'''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   :	str 							=   "src/diffusers"
_a   :	Dict 							=   "."


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

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Union[str, Any]:





       """simple docstring"""


       return line.startswith(lowerCamelCase__						) or len(lowerCamelCase__						) <= 1 or re.search(R"^\s*\)(\s*->.*:|:)\s*$"							,    lowerCamelCase__						) is not None

def _lowercase	(      lowerCamelCase__						)					->       str:





       """simple docstring"""


       __UpperCAmelCase  :				Any       				=		object_name.split("."						)
       __UpperCAmelCase  :				List[Any]       				=		0

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

       with open(os.path.join(lowerCamelCase__							,    f"""{module}.py"""						)							,    "r"							,    encoding="utf-8"							,    newline="\n"						) as f:
              __UpperCAmelCase  :				List[str]       				=		f.readlines()

       # Now let's find the class / func in the code!
       __UpperCAmelCase  :				Optional[Any]       				=		""
       __UpperCAmelCase  :				Optional[Any]       				=		0
       for name in parts[i + 1 :]:
              while (
                  line_index < len(lowerCamelCase__						) 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(lowerCamelCase__						):
              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  :				Any       				=		line_index
       while line_index < len(lowerCamelCase__						) and _should_continue(lines[line_index]							,    lowerCamelCase__						):
              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(lowerCamelCase__						)


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

def _lowercase	(      lowerCamelCase__						)					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[int]       				=		code.split("\n"						)
       __UpperCAmelCase  :				Any       				=		0
       while idx < len(lowerCamelCase__						) and len(lines[idx]						) == 0:
              idx += 1
       if idx < len(lowerCamelCase__						):
              return re.search(R"^(\s*)\S"							,    lines[idx]						).groups()[0]
       return ""

def _lowercase	(      lowerCamelCase__						)					->       Optional[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		len(get_indent(lowerCamelCase__						)						) > 0
       if has_indent:
              __UpperCAmelCase  :				Tuple       				=		f"""class Bla:\n{code}"""
       __UpperCAmelCase  :				int       				=		black.Mode(target_versions={black.TargetVersion.PYaa}							,    line_length=119							,    preview=lowerCamelCase__						)
       __UpperCAmelCase  :				Union[str, Any]       				=		black.format_str(lowerCamelCase__							,    mode=lowerCamelCase__						)
       __UpperCAmelCase				, __UpperCAmelCase  :				Union[str, Any]       				=		style_docstrings_in_code(lowerCamelCase__						)
       return result[len("class Bla:\n"						) :] if has_indent else result

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__=False						)					->       Any:





       """simple docstring"""


       with open(lowerCamelCase__							,    "r"							,    encoding="utf-8"							,    newline="\n"						) as f:
              __UpperCAmelCase  :				Optional[Any]       				=		f.readlines()
       __UpperCAmelCase  :				List[str]       				=		[]
       __UpperCAmelCase  :				Dict       				=		0
       # Not a for loop cause `lines` is going to change (if `overwrite=True`).
       while line_index < len(lowerCamelCase__						):
              __UpperCAmelCase  :				Dict       				=		_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  :				Any       				=		search.groups()
              __UpperCAmelCase  :				List[Any]       				=		find_code_in_diffusers(lowerCamelCase__						)
              __UpperCAmelCase  :				Optional[Any]       				=		get_indent(lowerCamelCase__						)

              __UpperCAmelCase  :				Tuple       				=		line_index + 1 if indent == theoretical_indent else line_index + 2
              __UpperCAmelCase  :				str       				=		theoretical_indent
              __UpperCAmelCase  :				Any       				=		start_index

              # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
              __UpperCAmelCase  :				Any       				=		True
              while line_index < len(lowerCamelCase__						) and should_continue:
                     line_index += 1
                     if line_index >= len(lowerCamelCase__						):
                            break
                     __UpperCAmelCase  :				Optional[int]       				=		lines[line_index]
                     __UpperCAmelCase  :				int       				=		_should_continue(lowerCamelCase__							,    lowerCamelCase__						) and re.search(f"""^{indent}# End copy"""							,    lowerCamelCase__						) 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  :				List[Any]       				=		"".join(lowerCamelCase__						)

              # Remove any nested `Copied from` comments to avoid circular copies
              __UpperCAmelCase  :				str       				=		[line for line in theoretical_code.split("\n"						) if _re_copy_warning.search(lowerCamelCase__						) is None]
              __UpperCAmelCase  :				int       				=		"\n".join(lowerCamelCase__						)

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

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

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

def _lowercase	(      lowerCamelCase__ = False						)					->       Optional[int]:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[int]       				=		glob.glob(os.path.join(lowerCamelCase__							,    "**/*.py"						)							,    recursive=lowerCamelCase__						)
       __UpperCAmelCase  :				Union[str, Any]       				=		[]
       for filename in all_files:
              __UpperCAmelCase  :				Tuple       				=		is_copy_consistent(lowerCamelCase__							,    lowerCamelCase__						)
              diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
       if not overwrite and len(lowerCamelCase__						) > 0:
              __UpperCAmelCase  :				Dict       				=		"\n".join(lowerCamelCase__						)
              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   :	Union[str, Any] 							=   argparse.ArgumentParser()
   parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
   _a   :	Any 							=   parser.parse_args()

   check_copies(args.fix_and_overwrite)







