

import argparse
import os
import re


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

# Matches is_xxx_available()
lowerCAmelCase							:  str   	=						re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
lowerCAmelCase							:  List[str]   	=						re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


lowerCAmelCase							:  Union[str, Any]   	=						"""
{0} = None
"""

lowerCAmelCase							:  List[str]   	=						"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


lowerCAmelCase							:  List[Any]   	=						"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       _re_backend.findall(_UpperCAmelCase    )
						if len(_UpperCAmelCase    ) == 0:
												return None

						return "_and_".join(_UpperCAmelCase    )







def 			A_      (    ):
						with open(os.path.join(_UpperCAmelCase				,	"__init__.py"    )				,	"r"				,	encoding="utf-8"				,	newline="\n"    ) as f:
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       f.readlines()

						# Get to the point we do the actual imports for type checking
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       0
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       {}
						# Go through the end of the file
						while line_index < len(_UpperCAmelCase    ):
												# If the line contains is_backend_available, we grab all objects associated with the `else` block
												SCREAMING_SNAKE_CASE_: List[str]  		=       find_backend(lines[line_index]    )
												if backend is not None:
																		while not lines[line_index].startswith("else:"    ):
																								line_index += 1
																		line_index += 1
																		SCREAMING_SNAKE_CASE_: str  		=       []
																		# Until we unindent, add backend objects to the list
																		while line_index < len(_UpperCAmelCase    ) and len(lines[line_index]    ) > 1:
																								SCREAMING_SNAKE_CASE_: List[Any]  		=       lines[line_index]
																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       _re_single_line_import.search(_UpperCAmelCase    )
																								if single_line_import_search is not None:
																														objects.extend(single_line_import_search.groups()[0].split(", "    )    )
																								elif line.startswith(" " * 8    ):
																														objects.append(line[8:-2]    )
																								line_index += 1

																		if len(_UpperCAmelCase    ) > 0:
																								SCREAMING_SNAKE_CASE_: Any  		=       objects
												else:
																		line_index += 1

						return backend_specific_objects







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						if name.isupper():
												return DUMMY_CONSTANT.format(_UpperCAmelCase    )
						elif name.islower():
												return DUMMY_FUNCTION.format(_UpperCAmelCase				,	_UpperCAmelCase    )
						else:
												return DUMMY_CLASS.format(_UpperCAmelCase				,	_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase=None    ):
						if backend_specific_objects is None:
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       {}

						for backend, objects in backend_specific_objects.items():
												SCREAMING_SNAKE_CASE_: Tuple  		=       "[" + ", ".join(f"\"{b}\"" for b in backend.split("_and_"    )    ) + "]"
												SCREAMING_SNAKE_CASE_: Tuple  		=       "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
												dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
												dummy_file += "\n".join([create_dummy_object(_UpperCAmelCase				,	_UpperCAmelCase    ) for o in objects]    )
												SCREAMING_SNAKE_CASE_: Any  		=       dummy_file

						return dummy_files







def 			A_      (    _UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						SCREAMING_SNAKE_CASE_: int  		=       {"torch": "pt"}

						# Locate actual dummy modules and read their content.
						SCREAMING_SNAKE_CASE_: Dict  		=       os.path.join(_UpperCAmelCase				,	"utils"    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       {
						    backend: os.path.join(_UpperCAmelCase				,	f"dummy_{short_names.get(_UpperCAmelCase				,	_UpperCAmelCase    )}_objects.py"    )
						    for backend in dummy_files.keys()
						}

						SCREAMING_SNAKE_CASE_: Any  		=       {}
						for backend, file_path in dummy_file_paths.items():
												if os.path.isfile(_UpperCAmelCase    ):
																		with open(_UpperCAmelCase				,	"r"				,	encoding="utf-8"				,	newline="\n"    ) as f:
																								SCREAMING_SNAKE_CASE_: Tuple  		=       f.read()
												else:
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       ""

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    f"Updating diffusers.utils.dummy_{short_names.get(_UpperCAmelCase				,	_UpperCAmelCase    )}_objects.py as the main "
																								    "__init__ has new objects."    )
																								with open(dummy_file_paths[backend]				,	"w"				,	encoding="utf-8"				,	newline="\n"    ) as f:
																														f.write(dummy_files[backend]    )
																		else:
																								raise ValueError(
																								    "The main __init__ has objects that are not present in "
																								    f"diffusers.utils.dummy_{short_names.get(_UpperCAmelCase				,	_UpperCAmelCase    )}_objects.py. Run `make fix-copies` "
																								    "to fix this."    )


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

						check_dummies(args.fix_and_overwrite)









import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    Adafactor,
						    AdamW,
						    get_constant_schedule,
						    get_constant_schedule_with_warmup,
						    get_cosine_schedule_with_warmup,
						    get_cosine_with_hard_restarts_schedule_with_warmup,
						    get_inverse_sqrt_schedule,
						    get_linear_schedule_with_warmup,
						    get_polynomial_decay_schedule_with_warmup,
						)







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=10    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       []
						for _ in range(_UpperCAmelCase    ):
												lrs.append(scheduler.get_lr()[0]    )
												scheduler.step()
						return lrs







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=10    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       []
						for step in range(_UpperCAmelCase    ):
												lrs.append(scheduler.get_lr()[0]    )
												scheduler.step()
												if step == num_steps // 2:
																		with tempfile.TemporaryDirectory() as tmpdirname:
																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       os.path.join(_UpperCAmelCase				,	"schedule.bin"    )
																								torch.save(scheduler.state_dict()				,	_UpperCAmelCase    )

