
"""simple docstring"""


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

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
			if is_torch_available():
						import torch
			if is_tf_available():
						import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


__A 		= logging.get_logger(__name__)

__A 		= {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__A 		= {
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
}

__A 		= {
    'Salesforce/codegen-350M-mono': 2048,
}



@lru_cache()
def 					_lowerCamelCase()       ->				Any:
	_lowerCAmelCase			=(
	    list(range(ord("""!""" )					,				ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" )					,				ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" )					,				ord("""ÿ""" ) + 1 ) )
	)
	_lowerCAmelCase			=bs[:]
	_lowerCAmelCase			=0
	for b in range(2**8 ):
		if b not in bs:
			bs.append(__A )
			cs.append(2**8 + n )
			n += 1
	_lowerCAmelCase			=[chr(__A ) for n in cs]
	return dict(zip(__A					,				__A ) )



def 					_lowerCamelCase(__UpperCamelCase )       ->				Union[str, Any]:
	_lowerCAmelCase			=set()
	_lowerCAmelCase			=word[0]
	for char in word[1:]:
		pairs.add((prev_char, char) )
		_lowerCAmelCase			=char
	return pairs







class 	lowerCamelCase__      (      UpperCAmelCase_       ):





			'''simple docstring'''
			lowerCamelCase						      =	VOCAB_FILES_NAMES
			lowerCamelCase						      =	PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase						      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase						      =	['''input_ids''', '''attention_mask''']



			def __init__(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase="replace"  ,  __UpperCAmelCase="<|endoftext|>"  ,  __UpperCAmelCase="<|endoftext|>"  ,  __UpperCAmelCase="<|endoftext|>"  ,  __UpperCAmelCase=None  ,  __UpperCAmelCase=False  ,  __UpperCAmelCase=False  ,  **__UpperCAmelCase  ,  )				->  Optional[int]:
				_lowerCAmelCase			=AddedToken(__UpperCAmelCase  ,  lstrip=__UpperCAmelCase  ,  rstrip=__UpperCAmelCase						) if isinstance(__UpperCAmelCase  ,  __UpperCAmelCase						) else bos_token
				_lowerCAmelCase			=AddedToken(__UpperCAmelCase  ,  lstrip=__UpperCAmelCase  ,  rstrip=__UpperCAmelCase						) if isinstance(__UpperCAmelCase  ,  __UpperCAmelCase						) else eos_token
				_lowerCAmelCase			=AddedToken(__UpperCAmelCase  ,  lstrip=__UpperCAmelCase  ,  rstrip=__UpperCAmelCase						) if isinstance(__UpperCAmelCase  ,  __UpperCAmelCase						) else unk_token
				_lowerCAmelCase			=AddedToken(__UpperCAmelCase  ,  lstrip=__UpperCAmelCase  ,  rstrip=__UpperCAmelCase						) if isinstance(__UpperCAmelCase  ,  __UpperCAmelCase						) else pad_token
				super().__init__(
				    errors=__UpperCAmelCase  ,  unk_token=__UpperCAmelCase  ,  bos_token=__UpperCAmelCase  ,  eos_token=__UpperCAmelCase  ,  pad_token=__UpperCAmelCase  ,  add_prefix_space=__UpperCAmelCase  ,  add_bos_token=__UpperCAmelCase  ,  **__UpperCAmelCase  ,  )
				_lowerCAmelCase			=add_bos_token

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

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



			@property
			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				return len(self.encoder						)



			def       _lowerCAmelCase	(							self						)				->  Any:
				return dict(self.encoder  ,  **self.added_tokens_encoder						)



