





"""simple docstring"""



from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


a_				=    logging.get_logger(__name__)

# General docstring
a_				=    """ResNetConfig"""

# Base docstring
a_				=    """microsoft/resnet-50"""
a_				=    [1, 2048, 7, 7]

# Image classification docstring
a_				=    """microsoft/resnet-50"""
a_				=    """tiger cat"""

a_				=    [
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]






class 	__snake_case      (	tf.keras.layers.Layer  ):





	"""simple docstring"""


	def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = 3      ,		__lowerCamelCase = 1      ,		__lowerCamelCase = "relu"      ,		**__lowerCamelCase						):



					'''simple docstring'''




					super().__init__(**__lowerCamelCase						)
					__A					:		List[str]						=  kernel_size // 2
					__A					:		int						=  tf.keras.layers.ConvaD(
					    __lowerCamelCase      ,		kernel_size=__lowerCamelCase      ,		strides=__lowerCamelCase      ,		padding='''valid'''      ,		use_bias=__lowerCamelCase      ,		name='''convolution'''						)
					# Use same default momentum and epsilon as PyTorch equivalent
					__A					:		Optional[int]						=  tf.keras.layers.BatchNormalization(epsilon=1e-5      ,		momentum=0.9      ,		name='''normalization'''						)
					__A					:		Union[str, Any]						=  ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('''linear'''						)

	def       UpperCamelCase__(					self      ,		__lowerCamelCase						):



					'''simple docstring'''




					__A					:		Union[str, Any]						=  (self.pad_value, self.pad_value)
					__A					:		List[str]						=  tf.pad(__lowerCamelCase      ,		[(0, 0), height_pad, width_pad, (0, 0)]						)
					__A					:		List[str]						=  self.conv(__lowerCamelCase						)
					return hidden_state







	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = False						):



					'''simple docstring'''




					__A					:		Optional[int]						=  self.convolution(__lowerCamelCase						)
					__A					:		Dict						=  self.normalization(__lowerCamelCase      ,		training=__lowerCamelCase						)
					__A					:		str						=  self.activation(__lowerCamelCase						)
					return hidden_state






class 	__snake_case      (	tf.keras.layers.Layer  ):





	"""simple docstring"""


	def __init__(					self      ,		__lowerCamelCase      ,		**__lowerCamelCase						):



					'''simple docstring'''




					super().__init__(**__lowerCamelCase						)
					__A					:		List[str]						=  TFResNetConvLayer(
					    config.embedding_size      ,		kernel_size=7      ,		stride=2      ,		activation=config.hidden_act      ,		name='''embedder'''      ,		)
					__A					:		Any						=  tf.keras.layers.MaxPoolaD(pool_size=3      ,		strides=2      ,		padding='''valid'''      ,		name='''pooler'''						)
					__A					:		Tuple						=  config.num_channels







	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = False						):



					'''simple docstring'''




					__A					:		Any						=  shape_list(__lowerCamelCase						)
					if tf.executing_eagerly() and num_channels != self.num_channels:
									raise ValueError(
									    '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''						)
					__A					:		List[Any]						=  pixel_values
					__A					:		Any						=  self.embedder(__lowerCamelCase						)
					__A					:		str						=  tf.pad(__lowerCamelCase      ,		[[0, 0], [1, 1], [1, 1], [0, 0]]						)
					__A					:		Dict						=  self.pooler(__lowerCamelCase						)
					return hidden_state






class 	__snake_case      (	tf.keras.layers.Layer  ):





	"""simple docstring"""


	def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = 2      ,		**__lowerCamelCase						):



					'''simple docstring'''




					super().__init__(**__lowerCamelCase						)
					__A					:		Optional[int]						=  tf.keras.layers.ConvaD(
					    __lowerCamelCase      ,		kernel_size=1      ,		strides=__lowerCamelCase      ,		use_bias=__lowerCamelCase      ,		name='''convolution'''						)
					# Use same default momentum and epsilon as PyTorch equivalent
					__A					:		Optional[int]						=  tf.keras.layers.BatchNormalization(epsilon=1e-5      ,		momentum=0.9      ,		name='''normalization'''						)







	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = False						):



					'''simple docstring'''




					__A					:		Tuple						=  x
					__A					:		Any						=  self.convolution(__lowerCamelCase						)
					__A					:		int						=  self.normalization(__lowerCamelCase      ,		training=__lowerCamelCase						)
					return hidden_state






class 	__snake_case      (	tf.keras.layers.Layer  ):





