




"""simple docstring"""
import argparse
import os
import re


SCREAMING_SNAKE_CASE__   =					"src/transformers"

# Pattern that looks at the indentation in a line.
SCREAMING_SNAKE_CASE__   =					re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
SCREAMING_SNAKE_CASE__   =					re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
SCREAMING_SNAKE_CASE__   =					re.compile(R"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
SCREAMING_SNAKE_CASE__   =					re.compile(R"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
SCREAMING_SNAKE_CASE__   =					re.compile(R"\[([^\]]+)\]")





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[str, Any]   )   ->			str:





						"""simple docstring"""






						snake_case        =     _re_indent.search(_UpperCamelCase   )
						return "" if search is None else search.groups()[0]





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Union[str, Any]=""	,     _UpperCamelCase    :			Any=None	,     _UpperCamelCase    :			int=None   )   ->			Dict:





						"""simple docstring"""






						snake_case        =     0
						snake_case        =     code.split('\n'   )
						if start_prompt is not None:
												while not lines[index].startswith(_UpperCamelCase   ):
																		index += 1
												snake_case        =     ['\n'.join(lines[:index]   )]
						else:
												snake_case        =     []

						# We split into blocks until we get to the `end_prompt` (or the end of the block).
						snake_case        =     [lines[index]]
						index += 1
						while index < len(_UpperCamelCase   ) and (end_prompt is None or not lines[index].startswith(_UpperCamelCase   )):
												if len(lines[index]   ) > 0 and get_indent(lines[index]   ) == indent_level:
																		if len(_UpperCamelCase   ) > 0 and get_indent(current_block[-1]   ).startswith(indent_level + ' '   ):
																								current_block.append(lines[index]   )
																								blocks.append('\n'.join(_UpperCamelCase   )   )
																								if index < len(_UpperCamelCase   ) - 1:
																														snake_case        =     [lines[index + 1]]
																														index += 1
																								else:
																														snake_case        =     []
																		else:
																								blocks.append('\n'.join(_UpperCamelCase   )   )
																								snake_case        =     [lines[index]]
												else:
																		current_block.append(lines[index]   )
												index += 1

						# Adds current block if it's nonempty.
						if len(_UpperCamelCase   ) > 0:
												blocks.append('\n'.join(_UpperCamelCase   )   )

						# Add final block after end_prompt if provided.
						if end_prompt is not None and index < len(_UpperCamelCase   ):
												blocks.append('\n'.join(lines[index:]   )   )

						return blocks





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			str:





						"""simple docstring"""






						def _inner(_UpperCamelCase    :			str   ):
												return key(_UpperCamelCase   ).lower().replace('_'	,     ''   )

						return _inner





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Any	,     _UpperCamelCase    :			List[str]=None   )   ->			List[str]:





						"""simple docstring"""






						def noop(_UpperCamelCase    :			int   ):
												return x

						if key is None:
												snake_case        =     noop
						# Constants are all uppercase, they go first.
						snake_case        =     [obj for obj in objects if key(_UpperCamelCase   ).isupper()]
						# Classes are not all uppercase but start with a capital, they go second.
						snake_case        =     [obj for obj in objects if key(_UpperCamelCase   )[0].isupper() and not key(_UpperCamelCase   ).isupper()]
						# Functions begin with a lowercase, they go last.
						snake_case        =     [obj for obj in objects if not key(_UpperCamelCase   )[0].isupper()]

						snake_case        =     ignore_underscore(_UpperCamelCase   )
						return sorted(_UpperCamelCase	,     key=_UpperCamelCase   ) + sorted(_UpperCamelCase	,     key=_UpperCamelCase   ) + sorted(_UpperCamelCase	,     key=_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[str, Any]   )   ->			Dict:





						"""simple docstring"""






						def _replace(_UpperCamelCase    :			Union[str, Any]   ):
												snake_case        =     match.groups()[0]
												if "," not in imports:
																		return f"""[{imports}]"""
												snake_case        =     [part.strip().replace('"'	,     ''   ) for part in imports.split(','   )]
												# We will have a final empty element if the line finished with a comma.
												if len(keys[-1]   ) == 0:
																		snake_case        =     keys[:-1]
												return "[" + ", ".join([f"""\"{k}\"""" for k in sort_objects(_UpperCamelCase   )]   ) + "]"

						snake_case        =     import_statement.split('\n'   )
						if len(_UpperCamelCase   ) > 3:
												# Here we have to sort internal imports that are on several lines (one per name):
												# key: [
												#     "object1",
												#     "object2",
												#     ...
												# ]

