


import argparse
import os
import re


UpperCamelCase__   : int					  = """src/diffusers"""

# Pattern that looks at the indentation in a line.
UpperCamelCase__   : List[Any]					  = re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCamelCase__   : int					  = re.compile(r"""^\s*\"([^\"]+)\":""")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
UpperCamelCase__   : Union[str, Any]					  = re.compile(r"""^\s*_import_structure\[\"([^\"]+)\"\]""")
# Pattern that matches `"key",` and puts `key` in group 0.
UpperCamelCase__   : List[str]					  = re.compile(r"""^\s*\"([^\"]+)\",\s*$""")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
UpperCamelCase__   : Dict					  = re.compile(r"""\[([^\]]+)\]""")




def    A_(			A   ):
	UpperCAmelCase_								=     _re_indent.search(A   )
	return "" if search is None else search.groups()[0]




def    A_(			A				,      A=""				,      A=None				,      A=None   ):
	UpperCAmelCase_								=     0
	UpperCAmelCase_								=     code.split("""\n"""   )
	if start_prompt is not None:
		while not lines[index].startswith(A   ):
			index += 1
		UpperCAmelCase_								=     ["""\n""".join(lines[:index]   )]
	else:
		UpperCAmelCase_								=     []

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

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

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

	return blocks




def    A_(			A   ):

	def _inner(A   ):
		return key(A   ).lower().replace("""_"""				,      """"""   )

	return _inner




def    A_(			A				,      A=None   ):

	# If no key is provided, we use a noop.
	def noop(A   ):
		return x

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

	UpperCAmelCase_								=     ignore_underscore(A   )
	return sorted(A				,      key=A   ) + sorted(A				,      key=A   ) + sorted(A				,      key=A   )




def    A_(			A   ):

	# This inner function sort imports between [ ].
	def _replace(A   ):
		UpperCAmelCase_								=     match.groups()[0]
		if "," not in imports:
			return f"""[{imports}]"""
		UpperCAmelCase_								=     [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:
			UpperCAmelCase_								=     keys[:-1]
		return "[" + ", ".join([f"""\"{k}\"""" for k in sort_objects(A   )]   ) + "]"

	UpperCAmelCase_								=     import_statement.split("""\n"""   )
	if len(A   ) > 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.
		UpperCAmelCase_								=     2 if lines[1].strip() == """[""" else 1
		UpperCAmelCase_								=     [(i, _re_strip_line.search(A   ).groups()[0]) for i, line in enumerate(lines[idx:-idx]   )]
		UpperCAmelCase_								=     sort_objects(A				,      key=lambda A   : x[1]   )
		UpperCAmelCase_								=     [lines[x[0] + idx] for x in sorted_indices]
		return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]   )
	elif len(A   ) == 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:
			UpperCAmelCase_								=     _re_bracket_content.sub(_replace				,      lines[1]   )
		else:
			UpperCAmelCase_								=     [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:
				UpperCAmelCase_								=     keys[:-1]
			UpperCAmelCase_								=     get_indent(lines[1]   ) + """, """.join([f"""\"{k}\"""" for k in sort_objects(A   )]   )
		return "\n".join(A   )
	else:
		# Finally we have to deal with imports fitting on one line
		UpperCAmelCase_								=     _re_bracket_content.sub(_replace				,      A   )
		return import_statement




def    A_(			A				,      A=True   ):
	with open(A				,      """r"""   ) as f:
		UpperCAmelCase_								=     f.read()

	if "_import_structure" not in code:
		return

	# Blocks of indent level 0
	UpperCAmelCase_								=     split_code_in_indented_blocks(
	    A				,      start_prompt="""_import_structure = {"""				,      end_prompt="""if TYPE_CHECKING:"""   )

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

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

		# Ignore beginning and last line: they don't contain anything.
		UpperCAmelCase_								=     """\n""".join(block_lines[line_idx:-1]   )
		UpperCAmelCase_								=     get_indent(block_lines[1]   )
		# Slit the internal block into blocks of indent level 1.
		UpperCAmelCase_								=     split_code_in_indented_blocks(A				,      indent_level=A   )
		# We have two categories of import key: list or _import_structure[key].append/extend
		UpperCAmelCase_								=     _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.
		UpperCAmelCase_								=     [(pattern.search(A   ).groups()[0] if pattern.search(A   ) is not None else None) for b in internal_blocks]
		# We only sort the lines with a key.
		UpperCAmelCase_								=     [(i, key) for i, key in enumerate(A   ) if key is not None]
		UpperCAmelCase_								=     [x[0] for x in sorted(A				,      key=lambda A   : x[1]   )]

