



"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
A:   Optional[Any]								=		"src/diffusers"
A:   List[Any]								=		"."


# This is to make sure the diffusers module imported is the one in the repo.
A:   Optional[Any]								=		importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(DIFFUSERS_PATH, "__init__.py"),
    submodule_search_locations=[DIFFUSERS_PATH],
)
A:   int								=		spec.loader.load_module()

def       _snake_case						(						UpperCamelCase						:		List[str]			,		UpperCamelCase						:		Any						):
 return line.startswith(__snake_case						) or len(__snake_case						) <= 1 or re.search(R"""^\s*\)(\s*->.*:|:)\s*$"""			,		__snake_case						) is not None

def       _snake_case						(						UpperCamelCase						:		Optional[Any]						):
 UpperCAmelCase	:					Union[str, Any]	=	object_name.split("""."""						)
 UpperCAmelCase	:					Union[str, Any]	=	0

 # First let's find the module where our object lives.
 UpperCAmelCase	:					Dict	=	parts[i]
 while i < len(__snake_case						) and not os.path.isfile(os.path.join(__snake_case			,		F"{module}.py"						)						):
  i += 1
  if i < len(__snake_case						):
   UpperCAmelCase	:					Tuple	=	os.path.join(__snake_case			,		parts[i]						)
 if i >= len(__snake_case						):
  raise ValueError(F"`object_name` should begin with the name of a module of diffusers but got {object_name}."						)

 with open(os.path.join(__snake_case			,		F"{module}.py"						)			,		"""r"""			,		encoding="""utf-8"""			,		newline="""\n"""						) as f:
  UpperCAmelCase	:					Optional[int]	=	f.readlines()

 # Now let's find the class / func in the code!
 UpperCAmelCase	:					Any	=	""""""
 UpperCAmelCase	:					Tuple	=	0
 for name in parts[i + 1 :]:
  while (
      line_index < len(__snake_case						) and re.search(RF"^{indent}(class|def)\s+{name}(\(|\:)"			,		lines[line_index]						) is None
  ):
   line_index += 1
  indent += "    "
  line_index += 1

 if line_index >= len(__snake_case						):
  raise ValueError(F" {object_name} does not match any function or class in {module}."						)

 # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
 UpperCAmelCase	:					List[Any]	=	line_index
 while line_index < len(__snake_case						) and _should_continue(lines[line_index]			,		__snake_case						):
  line_index += 1
 # Clean up empty lines at the end (if any).
 while len(lines[line_index - 1]						) <= 1:
  line_index -= 1

 UpperCAmelCase	:					Any	=	lines[start_index:line_index]
 return "".join(__snake_case						)


A:   List[str]								=		re.compile(R"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
A:   Union[str, Any]								=		re.compile(R"^\s*(\S+)->(\S+)(\s+.*|$)")
A:   Optional[Any]								=		re.compile(R"<FILL\s+[^>]*>")

def       _snake_case						(						UpperCamelCase						:		List[Any]						):
 UpperCAmelCase	:					Dict	=	code.split("""\n"""						)
 UpperCAmelCase	:					Tuple	=	0
 while idx < len(__snake_case						) and len(lines[idx]						) == 0:
  idx += 1
 if idx < len(__snake_case						):
  return re.search(R"""^(\s*)\S"""			,		lines[idx]						).groups()[0]
 return ""

def       _snake_case						(						UpperCamelCase						:		Dict						):
 UpperCAmelCase	:					Dict	=	len(get_indent(__snake_case						)						) > 0
 if has_indent:
  UpperCAmelCase	:					Optional[Any]	=	F"class Bla:\n{code}"
 UpperCAmelCase	:					str	=	black.Mode(target_versions={black.TargetVersion.PYaa}			,		line_length=119			,		preview=__snake_case						)
 UpperCAmelCase	:					Dict	=	black.format_str(__snake_case			,		mode=__snake_case						)
 UpperCAmelCase	,		UpperCAmelCase	:					int	=	style_docstrings_in_code(__snake_case						)
 return result[len("""class Bla:\n"""						) :] if has_indent else result

def       _snake_case						(						UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		Any=False						):







 with open(__snake_case			,		"""r"""			,		encoding="""utf-8"""			,		newline="""\n"""						) as f:
  UpperCAmelCase	:					List[Any]	=	f.readlines()
 UpperCAmelCase	:					Union[str, Any]	=	[]
 UpperCAmelCase	:					Optional[Any]	=	0
 # Not a for loop cause `lines` is going to change (if `overwrite=True`).
 while line_index < len(__snake_case						):
  UpperCAmelCase	:					Tuple	=	_re_copy_warning.search(lines[line_index]						)
  if search is None:
   line_index += 1
   continue

  # There is some copied code here, let's retrieve the original.
  UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					List[Any]	=	search.groups()
  UpperCAmelCase	:					List[Any]	=	find_code_in_diffusers(__snake_case						)
  UpperCAmelCase	:					List[str]	=	get_indent(__snake_case						)

