


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[int]  				='src/diffusers'

# Matches is_xxx_available()
__lowerCAmelCase	: str  				=re.compile(r'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
__lowerCAmelCase	: Tuple  				=re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


__lowerCAmelCase	: Union[str, Any]  				='\n{0} = None\n'

__lowerCAmelCase	: Optional[Any]  				='\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


__lowerCAmelCase	: List[Any]  				='\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     str					=		_re_backend.findall(lowercase__      )
						if len(lowercase__      ) == 0:
												return None

						return "_and_".join(lowercase__      )


def        _UpperCamelCase							(      ):
						with open(os.path.join(lowercase__    ,       '''__init__.py'''      )    ,       '''r'''    ,       encoding='''utf-8'''    ,       newline='''\n'''      ) as f:
												__SCREAMING_SNAKE_CASE						:     Dict					=		f.readlines()

						# Get to the point we do the actual imports for type checking
						__SCREAMING_SNAKE_CASE						:     List[str]					=		0
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{}
						# Go through the end of the file
						while line_index < len(lowercase__      ):
												# If the line contains is_backend_available, we grab all objects associated with the `else` block
												__SCREAMING_SNAKE_CASE						:     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(lowercase__      ) and len(lines[line_index]      ) > 1:
																								__SCREAMING_SNAKE_CASE						:     Tuple					=		lines[line_index]
																								__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		_re_single_line_import.search(lowercase__      )
																								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(lowercase__      ) > 0:
																								__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		objects
												else:
																		line_index += 1

						return backend_specific_objects


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


def        _UpperCamelCase							(      lowercase__=None      ):
						if backend_specific_objects is None:
												__SCREAMING_SNAKE_CASE						:     List[str]					=		read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						__SCREAMING_SNAKE_CASE						:     List[str]					=		{}

						for backend, objects in backend_specific_objects.items():
												__SCREAMING_SNAKE_CASE						:     int					=		'''[''' + ''', '''.join(F'''"{b}"''' for b in backend.split('''_and_'''      )      ) + ''']'''
												__SCREAMING_SNAKE_CASE						:     List[str]					=		'''# 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(lowercase__    ,       lowercase__      ) for o in objects]      )
												__SCREAMING_SNAKE_CASE						:     int					=		dummy_file

						return dummy_files


def        _UpperCamelCase							(      lowercase__=False      ):
						__SCREAMING_SNAKE_CASE						:     List[str]					=		create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		{'''torch''': '''pt'''}

						# Locate actual dummy modules and read their content.
						__SCREAMING_SNAKE_CASE						:     Tuple					=		os.path.join(lowercase__    ,       '''utils'''      )
						__SCREAMING_SNAKE_CASE						:     str					=		{
						    backend: os.path.join(lowercase__    ,       F'''dummy_{short_names.get(lowercase__    ,       lowercase__      )}_objects.py'''      )
						    for backend in dummy_files.keys()
						}

						__SCREAMING_SNAKE_CASE						:     Tuple					=		{}
						for backend, file_path in dummy_file_paths.items():
												if os.path.isfile(lowercase__      ):
																		with open(lowercase__    ,       '''r'''    ,       encoding='''utf-8'''    ,       newline='''\n'''      ) as f:
																								__SCREAMING_SNAKE_CASE						:     Dict					=		f.read()
												else:
																		__SCREAMING_SNAKE_CASE						:     int					=		''''''

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    F'''Updating diffusers.utils.dummy_{short_names.get(lowercase__    ,       lowercase__      )}_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(lowercase__    ,       lowercase__      )}_objects.py. Run `make fix-copies` '''
																								    '''to fix this.'''      )


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

						check_dummies(args.fix_and_overwrite)




import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__lowerCAmelCase	: List[str]  				='true'


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=82    ,       lowercase__=16      ):
						set_seed(42      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		RegressionModel()
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		deepcopy(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Any					=		RegressionDataset(length=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		DataLoader(lowercase__    ,       batch_size=lowercase__      )
						model.to(accelerator.device      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		accelerator.prepare(lowercase__    ,       lowercase__      )
						return model, ddp_model, dataloader


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=False      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''      )
						__SCREAMING_SNAKE_CASE						:     str					=		load_dataset('''glue'''    ,       '''mrpc'''    ,       split='''validation'''      )

						def tokenize_function(lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     Dict					=		tokenizer(examples['''sentence1''']    ,       examples['''sentence2''']    ,       truncation=lowercase__    ,       max_length=lowercase__      )
												return outputs

						with accelerator.main_process_first():
												__SCREAMING_SNAKE_CASE						:     Tuple					=		dataset.map(
												    lowercase__    ,       batched=lowercase__    ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']    ,       )

