





'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	pass





def      lowercase  (  lowerCAmelCase :						str   , lowerCAmelCase :						Dict[str, Callable]   , lowerCAmelCase :						int=None   , lowerCAmelCase :						Optional[int]=False):



				"""simple docstring"""


				try:
								_A :				Any				=  ast.parse(lowerCAmelCase)
				except SyntaxError as e:
								print('''The code generated by the agent is not valid.\n'''   , lowerCAmelCase)
								return
				if state is None:
								_A :				Any				=  {}
				_A :				List[Any]				=  None
				for idx, node in enumerate(expression.body):
								try:
												_A :				Optional[Any]				=  evaluate_ast(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
								except InterpretorError as e:
												_A :				Tuple				=  f"""Evaluation of the code stopped at line {idx} before the end because of the following error"""
												if chat_mode:
																msg += (
																    f""". Copy paste the following error message and send it back to the agent:\nI get an error: '{e}'"""
																)
												else:
																msg += f""":\n{e}"""
												print(lowerCAmelCase)
												break
								if line_result is not None:
												_A :				Optional[Any]				=  line_result

				return result





def      lowercase  (  lowerCAmelCase :						ast.AST   , lowerCAmelCase :						Dict[str, Any]   , lowerCAmelCase :						Dict[str, Callable]):



				"""simple docstring"""


				if isinstance(lowerCAmelCase   , ast.Assign):
								# Assignement -> we evaluate the assignement which should update the state
								# We return the variable assigned as it may be used to determine the final result.
								return evaluate_assign(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
				elif isinstance(lowerCAmelCase   , ast.Call):
								# Function call -> we return the value of the function call
								return evaluate_call(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
				elif isinstance(lowerCAmelCase   , ast.Constant):
								# Constant -> just return the value
								return expression.value
				elif isinstance(lowerCAmelCase   , ast.Dict):
								# Dict -> evaluate all keys and values
								_A :				Tuple				=  [evaluate_ast(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase) for k in expression.keys]
								_A :				Dict				=  [evaluate_ast(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase) for v in expression.values]
								return dict(zip(lowerCAmelCase   , lowerCAmelCase))
				elif isinstance(lowerCAmelCase   , ast.Expr):
								# Expression -> evaluate the content
								return evaluate_ast(expression.value   , lowerCAmelCase   , lowerCAmelCase)
				elif isinstance(lowerCAmelCase   , ast.For):
								# For loop -> execute the loop
								return evaluate_for(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
				elif isinstance(lowerCAmelCase   , ast.FormattedValue):
								# Formatted value (part of f-string) -> evaluate the content and return
								return evaluate_ast(expression.value   , lowerCAmelCase   , lowerCAmelCase)
				elif isinstance(lowerCAmelCase   , ast.If):
								# If -> execute the right branch
								return evaluate_if(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
				elif hasattr(lowerCAmelCase   , '''Index''') and isinstance(lowerCAmelCase   , ast.Index):
								return evaluate_ast(expression.value   , lowerCAmelCase   , lowerCAmelCase)
				elif isinstance(lowerCAmelCase   , ast.JoinedStr):
								return "".join([str(evaluate_ast(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)) for v in expression.values])
				elif isinstance(lowerCAmelCase   , ast.List):
								# List -> evaluate all elements
								return [evaluate_ast(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase) for elt in expression.elts]
				elif isinstance(lowerCAmelCase   , ast.Name):
								# Name -> pick up the value in the state
								return evaluate_name(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
				elif isinstance(lowerCAmelCase   , ast.Subscript):
								# Subscript -> return the value of the indexing
								return evaluate_subscript(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
				else:
								# For now we refuse anything else. Let's add things as we need them.
								raise InterpretorError(f"""{expression.__class__.__name__} is not supported.""")





def      lowercase  (  lowerCAmelCase :						Union[str, Any]   , lowerCAmelCase :						Union[str, Any]   , lowerCAmelCase :						List[str]):



				"""simple docstring"""