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase						)				->  Optional[Any]:
				if token in self.cache:
					return self.cache[token]
				_lowerCAmelCase			=tuple(__UpperCAmelCase						)
				_lowerCAmelCase			=get_pairs(__UpperCAmelCase						)

				if not pairs:
					return token

				while True:
					_lowerCAmelCase			=min(__UpperCAmelCase  ,  key=lambda __UpperCAmelCase						: self.bpe_ranks.get(__UpperCAmelCase  ,  float("""inf"""						)						)						)
					if bigram not in self.bpe_ranks:
						break
					_lowerCAmelCase		,							_lowerCAmelCase			=bigram
					_lowerCAmelCase			=[]
					_lowerCAmelCase			=0
					while i < len(__UpperCAmelCase						):
						try:
							_lowerCAmelCase			=word.index(__UpperCAmelCase  ,  __UpperCAmelCase						)
						except ValueError:
							new_word.extend(word[i:]						)
							break
						else:
							new_word.extend(word[i:j]						)
							_lowerCAmelCase			=j

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



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase=None						)				->  str:
				if self.add_bos_token:
					_lowerCAmelCase			=[self.bos_token_id]
				else:
					_lowerCAmelCase			=[]

				_lowerCAmelCase			=bos_token_ids + token_ids_a

				if token_ids_a is None:
					return output

				return output + bos_token_ids + token_ids_a



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase						)				->  Any:
				_lowerCAmelCase			=[]
				for token in re.findall(self.pat  ,  __UpperCAmelCase						):
					_lowerCAmelCase			="""""".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(__UpperCAmelCase						).split(""" """						)						)
				return bpe_tokens



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase						)				->  Optional[int]:
				return self.encoder.get(__UpperCAmelCase  ,  self.encoder.get(self.unk_token						)						)



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase						)				->  List[Any]:
				return self.decoder.get(__UpperCAmelCase						)



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase						)				->  Optional[int]:
				_lowerCAmelCase			="""""".join(__UpperCAmelCase						)
				_lowerCAmelCase			=bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8"""  ,  errors=self.errors						)
				return text



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase = None						)				->  Tuple[str]:
				if not os.path.isdir(__UpperCAmelCase						):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
					return
				_lowerCAmelCase			=os.path.join(
				    __UpperCAmelCase  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
				_lowerCAmelCase			=os.path.join(
				    __UpperCAmelCase  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]						)

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

				_lowerCAmelCase			=0
				with open(__UpperCAmelCase  ,  """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 __UpperCAmelCase						: 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			=token_index
						writer.write(""" """.join(__UpperCAmelCase						) + """\n"""						)
						index += 1

				return vocab_file, merge_file



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase=False  ,  **__UpperCAmelCase						)				->  Optional[int]:
				_lowerCAmelCase			=kwargs.pop("""add_prefix_space"""  ,  self.add_prefix_space						)
				if is_split_into_words or add_prefix_space:
					_lowerCAmelCase			=""" """ + text
				return (text, kwargs)



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase = False  ,  __UpperCAmelCase = None  ,  __UpperCAmelCase = None  ,  **__UpperCAmelCase  ,  )				->  str:
				_lowerCAmelCase			=super()._decode(
				    token_ids=__UpperCAmelCase  ,  skip_special_tokens=__UpperCAmelCase  ,  clean_up_tokenization_spaces=__UpperCAmelCase  ,  **__UpperCAmelCase  ,  )

				if truncate_before_pattern is not None and len(__UpperCAmelCase						) > 0:
					_lowerCAmelCase			=self.truncate(__UpperCAmelCase  ,  __UpperCAmelCase						)

				return decoded_text



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  Optional[Any]:
				def find_re(__UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						):
					_lowerCAmelCase			=pattern.search(__UpperCAmelCase  ,  __UpperCAmelCase						)
					return m.start() if m else -1

				_lowerCAmelCase			=[re.compile(__UpperCAmelCase  ,  re.MULTILINE						) for pattern in truncate_before_pattern]

				_lowerCAmelCase			=list(re.finditer("""^print"""  ,  __UpperCAmelCase  ,  re.MULTILINE						)						)

				if len(__UpperCAmelCase						) > 1:
					_lowerCAmelCase			=completion[: prints[1].start()]