						__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokenized_datasets.rename_column('''label'''    ,       '''labels'''      )

						def collate_fn(lowercase__      ):
												if use_longest:
																		return tokenizer.pad(lowercase__    ,       padding='''longest'''    ,       return_tensors='''pt'''      )
												return tokenizer.pad(lowercase__    ,       padding='''max_length'''    ,       max_length=128    ,       return_tensors='''pt'''      )

						return DataLoader(lowercase__    ,       shuffle=lowercase__    ,       collate_fn=lowercase__    ,       batch_size=16      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     str					=		Accelerator(dispatch_batches=lowercase__    ,       split_batches=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		get_dataloader(lowercase__    ,       not dispatch_batches      )
						__SCREAMING_SNAKE_CASE						:     List[str]					=		AutoModelForSequenceClassification.from_pretrained(
						    '''hf-internal-testing/mrpc-bert-base-cased'''    ,       return_dict=lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		accelerator.prepare(lowercase__    ,       lowercase__      )
						return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[str]					=		[]
						for batch in dataloader:
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		batch.values()
												with torch.no_grad():
																		__SCREAMING_SNAKE_CASE						:     Dict					=		model(lowercase__      )
																		__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		accelerator.gather_for_metrics((logit, target)      )
																		logits_and_targets.append((logit, target)      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		[], []
						for logit, targ in logits_and_targets:
												logits.append(lowercase__      )
												targs.append(lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		torch.cat(lowercase__      ), torch.cat(lowercase__      )
						return logits, targs


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=82    ,       lowercase__=False    ,       lowercase__=False    ,       lowercase__=16      ):
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Tuple					=		get_basic_setup(lowercase__    ,       lowercase__    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		generate_predictions(lowercase__    ,       lowercase__    ,       lowercase__      )
						assert (
						    len(lowercase__      ) == num_samples
						), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowercase__      )}'''


def        _UpperCamelCase							(      lowercase__ = False    ,       lowercase__ = False      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		evaluate.load('''glue'''    ,       '''mrpc'''      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[Any]					=		get_mrpc_setup(lowercase__    ,       lowercase__      )
						# First do baseline
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		setup['''no''']
						model.to(lowercase__      )
						model.eval()
						for batch in dataloader:
												batch.to(lowercase__      )
												with torch.inference_mode():
																		__SCREAMING_SNAKE_CASE						:     Dict					=		model(**lowercase__      )
												__SCREAMING_SNAKE_CASE						:     Dict					=		outputs.logits.argmax(dim=-1      )
												metric.add_batch(predictions=lowercase__    ,       references=batch['''labels''']      )
						__SCREAMING_SNAKE_CASE						:     int					=		metric.compute()

						# Then do distributed
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		setup['''ddp''']
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		__SCREAMING_SNAKE_CASE						:     int					=		model(**lowercase__      )
												__SCREAMING_SNAKE_CASE						:     str					=		outputs.logits.argmax(dim=-1      )
												__SCREAMING_SNAKE_CASE						:     Any					=		batch['''labels''']
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		accelerator.gather_for_metrics((preds, references)      )
												metric.add_batch(predictions=lowercase__    ,       references=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		metric.compute()

						for key in "accuracy f1".split():
												assert math.isclose(
												    baseline[key]    ,       distributed[key]      ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''


def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		Accelerator(split_batches=lowercase__    ,       dispatch_batches=lowercase__      )
						if accelerator.is_local_main_process:
												datasets.utils.logging.set_verbosity_warning()
												transformers.utils.logging.set_verbosity_warning()
						else:
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						# These are a bit slower so they should only be ran on the GPU or TPU
						if torch.cuda.is_available() or is_tpu_available():
												if accelerator.is_local_main_process:
																		print('''**Testing gather_for_metrics**'''      )
												for split_batches in [True, False]:
																		for dispatch_batches in [True, False]:
																								if accelerator.is_local_main_process:
																														print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''      )
																								test_mrpc(lowercase__    ,       lowercase__      )
																								accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('''**Test torch metrics**'''      )
						for split_batches in [True, False]:
												for dispatch_batches in [True, False]:
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		Accelerator(split_batches=lowercase__    ,       dispatch_batches=lowercase__      )
																		if accelerator.is_local_main_process:
																								print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''      )
																		test_torch_metrics(lowercase__    ,       99      )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('''**Test last batch is not dropped when perfectly divisible**'''      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		Accelerator()
						test_torch_metrics(lowercase__    ,       512      )
						accelerator.state._reset_state()


def        _UpperCamelCase							(      lowercase__      ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()