				_A :				str				=  assign.targets
				_A :				Optional[Any]				=  evaluate_ast(assign.value   , lowerCAmelCase   , lowerCAmelCase)

				if len(lowerCAmelCase) == 1:
								_A :				List[str]				=  result
				else:
								if len(lowerCAmelCase) != len(lowerCAmelCase):
												raise InterpretorError(f"""Expected {len(lowerCAmelCase)} values but got {len(lowerCAmelCase)}.""")
								for var_name, r in zip(lowerCAmelCase   , lowerCAmelCase):
												_A :				List[Any]				=  r
				return result





def      lowercase  (  lowerCAmelCase :						Dict   , lowerCAmelCase :						Dict   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


				if not isinstance(call.func   , ast.Name):
								raise InterpretorError(
								    f"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of """
								    f"""type {type(call.func)}.""")
				_A :				Union[str, Any]				=  call.func.id
				if func_name not in tools:
								raise InterpretorError(
								    f"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id}).""")

				_A :				str				=  tools[func_name]
				# Todo deal with args
				_A :				Union[str, Any]				=  [evaluate_ast(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase) for arg in call.args]
				_A :				Optional[int]				=  {keyword.arg: evaluate_ast(keyword.value   , lowerCAmelCase   , lowerCAmelCase) for keyword in call.keywords}
				return func(*lowerCAmelCase   , **lowerCAmelCase)





def      lowercase  (  lowerCAmelCase :						Tuple   , lowerCAmelCase :						int   , lowerCAmelCase :						List[Any]):



				"""simple docstring"""


				_A :				Dict				=  evaluate_ast(subscript.slice   , lowerCAmelCase   , lowerCAmelCase)
				_A :				str				=  evaluate_ast(subscript.value   , lowerCAmelCase   , lowerCAmelCase)
				if isinstance(lowerCAmelCase   , (list, tuple)):
								return value[int(lowerCAmelCase)]
				if index in value:
								return value[index]
				if isinstance(lowerCAmelCase   , lowerCAmelCase) and isinstance(lowerCAmelCase   , lowerCAmelCase):
								_A :				Tuple				=  difflib.get_close_matches(lowerCAmelCase   , list(value.keys()))
								if len(lowerCAmelCase) > 0:
												return value[close_matches[0]]

				raise InterpretorError(f"""Could not index {value} with '{index}'.""")





def      lowercase  (  lowerCAmelCase :						List[Any]   , lowerCAmelCase :						Optional[int]   , lowerCAmelCase :						Dict):



				"""simple docstring"""


				if name.id in state:
								return state[name.id]
				_A :				List[str]				=  difflib.get_close_matches(name.id   , list(state.keys()))
				if len(lowerCAmelCase) > 0:
								return state[close_matches[0]]
				raise InterpretorError(f"""The variable `{name.id}` is not defined.""")





def      lowercase  (  lowerCAmelCase :						Dict   , lowerCAmelCase :						Tuple   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


				if len(condition.ops) > 1:
								raise InterpretorError('''Cannot evaluate conditions with multiple operators''')

				_A :				Tuple				=  evaluate_ast(condition.left   , lowerCAmelCase   , lowerCAmelCase)
				_A :				List[str]				=  condition.ops[0]
				_A :				List[Any]				=  evaluate_ast(condition.comparators[0]   , lowerCAmelCase   , lowerCAmelCase)

				if isinstance(lowerCAmelCase   , ast.Eq):
								return left == right
				elif isinstance(lowerCAmelCase   , ast.NotEq):
								return left != right
				elif isinstance(lowerCAmelCase   , ast.Lt):
								return left < right
				elif isinstance(lowerCAmelCase   , ast.LtE):
								return left <= right
				elif isinstance(lowerCAmelCase   , ast.Gt):
								return left > right
				elif isinstance(lowerCAmelCase   , ast.GtE):
								return left >= right
				elif isinstance(lowerCAmelCase   , ast.Is):
								return left is right
				elif isinstance(lowerCAmelCase   , ast.IsNot):
								return left is not right
				elif isinstance(lowerCAmelCase   , ast.In):
								return left in right
				elif isinstance(lowerCAmelCase   , ast.NotIn):
								return left not in right
				else:
								raise InterpretorError(f"""Operator not supported: {comparator}""")





def      lowercase  (  lowerCAmelCase :						Any   , lowerCAmelCase :						Union[str, Any]   , lowerCAmelCase :						int):



				"""simple docstring"""