'''simple docstring'''




import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_a   :	Union[str, Any] 							=   HfApi()

_a   :	int 							=   {}
# fmt: off
_a   :	Optional[int] 							=   torch.tensor([
    -0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467,
    1.2_342, -2.2_485, 0.4_636, 0.8_076, -0.7_991, 0.3_969, 0.8_498, 0.9_189,
    -1.8_887, -3.3_522, 0.7_639, 0.2_040, 0.6_271, -2.7_148, -1.6_316, 3.0_839,
    0.3_186, 0.2_721, -0.9_759, -1.2_461, 2.6_257, 1.3_557
])
_a   :	Optional[Any] 							=   torch.tensor([
    -2.3_639, -2.5_344, 0.0_054, -0.6_674, 1.5_990, 1.0_158, 0.3_124, -2.1_436,
    1.8_795, -2.5_429, -0.1_566, -0.3_973, 1.2_490, 2.6_447, 1.2_283, -0.5_208,
    -2.8_154, -3.5_119, 2.3_838, 1.2_033, 1.7_201, -2.1_256, -1.4_576, 2.7_948,
    2.4_204, -0.9_752, -1.2_546, 0.8_027, 3.2_758, 3.1_365
])
_a   :	int 							=   torch.tensor([
    -0.6_531, -0.6_891, -0.3_172, -0.5_375, -0.9_140, -0.5_367, -0.1_175, -0.7_869,
    -0.3_808, -0.4_513, -0.2_098, -0.0_083, 0.3_183, 0.5_140, 0.2_247, -0.1_304,
    -0.1_302, -0.2_802, -0.2_084, -0.2_025, -0.4_967, -0.4_873, -0.0_861, 0.6_925,
    0.0_250, 0.1_290, -0.1_543, 0.6_316, 1.0_460, 1.4_943
])
_a   :	str 							=   torch.tensor([
    0.0_911, 0.1_107, 0.0_182, 0.0_435, -0.0_805, -0.0_608, 0.0_381, 0.2_172,
    -0.0_280, 0.1_327, -0.0_299, -0.0_255, -0.0_050, -0.1_170, -0.1_046, 0.0_309,
    0.1_367, 0.1_728, -0.0_533, -0.0_748, -0.0_534, 0.1_624, 0.0_384, -0.1_805,
    -0.0_707, 0.0_642, 0.0_220, -0.0_134, -0.1_333, -0.1_505
])
_a   :	Union[str, Any] 							=   torch.tensor([
    0.1_321, 0.1_337, 0.0_440, 0.0_622, -0.0_591, -0.0_370, 0.0_503, 0.2_133,
    -0.0_177, 0.1_415, -0.0_116, -0.0_112, 0.0_044, -0.0_980, -0.0_789, 0.0_395,
    0.1_502, 0.1_785, -0.0_488, -0.0_514, -0.0_404, 0.1_539, 0.0_454, -0.1_559,
    -0.0_665, 0.0_659, 0.0_383, -0.0_005, -0.1_266, -0.1_386
])
_a   :	Any 							=   torch.tensor([
    0.1_154, 0.1_218, 0.0_307, 0.0_526, -0.0_711, -0.0_541, 0.0_366, 0.2_078,
    -0.0_267, 0.1_317, -0.0_226, -0.0_193, -0.0_014, -0.1_055, -0.0_902, 0.0_330,
    0.1_391, 0.1_709, -0.0_562, -0.0_693, -0.0_560, 0.1_482, 0.0_381, -0.1_683,
    -0.0_681, 0.0_661, 0.0_331, -0.0_046, -0.1_268, -0.1_431
])
_a   :	List[Any] 							=   torch.tensor([
    0.1_192, 0.1_240, 0.0_414, 0.0_606, -0.0_557, -0.0_412, 0.0_430, 0.2_042,
    -0.0_200, 0.1_385, -0.0_115, -0.0_132, 0.0_017, -0.0_965, -0.0_802, 0.0_398,
    0.1_433, 0.1_747, -0.0_458, -0.0_533, -0.0_407, 0.1_545, 0.0_419, -0.1_574,
    -0.0_645, 0.0_626, 0.0_341, -0.0_010, -0.1_199, -0.1_390
])
_a   :	Optional[int] 							=   torch.tensor([
    0.1_075, 0.1_074, 0.0_205, 0.0_431, -0.0_774, -0.0_607, 0.0_298, 0.2_042,
    -0.0_320, 0.1_267, -0.0_281, -0.0_250, -0.0_064, -0.1_091, -0.0_946, 0.0_290,
    0.1_328, 0.1_650, -0.0_580, -0.0_738, -0.0_586, 0.1_440, 0.0_337, -0.1_746,
    -0.0_712, 0.0_605, 0.0_250, -0.0_099, -0.1_316, -0.1_473
])
_a   :	Tuple 							=   torch.tensor([
    -1.4_572, -2.0_481, -0.0_414, -0.6_005, 1.4_136, 0.5_848, 0.4_028, -2.7_330,
    1.2_212, -2.1_228, 0.2_155, 0.4_039, 0.7_662, 2.0_535, 0.7_477, -0.3_243,
    -2.1_758, -2.7_648, 1.6_947, 0.7_026, 1.2_338, -1.6_078, -0.8_682, 2.2_810,
