"""simple docstring"""



import argparse

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
#
# 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								=		16
_UpperCamelCase								=		32

def  _A(		lowerCAmelCase			,  lowerCAmelCase = 16							):
							A__       :     int								=			AutoTokenizer.from_pretrained("""bert-base-cased"""							)
							A__       :     Union[str, Any]								=			load_dataset("""glue"""			,  """mrpc"""							)

							def tokenize_function(lowerCAmelCase							):
														# max_length=None => use the model max length (it's actually the default)
														A__       :     Tuple								=			tokenizer(examples["""sentence1"""]			,  examples["""sentence2"""]			,  truncation=lowerCAmelCase			,  max_length=lowerCAmelCase							)
														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():
														A__       :     List[str]								=			datasets.map(
														    lowerCAmelCase			,  batched=lowerCAmelCase			,  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
							A__       :     int								=			tokenized_datasets.rename_column("""label"""			,  """labels"""							)

							def collate_fn(lowerCAmelCase							):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														A__       :     int								=			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":
																					A__       :     Union[str, Any]								=			16
														elif accelerator.mixed_precision != "no":
																					A__       :     Any								=			8
														else:
																					A__       :     Dict								=			None

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

							# Instantiate dataloaders.
							A__       :     Any								=			DataLoader(
							    tokenized_datasets["""train"""]			,  shuffle=lowerCAmelCase			,  collate_fn=lowerCAmelCase			,  batch_size=lowerCAmelCase			,  drop_last=lowerCAmelCase							)
							A__       :     Union[str, Any]								=			DataLoader(
							    tokenized_datasets["""validation"""]			,  shuffle=lowerCAmelCase			,  collate_fn=lowerCAmelCase			,  batch_size=lowerCAmelCase			,  drop_last=(accelerator.mixed_precision == """fp8""")			,  )

							return train_dataloader, eval_dataloader

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							# Initialize accelerator
							A__       :     List[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__       :     Any								=			config["""lr"""]
							A__       :     List[str]								=			int(config["""num_epochs"""]							)
							A__       :     List[str]								=			int(config["""seed"""]							)
							A__       :     List[str]								=			int(config["""batch_size"""]							)

							A__       :     Dict								=			evaluate.load("""glue"""			,  """mrpc"""							)

							# If the batch size is too big we use gradient accumulation
							A__       :     Dict								=			1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														A__       :     List[Any]								=			batch_size // MAX_GPU_BATCH_SIZE
														A__       :     Optional[Any]								=			MAX_GPU_BATCH_SIZE

							set_seed(lowerCAmelCase							)
							A__				,							A__       :     Union[str, Any]								=			get_dataloaders(lowerCAmelCase			,  lowerCAmelCase							)
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							A__       :     str								=			AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""			,  return_dict=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__       :     Dict								=			model.to(accelerator.device							)
							# Instantiate optimizer
							A__       :     List[Any]								=			AdamW(params=model.parameters()			,  lr=lowerCAmelCase							)

							# Instantiate scheduler
							A__       :     Union[str, Any]								=			get_linear_schedule_with_warmup(
							    optimizer=lowerCAmelCase			,  num_warmup_steps=100			,  num_training_steps=(len(lowerCAmelCase							) * num_epochs) // gradient_accumulation_steps			,  )

							# 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__       :     Union[str, Any]								=			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`.
																					batch.to(accelerator.device							)
																					A__       :     Optional[Any]								=			model(**lowerCAmelCase							)
																					A__       :     Union[str, Any]								=			outputs.loss
																					A__       :     str								=			loss / gradient_accumulation_steps
																					accelerator.backward(lowerCAmelCase							)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

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

														A__       :     Dict								=			metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}:'''			,  lowerCAmelCase							)

def  _A(		):
							A__       :     int								=			argparse.ArgumentParser(description="""Simple example of training script."""							)
							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("""--cpu"""			,  action="""store_true"""			,  help="""If passed, will train on the CPU."""							)
							A__       :     Tuple								=			parser.parse_args()
							A__       :     Optional[int]								=			{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(lowerCAmelCase			,  lowerCAmelCase							)


if __name__ == "__main__":
				main()






