					) + 1					)					)
       )
       _lowerCAmelCase    :							List[str]   			=       bs[:]
       _lowerCAmelCase    :							str   			=       0
       for b in range(2**8					):
              if b not in bs:
                     bs.append(lowercase_					)
                     cs.append(2**8 + n					)
                     n += 1
       _lowerCAmelCase    :							str   			=       [chr(lowercase_					) for n in cs]
       return dict(zip(lowercase_   ,						lowercase_					)					)
def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Dict   			=       set()
       _lowerCAmelCase    :							Optional[Any]   			=       word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)					)
              _lowerCAmelCase    :							Dict   			=       char
       return pairs
class 	UpperCamelCase__					( _UpperCamelCase	):



     """simple docstring"""






     __magic_name__    			=    VOCAB_FILES_NAMES
     __magic_name__    			=    PRETRAINED_VOCAB_FILES_MAP
     __magic_name__    			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__    			=    ["input_ids", "attention_mask"]







     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__="replace"      ,	snake_case__="<s>"      ,	snake_case__="</s>"      ,	snake_case__="</s>"      ,	snake_case__="<s>"      ,	snake_case__="<unk>"      ,	snake_case__="<pad>"      ,	snake_case__="<mask>"      ,	snake_case__=False      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       AddedToken(__a      ,	lstrip=__a      ,	rstrip=__a   ) if isinstance(__a      ,	__a   ) else bos_token
            _lowerCAmelCase    :							List[str]   			=       AddedToken(__a      ,	lstrip=__a      ,	rstrip=__a   ) if isinstance(__a      ,	__a   ) else eos_token
            _lowerCAmelCase    :							Tuple   			=       AddedToken(__a      ,	lstrip=__a      ,	rstrip=__a   ) if isinstance(__a      ,	__a   ) else sep_token
            _lowerCAmelCase    :							str   			=       AddedToken(__a      ,	lstrip=__a      ,	rstrip=__a   ) if isinstance(__a      ,	__a   ) else cls_token
            _lowerCAmelCase    :							Any   			=       AddedToken(__a      ,	lstrip=__a      ,	rstrip=__a   ) if isinstance(__a      ,	__a   ) else unk_token
            _lowerCAmelCase    :							List[Any]   			=       AddedToken(__a      ,	lstrip=__a      ,	rstrip=__a   ) if isinstance(__a      ,	__a   ) else pad_token

            # Mask token behave like a normal word, i.e. include the space before it
            _lowerCAmelCase    :							int   			=       AddedToken(__a      ,	lstrip=__a      ,	rstrip=__a   ) if isinstance(__a      ,	__a   ) else mask_token

            super().__init__(
                errors=__a      ,	bos_token=__a      ,	eos_token=__a      ,	unk_token=__a      ,	sep_token=__a      ,	cls_token=__a      ,	pad_token=__a      ,	mask_token=__a      ,	add_prefix_space=__a      ,	**__a      ,	)

            with open(__a      ,	encoding='utf-8'   ) as vocab_handle:
                   _lowerCAmelCase    :							int   			=       json.load(__a   )
            _lowerCAmelCase    :							str   			=       {v: k for k, v in self.encoder.items()}
            _lowerCAmelCase    :							int   			=       errors  # how to handle errors in decoding
            _lowerCAmelCase    :							str   			=       bytes_to_unicode()
            _lowerCAmelCase    :							Optional[Any]   			=       {v: k for k, v in self.byte_encoder.items()}
            with open(__a      ,	encoding='utf-8'   ) as merges_handle:
                   _lowerCAmelCase    :							List[Any]   			=       merges_handle.read().split('\n'   )[1:-1]
            _lowerCAmelCase    :							Optional[int]   			=       [tuple(merge.split()   ) for merge in bpe_merges]
            _lowerCAmelCase    :							List[Any]   			=       dict(zip(__a      ,	range(len(__a   )   )   )   )
            _lowerCAmelCase    :							Any   			=       {}
            _lowerCAmelCase    :							List[Any]   			=       add_prefix_space

            # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
            _lowerCAmelCase    :							Optional[Any]   			=       re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'   )







     @property
     # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
     def a	( self   ):

            '''simple docstring'''

            return len(self.encoder   )







     def a	( self   ):

