





from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


UpperCamelCase__   : Union[str, Any]					  = logging.get_logger(__name__)


# General docstring
UpperCamelCase__   : Any					  = """MobileNetV1Config"""

# Base docstring
UpperCamelCase__   : Union[str, Any]					  = """google/mobilenet_v1_1.0_224"""
UpperCamelCase__   : Dict					  = [1, 10_24, 7, 7]

# Image classification docstring
UpperCamelCase__   : Union[str, Any]					  = """google/mobilenet_v1_1.0_224"""
UpperCamelCase__   : int					  = """tabby, tabby cat"""


UpperCamelCase__   : Optional[Any]					  = [
    """google/mobilenet_v1_1.0_224""",
    """google/mobilenet_v1_0.75_192""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]




def    A_(			A				,      A				,      A=None   ):
	UpperCAmelCase_								=     {}

	if isinstance(A				,      A   ):
		UpperCAmelCase_								=     model.mobilenet_va
	else:
		UpperCAmelCase_								=     model

	UpperCAmelCase_								=     """MobilenetV1/Conv2d_0/"""
	UpperCAmelCase_								=     backbone.conv_stem.convolution.weight
	UpperCAmelCase_								=     backbone.conv_stem.normalization.bias
	UpperCAmelCase_								=     backbone.conv_stem.normalization.weight
	UpperCAmelCase_								=     backbone.conv_stem.normalization.running_mean
	UpperCAmelCase_								=     backbone.conv_stem.normalization.running_var

	for i in range(13   ):
		UpperCAmelCase_								=     i + 1
		UpperCAmelCase_								=     i * 2

		UpperCAmelCase_								=     backbone.layer[pt_index]
		UpperCAmelCase_								=     f"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
		UpperCAmelCase_								=     pointer.convolution.weight
		UpperCAmelCase_								=     pointer.normalization.bias
		UpperCAmelCase_								=     pointer.normalization.weight
		UpperCAmelCase_								=     pointer.normalization.running_mean
		UpperCAmelCase_								=     pointer.normalization.running_var

		UpperCAmelCase_								=     backbone.layer[pt_index + 1]
		UpperCAmelCase_								=     f"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
		UpperCAmelCase_								=     pointer.convolution.weight
		UpperCAmelCase_								=     pointer.normalization.bias
		UpperCAmelCase_								=     pointer.normalization.weight
		UpperCAmelCase_								=     pointer.normalization.running_mean
		UpperCAmelCase_								=     pointer.normalization.running_var

	if isinstance(A				,      A   ):
		UpperCAmelCase_								=     """MobilenetV1/Logits/Conv2d_1c_1x1/"""
		UpperCAmelCase_								=     model.classifier.weight
		UpperCAmelCase_								=     model.classifier.bias

	return tf_to_pt_map




def    A_(			A				,      A				,      A   ):
	try:
		import numpy as np
		import tensorflow as tf
	except ImportError:
		logger.error(
		    """Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see """
		    """https://www.tensorflow.org/install/ for installation instructions."""   )
		raise

	# Load weights from TF model
	UpperCAmelCase_								=     tf.train.list_variables(A   )
	UpperCAmelCase_								=     {}
	for name, shape in init_vars:
		logger.info(f"""Loading TF weight {name} with shape {shape}"""   )
		UpperCAmelCase_								=     tf.train.load_variable(A				,      A   )
		UpperCAmelCase_								=     array

	# Build TF to PyTorch weights loading map
	UpperCAmelCase_								=     _build_tf_to_pytorch_map(A				,      A				,      A   )

	for name, pointer in tf_to_pt_map.items():
		logger.info(f"""Importing {name}"""   )
		if name not in tf_weights:
			logger.info(f"""{name} not in tf pre-trained weights, skipping"""   )
			continue