"""simple docstring"""



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor
class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=3   ,      snake_case_=32   ,      snake_case_=3   ,      snake_case_=10   ,      snake_case_=[10, 20, 30, 40]   ,      snake_case_=[1, 1, 2, 1]   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_="relu"   ,      snake_case_=3   ,      snake_case_=None   ,      ):







										'''simple docstring'''


										A__       :     Tuple								=			parent
										A__       :     int								=			batch_size
										A__       :     List[Any]								=			image_size
										A__       :     Any								=			num_channels
										A__       :     Tuple								=			embeddings_size
										A__       :     Union[str, Any]								=			hidden_sizes
										A__       :     Tuple								=			depths
										A__       :     Union[str, Any]								=			is_training
										A__       :     Any								=			use_labels
										A__       :     Optional[Any]								=			hidden_act
										A__       :     str								=			num_labels
										A__       :     Optional[Any]								=			scope
										A__       :     List[Any]								=			len(snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

										A__       :     str								=			None
										if self.use_labels:
																	A__       :     Tuple								=			ids_tensor([self.batch_size]   ,      self.num_labels							)

										A__       :     Dict								=			self.get_config()
										return config, pixel_values, labels





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return RegNetConfig(
										    num_channels=self.num_channels   ,      embeddings_size=self.embeddings_size   ,      hidden_sizes=self.hidden_sizes   ,      depths=self.depths   ,      hidden_act=self.hidden_act   ,      num_labels=self.num_labels   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			TFRegNetModel(config=snake_case_							)
										A__       :     Optional[int]								=			model(snake_case_   ,      training=snake_case_							)
										# expected last hidden states: B, C, H // 32, W // 32
										self.parent.assertEqual(
										    result.last_hidden_state.shape   ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[int]								=			self.num_labels
										A__       :     Tuple								=			TFRegNetForImageClassification(snake_case_							)
										A__       :     Optional[int]								=			model(snake_case_   ,      labels=snake_case_   ,      training=snake_case_							)
										self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			self.prepare_config_and_inputs()
										A__				,							A__				,							A__       :     Optional[Any]								=			config_and_inputs
										A__       :     str								=			{"""pixel_values""": pixel_values}
										return config, inputs_dict





@require_tf
class 						__UpperCAmelCase    (__A							,   __A							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
			_UpperCamelCase  :						Tuple					     =				(
			    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
			    if is_tf_available()
			    else {}
			)

			_UpperCamelCase  :						str					     =				False
			_UpperCamelCase  :						int					     =				False
			_UpperCamelCase  :						Tuple					     =				False
			_UpperCamelCase  :						Any					     =				False
			_UpperCamelCase  :						Union[str, Any]					     =				False





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Dict								=			TFRegNetModelTester(self							)
										A__       :     str								=			ConfigTester(self   ,      config_class=snake_case_   ,      has_text_modality=snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return





			@unittest.skip(reason="""RegNet does not use inputs_embeds"""							)
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""							)							) == 0   ,      reason="""TF does not support backprop for grouped convolutions on CPU."""   ,      )
			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										super().test_keras_fit()





			@unittest.skip(reason="""RegNet does not support input and output embeddings"""							)
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     int								=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	A__       :     int								=			model_class(snake_case_							)
																	A__       :     Optional[int]								=			inspect.signature(model.call							)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	A__       :     Union[str, Any]								=			[*signature.parameters.keys()]

																	A__       :     Tuple								=			["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										def check_hidden_states_output(snake_case_   ,      snake_case_   ,      snake_case_							):
																	A__       :     str								=			model_class(snake_case_							)
																	A__       :     Union[str, Any]								=			model(**self._prepare_for_class(snake_case_   ,      snake_case_							)   ,      training=snake_case_							)