	"""simple docstring"""


	def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase = 1      ,		__lowerCamelCase = "relu"      ,		**__lowerCamelCase						):



					'''simple docstring'''




					super().__init__(**__lowerCamelCase						)
					__A					:		int						=  in_channels != out_channels or stride != 1
					__A					:		Union[str, Any]						=  TFResNetConvLayer(__lowerCamelCase      ,		stride=__lowerCamelCase      ,		name='''layer.0'''						)
					__A					:		Tuple						=  TFResNetConvLayer(__lowerCamelCase      ,		activation=__lowerCamelCase      ,		name='''layer.1'''						)
					__A					:		Any						=  (
					    TFResNetShortCut(__lowerCamelCase      ,		stride=__lowerCamelCase      ,		name='''shortcut'''						)
					    if should_apply_shortcut
					    else tf.keras.layers.Activation('''linear'''      ,		name='''shortcut'''						)
					)
					__A					:		List[Any]						=  ACTaFN[activation]







	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = False						):



					'''simple docstring'''




					__A					:		Optional[Any]						=  hidden_state
					__A					:		Tuple						=  self.conva(__lowerCamelCase      ,		training=__lowerCamelCase						)
					__A					:		Optional[int]						=  self.conva(__lowerCamelCase      ,		training=__lowerCamelCase						)
					__A					:		Dict						=  self.shortcut(__lowerCamelCase      ,		training=__lowerCamelCase						)
					hidden_state += residual
					__A					:		int						=  self.activation(__lowerCamelCase						)
					return hidden_state






class 	__snake_case      (	tf.keras.layers.Layer  ):





	"""simple docstring"""


	def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase = 1      ,		__lowerCamelCase = "relu"      ,		__lowerCamelCase = 4      ,		**__lowerCamelCase      ,		):



					'''simple docstring'''




					super().__init__(**__lowerCamelCase						)
					__A					:		Optional[int]						=  in_channels != out_channels or stride != 1
					__A					:		str						=  out_channels // reduction
					__A					:		Tuple						=  TFResNetConvLayer(__lowerCamelCase      ,		kernel_size=1      ,		name='''layer.0'''						)
					__A					:		Dict						=  TFResNetConvLayer(__lowerCamelCase      ,		stride=__lowerCamelCase      ,		name='''layer.1'''						)
					__A					:		Any						=  TFResNetConvLayer(__lowerCamelCase      ,		kernel_size=1      ,		activation=__lowerCamelCase      ,		name='''layer.2'''						)
					__A					:		str						=  (
					    TFResNetShortCut(__lowerCamelCase      ,		stride=__lowerCamelCase      ,		name='''shortcut'''						)
					    if should_apply_shortcut
					    else tf.keras.layers.Activation('''linear'''      ,		name='''shortcut'''						)
					)
					__A					:		Dict						=  ACTaFN[activation]







	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = False						):



					'''simple docstring'''




					__A					:		List[str]						=  hidden_state
					__A					:		int						=  self.conva(__lowerCamelCase      ,		training=__lowerCamelCase						)
					__A					:		int						=  self.conva(__lowerCamelCase      ,		training=__lowerCamelCase						)
					__A					:		Tuple						=  self.conva(__lowerCamelCase      ,		training=__lowerCamelCase						)
					__A					:		Optional[Any]						=  self.shortcut(__lowerCamelCase      ,		training=__lowerCamelCase						)
					hidden_state += residual
					__A					:		int						=  self.activation(__lowerCamelCase						)
					return hidden_state






class 	__snake_case      (	tf.keras.layers.Layer  ):





	"""simple docstring"""


	def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase = 2      ,		__lowerCamelCase = 2      ,		**__lowerCamelCase						):



					'''simple docstring'''




					super().__init__(**__lowerCamelCase						)

					__A					:		List[Any]						=  TFResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else TFResNetBasicLayer

					__A					:		List[str]						=  [layer(__lowerCamelCase      ,		__lowerCamelCase      ,		stride=__lowerCamelCase      ,		activation=config.hidden_act      ,		name='''layers.0'''						)]
					layers += [
					    layer(__lowerCamelCase      ,		__lowerCamelCase      ,		activation=config.hidden_act      ,		name=F"""layers.{i + 1}"""						)
					    for i in range(depth - 1						)
					]
					__A					:		Optional[int]						=  layers