            '''simple docstring'''

            return dict(self.encoder      ,	**self.added_tokens_encoder   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            if token in self.cache:
                   return self.cache[token]
            _lowerCAmelCase    :							Optional[int]   			=       tuple(__a   )
            _lowerCAmelCase    :							List[Any]   			=       get_pairs(__a   )

            if not pairs:
                   return token

            while True:
                   _lowerCAmelCase    :							List[str]   			=       min(__a      ,	key=lambda snake_case__   : self.bpe_ranks.get(__a      ,	float('inf'   )   )   )
                   if bigram not in self.bpe_ranks:
                          break
                   _lowerCAmelCase    :							Union[str, Any]   			=       bigram
                   _lowerCAmelCase    :							Dict   			=       []
                   _lowerCAmelCase    :							Optional[Any]   			=       0
                   while i < len(__a   ):
                          try:
                                 _lowerCAmelCase    :							Dict   			=       word.index(__a      ,	__a   )
                          except ValueError:
                                 new_word.extend(word[i:]   )
                                 break
                          else:
                                 new_word.extend(word[i:j]   )
                                 _lowerCAmelCase    :							str   			=       j

                          if word[i] == first and i < len(__a   ) - 1 and word[i + 1] == second:
                                 new_word.append(first + second   )
                                 i += 2
                          else:
                                 new_word.append(word[i]   )
                                 i += 1
                   _lowerCAmelCase    :							str   			=       tuple(__a   )
                   _lowerCAmelCase    :							Optional[int]   			=       new_word
                   if len(__a   ) == 1:
                          break
                   else:
                          _lowerCAmelCase    :							Any   			=       get_pairs(__a   )
            _lowerCAmelCase    :							str   			=       " ".join(__a   )
            _lowerCAmelCase    :							Dict   			=       word
            return word







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       []
            for token in re.findall(self.pat      ,	__a   ):
                   _lowerCAmelCase    :							List[str]   			=       "".join(
                       self.byte_encoder[b] for b in token.encode('utf-8'   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                   bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__a   ).split(' '   )   )
            return bpe_tokens







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            return self.encoder.get(__a      ,	self.encoder.get(self.unk_token   )   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            return self.decoder.get(__a   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       "".join(__a   )
            _lowerCAmelCase    :							List[Any]   			=       bytearray([self.byte_decoder[c] for c in text]   ).decode('utf-8'      ,	errors=self.errors   )
            return text







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            if not os.path.isdir(__a   ):
                   logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
                   return
            _lowerCAmelCase    :							Tuple   			=       os.path.join(
                __a      ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )
            _lowerCAmelCase    :							Dict   			=       os.path.join(
                __a      ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']   )

            with open(__a      ,	'w'      ,	encoding='utf-8'   ) as f:
                   f.write(json.dumps(self.encoder      ,	indent=2      ,	sort_keys=__a      ,	ensure_ascii=__a   ) + '\n'   )

            _lowerCAmelCase    :							str   			=       0
            with open(__a      ,	'w'      ,	encoding='utf-8'   ) as writer:
                   writer.write('#version: 0.2\n'   )
                   for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,	key=lambda snake_case__   : kv[1]   ):
                          if index != token_index:
                                 logger.warning(
                                     F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                                     ' Please check that the tokenizer is not corrupted!'   )
                                 _lowerCAmelCase    :							Union[str, Any]   			=       token_index
                          writer.write(' '.join(__a   ) + '\n'   )
                          index += 1

            return vocab_file, merge_file







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            _lowerCAmelCase    :							Tuple   			=       [self.cls_token_id]
            _lowerCAmelCase    :							List[Any]   			=       [self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep







     def a	( self      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = False   ):

            '''simple docstring'''

            if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=__a      ,	token_ids_a=__a      ,	already_has_special_tokens=__a   )

            if token_ids_a is None:
                   return [1] + ([0] * len(__a   )) + [1]
            return [1] + ([0] * len(__a   )) + [1, 1] + ([0] * len(__a   )) + [1]







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       [self.sep_token_id]
            _lowerCAmelCase    :							List[Any]   			=       [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 + sep + token_ids_a + sep   ) * [0]







     def a	( self      ,	snake_case__      ,	snake_case__=False      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       kwargs.pop('add_prefix_space'      ,	self.add_prefix_space   )
            if (is_split_into_words or add_prefix_space) and (len(__a   ) > 0 and not text[0].isspace()):
                   _lowerCAmelCase    :							List[Any]   			=       " " + text
            return (text, kwargs)







     def a	( self      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = PaddingStrategy.DO_NOT_PAD      ,	snake_case__ = None      ,	snake_case__ = None      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       super()._pad(
                encoded_inputs=__a      ,	max_length=__a      ,	padding_strategy=__a      ,	pad_to_multiple_of=__a      ,	return_attention_mask=__a      ,	)