    1.8_574, -0.5_718, -0.5_586, -0.0_186, 2.3_415, 2.1_251])
_a   :	List[Any] 							=   torch.tensor([
    -1.3_690, -1.9_720, -0.4_090, -0.6_966, 1.4_660, 0.9_938, -0.1_385, -2.7_324,
    0.7_736, -1.8_917, 0.2_923, 0.4_293, 0.1_693, 1.4_112, 1.1_887, -0.3_181,
    -2.2_160, -2.6_381, 1.3_170, 0.8_163, 0.9_240, -1.6_544, -0.6_099, 2.5_259,
    1.6_430, -0.9_090, -0.9_392, -0.0_126, 2.4_268, 2.3_266
])
_a   :	Optional[Any] 							=   torch.tensor([
    -1.3_525, -1.9_628, -0.3_956, -0.6_860, 1.4_664, 1.0_014, -0.1_259, -2.7_212,
    0.7_772, -1.8_811, 0.2_996, 0.4_388, 0.1_704, 1.4_029, 1.1_701, -0.3_027,
    -2.2_053, -2.6_287, 1.3_350, 0.8_131, 0.9_274, -1.6_292, -0.6_098, 2.5_131,
    1.6_505, -0.8_958, -0.9_298, -0.0_151, 2.4_257, 2.3_355
])
_a   :	Union[str, Any] 							=   torch.tensor([
    -2.0_585, -2.7_897, -0.2_850, -0.8_940, 1.9_052, 0.5_702, 0.6_345, -3.8_959,
    1.5_932, -3.2_319, 0.1_974, 0.0_287, 1.7_566, 2.6_543, 0.8_387, -0.5_351,
    -3.2_736, -4.3_375, 2.9_029, 1.6_390, 1.4_640, -2.1_701, -1.9_013, 2.9_341,
    3.4_981, -0.6_255, -1.1_644, -0.1_591, 3.7_097, 3.2_066
])
_a   :	Optional[int] 							=   torch.tensor([
    -2.3_139, -2.5_594, -0.0_197, -0.6_785, 1.7_001, 1.1_606, 0.3_075, -2.1_740,
    1.8_071, -2.5_630, -0.0_926, -0.3_811, 1.2_116, 2.6_246, 1.2_731, -0.5_398,
    -2.8_153, -3.6_140, 2.3_893, 1.3_262, 1.6_258, -2.1_856, -1.3_267, 2.8_395,
    2.3_779, -1.0_623, -1.2_468, 0.8_959, 3.3_367, 3.2_243
])
_a   :	Union[str, Any] 							=   torch.tensor([
    -2.0_628, -2.7_667, -0.2_089, -0.8_263, 2.0_539, 0.5_992, 0.6_495, -3.8_336,
    1.6_025, -3.2_817, 0.1_721, -0.0_633, 1.7_516, 2.7_039, 0.8_100, -0.5_908,
    -3.2_113, -4.4_343, 2.9_257, 1.3_632, 1.5_562, -2.1_489, -1.9_894, 3.0_560,
    3.3_396, -0.7_328, -1.0_417, 0.0_383, 3.7_093, 3.2_343
])
_a   :	str 							=   torch.tensor([
    -1.4_574, -2.0_569, -0.0_473, -0.6_117, 1.4_018, 0.5_769, 0.4_129, -2.7_344,
    1.2_241, -2.1_397, 0.2_000, 0.3_937, 0.7_616, 2.0_453, 0.7_324, -0.3_391,
    -2.1_746, -2.7_744, 1.6_963, 0.6_921, 1.2_187, -1.6_172, -0.8_877, 2.2_439,
    1.8_471, -0.5_839, -0.5_605, -0.0_464, 2.3_250, 2.1_219
])
# fmt: on

_a   :	Optional[Any] 							=   api.list_models(filter="diffusers")
for mod in models:
   if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
      _a   :	List[str] 							=   "/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]

      print(f"""Started running {mod.modelId}!!!""")

      if mod.modelId.startswith("CompVis"):
         _a   :	int 							=   UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
      else:
         _a   :	Optional[int] 							=   UNetaDModel.from_pretrained(local_checkpoint)

      torch.manual_seed(0)
      random.seed(0)

      _a   :	str 							=   torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
      _a   :	str 							=   torch.tensor([10] * noise.shape[0])
      with torch.no_grad():
         _a   :	str 							=   model(noise, time_step).sample

      assert torch.allclose(
          logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1e-3
      )
      print(f"""{mod.modelId} has passed successfully!!!""")






