





import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
UpperCamelCase__   : int					  = {
    """/attention/""": """/0/SelfAttention/""",
    """/self_attention/""": """/0/SelfAttention/""",
    """/encoder_decoder_attention/""": """/1/EncDecAttention/""",
    """value""": """v""",
    """query""": """q""",
    """key""": """k""",
    """out""": """o""",
    """pre_self_attention_layer_norm""": """0/layer_norm""",
    """pre_cross_attention_layer_norm""": """1/layer_norm""",
    """pre_attention_layer_norm""": """0/layer_norm""",  # previously 1, but seems wrong
    """token_embedder""": """shared""",
    """encoder_norm""": """final_layer_norm""",
    """decoder_norm""": """final_layer_norm""",
    """relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
    """router/router_weights/w/""": """router/classifier/""",
    """roer/roer_weights/w/""": """router/classifier/""",
    """logits_dense""": """lm_head""",
}




def    A_(			A   ):
	# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
	# the original model
	UpperCAmelCase_								=     list(s_dict.keys()   )
	for key in keys:
		UpperCAmelCase_								=     R""".*/layers_(\d+)"""
		UpperCAmelCase_								=     key
		if re.match(A				,      A   ):
			UpperCAmelCase_								=     re.sub(R"""layers_(\d+)"""				,      R"""block/\1/layer"""				,      A   )

		UpperCAmelCase_								=     R"""(encoder|decoder)\/"""

		if re.match(A				,      A   ):
			UpperCAmelCase_								=     re.match(A				,      A   ).groups()
			if groups[0] == "encoder":
				UpperCAmelCase_								=     re.sub(R"""/mlp/"""				,      R"""/1/mlp/"""				,      A   )
				UpperCAmelCase_								=     re.sub(R"""/pre_mlp_layer_norm/"""				,      R"""/1/layer_norm/"""				,      A   )

			elif groups[0] == "decoder":
				UpperCAmelCase_								=     re.sub(R"""/mlp/"""				,      R"""/2/mlp/"""				,      A   )
				UpperCAmelCase_								=     re.sub(R"""/pre_mlp_layer_norm/"""				,      R"""/2/layer_norm/"""				,      A   )

        # 2. Convert other classic mappings
		for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
			if old_key in new_key:
				UpperCAmelCase_								=     new_key.replace(A				,      A   )

		print(f"""{key} -> {new_key}"""   )
		UpperCAmelCase_								=     s_dict.pop(A   )

	if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
		UpperCAmelCase_								=     s_dict[
		    """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
		].T
	if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
		UpperCAmelCase_								=     s_dict[
		    """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
		].T

	# 3. Take extra care of the EXPERTS layer
	for key in list(s_dict.keys()   ):
		if "expert" in key:
			UpperCAmelCase_								=     s_dict[key].shape[0]
			UpperCAmelCase_								=     s_dict[key]
			for idx in range(A   ):
				UpperCAmelCase_								=     expert_weihts[idx]
				print(f"""{key} -> {key.replace('expert/'				,      'nested fstring'   )}"""   )

			s_dict.pop(A   )

	return s_dict


UpperCamelCase__   : Optional[int]					  = {
    """NUM_ENCODER_LAYERS""": """num_layers""",
    """NUM_DECODER_LAYERS""": """num_decoder_layers""",
    """NUM_HEADS""": """num_heads""",
    """HEAD_DIM""": """d_kv""",
    """EMBED_DIM""": """d_model""",
    """MLP_DIM""": """d_ff""",
    """NUM_SELECTED_EXPERTS""": """num_selected_experts""",
    """NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
    """NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
    """dense.MlpBlock.activations""": """feed_forward_proj""",
}




def    A_(			A				,      A   ):
	# Convert a google style config to the hugging face fromat
	import regex as re

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

	UpperCAmelCase_								=     re.findall(R"""(.*) = ([0-9.]*)"""				,      A   )
	UpperCAmelCase_								=     {}
	for param, value in regex_match:
		if param in GIN_TO_CONFIG_MAPPING and value != "":
			UpperCAmelCase_								=     float(A   ) if """.""" in value else int(A   )

	UpperCAmelCase_								=     re.findall(R"""(.*activations) = \(\'(.*)\',\)"""				,      A   )[0]
	UpperCAmelCase_								=     str(activation[1]   )

	UpperCAmelCase_								=     num_experts
	UpperCAmelCase_								=     SwitchTransformersConfig(**A   )
	return config




def    A_(			A				,      A				,      A=None				,      A="./"				,      A=8   ):
	# Initialise PyTorch model

	print(f"""Loading flax weights from : {flax_checkpoint_path}"""   )
	UpperCAmelCase_								=     checkpoints.load_tax_checkpoint(A   )

	if gin_file is not None:
		UpperCAmelCase_								=     convert_gin_to_config(A				,      A   )
	else:
		UpperCAmelCase_								=     SwitchTransformersConfig.from_pretrained(A   )

	UpperCAmelCase_								=     SwitchTransformersForConditionalGeneration(A   )

	UpperCAmelCase_								=     flax_params["""target"""]
	UpperCAmelCase_								=     flatten_dict(A				,      sep="""/"""   )
	UpperCAmelCase_								=     rename_keys(A   )
	UpperCAmelCase_								=     unflatten_dict(A				,      sep="""/"""   )

	# Load the flax params in the PT model
	load_flax_weights_in_pytorch_model(A				,      A   )

	print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
	pt_model.save_pretrained(A   )


if __name__ == "__main__":
				UpperCamelCase__   : Optional[Any]					  = argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--switch_t5x_checkpoint_path""",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        """The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"""
				        """ model architecture. If not provided, a `gin_file` has to be provided."""
				    ),
				)
				parser.add_argument(
				    """--gin_file""",
				    default=None,
				    type=str,
				    required=False,
				    help="""Path to the gin config file. If not provided, a `config_file` has to be passed   """,
				)
				parser.add_argument(
				    """--config_name""", default=None, type=str, required=False, help="""Config name of SwitchTransformers model."""
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output pytorch model."""
				)
				parser.add_argument("""--num_experts""", default=8, type=int, required=False, help="""Number of experts""")
				UpperCamelCase__   : Union[str, Any]					  = parser.parse_args()
				convert_flax_checkpoint_to_pytorch(
				    args.switch_tax_checkpoint_path,
				    args.config_name,
				    args.gin_file,
				    args.pytorch_dump_folder_path,
				    args.num_experts,
				)









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()