				_A :				Optional[Any]				=  None
				if evaluate_condition(if_statement.test   , lowerCAmelCase   , lowerCAmelCase):
								for line in if_statement.body:
												_A :				str				=  evaluate_ast(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
												if line_result is not None:
																_A :				Dict				=  line_result
				else:
								for line in if_statement.orelse:
												_A :				Tuple				=  evaluate_ast(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
												if line_result is not None:
																_A :				int				=  line_result
				return result





def      lowercase  (  lowerCAmelCase :						str   , lowerCAmelCase :						Optional[int]   , lowerCAmelCase :						Optional[Any]):



				"""simple docstring"""


				_A :				Optional[Any]				=  None
				_A :				Optional[int]				=  evaluate_ast(for_loop.iter   , lowerCAmelCase   , lowerCAmelCase)
				for counter in iterator:
								_A :				Union[str, Any]				=  counter
								for expression in for_loop.body:
												_A :				Any				=  evaluate_ast(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
												if line_result is not None:
																_A :				Optional[int]				=  line_result
				return result







'''simple docstring'''






import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator





def      lowercase  (  lowerCAmelCase :						Any):



				"""simple docstring"""


				_A :				Union[str, Any]				=  fname.split(os.path.sep)[-1]
				return re.search(R'''^(.*)_\d+\.jpg$'''   , lowerCAmelCase).groups()[0]



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=None			)       ->				int:
					_A :				Tuple				=  file_names
					_A :				Any				=  image_transform
					_A :				str				=  label_to_id




	def __len__(					self			)       ->				Dict:
					return len(self.file_names			)




	def __getitem__(					self   ,     UpperCAmelCase__			)       ->				Tuple:
					_A :				Union[str, Any]				=  self.file_names[idx]
					_A :				str				=  PIL.Image.open(UpperCAmelCase__			)
					_A :				str				=  raw_image.convert('''RGB'''			)
					if self.image_transform is not None:
									_A :				Tuple				=  self.image_transform(UpperCAmelCase__			)
					_A :				List[Any]				=  extract_label(UpperCAmelCase__			)
					if self.label_to_id is not None:
									_A :				Tuple				=  self.label_to_id[label]
					return {"image": image, "label": label}





def      lowercase  (  lowerCAmelCase :						str   , lowerCAmelCase :						List[Any]):



				"""simple docstring"""


				_A :				Union[str, Any]				=  Accelerator(cpu=args.cpu   , mixed_precision=args.mixed_precision)

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_A :				int				=  config['''lr''']
				_A :				str				=  int(config['''num_epochs'''])
				_A :				Dict				=  int(config['''seed'''])
				_A :				Any				=  int(config['''batch_size'''])
				_A :				List[str]				=  config['''image_size''']
				if not isinstance(lowerCAmelCase   , (list, tuple)):
								_A :				List[str]				=  (image_size, image_size)

				# Grab all the image filenames
				_A :				Optional[Any]				=  [os.path.join(args.data_dir   , lowerCAmelCase) for fname in os.listdir(args.data_dir) if fname.endswith('''.jpg''')]

				# Build the label correspondences
				_A :				str				=  [extract_label(lowerCAmelCase) for fname in file_names]
				_A :				List[Any]				=  list(set(lowerCAmelCase))
				id_to_label.sort()
				_A :				Dict				=  {lbl: i for i, lbl in enumerate(lowerCAmelCase)}

				# Set the seed before splitting the data.
				np.random.seed(lowerCAmelCase)
				torch.manual_seed(lowerCAmelCase)
				torch.cuda.manual_seed_all(lowerCAmelCase)

				# Split our filenames between train and validation
				_A :				Dict				=  np.random.permutation(len(lowerCAmelCase))
				_A :				List[str]				=  int(0.8 * len(lowerCAmelCase))
				_A :				Tuple				=  random_perm[:cut]
				_A :				Union[str, Any]				=  random_perm[cut:]

				# For training we use a simple RandomResizedCrop
				_A :				str				=  Compose([RandomResizedCrop(lowerCAmelCase   , scale=(0.5, 1.0)), ToTensor()])
				_A :				Tuple				=  PetsDataset(
				    [file_names[i] for i in train_split]   , image_transform=lowerCAmelCase   , label_to_id=lowerCAmelCase)