		UpperCAmelCase_								=     tf_weights[name]

		if "depthwise_weights" in name:
			logger.info("""Transposing depthwise"""   )
			UpperCAmelCase_								=     np.transpose(A				,      (2, 3, 0, 1)   )
		elif "weights" in name:
			logger.info("""Transposing"""   )
			if len(pointer.shape   ) == 2:  # copying into linear layer
				UpperCAmelCase_								=     array.squeeze().transpose()
			else:
				UpperCAmelCase_								=     np.transpose(A				,      (3, 2, 0, 1)   )

		if pointer.shape != array.shape:
			raise ValueError(f"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched"""   )

		logger.info(f"""Initialize PyTorch weight {name} {array.shape}"""   )
		UpperCAmelCase_								=     torch.from_numpy(A   )

		tf_weights.pop(A				,      A   )
		tf_weights.pop(name + """/RMSProp"""				,      A   )
		tf_weights.pop(name + """/RMSProp_1"""				,      A   )
		tf_weights.pop(name + """/ExponentialMovingAverage"""				,      A   )

	logger.info(f"""Weights not copied to PyTorch model: {', '.join(tf_weights.keys()   )}"""   )
	return model




def    A_(			A				,      A   ):
	UpperCAmelCase_						,	UpperCAmelCase_								=     features.shape[-2:]
	UpperCAmelCase_						,	UpperCAmelCase_								=     conv_layer.stride
	UpperCAmelCase_						,	UpperCAmelCase_								=     conv_layer.kernel_size

	if in_height % stride_height == 0:
		UpperCAmelCase_								=     max(kernel_height - stride_height				,      0   )
	else:
		UpperCAmelCase_								=     max(kernel_height - (in_height % stride_height)				,      0   )

	if in_width % stride_width == 0:
		UpperCAmelCase_								=     max(kernel_width - stride_width				,      0   )
	else:
		UpperCAmelCase_								=     max(kernel_width - (in_width % stride_width)				,      0   )

	UpperCAmelCase_								=     pad_along_width // 2
	UpperCAmelCase_								=     pad_along_width - pad_left
	UpperCAmelCase_								=     pad_along_height // 2
	UpperCAmelCase_								=     pad_along_height - pad_top

	UpperCAmelCase_								=     (pad_left, pad_right, pad_top, pad_bottom)
	return nn.functional.pad(A				,      A				,      """constant"""				,      0.0   )







class   _UpperCamelCase  (       nn.Module  ):







					'''simple docstring'''

					def __init__(						self    :  int ,					__lowercase    :  MobileNetVaConfig ,					__lowercase    :  int ,					__lowercase    :  int ,					__lowercase    :  int ,					__lowercase    :  Optional[int] = 1 ,					__lowercase    :  Optional[int] = 1 ,					__lowercase    :  bool = False ,					__lowercase    :  Optional[bool] = True ,					__lowercase    :  Optional[bool or str] = True ,					):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_								=     config

						if in_channels % groups != 0:
							raise ValueError(F"""Input channels ({in_channels}) are not divisible by {groups} groups."""		)
						if out_channels % groups != 0:
							raise ValueError(F"""Output channels ({out_channels}) are not divisible by {groups} groups."""		)

						UpperCAmelCase_								=     0 if config.tf_padding else int((kernel_size - 1) / 2		)

						UpperCAmelCase_								=     nn.Convad(
						    in_channels=__lowercase ,					out_channels=__lowercase ,					kernel_size=__lowercase ,					stride=__lowercase ,					padding=__lowercase ,					groups=__lowercase ,					bias=__lowercase ,					padding_mode="""zeros""" ,					)

						if use_normalization:
							UpperCAmelCase_								=     nn.BatchNormad(
							    num_features=__lowercase ,					eps=config.layer_norm_eps ,					momentum=0.9997 ,					affine=__lowercase ,					track_running_stats=__lowercase ,					)
						else:
							UpperCAmelCase_								=     None

						if use_activation:
							if isinstance(__lowercase ,					__lowercase		):
								UpperCAmelCase_								=     ACTaFN[use_activation]
							elif isinstance(config.hidden_act ,					__lowercase		):
								UpperCAmelCase_								=     ACTaFN[config.hidden_act]
							else:
								UpperCAmelCase_								=     config.hidden_act
						else:
							UpperCAmelCase_								=     None





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  torch.Tensor		):