																	A__       :     Optional[int]								=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																	A__       :     Union[str, Any]								=			self.model_tester.num_stages
																	self.assertEqual(len(snake_case_							)   ,      expected_num_stages + 1							)

																	# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
																	self.assertListEqual(
																	    list(hidden_states[0].shape[-2:]							)   ,      [self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,      )

										A__				,							A__       :     int								=			self.model_tester.prepare_config_and_inputs_for_common()
										A__       :     Optional[int]								=			["""basic""", """bottleneck"""]
										for model_class in self.all_model_classes:
																	for layer_type in layers_type:
																								A__       :     Optional[Any]								=			layer_type
																								A__       :     Dict								=			True
																								check_hidden_states_output(snake_case_   ,      snake_case_   ,      snake_case_							)

																								# check that output_hidden_states also work using config
																								del inputs_dict["output_hidden_states"]
																								A__       :     List[Any]								=			True

																								check_hidden_states_output(snake_case_   ,      snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     Optional[Any]								=			self.model_tester.prepare_config_and_inputs_for_common()

										def check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_={}							):
																	A__       :     List[str]								=			model(snake_case_   ,      return_dict=snake_case_   ,      **snake_case_							)
																	A__       :     Union[str, Any]								=			model(snake_case_   ,      return_dict=snake_case_   ,      **snake_case_							).to_tuple()

																	def recursive_check(snake_case_   ,      snake_case_							):
																								if isinstance(snake_case_   ,      (List, Tuple)							):
																															for tuple_iterable_value, dict_iterable_value in zip(snake_case_   ,      snake_case_							):
																																						recursive_check(snake_case_   ,      snake_case_							)
																								elif tuple_object is None:
																															return
																								else:
																															self.assertTrue(
																															    all(tf.equal(snake_case_   ,      snake_case_							)							)   ,      msg=(
																															        """Tuple and dict output are not equal. Difference:"""
																															        F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object							)							)}'''
																															    )   ,      )

																								recursive_check(snake_case_   ,      snake_case_							)

										for model_class in self.all_model_classes:
																	A__       :     Any								=			model_class(snake_case_							)

																	A__       :     Dict								=			self._prepare_for_class(snake_case_   ,      snake_case_							)
																	A__       :     List[Any]								=			self._prepare_for_class(snake_case_   ,      snake_case_							)
																	check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_							)

																	A__       :     Tuple								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	A__       :     List[str]								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_							)

																	A__       :     Optional[int]								=			self._prepare_for_class(snake_case_   ,      snake_case_							)
																	A__       :     List[str]								=			self._prepare_for_class(snake_case_   ,      snake_case_							)
																	check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_   ,      {"""output_hidden_states""": True}							)

																	A__       :     Dict								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	A__       :     Union[str, Any]								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	check_equivalence(snake_case_   ,      snake_case_   ,      snake_case_   ,      {"""output_hidden_states""": True}							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Tuple								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*snake_case_							)





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	A__       :     Any								=			TFRegNetModel.from_pretrained(snake_case_							)
																	self.assertIsNotNone(snake_case_							)





def  _A(		):
							A__       :     Optional[int]								=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
							return image



@require_tf
@require_vision
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			@cached_property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return (
										    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
										    if is_vision_available()
										    else None
										)





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

										A__       :     str								=			self.default_image_processor
										A__       :     Any								=			prepare_img()
										A__       :     Tuple								=			image_processor(images=snake_case_   ,      return_tensors="""tf"""							)

										# forward pass
										A__       :     List[Any]								=			model(**snake_case_   ,      training=snake_case_							)

										# verify the logits
										A__       :     Tuple								=			tf.TensorShape((1, 1_000)							)
										self.assertEqual(outputs.logits.shape   ,      snake_case_							)

										A__       :     Tuple								=			tf.constant([-0.41_80, -1.50_51, -3.48_36]							)

										tf.debugging.assert_near(outputs.logits[0, :3]   ,      snake_case_   ,      atol=1E-4							)