				# For evaluation, we use a deterministic Resize
				_A :				List[Any]				=  Compose([Resize(lowerCAmelCase), ToTensor()])
				_A :				str				=  PetsDataset([file_names[i] for i in eval_split]   , image_transform=lowerCAmelCase   , label_to_id=lowerCAmelCase)

				# Instantiate dataloaders.
				_A :				str				=  DataLoader(lowerCAmelCase   , shuffle=lowerCAmelCase   , batch_size=lowerCAmelCase   , num_workers=4)
				_A :				Optional[int]				=  DataLoader(lowerCAmelCase   , shuffle=lowerCAmelCase   , batch_size=lowerCAmelCase   , num_workers=4)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_A :				Tuple				=  create_model('''resnet50d'''   , pretrained=lowerCAmelCase   , num_classes=len(lowerCAmelCase))

				# 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).
				_A :				Tuple				=  model.to(accelerator.device)

				# Freezing the base model
				for param in model.parameters():
								_A :				int				=  False
				for param in model.get_classifier().parameters():
								_A :				Tuple				=  True

				# We normalize the batches of images to be a bit faster.
				_A :				List[Any]				=  torch.tensor(model.default_cfg['''mean'''])[None, :, None, None].to(accelerator.device)
				_A :				int				=  torch.tensor(model.default_cfg['''std'''])[None, :, None, None].to(accelerator.device)

				# Instantiate optimizer
				_A :				Optional[Any]				=  torch.optim.Adam(params=model.parameters()   , lr=lr / 25)

				# Instantiate learning rate scheduler
				_A :				List[str]				=  OneCycleLR(optimizer=lowerCAmelCase   , max_lr=lowerCAmelCase   , epochs=lowerCAmelCase   , steps_per_epoch=len(lowerCAmelCase))

				# 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.
				_A	,       _A	,       _A	,       _A	,       _A :				List[str]				=  accelerator.prepare(
				    lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)

				# Now we train the model
				for epoch in range(lowerCAmelCase):
								model.train()
								for step, batch in enumerate(lowerCAmelCase):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												_A :				Optional[Any]				=  {k: v.to(accelerator.device) for k, v in batch.items()}
												_A :				Tuple				=  (batch['''image'''] - mean) / std
												_A :				Any				=  model(lowerCAmelCase)
												_A :				Optional[Any]				=  torch.nn.functional.cross_entropy(lowerCAmelCase   , batch['''label'''])
												accelerator.backward(lowerCAmelCase)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

								model.eval()
								_A :				Optional[Any]				=  0
								_A :				Union[str, Any]				=  0
								for _, batch in enumerate(lowerCAmelCase):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												_A :				int				=  {k: v.to(accelerator.device) for k, v in batch.items()}
												_A :				List[Any]				=  (batch['''image'''] - mean) / std
												with torch.no_grad():
																_A :				Tuple				=  model(lowerCAmelCase)
												_A :				List[str]				=  outputs.argmax(dim=-1)
												_A	,       _A :				Union[str, Any]				=  accelerator.gather_for_metrics((predictions, batch['''label''']))
												_A :				Dict				=  predictions == references
												num_elems += accurate_preds.shape[0]
												accurate += accurate_preds.long().sum()

								_A :				int				=  accurate.item() / num_elems
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"""epoch {epoch}: {100 * eval_metric:.2f}""")





def      lowercase  (  ):



				"""simple docstring"""


				_A :				Any				=  argparse.ArgumentParser(description='''Simple example of training script.''')
				parser.add_argument('''--data_dir'''   , required=lowerCAmelCase   , help='''The data folder on disk.''')
				parser.add_argument(
				    '''--mixed_precision'''   , type=lowerCAmelCase   , default=lowerCAmelCase   , 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.'''   , )
				parser.add_argument(
				    '''--checkpointing_steps'''   , type=lowerCAmelCase   , default=lowerCAmelCase   , help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''   , )
				parser.add_argument('''--cpu'''   , action='''store_true'''   , help='''If passed, will train on the CPU.''')
				_A :				Optional[int]				=  parser.parse_args()
				_A :				Optional[Any]				=  {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
				training_function(lowerCAmelCase   , lowerCAmelCase)


if __name__ == "__main__":
	main()