				_lowerCAmelCase			=list(re.finditer("""^def"""  ,  __UpperCAmelCase  ,  re.MULTILINE						)						)

				if len(__UpperCAmelCase						) > 1:
					_lowerCAmelCase			=completion[: defs[1].start()]

				_lowerCAmelCase			=0

				_lowerCAmelCase			=[
				    pos for pos in [find_re(__UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						) for terminal in terminals] if pos != -1
				]

				if len(__UpperCAmelCase						) > 0:
					return completion[: min(__UpperCAmelCase						)]
				else:
					return completion



"""simple docstring"""


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				Dict:
	_lowerCAmelCase			=UNetaDModel(**__UpperCamelCase )
	_lowerCAmelCase			=checkpoint["""all_modules.0.W"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.0.W"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.1.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.1.bias"""].data

	_lowerCAmelCase			=checkpoint["""all_modules.2.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.2.bias"""].data

	_lowerCAmelCase			=checkpoint["""all_modules.3.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.3.bias"""].data

	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-4]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-3]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-2]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-1]].data

	_lowerCAmelCase			=4

	def set_attention_weights(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase ):
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

	def set_resnet_weights(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase ):
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Dense_0.bias'''].data

		if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
			_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
			_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

	for i, block in enumerate(new_model_architecture.downsample_blocks ):
		_lowerCAmelCase			=hasattr(__UpperCamelCase					,				"""attentions""" )
		for j in range(len(block.resnets ) ):
			set_resnet_weights(block.resnets[j]					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
			if has_attentions:
				set_attention_weights(block.attentions[j]					,				__UpperCamelCase					,				__UpperCamelCase )
				module_index += 1

		if hasattr(__UpperCamelCase					,				"""downsamplers""" ) and block.downsamplers is not None:
			set_resnet_weights(block.resnet_down					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
			module_index += 1

	set_resnet_weights(new_model_architecture.mid_block.resnets[0]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1
	set_attention_weights(new_model_architecture.mid_block.attentions[0]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1
	set_resnet_weights(new_model_architecture.mid_block.resnets[1]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1

	for i, block in enumerate(new_model_architecture.up_blocks ):
		_lowerCAmelCase			=hasattr(__UpperCamelCase					,				"""attentions""" )
		for j in range(len(block.resnets ) ):
			set_resnet_weights(block.resnets[j]					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
		if has_attentions:
			set_attention_weights(
			    block.attentions[0]					,				__UpperCamelCase					,				__UpperCamelCase )  # why can there only be a single attention layer for up?
			module_index += 1

		if hasattr(__UpperCamelCase					,				"""resnet_up""" ) and block.resnet_up is not None:
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
			module_index += 1
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
			module_index += 1
			set_resnet_weights(block.resnet_up					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1

	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
	module_index += 1
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data

	return new_model_architecture.state_dict()


if __name__ == "__main__":
			__A 		= argparse.ArgumentParser()

			parser.add_argument(
			    '--checkpoint_path',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
			    type=str,
			    required=False,
			    help='Path to the checkpoint to convert.',
			)

			parser.add_argument(
			    '--config_file',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
			    type=str,
			    required=False,
			    help='The config json file corresponding to the architecture.',
			)

			parser.add_argument(
			    '--dump_path',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
			    type=str,
			    required=False,
			    help='Path to the output model.',
			)

			__A 		= parser.parse_args()

			__A 		= torch.load(args.checkpoint_path, map_location='cpu')

			with open(args.config_file) as f:
						__A 		= json.loads(f.read())

			__A 		= convert_ncsnpp_checkpoint(
			    checkpoint,
			    config,
			)

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

			__A 		= UNetaDModel(**config)
			model.load_state_dict(converted_checkpoint)

			try:
						__A 		= ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

						__A 		= ScoreSdeVePipeline(unet=model, scheduler=scheduler)
						pipe.save_pretrained(args.dump_path)
			except:  # noqa: E722
						model.save_pretrained(args.dump_path)