						'''simple docstring'''







						if self.config.tf_padding:
							UpperCAmelCase_								=     apply_tf_padding(__lowercase ,					self.convolution		)
						UpperCAmelCase_								=     self.convolution(__lowercase		)
						if self.normalization is not None:
							UpperCAmelCase_								=     self.normalization(__lowercase		)
						if self.activation is not None:
							UpperCAmelCase_								=     self.activation(__lowercase		)
						return features







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Any			    =       MobileNetVaConfig
					lowerCamelCase     :     str			    =       load_tf_weights_in_mobilenet_va
					lowerCamelCase     :     Optional[Any]			    =       'mobilenet_v1'
					lowerCamelCase     :     Tuple			    =       'pixel_values'
					lowerCamelCase     :     int			    =       False





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					__lowercase    :  Union[nn.Linear, nn.Convad]		):






						'''simple docstring'''







						if isinstance(__lowercase ,					(nn.Linear, nn.Convad)		):
							module.weight.data.normal_(mean=0.0 ,					std=self.config.initializer_range		)
							if module.bias is not None:
								module.bias.data.zero_()
						elif isinstance(__lowercase ,					nn.BatchNormad		):
							module.bias.data.zero_()
							module.weight.data.fill_(1.0		)


UpperCamelCase__   : Tuple					  = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

UpperCamelCase__   : str					  = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__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.
"""







@add_start_docstrings(
    'The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'						,       A_						,       )
class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					def __init__(						self    :  List[str] ,					__lowercase    :  MobileNetVaConfig ,					__lowercase    :  bool = True		):






						'''simple docstring'''







						super().__init__(__lowercase		)
						UpperCAmelCase_								=     config

						UpperCAmelCase_								=     32
						UpperCAmelCase_								=     max(int(depth * config.depth_multiplier		) ,					config.min_depth		)

						UpperCAmelCase_								=     MobileNetVaConvLayer(
						    __lowercase ,					in_channels=config.num_channels ,					out_channels=__lowercase ,					kernel_size=3 ,					stride=2 ,					)

						UpperCAmelCase_								=     [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

						UpperCAmelCase_								=     nn.ModuleList()
						for i in range(13		):
							UpperCAmelCase_								=     out_channels

							if strides[i] == 2 or i == 0:
								depth *= 2
								UpperCAmelCase_								=     max(int(depth * config.depth_multiplier		) ,					config.min_depth		)

							self.layer.append(
							    MobileNetVaConvLayer(
							        __lowercase ,					in_channels=__lowercase ,					out_channels=__lowercase ,					kernel_size=3 ,					stride=strides[i] ,					groups=__lowercase ,					)		)

							self.layer.append(
							    MobileNetVaConvLayer(
							        __lowercase ,					in_channels=__lowercase ,					out_channels=__lowercase ,					kernel_size=1 ,					)		)

						UpperCAmelCase_								=     nn.AdaptiveAvgPoolad((1, 1)		) if add_pooling_layer else None

						# Initialize weights and apply final processing
						self.post_init()





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  List[Any]		):






						'''simple docstring'''







						raise NotImplementedError





					@add_start_docstrings_to_model_forward(__lowercase		)
					@add_code_sample_docstrings(
					    checkpoint=_CHECKPOINT_FOR_DOC ,					output_type=__lowercase ,					config_class=_CONFIG_FOR_DOC ,					modality="""vision""" ,					expected_output=_EXPECTED_OUTPUT_SHAPE ,					)
					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  Optional[torch.Tensor] = None ,					__lowercase    :  Optional[bool] = None ,					__lowercase    :  Optional[bool] = None ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						UpperCAmelCase_								=     return_dict if return_dict is not None else self.config.use_return_dict

						if pixel_values is None:
							raise ValueError("""You have to specify pixel_values"""		)

						UpperCAmelCase_								=     self.conv_stem(__lowercase		)

						UpperCAmelCase_								=     () if output_hidden_states else None

						for i, layer_module in enumerate(self.layer		):
							UpperCAmelCase_								=     layer_module(__lowercase		)

							if output_hidden_states:
								UpperCAmelCase_								=     all_hidden_states + (hidden_states,)