	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = False						):



					'''simple docstring'''




					for layer in self.stage_layers:
									__A					:		Tuple						=  layer(__lowerCamelCase      ,		training=__lowerCamelCase						)
					return hidden_state






class 	__snake_case      (	tf.keras.layers.Layer  ):





	"""simple docstring"""


	def __init__(					self      ,		__lowerCamelCase      ,		**__lowerCamelCase						):



					'''simple docstring'''




					super().__init__(**__lowerCamelCase						)
					# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
					__A					:		Union[str, Any]						=  [
					    TFResNetStage(
					        __lowerCamelCase      ,		config.embedding_size      ,		config.hidden_sizes[0]      ,		stride=2 if config.downsample_in_first_stage else 1      ,		depth=config.depths[0]      ,		name='''stages.0'''      ,		)
					]
					for i, (in_channels, out_channels, depth) in enumerate(
					    zip(config.hidden_sizes      ,		config.hidden_sizes[1:]      ,		config.depths[1:]						)						):
									self.stages.append(TFResNetStage(__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		depth=__lowerCamelCase      ,		name=F"""stages.{i + 1}"""						)						)







	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = False      ,		__lowerCamelCase = True      ,		__lowerCamelCase = False      ,		):



					'''simple docstring'''




					__A					:		Any						=  () if output_hidden_states else None

					for stage_module in self.stages:
									if output_hidden_states:
													__A					:		Tuple						=  hidden_states + (hidden_state,)

									__A					:		Optional[Any]						=  stage_module(__lowerCamelCase      ,		training=__lowerCamelCase						)

					if output_hidden_states:
									__A					:		Any						=  hidden_states + (hidden_state,)

					if not return_dict:
									return tuple(v for v in [hidden_state, hidden_states] if v is not None						)

					return TFBaseModelOutputWithNoAttention(last_hidden_state=__lowerCamelCase      ,		hidden_states=__lowerCamelCase						)






class 	__snake_case      (	SCREAMING_SNAKE_CASE__  ):





	"""simple docstring"""


	_lowerCamelCase				 =  ResNetConfig
	_lowerCamelCase				 =  """resnet"""
	_lowerCamelCase				 =  """pixel_values"""

	@property
	def       UpperCamelCase__(					self						):



					'''simple docstring'''




					return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)      ,		dtype=tf.floataa						)}






a_				=    R"""
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""


a_				=    R"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""






@keras_serializable
class 	__snake_case      (	tf.keras.layers.Layer  ):





	"""simple docstring"""


	_lowerCamelCase				 =  ResNetConfig

	def __init__(					self      ,		__lowerCamelCase      ,		**__lowerCamelCase						):



					'''simple docstring'''




					super().__init__(**__lowerCamelCase						)
					__A					:		str						=  config
					__A					:		List[Any]						=  TFResNetEmbeddings(__lowerCamelCase      ,		name='''embedder'''						)
					__A					:		Optional[int]						=  TFResNetEncoder(__lowerCamelCase      ,		name='''encoder'''						)
					__A					:		str						=  tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowerCamelCase						)







	@unpack_inputs
	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = None      ,		__lowerCamelCase = None      ,		__lowerCamelCase = False      ,		):



					'''simple docstring'''




					__A					:		Union[str, Any]						=  (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					__A					:		Tuple						=  return_dict if return_dict is not None else self.config.use_return_dict

					# TF 2.0 image layers can't use NCHW format when running on CPU.
					# We transpose to NHWC format and then transpose back after the full forward pass.
					# (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
					__A					:		Tuple						=  tf.transpose(__lowerCamelCase      ,		perm=[0, 2, 3, 1]						)
					__A					:		Any						=  self.embedder(__lowerCamelCase      ,		training=__lowerCamelCase						)

					__A					:		List[str]						=  self.encoder(
					    __lowerCamelCase      ,		output_hidden_states=__lowerCamelCase      ,		return_dict=__lowerCamelCase      ,		training=__lowerCamelCase						)