												# We may have to ignore one or two lines on each side.
												snake_case        =     2 if lines[1].strip() == '[' else 1
												snake_case        =     [(i, _re_strip_line.search(_UpperCamelCase   ).groups()[0]) for i, line in enumerate(lines[idx:-idx]   )]
												snake_case        =     sort_objects(_UpperCamelCase	,     key=lambda _UpperCamelCase   : x[1]   )
												snake_case        =     [lines[x[0] + idx] for x in sorted_indices]
												return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]   )
						elif len(_UpperCamelCase   ) == 3:
												# Here we have to sort internal imports that are on one separate line:
												# key: [
												#     "object1", "object2", ...
												# ]
												if _re_bracket_content.search(lines[1]   ) is not None:
																		snake_case        =     _re_bracket_content.sub(_replace	,     lines[1]   )
												else:
																		snake_case        =     [part.strip().replace('"'	,     ''   ) for part in lines[1].split(','   )]
																		# We will have a final empty element if the line finished with a comma.
																		if len(keys[-1]   ) == 0:
																								snake_case        =     keys[:-1]
																		snake_case        =     get_indent(lines[1]   ) + ', '.join([f"""\"{k}\"""" for k in sort_objects(_UpperCamelCase   )]   )
												return "\n".join(_UpperCamelCase   )
						else:
												# Finally we have to deal with imports fitting on one line
												snake_case        =     _re_bracket_content.sub(_replace	,     _UpperCamelCase   )
												return import_statement





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			List[Any]=True   )   ->			Any:





						"""simple docstring"""






						with open(_UpperCamelCase	,     encoding='utf-8'   ) as f:
												snake_case        =     f.read()

						if "_import_structure" not in code:
												return

						# Blocks of indent level 0
						snake_case        =     split_code_in_indented_blocks(
						    _UpperCamelCase	,     start_prompt='_import_structure = {'	,     end_prompt='if TYPE_CHECKING:'   )

						# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
						for block_idx in range(1	,     len(_UpperCamelCase   ) - 1   ):
												# Check if the block contains some `_import_structure`s thingy to sort.
												snake_case        =     main_blocks[block_idx]
												snake_case        =     block.split('\n'   )

												# Get to the start of the imports.
												snake_case        =     0
												while line_idx < len(_UpperCamelCase   ) and "_import_structure" not in block_lines[line_idx]:
																		# Skip dummy import blocks
																		if "import dummy" in block_lines[line_idx]:
																								snake_case        =     len(_UpperCamelCase   )
																		else:
																								line_idx += 1
												if line_idx >= len(_UpperCamelCase   ):
																		continue

												# Ignore beginning and last line: they don't contain anything.
												snake_case        =     '\n'.join(block_lines[line_idx:-1]   )
												snake_case        =     get_indent(block_lines[1]   )
												# Slit the internal block into blocks of indent level 1.
												snake_case        =     split_code_in_indented_blocks(_UpperCamelCase	,     indent_level=_UpperCamelCase   )
												# We have two categories of import key: list or _import_structure[key].append/extend
												snake_case        =     _re_direct_key if '_import_structure = {' in block_lines[0] else _re_indirect_key
												# Grab the keys, but there is a trap: some lines are empty or just comments.
												snake_case        =     [(pattern.search(_UpperCamelCase   ).groups()[0] if pattern.search(_UpperCamelCase   ) is not None else None) for b in internal_blocks]
												# We only sort the lines with a key.
												snake_case        =     [(i, key) for i, key in enumerate(_UpperCamelCase   ) if key is not None]
												snake_case        =     [x[0] for x in sorted(_UpperCamelCase	,     key=lambda _UpperCamelCase   : x[1]   )]

												# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
												snake_case        =     0
												snake_case        =     []
												for i in range(len(_UpperCamelCase   )   ):
																		if keys[i] is None:
																								reorderded_blocks.append(internal_blocks[i]   )
																		else:
																								snake_case        =     sort_objects_in_import(internal_blocks[sorted_indices[count]]   )
																								reorderded_blocks.append(_UpperCamelCase   )
																								count += 1

        # And we put our main block back together with its first and last line.
												snake_case        =     '\n'.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]   )

						if code != "\n".join(_UpperCamelCase   ):
												if check_only:
																		return True
												else:
																		print(f"""Overwriting {file}."""   )
																		with open(_UpperCamelCase	,     'w'	,     encoding='utf-8'   ) as f:
																								f.write('\n'.join(_UpperCamelCase   )   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[Any]=True   )   ->			List[str]:





						"""simple docstring"""






						snake_case        =     []
						for root, _, files in os.walk(_UpperCamelCase   ):
												if "__init__.py" in files:
																		snake_case        =     sort_imports(os.path.join(_UpperCamelCase	,     '__init__.py'   )	,     check_only=_UpperCamelCase   )
																		if result:
																								snake_case        =     [os.path.join(_UpperCamelCase	,     '__init__.py'   )]
						if len(_UpperCamelCase   ) > 0:
												raise ValueError(f"""Would overwrite {len(_UpperCamelCase   )} files, run `make style`."""   )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					argparse.ArgumentParser()
					parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
					SCREAMING_SNAKE_CASE__   =					parser.parse_args()

					sort_imports_in_all_inits(check_only=args.check_only)



"""simple docstring"""
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


SCREAMING_SNAKE_CASE__   =					"true"