						UpperCAmelCase_								=     hidden_states

						if self.pooler is not None:
							UpperCAmelCase_								=     torch.flatten(self.pooler(__lowercase		) ,					start_dim=1		)
						else:
							UpperCAmelCase_								=     None

						if not return_dict:
							return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None		)

						return BaseModelOutputWithPoolingAndNoAttention(
						    last_hidden_state=__lowercase ,					pooler_output=__lowercase ,					hidden_states=__lowercase ,					)







@add_start_docstrings(
    '\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '						,       A_						,       )
class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					def __init__(						self    :  Tuple ,					__lowercase    :  MobileNetVaConfig		):






						'''simple docstring'''







						super().__init__(__lowercase		)

						UpperCAmelCase_								=     config.num_labels
						UpperCAmelCase_								=     MobileNetVaModel(__lowercase		)

						UpperCAmelCase_								=     self.mobilenet_va.layer[-1].convolution.out_channels

						# Classifier head
						UpperCAmelCase_								=     nn.Dropout(config.classifier_dropout_prob ,					inplace=__lowercase		)
						UpperCAmelCase_								=     nn.Linear(__lowercase ,					config.num_labels		) if config.num_labels > 0 else nn.Identity()

						# Initialize weights and apply final processing
						self.post_init()





					@add_start_docstrings_to_model_forward(__lowercase		)
					@add_code_sample_docstrings(
					    checkpoint=_IMAGE_CLASS_CHECKPOINT ,					output_type=__lowercase ,					config_class=_CONFIG_FOR_DOC ,					expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,					)
					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Optional[torch.Tensor] = None ,					__lowercase    :  Optional[bool] = None ,					__lowercase    :  Optional[torch.Tensor] = None ,					__lowercase    :  Optional[bool] = None ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     return_dict if return_dict is not None else self.config.use_return_dict

						UpperCAmelCase_								=     self.mobilenet_va(__lowercase ,					output_hidden_states=__lowercase ,					return_dict=__lowercase		)

						UpperCAmelCase_								=     outputs.pooler_output if return_dict else outputs[1]

						UpperCAmelCase_								=     self.classifier(self.dropout(__lowercase		)		)

						UpperCAmelCase_								=     None
						if labels is not None:
							if self.config.problem_type is None:
								if self.num_labels == 1:
									UpperCAmelCase_								=     """regression"""
								elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
									UpperCAmelCase_								=     """single_label_classification"""
								else:
									UpperCAmelCase_								=     """multi_label_classification"""

							if self.config.problem_type == "regression":
								UpperCAmelCase_								=     MSELoss()
								if self.num_labels == 1:
									UpperCAmelCase_								=     loss_fct(logits.squeeze() ,					labels.squeeze()		)
								else:
									UpperCAmelCase_								=     loss_fct(__lowercase ,					__lowercase		)
							elif self.config.problem_type == "single_label_classification":
								UpperCAmelCase_								=     CrossEntropyLoss()
								UpperCAmelCase_								=     loss_fct(logits.view(-1 ,					self.num_labels		) ,					labels.view(-1		)		)
							elif self.config.problem_type == "multi_label_classification":
								UpperCAmelCase_								=     BCEWithLogitsLoss()
								UpperCAmelCase_								=     loss_fct(__lowercase ,					__lowercase		)

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

						return ImageClassifierOutputWithNoAttention(
						    loss=__lowercase ,					logits=__lowercase ,					hidden_states=outputs.hidden_states ,					)









import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)




def    A_(			):
	UpperCAmelCase_								=     argparse.ArgumentParser()
	parser.add_argument("""--model_ckpt"""				,      type=A				,      default="""microsoft/unixcoder-base-nine"""   )
	parser.add_argument("""--num_epochs"""				,      type=A				,      default=5   )
	parser.add_argument("""--batch_size"""				,      type=A				,      default=6   )
	parser.add_argument("""--gradient_accumulation_steps"""				,      type=A				,      default=1   )
	parser.add_argument("""--freeze"""				,      type=A				,      default=A   )
	parser.add_argument("""--learning_rate"""				,      type=A				,      default=5E-4   )
	parser.add_argument("""--seed"""				,      type=A				,      default=0   )
	parser.add_argument("""--lr_scheduler_type"""				,      type=A				,      default="""cosine"""   )
	parser.add_argument("""--num_warmup_steps"""				,      type=A				,      default=10   )
	parser.add_argument("""--weight_decay"""				,      type=A				,      default=0.01   )
	parser.add_argument("""--output_dir"""				,      type=A				,      default="""./results"""   )
	return parser.parse_args()