					__A					:		Optional[Any]						=  encoder_outputs[0]

					__A					:		Tuple						=  self.pooler(__lowerCamelCase						)

					# Transpose all the outputs to the NCHW format
					# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
					__A					:		Optional[Any]						=  tf.transpose(__lowerCamelCase      ,		(0, 3, 1, 2)						)
					__A					:		List[str]						=  tf.transpose(__lowerCamelCase      ,		(0, 3, 1, 2)						)
					__A					:		List[str]						=  ()
					for hidden_state in encoder_outputs[1:]:
									__A					:		Optional[Any]						=  hidden_states + tuple(tf.transpose(__lowerCamelCase      ,		(0, 3, 1, 2)						) for h in hidden_state						)

					if not return_dict:
									return (last_hidden_state, pooled_output) + hidden_states

					__A					:		Dict						=  hidden_states if output_hidden_states else None

					return TFBaseModelOutputWithPoolingAndNoAttention(
					    last_hidden_state=__lowerCamelCase      ,		pooler_output=__lowerCamelCase      ,		hidden_states=__lowerCamelCase      ,		)






@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top."""							,							SCREAMING_SNAKE_CASE__							,							)
class 	__snake_case      (	SCREAMING_SNAKE_CASE__  ):





	"""simple docstring"""


	def __init__(					self      ,		__lowerCamelCase      ,		**__lowerCamelCase						):



					'''simple docstring'''




					super().__init__(__lowerCamelCase      ,		**__lowerCamelCase						)
					__A					:		List[Any]						=  TFResNetMainLayer(config=__lowerCamelCase      ,		name='''resnet'''						)







	@add_start_docstrings_to_model_forward(__lowerCamelCase						)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC      ,		output_type=__lowerCamelCase      ,		config_class=_CONFIG_FOR_DOC      ,		modality='''vision'''      ,		expected_output=_EXPECTED_OUTPUT_SHAPE      ,		)
	@unpack_inputs
	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = None      ,		__lowerCamelCase = None      ,		__lowerCamelCase = False      ,		):



					'''simple docstring'''




					__A					:		Union[str, Any]						=  (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					__A					:		str						=  return_dict if return_dict is not None else self.config.use_return_dict

					__A					:		int						=  self.resnet(
					    pixel_values=__lowerCamelCase      ,		output_hidden_states=__lowerCamelCase      ,		return_dict=__lowerCamelCase      ,		training=__lowerCamelCase      ,		)
					return resnet_outputs






@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """							,							SCREAMING_SNAKE_CASE__							,							)
class 	__snake_case      (	SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__  ):





	"""simple docstring"""


	def __init__(					self      ,		__lowerCamelCase      ,		**__lowerCamelCase						):



					'''simple docstring'''




					super().__init__(__lowerCamelCase      ,		**__lowerCamelCase						)
					__A					:		Dict						=  config.num_labels
					__A					:		List[str]						=  TFResNetMainLayer(__lowerCamelCase      ,		name='''resnet'''						)
					# classification head
					__A					:		List[Any]						=  (
					    tf.keras.layers.Dense(config.num_labels      ,		name='''classifier.1'''						)
					    if config.num_labels > 0
					    else tf.keras.layers.Activation('''linear'''      ,		name='''classifier.1'''						)
					)

	def       UpperCamelCase__(					self      ,		__lowerCamelCase						):



					'''simple docstring'''




					__A					:		Optional[Any]						=  tf.keras.layers.Flatten()(__lowerCamelCase						)
					__A					:		Optional[Any]						=  self.classifier_layer(__lowerCamelCase						)
					return logits







	@add_start_docstrings_to_model_forward(__lowerCamelCase						)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT      ,		output_type=__lowerCamelCase      ,		config_class=_CONFIG_FOR_DOC      ,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,		)
	@unpack_inputs
	def       UpperCamelCase__(					self      ,		__lowerCamelCase = None      ,		__lowerCamelCase = None      ,		__lowerCamelCase = None      ,		__lowerCamelCase = None      ,		__lowerCamelCase = False      ,		):



					'''simple docstring'''




					__A					:		Optional[Any]						=  return_dict if return_dict is not None else self.config.use_return_dict

					__A					:		List[Any]						=  self.resnet(
					    __lowerCamelCase      ,		output_hidden_states=__lowerCamelCase      ,		return_dict=__lowerCamelCase      ,		training=__lowerCamelCase						)