																								SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.load(_UpperCAmelCase    )
																								scheduler.load_state_dict(_UpperCAmelCase    )
						return lrs



@require_torch
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Tuple):
									self.assertEqual(len(lowerCAmelCase__)   ,						len(lowerCAmelCase__))
									for a, b in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
															self.assertAlmostEqual(lowerCAmelCase__   ,						lowerCAmelCase__   ,						delta=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.tensor([0.1, -0.2, -0.1]   ,						requires_grad=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: str  		=       torch.tensor([0.4, 0.2, -0.5])
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									SCREAMING_SNAKE_CASE_: int  		=       AdamW(params=[w]   ,						lr=2E-1   ,						weight_decay=0.0)
									for _ in range(100):
															SCREAMING_SNAKE_CASE_: Dict  		=       criterion(lowerCAmelCase__   ,						lowerCAmelCase__)
															loss.backward()
															optimizer.step()
															w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
															w.grad.zero_()
									self.assertListAlmostEqual(w.tolist()   ,						[0.4, 0.2, -0.5]   ,						tol=1E-2)




			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.tensor([0.1, -0.2, -0.1]   ,						requires_grad=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       torch.tensor([0.4, 0.2, -0.5])
									SCREAMING_SNAKE_CASE_: Any  		=       nn.MSELoss()
									# No warmup, constant schedule, no gradient clipping
									SCREAMING_SNAKE_CASE_: int  		=       Adafactor(
									    params=[w]   ,						lr=1E-2   ,						eps=(1E-30, 1E-3)   ,						clip_threshold=1.0   ,						decay_rate=-0.8   ,						betaa=lowerCAmelCase__   ,						weight_decay=0.0   ,						relative_step=lowerCAmelCase__   ,						scale_parameter=lowerCAmelCase__   ,						warmup_init=lowerCAmelCase__   ,						)
									for _ in range(1000):
															SCREAMING_SNAKE_CASE_: List[Any]  		=       criterion(lowerCAmelCase__   ,						lowerCAmelCase__)
															loss.backward()
															optimizer.step()
															w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
															w.grad.zero_()
									self.assertListAlmostEqual(w.tolist()   ,						[0.4, 0.2, -0.5]   ,						tol=1E-2)







@require_torch
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Union[str, Any]										=     nn.Linear(50		,       50	) if is_torch_available() else None
			_UpperCAmelCase   :							List[Any]										=     AdamW(m.parameters()		,       lr=10.0	) if is_torch_available() else None
			_UpperCAmelCase   :							Optional[Any]										=     10



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[Any]=None):
									self.assertEqual(len(lowerCAmelCase__)   ,						len(lowerCAmelCase__))
									for a, b in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
															self.assertAlmostEqual(lowerCAmelCase__   ,						lowerCAmelCase__   ,						delta=lowerCAmelCase__   ,						msg=lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Dict  		=       {"num_warmup_steps": 2, "num_training_steps": 10}
									# schedulers doct format
									# function: (sched_args_dict, expected_learning_rates)
									SCREAMING_SNAKE_CASE_: Dict  		=       {
									    get_constant_schedule: ({}, [10.0] * self.num_steps),
									    get_constant_schedule_with_warmup: (
									        {"num_warmup_steps": 4},
									        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
									    ),
									    get_linear_schedule_with_warmup: (
									        {**common_kwargs},
									        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
									    ),
									    get_cosine_schedule_with_warmup: (
									        {**common_kwargs},
									        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
									    ),
									    get_cosine_with_hard_restarts_schedule_with_warmup: (
									        {**common_kwargs, "num_cycles": 2},
									        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
									    ),
									    get_polynomial_decay_schedule_with_warmup: (
									        {**common_kwargs, "power": 2.0, "lr_end": 1E-7},
									        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
									    ),
									    get_inverse_sqrt_schedule: (
									        {"num_warmup_steps": 2},
									        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
									    ),
									}

									for scheduler_func, data in scheds.items():
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       data

															SCREAMING_SNAKE_CASE_: List[Any]  		=       scheduler_func(self.optimizer   ,						**lowerCAmelCase__)
															self.assertEqual(len([scheduler.get_lr()[0]])   ,						1)
															SCREAMING_SNAKE_CASE_: int  		=       unwrap_schedule(lowerCAmelCase__   ,						self.num_steps)
															self.assertListAlmostEqual(
															    lowerCAmelCase__   ,						lowerCAmelCase__   ,						tol=1E-2   ,						msg=F"failed for {scheduler_func} in normal scheduler"   ,						)

															SCREAMING_SNAKE_CASE_: List[str]  		=       scheduler_func(self.optimizer   ,						**lowerCAmelCase__)
															if scheduler_func.__name__ != "get_constant_schedule":
																					LambdaScheduleWrapper.wrap_scheduler(lowerCAmelCase__)  # wrap to test picklability of the schedule
															SCREAMING_SNAKE_CASE_: Tuple  		=       unwrap_and_save_reload_schedule(lowerCAmelCase__   ,						self.num_steps)
															self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__   ,						msg=F"failed for {scheduler_func} in save and reload")







class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     str   ,						lowerCAmelCase__  :     List[str]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       fn



			def __call__( self  :     Optional[int]   ,						*lowerCAmelCase__  :     List[Any]   ,						**lowerCAmelCase__  :     Tuple):
									return self.fn(*lowerCAmelCase__   ,						**lowerCAmelCase__)




			@classmethod
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: str  		=       list(map(self   ,						scheduler.lr_lambdas))