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			Dict=8_2	,     _UpperCamelCase    :			int=1_6   )   ->			str:





						"""simple docstring"""






						set_seed(4_2   )
						snake_case        =     RegressionModel()
						snake_case        =     deepcopy(_UpperCamelCase   )
						snake_case        =     RegressionDataset(length=_UpperCamelCase   )
						snake_case        =     DataLoader(_UpperCamelCase	,     batch_size=_UpperCamelCase   )
						model.to(accelerator.device   )
						snake_case   ,snake_case        =     accelerator.prepare(_UpperCamelCase	,     _UpperCamelCase   )
						return model, ddp_model, dataloader





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Accelerator	,     _UpperCamelCase    :			Optional[Any]=False   )   ->			List[str]:





						"""simple docstring"""






						snake_case        =     AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'   )
						snake_case        =     load_dataset('glue'	,     'mrpc'	,     split='validation'   )

						def tokenize_function(_UpperCamelCase    :			Optional[Any]   ):
												snake_case        =     tokenizer(examples['sentence1']	,     examples['sentence2']	,     truncation=_UpperCamelCase	,     max_length=_UpperCamelCase   )
												return outputs

						with accelerator.main_process_first():
												snake_case        =     dataset.map(
												    _UpperCamelCase	,     batched=_UpperCamelCase	,     remove_columns=['idx', 'sentence1', 'sentence2']	,     )

						snake_case        =     tokenized_datasets.rename_column('label'	,     'labels'   )

						def collate_fn(_UpperCamelCase    :			Optional[Any]   ):
												if use_longest:
																		return tokenizer.pad(_UpperCamelCase	,     padding='longest'	,     return_tensors='pt'   )
												return tokenizer.pad(_UpperCamelCase	,     padding='max_length'	,     max_length=1_2_8	,     return_tensors='pt'   )

						return DataLoader(_UpperCamelCase	,     shuffle=_UpperCamelCase	,     collate_fn=_UpperCamelCase	,     batch_size=1_6   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			Any   )   ->			List[Any]:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Any	,     _UpperCamelCase    :			Optional[Any]   )   ->			Dict:





						"""simple docstring"""






						snake_case        =     []
						for batch in dataloader:
												snake_case   ,snake_case        =     batch.values()
												with torch.no_grad():
																		snake_case        =     model(_UpperCamelCase   )
																		snake_case   ,snake_case        =     accelerator.gather_for_metrics((logit, target)   )
																		logits_and_targets.append((logit, target)   )
						snake_case   ,snake_case        =     [], []
						for logit, targ in logits_and_targets:
												logits.append(_UpperCamelCase   )
												targs.append(_UpperCamelCase   )
						snake_case   ,snake_case        =     torch.cat(_UpperCamelCase   ), torch.cat(_UpperCamelCase   )
						return logits, targs





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Accelerator	,     _UpperCamelCase    :			Tuple=8_2	,     _UpperCamelCase    :			Optional[Any]=False	,     _UpperCamelCase    :			int=False	,     _UpperCamelCase    :			List[str]=1_6   )   ->			Optional[Any]:





						"""simple docstring"""






						snake_case   ,snake_case   ,snake_case        =     get_basic_setup(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )
						snake_case   ,snake_case        =     generate_predictions(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )
						assert (
						    len(_UpperCamelCase   ) == num_samples
						), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCamelCase   )}"""





def 				lowerCAmelCase__      (     _UpperCamelCase    :			bool = False	,     _UpperCamelCase    :			bool = False   )   ->			Tuple:





						"""simple docstring"""






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

						# Then do distributed
						snake_case   ,snake_case   ,snake_case        =     setup['ddp']
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		snake_case        =     model(**_UpperCamelCase   )
												snake_case        =     outputs.logits.argmax(dim=-1   )
												snake_case        =     batch['labels']
												snake_case   ,snake_case        =     accelerator.gather_for_metrics((preds, references)   )
												metric.add_batch(predictions=_UpperCamelCase	,     references=_UpperCamelCase   )
						snake_case        =     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 				lowerCAmelCase__      (     )   ->			Tuple:





						"""simple docstring"""






						snake_case        =     Accelerator(split_batches=_UpperCamelCase	,     dispatch_batches=_UpperCamelCase   )
						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(_UpperCamelCase	,     _UpperCamelCase   )
																								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]:
																		snake_case        =     Accelerator(split_batches=_UpperCamelCase	,     dispatch_batches=_UpperCamelCase   )
																		if accelerator.is_local_main_process:
																								print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""   )
																		test_torch_metrics(_UpperCamelCase	,     9_9   )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('**Test last batch is not dropped when perfectly divisible**'   )
						snake_case        =     Accelerator()
						test_torch_metrics(_UpperCamelCase	,     5_1_2   )
						accelerator.state._reset_state()





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple   )   ->			str:





						"""simple docstring"""






						main()


if __name__ == "__main__":
					main()