					__A					:		str						=  outputs.pooler_output if return_dict else outputs[1]

					__A					:		Union[str, Any]						=  self.classifier(__lowerCamelCase						)

					__A					:		Tuple						=  None if labels is None else self.hf_compute_loss(__lowerCamelCase      ,		__lowerCamelCase						)

					if not return_dict:
									__A					:		List[str]						=  (logits,) + outputs[2:]
									return (loss,) + output if loss is not None else output

					return TFImageClassifierOutputWithNoAttention(loss=__lowerCamelCase      ,		logits=__lowerCamelCase      ,		hidden_states=outputs.hidden_states						)











"""simple docstring"""



import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin







def 	__lowercase  (     snake_case_						:						Dict=2       ,snake_case_						:						str=True       ,snake_case_						:						Tuple=False		)     ->Any:



    '''simple docstring'''







    __A					:		List[str]						=  Accelerator(step_scheduler_with_optimizer=snake_case_       ,split_batches=snake_case_		)
    __A					:		Dict						=  torch.nn.Linear(2       ,4		)
    __A					:		Tuple						=  torch.optim.AdamW(model.parameters()       ,lr=1.0		)
    __A					:		int						=  torch.optim.lr_scheduler.OneCycleLR(snake_case_       ,max_lr=0.01       ,steps_per_epoch=2       ,epochs=1		)
    __A				, __A				, __A					:		Optional[int]						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

    # Optimizer has stepped
    scheduler.step()
    if step_scheduler_with_optimizer or (num_processes == 1):
        assert (
            scheduler.scheduler.last_epoch == num_processes
        ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
    else:
        assert (
            scheduler.scheduler.last_epoch != num_processes
        ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""







def 	__lowercase  (     snake_case_						:						str=2       ,snake_case_						:						Dict=True       ,snake_case_						:						Tuple=False		)     ->List[Any]:



    '''simple docstring'''







    __A					:		int						=  Accelerator(step_scheduler_with_optimizer=snake_case_       ,split_batches=snake_case_		)
    __A					:		Any						=  torch.nn.Linear(2       ,4		)
    __A					:		Optional[int]						=  torch.optim.AdamW(model.parameters()       ,lr=1.0		)
    __A					:		Tuple						=  torch.optim.lr_scheduler.LambdaLR(snake_case_       ,lr_lambda=lambda snake_case_		: 1 - n / 10		)
    __A				, __A				, __A					:		Tuple						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

    # Optimizer has stepped
    __A					:		List[Any]						=  False
    scheduler.step()
    __A					:		List[Any]						=  1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

    # Optimizer has not stepped
    __A					:		List[str]						=  True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        __A					:		List[Any]						=  1 - 2 / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""







def 	__lowercase  (     snake_case_						:						int = 2		)     ->str:



    '''simple docstring'''







    from transformers import get_linear_schedule_with_warmup

    __A					:		Any						=  [1, 2, 4]
    for num_steps in steps:
        __A					:		Tuple						=  GradientAccumulationPlugin(num_steps=snake_case_       ,adjust_scheduler=num_steps > 1		)
        __A					:		Optional[Any]						=  Accelerator(gradient_accumulation_plugin=snake_case_		)
        __A					:		Optional[int]						=  torch.nn.Linear(2       ,4		)
        __A					:		Any						=  torch.optim.AdamW(model.parameters()       ,lr=10.0		)
        __A					:		Dict						=  get_linear_schedule_with_warmup(optimizer=snake_case_       ,num_warmup_steps=0       ,num_training_steps=20		)

        __A				, __A				, __A					:		Union[str, Any]						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

        for i in range(10 * num_steps		):
            with accelerator.accumulate(snake_case_		):
                optimizer.step()
                scheduler.step()

            if i == (10 * num_steps - 2):
                assert (
                    scheduler.get_last_lr()[0] != 0
                ), F"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
        assert (
            scheduler.get_last_lr()[0] == 0
        ), F"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
        GradientState._reset_state()






@require_cpu
class 	__snake_case      (	unittest.TestCase  ):





 """simple docstring"""


 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)      ,		num_processes=1						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)      ,		num_processes=1						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		step_scheduler_with_optimizer=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		step_scheduler_with_optimizer=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		step_scheduler_with_optimizer=__lowerCamelCase						)						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		step_scheduler_with_optimizer=__lowerCamelCase						)						)







 @require_huggingface_suite
 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)						)
     debug_launcher(__lowerCamelCase						)