UpperCamelCase__   : Any					  = load("""accuracy""")




def    A_(			A   ):
	UpperCAmelCase_						,	UpperCAmelCase_								=     eval_pred
	UpperCAmelCase_								=     np.argmax(A				,      axis=1   )
	return metric.compute(predictions=A				,      references=A   )







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					def __init__(						self    :  str ,					__lowercase    :  Any		):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_								=     trainer





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  int ,					__lowercase    :  Tuple ,					__lowercase    :  Dict ,					**__lowercase    :  Dict		):






						'''simple docstring'''







						if control.should_evaluate:
							UpperCAmelCase_								=     deepcopy(__lowercase		)
							self._trainer.evaluate(eval_dataset=self._trainer.train_dataset ,					metric_key_prefix="""train"""		)
							return control_copy




def    A_(			):
	UpperCAmelCase_								=     get_args()
	set_seed(args.seed   )

	UpperCAmelCase_								=     load_dataset("""codeparrot/codecomplex"""				,      split="""train"""   )
	UpperCAmelCase_								=     dataset.train_test_split(test_size=0.2   )
	UpperCAmelCase_								=     train_test["""test"""].train_test_split(test_size=0.5   )
	UpperCAmelCase_								=     DatasetDict(
	    {
	        """train""": train_test["""train"""],
	        """test""": test_validation["""train"""],
	        """valid""": test_validation["""test"""],
	    }   )

	print("""Loading tokenizer and model"""   )
	UpperCAmelCase_								=     AutoTokenizer.from_pretrained(args.model_ckpt   )
	UpperCAmelCase_								=     tokenizer.eos_token
	UpperCAmelCase_								=     AutoModelForSequenceClassification.from_pretrained(args.model_ckpt				,      num_labels=7   )
	UpperCAmelCase_								=     model.config.eos_token_id

	if args.freeze:
		for param in model.roberta.parameters():
			UpperCAmelCase_								=     False

	UpperCAmelCase_								=     ClassLabel(num_classes=7				,      names=list(set(train_test_validation["""train"""]["""complexity"""]   )   )   )

	def tokenize(A   ):
		UpperCAmelCase_								=     tokenizer(example["""src"""]				,      truncation=A				,      max_length=1024   )
		UpperCAmelCase_								=     labels.straint(example["""complexity"""]   )
		return {
		    "input_ids": inputs["input_ids"],
		    "attention_mask": inputs["attention_mask"],
		    "label": label,
		}

	UpperCAmelCase_								=     train_test_validation.map(
	    A				,      batched=A				,      remove_columns=train_test_validation["""train"""].column_names				,      )
	UpperCAmelCase_								=     DataCollatorWithPadding(tokenizer=A   )

	UpperCAmelCase_								=     TrainingArguments(
	    output_dir=args.output_dir				,      learning_rate=args.learning_rate				,      lr_scheduler_type=args.lr_scheduler_type				,      evaluation_strategy="""epoch"""				,      save_strategy="""epoch"""				,      logging_strategy="""epoch"""				,      per_device_train_batch_size=args.batch_size				,      per_device_eval_batch_size=args.batch_size				,      num_train_epochs=args.num_epochs				,      gradient_accumulation_steps=args.gradient_accumulation_steps				,      weight_decay=0.01				,      metric_for_best_model="""accuracy"""				,      run_name="""complexity-java"""				,      report_to="""wandb"""				,      )

	UpperCAmelCase_								=     Trainer(
	    model=A				,      args=A				,      train_dataset=tokenized_datasets["""train"""]				,      eval_dataset=tokenized_datasets["""valid"""]				,      tokenizer=A				,      data_collator=A				,      compute_metrics=A				,      )

	print("""Training..."""   )
	trainer.add_callback(CustomCallback(A   )   )
	trainer.train()


if __name__ == "__main__":
				main()