            # Load from model defaults
            if return_attention_mask is None:
                   _lowerCAmelCase    :							str   			=       "attention_mask" in self.model_input_names

            if return_attention_mask and "global_attention_mask" in encoded_inputs:
                   _lowerCAmelCase    :							Dict   			=       encoded_inputs[self.model_input_names[0]]
                   # `global_attention_mask` need to have the same length as other (sequential) inputs.
                   _lowerCAmelCase    :							str   			=       len(encoded_inputs['global_attention_mask']   ) != len(__a   )

                   if needs_to_be_padded:
                          _lowerCAmelCase    :							List[str]   			=       len(__a   ) - len(encoded_inputs['global_attention_mask']   )

                          if self.padding_side == "right":
                                 # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                                 _lowerCAmelCase    :							str   			=       (
                                     encoded_inputs["global_attention_mask"] + [-1] * difference
                                 )
                          elif self.padding_side == "left":
                                 _lowerCAmelCase    :							List[Any]   			=       [-1] * difference + encoded_inputs[
                                     "global_attention_mask"
                                 ]
                          else:
                                 raise ValueError('Invalid padding strategy:' + str(self.padding_side   )   )

            return encoded_inputs





'''simple docstring'''







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCAmelCase    :     Tuple					=						False
lowerCAmelCase    :     str					=						True
lowerCAmelCase    :     List[Any]					=						False


if __name__ == "__main__":
       lowerCAmelCase    :     Any					=						argparse.ArgumentParser()

       parser.add_argument(
           """--repo_path""",
           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.""")

       lowerCAmelCase    :     Optional[int]					=						parser.parse_args()

       lowerCAmelCase    :     int					=						{
           """image_size""": """sample_size""",
           """num_res_blocks""": """layers_per_block""",
           """block_channels""": """block_out_channels""",
           """down_blocks""": """down_block_types""",
           """up_blocks""": """up_block_types""",
           """downscale_freq_shift""": """freq_shift""",
           """resnet_num_groups""": """norm_num_groups""",
           """resnet_act_fn""": """act_fn""",
           """resnet_eps""": """norm_eps""",
           """num_head_channels""": """attention_head_dim""",
       }

       lowerCAmelCase    :     int					=						{
           """time_steps""": """time_proj""",
           """mid""": """mid_block""",
           """downsample_blocks""": """down_blocks""",
           """upsample_blocks""": """up_blocks""",
       }

       lowerCAmelCase    :     Optional[Any]					=						"""""" if has_file(args.repo_path, """config.json""") else """unet"""

       with open(os.path.join(args.repo_path, subfolder, """config.json"""), """r""", encoding="""utf-8""") as reader:
              lowerCAmelCase    :     int					=						reader.read()
              lowerCAmelCase    :     List[str]					=						json.loads(text)

       if do_only_config:
              for key in config_parameters_to_change.keys():
                     config.pop(key, None)

       if has_file(args.repo_path, """config.json"""):
              lowerCAmelCase    :     str					=						UNetaDModel(**config)
       else:
              lowerCAmelCase    :     Union[str, Any]					=						UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
              lowerCAmelCase    :     Dict					=						class_name(**config)

       if do_only_config:
              model.save_config(os.path.join(args.repo_path, subfolder))

       lowerCAmelCase    :     Union[str, Any]					=						dict(model.config)

       if do_only_renaming:
              for key, value in config_parameters_to_change.items():
                     if key in config:
                            lowerCAmelCase    :     str					=						config[key]
                            del config[key]

              lowerCAmelCase    :     Optional[int]					=						[k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
              lowerCAmelCase    :     Dict					=						[k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

       if do_only_weights:
              lowerCAmelCase    :     Tuple					=						torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

              lowerCAmelCase    :     str					=						{}
              for param_key, param_value in state_dict.items():
                     if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
                            continue
                     lowerCAmelCase    :     str					=						False
                     for key, new_key in key_parameters_to_change.items():
                            if not has_changed and param_key.split(""".""")[0] == key:
                                   lowerCAmelCase    :     Dict					=						param_value
                                   lowerCAmelCase    :     Tuple					=						True
                     if not has_changed:
                            lowerCAmelCase    :     Tuple					=						param_value

              model.load_state_dict(new_state_dict)
              model.save_pretrained(os.path.join(args.repo_path, subfolder))