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

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

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




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


if __name__ == "__main__":
				UpperCamelCase__   : List[str]					  = argparse.ArgumentParser()
				parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
				UpperCamelCase__   : Optional[Any]					  = parser.parse_args()

				sort_imports_in_all_inits(check_only=args.check_only)









import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase__   : Union[str, Any]					  = 16
UpperCamelCase__   : Tuple					  = 32




def    A_(			A				,      A = 16   ):
	UpperCAmelCase_								=     AutoTokenizer.from_pretrained("""bert-base-cased"""   )
	UpperCAmelCase_								=     load_dataset("""glue"""				,      """mrpc"""   )

	def tokenize_function(A   ):
		# max_length=None => use the model max length (it's actually the default)
		UpperCAmelCase_								=     tokenizer(examples["""sentence1"""]				,      examples["""sentence2"""]				,      truncation=A				,      max_length=A   )
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		UpperCAmelCase_								=     datasets.map(
		    A				,      batched=A				,      remove_columns=["""idx""", """sentence1""", """sentence2"""]				,      )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	UpperCAmelCase_								=     tokenized_datasets.rename_column("""label"""				,      """labels"""   )

	def collate_fn(A   ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		UpperCAmelCase_								=     128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			UpperCAmelCase_								=     16
		elif accelerator.mixed_precision != "no":
			UpperCAmelCase_								=     8
		else:
			UpperCAmelCase_								=     None

		return tokenizer.pad(
		    A				,      padding="""longest"""				,      max_length=A				,      pad_to_multiple_of=A				,      return_tensors="""pt"""				,      )

	# Instantiate dataloaders.
	UpperCAmelCase_								=     DataLoader(
	    tokenized_datasets["""train"""]				,      shuffle=A				,      collate_fn=A				,      batch_size=A   )
	UpperCAmelCase_								=     DataLoader(
	    tokenized_datasets["""validation"""]				,      shuffle=A				,      collate_fn=A				,      batch_size=A   )

	return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
				from accelerate.test_utils.training import mocked_dataloaders

				UpperCamelCase__   : str					  = mocked_dataloaders  # noqa: F811




def    A_(			A				,      A   ):
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,      A   ) == "1":
		UpperCAmelCase_								=     2
	# New Code #
	UpperCAmelCase_								=     int(args.gradient_accumulation_steps   )
	# Initialize accelerator
	UpperCAmelCase_								=     Accelerator(
	    cpu=args.cpu				,      mixed_precision=args.mixed_precision				,      gradient_accumulation_steps=A   )
	if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
		raise NotImplementedError(
		    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""   )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	UpperCAmelCase_								=     config["""lr"""]
	UpperCAmelCase_								=     int(config["""num_epochs"""]   )
	UpperCAmelCase_								=     int(config["""seed"""]   )
	UpperCAmelCase_								=     int(config["""batch_size"""]   )

	UpperCAmelCase_								=     evaluate.load("""glue"""				,      """mrpc"""   )

	set_seed(A   )
	UpperCAmelCase_						,	UpperCAmelCase_								=     get_dataloaders(A				,      A   )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCAmelCase_								=     AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""				,      return_dict=A   )

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	UpperCAmelCase_								=     model.to(accelerator.device   )

	# Instantiate optimizer
	UpperCAmelCase_								=     AdamW(params=model.parameters()				,      lr=A   )

	# Instantiate scheduler
	UpperCAmelCase_								=     get_linear_schedule_with_warmup(
	    optimizer=A				,      num_warmup_steps=100				,      num_training_steps=(len(A   ) * num_epochs)				,      )

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     accelerator.prepare(
	    A				,      A				,      A				,      A				,      A   )

	# Now we train the model
	for epoch in range(A   ):
		model.train()
		for step, batch in enumerate(A   ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device   )
			# New code #
			# We use the new `accumulate` context manager to perform gradient accumulation
			# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
			with accelerator.accumulate(A   ):
				UpperCAmelCase_								=     model(**A   )
				UpperCAmelCase_								=     output.loss
				accelerator.backward(A   )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		for step, batch in enumerate(A   ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device   )
			with torch.no_grad():
				UpperCAmelCase_								=     model(**A   )
			UpperCAmelCase_								=     outputs.logits.argmax(dim=-1   )
			UpperCAmelCase_						,	UpperCAmelCase_								=     accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
			metric.add_batch(
			    predictions=A				,      references=A				,      )

		UpperCAmelCase_								=     metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""				,      A   )




def    A_(			):
	UpperCAmelCase_								=     argparse.ArgumentParser(description="""Simple example of training script."""   )
	parser.add_argument(
	    """--mixed_precision"""				,      type=A				,      default=A				,      choices=["""no""", """fp16""", """bf16""", """fp8"""]				,      help="""Whether to use mixed precision. Choose"""
	    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
	    """and an Nvidia Ampere GPU."""				,      )
	# New Code #
	parser.add_argument(
	    """--gradient_accumulation_steps"""				,      type=A				,      default=1				,      help="""The number of minibatches to be ran before gradients are accumulated."""				,      )
	parser.add_argument("""--cpu"""				,      action="""store_true"""				,      help="""If passed, will train on the CPU."""   )
	UpperCAmelCase_								=     parser.parse_args()
	UpperCAmelCase_								=     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(A				,      A   )


if __name__ == "__main__":
				main()