  UpperCAmelCase	:					Optional[int]	=	line_index + 1 if indent == theoretical_indent else line_index + 2
  UpperCAmelCase	:					Dict	=	theoretical_indent
  UpperCAmelCase	:					Tuple	=	start_index

  # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
  UpperCAmelCase	:					Optional[int]	=	True
  while line_index < len(__snake_case						) and should_continue:
   line_index += 1
   if line_index >= len(__snake_case						):
    break
   UpperCAmelCase	:					Tuple	=	lines[line_index]
   UpperCAmelCase	:					List[str]	=	_should_continue(__snake_case			,		__snake_case						) and re.search(F"^{indent}# End copy"			,		__snake_case						) is None
  # Clean up empty lines at the end (if any).
  while len(lines[line_index - 1]						) <= 1:
   line_index -= 1

  UpperCAmelCase	:					Dict	=	lines[start_index:line_index]
  UpperCAmelCase	:					Tuple	=	"""""".join(__snake_case						)

  # Remove any nested `Copied from` comments to avoid circular copies
  UpperCAmelCase	:					Union[str, Any]	=	[line for line in theoretical_code.split("""\n"""						) if _re_copy_warning.search(__snake_case						) is None]
  UpperCAmelCase	:					List[str]	=	"""\n""".join(__snake_case						)

  # Before comparing, use the `replace_pattern` on the original code.
  if len(__snake_case						) > 0:
   UpperCAmelCase	:					List[Any]	=	replace_pattern.replace("""with"""			,		""""""						).split(""","""						)
   UpperCAmelCase	:					Optional[int]	=	[_re_replace_pattern.search(__snake_case						) for p in patterns]
   for pattern in patterns:
    if pattern is None:
     continue
    UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Union[str, Any]	=	pattern.groups()
    UpperCAmelCase	:					int	=	re.sub(__snake_case			,		__snake_case			,		__snake_case						)
    if option.strip() == "all-casing":
     UpperCAmelCase	:					int	=	re.sub(obja.lower()			,		obja.lower()			,		__snake_case						)
     UpperCAmelCase	:					List[Any]	=	re.sub(obja.upper()			,		obja.upper()			,		__snake_case						)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
   UpperCAmelCase	:					Optional[Any]	=	blackify(lines[start_index - 1] + theoretical_code						)
   UpperCAmelCase	:					Tuple	=	theoretical_code[len(lines[start_index - 1]						) :]

  # Test for a diff and act accordingly.
  if observed_code != theoretical_code:
   diffs.append([object_name, start_index]						)
   if overwrite:
    UpperCAmelCase	:					int	=	lines[:start_index] + [theoretical_code] + lines[line_index:]
    UpperCAmelCase	:					Dict	=	start_index + 1

 if overwrite and len(__snake_case						) > 0:
  # Warn the user a file has been modified.
  print(F"Detected changes, rewriting {filename}."						)
  with open(__snake_case			,		"""w"""			,		encoding="""utf-8"""			,		newline="""\n"""						) as f:
   f.writelines(__snake_case						)
 return diffs

def       _snake_case						(						UpperCamelCase						:		int = False						):
 UpperCAmelCase	:					Dict	=	glob.glob(os.path.join(__snake_case			,		"""**/*.py"""						)			,		recursive=__snake_case						)
 UpperCAmelCase	:					List[str]	=	[]
 for filename in all_files:
  UpperCAmelCase	:					List[Any]	=	is_copy_consistent(__snake_case			,		__snake_case						)
  diffs += [F"- {filename}: copy does not match {d[0]} at line {d[1]}" for d in new_diffs]
 if not overwrite and len(__snake_case						) > 0:
  UpperCAmelCase	:					Tuple	=	"""\n""".join(__snake_case						)
  raise Exception(
      """Found the following copy inconsistencies:\n"""
      + diff
      + """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."""						)


if __name__ == "__main__":
      A:   Tuple								=		argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      A:   Optional[Any]								=		parser.parse_args()

      check_copies(args.fix_and_overwrite)







import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
			import torch


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


_a 						=      logging.get_logger(__name__)
_a 						=      {"vocab_file": "spiece.model"}

_a 						=      {
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

_a 						=      {
    "AI-Sweden/gpt-sw3-126m": 2_048,
    "AI-Sweden/gpt-sw3-350m": 2_048,
    "AI-Sweden/gpt-sw3-1.6b": 2_048,
    "AI-Sweden/gpt-sw3-6.7b": 2_048,
    "AI-Sweden/gpt-sw3-20b": 2_048,
}






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







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





			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase = None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		{} if sp_model_kwargs is None else sp_model_kwargs

				lowerCamelCase__								=		kwargs.get('''name_or_path'''							)
				if name_or_path is None:
					logger.warning(
					    '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
					    ''' you are testing the model, this can safely be ignored'''							)
					lowerCamelCase__								=		'''None'''

				# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
				lowerCamelCase__								=		'''<|endoftext|>''' if eos_token is None else eos_token
				lowerCamelCase__								=		'''<unk>''' if unk_token is None else unk_token
				if "gpt-sw3-7b" in name_or_path:
					lowerCamelCase__								=		unk_token if pad_token is None else pad_token
					lowerCamelCase__								=		eos_token if bos_token is None else bos_token
				else:
					lowerCamelCase__								=		'''<pad>''' if pad_token is None else pad_token
					lowerCamelCase__								=		'''<s>''' if bos_token is None else bos_token

				super().__init__(
				    do_lower_case=__lowerCAmelCase       ,  remove_space=__lowerCAmelCase       ,  keep_accents=__lowerCAmelCase       ,  bos_token=__lowerCAmelCase       ,  eos_token=__lowerCAmelCase       ,  unk_token=__lowerCAmelCase       ,  pad_token=__lowerCAmelCase       ,  sp_model_kwargs=self.sp_model_kwargs       ,  **__lowerCAmelCase       ,  )

				lowerCamelCase__								=		do_lower_case
				lowerCamelCase__								=		remove_space
				lowerCamelCase__								=		keep_accents
				lowerCamelCase__								=		vocab_file

				lowerCamelCase__								=		spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(__lowerCAmelCase							)

				# Used for whitespace normalization in input texts
				# fmt : off
				lowerCamelCase__								=		{''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
				# fmt : on

				# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
				lowerCamelCase__								=		re.compile(
				    F'[{"".join(map(__lowerCAmelCase       ,  list(range(0       ,  9							)							) + list(range(1_1       ,  3_2							)							) + list(range(1_2_7       ,  1_6_0							)							) + [1_6_0, 1_7_3, 8_2_0_3]							)							)}]'							)





			def __getstate__(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.__dict__.copy()
				lowerCamelCase__								=		None
				return state





			def __setstate__(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		d

				# for backward compatibility
				if not hasattr(self       ,  '''sp_model_kwargs'''							):
					lowerCamelCase__								=		{}

				lowerCamelCase__								=		spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(self.vocab_file							)





			@property
			# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return len(self.sp_model							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.non_printing_characters_re.sub(''''''       ,  __lowerCAmelCase							)

				# Normalize whitespaces
				lowerCamelCase__								=		''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]							)

				# NFC Unicode normalization
				lowerCamelCase__								=		unicodedata.normalize('''NFC'''       ,  __lowerCAmelCase							)
				return text





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.preprocess_text(__lowerCAmelCase							)
				return self.sp_model.encode(__lowerCAmelCase       ,  out_type=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.sp_model.PieceToId(__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.sp_model.IdToPiece(__lowerCAmelCase							)





			@staticmethod
			def 		__lowerCamelCase				(    __lowerCAmelCase							):



				'''simple docstring'''







				return out_string





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		[]
				lowerCamelCase__								=		''''''
				lowerCamelCase__								=		False
				for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
						# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
						if not prev_is_special:
							out_string += " "

						out_string += self.sp_model.decode(__lowerCAmelCase							) + token
						lowerCamelCase__								=		True
						lowerCamelCase__								=		[]
					else:
						current_sub_tokens.append(__lowerCAmelCase							)
						lowerCamelCase__								=		False
				out_string += self.sp_model.decode(__lowerCAmelCase							)

				return out_string





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		{self.convert_ids_to_tokens(__lowerCAmelCase							): i for i in range(self.vocab_size							)}
				vocab.update(self.added_tokens_encoder							)
				return vocab





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







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

				if os.path.abspath(self.vocab_file							) != os.path.abspath(__lowerCAmelCase							) and os.path.isfile(self.vocab_file							):
					copyfile(self.vocab_file       ,  __lowerCAmelCase							)
				elif not os.path.isfile(self.vocab_file							):
					with open(__lowerCAmelCase       ,  '''wb'''							) as fi:
						lowerCamelCase__								=		self.sp_model.serialized_model_proto()
						fi.write(__lowerCAmelCase							)

				return (out_vocab_file,)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = False							):



				'''simple docstring'''







				if isinstance(__lowerCAmelCase       ,  __lowerCAmelCase							):
					lowerCamelCase__								=		self.preprocess_text(__lowerCAmelCase							)
					lowerCamelCase__								=		self.sp_model.encode(__lowerCAmelCase							)
				else:
					lowerCamelCase__								=		[self.preprocess_text(__lowerCAmelCase							) for t in text]
					lowerCamelCase__								=		self.sp_model.encode(__lowerCAmelCase							)

				if return_tensors is True or return_tensors == "pt":
					lowerCamelCase__								=		torch.tensor(__lowerCAmelCase							)

				return token_ids





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.sp_model.decode(__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		[F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
				lowerCamelCase__								=		(
				    F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(__lowerCAmelCase							) + F'{self.bos_token}Bot:'
				)
				return self.encode(text=__lowerCAmelCase							)



