





'''simple docstring'''





import unittest

from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    FalconForCausalLM,
				    FalconForQuestionAnswering,
				    FalconForSequenceClassification,
				    FalconForTokenClassification,
				    FalconModel,
				)







class   _UpperCamelCase  :







					'''simple docstring'''

					def __init__(						self    :  Optional[Any] ,					__lowercase    :  Tuple ,					__lowercase    :  Dict=3 ,					__lowercase    :  str=7 ,					__lowercase    :  Dict=True ,					__lowercase    :  Tuple=True ,					__lowercase    :  int=False ,					__lowercase    :  Optional[Any]=True ,					__lowercase    :  List[Any]=99 ,					__lowercase    :  Union[str, Any]=32 ,					__lowercase    :  Optional[Any]=5 ,					__lowercase    :  Any=4 ,					__lowercase    :  Tuple=37 ,					__lowercase    :  Optional[int]="gelu" ,					__lowercase    :  int=0.1 ,					__lowercase    :  Tuple=0.1 ,					__lowercase    :  List[str]=5_12 ,					__lowercase    :  Any=16 ,					__lowercase    :  Any=2 ,					__lowercase    :  int=0.02 ,					__lowercase    :  Dict=3 ,					__lowercase    :  List[Any]=4 ,					__lowercase    :  Optional[int]=None ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     parent
						UpperCAmelCase_								=     batch_size
						UpperCAmelCase_								=     seq_length
						UpperCAmelCase_								=     is_training
						UpperCAmelCase_								=     use_input_mask
						UpperCAmelCase_								=     use_token_type_ids
						UpperCAmelCase_								=     use_labels
						UpperCAmelCase_								=     vocab_size
						UpperCAmelCase_								=     hidden_size
						UpperCAmelCase_								=     num_hidden_layers
						UpperCAmelCase_								=     num_attention_heads
						UpperCAmelCase_								=     intermediate_size
						UpperCAmelCase_								=     hidden_act
						UpperCAmelCase_								=     hidden_dropout_prob
						UpperCAmelCase_								=     attention_probs_dropout_prob
						UpperCAmelCase_								=     max_position_embeddings
						UpperCAmelCase_								=     type_vocab_size
						UpperCAmelCase_								=     type_sequence_label_size
						UpperCAmelCase_								=     initializer_range
						UpperCAmelCase_								=     num_labels
						UpperCAmelCase_								=     num_choices
						UpperCAmelCase_								=     scope





					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						UpperCAmelCase_								=     ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size		)

						UpperCAmelCase_								=     None
						if self.use_input_mask:
							UpperCAmelCase_								=     random_attention_mask([self.batch_size, self.seq_length]		)

						UpperCAmelCase_								=     None

						UpperCAmelCase_								=     None
						UpperCAmelCase_								=     None
						UpperCAmelCase_								=     None
						if self.use_labels:
							UpperCAmelCase_								=     ids_tensor([self.batch_size] ,					self.type_sequence_label_size		)
							UpperCAmelCase_								=     ids_tensor([self.batch_size, self.seq_length] ,					self.num_labels		)
							UpperCAmelCase_								=     ids_tensor([self.batch_size] ,					self.num_choices		)

						UpperCAmelCase_								=     self.get_config()

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						return FalconConfig(
						    vocab_size=self.vocab_size ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					num_attention_heads=self.num_attention_heads ,					intermediate_size=self.intermediate_size ,					hidden_act=self.hidden_act ,					hidden_dropout_prob=self.hidden_dropout_prob ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob ,					max_position_embeddings=self.max_position_embeddings ,					type_vocab_size=self.type_vocab_size ,					is_decoder=lowerCAmelCase__ ,					initializer_range=self.initializer_range ,					pad_token_id=1 ,					new_decoder_architecture=lowerCAmelCase__ ,					)





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					__lowercase    :  Optional[int] ,					__lowercase    :  List[Any] ,					__lowercase    :  Optional[int] ,					__lowercase    :  Tuple ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  str ,					__lowercase    :  Union[str, Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     FalconModel(config=lowerCAmelCase__		)
						model.to(lowerCAmelCase__		)
						model.eval()
						UpperCAmelCase_								=     model(lowerCAmelCase__ ,					attention_mask=lowerCAmelCase__		)
						UpperCAmelCase_								=     model(lowerCAmelCase__		)
						self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  Tuple ,					__lowercase    :  Tuple ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  Optional[int] ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  str ,					__lowercase    :  Tuple ,					__lowercase    :  int ,					__lowercase    :  Union[str, Any] ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     True
						UpperCAmelCase_								=     FalconModel(lowerCAmelCase__		)
						model.to(lowerCAmelCase__		)
						model.eval()
						UpperCAmelCase_								=     model(
						    lowerCAmelCase__ ,					attention_mask=lowerCAmelCase__ ,					encoder_hidden_states=lowerCAmelCase__ ,					encoder_attention_mask=lowerCAmelCase__ ,					)
						UpperCAmelCase_								=     model(
						    lowerCAmelCase__ ,					attention_mask=lowerCAmelCase__ ,					encoder_hidden_states=lowerCAmelCase__ ,					)
						UpperCAmelCase_								=     model(lowerCAmelCase__ ,					attention_mask=lowerCAmelCase__		)
						self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)		)





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






						'''simple docstring'''







						UpperCAmelCase_								=     FalconForCausalLM(config=lowerCAmelCase__		)
						model.to(lowerCAmelCase__		)
						model.eval()
						UpperCAmelCase_								=     model(lowerCAmelCase__ ,					attention_mask=lowerCAmelCase__ ,					labels=lowerCAmelCase__		)
						self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  Optional[int] ,					__lowercase    :  Dict ,					__lowercase    :  int ,					__lowercase    :  Optional[int] ,					__lowercase    :  Tuple ,					__lowercase    :  Optional[Any] ,					__lowercase    :  List[Any] ,					__lowercase    :  str ,					__lowercase    :  Dict ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     True
						UpperCAmelCase_								=     True
						UpperCAmelCase_								=     FalconForCausalLM(config=lowerCAmelCase__		)
						model.to(lowerCAmelCase__		)
						model.eval()

						# first forward pass
						UpperCAmelCase_								=     model(
						    lowerCAmelCase__ ,					attention_mask=lowerCAmelCase__ ,					encoder_hidden_states=lowerCAmelCase__ ,					encoder_attention_mask=lowerCAmelCase__ ,					use_cache=lowerCAmelCase__ ,					)
						UpperCAmelCase_								=     outputs.past_key_values

						# create hypothetical multiple next token and extent to next_input_ids
						UpperCAmelCase_								=     ids_tensor((self.batch_size, 3) ,					config.vocab_size		)
						UpperCAmelCase_								=     ids_tensor((self.batch_size, 3) ,					vocab_size=2		)

						# append to next input_ids and
						UpperCAmelCase_								=     torch.cat([input_ids, next_tokens] ,					dim=-1		)
						UpperCAmelCase_								=     torch.cat([input_mask, next_mask] ,					dim=-1		)

						UpperCAmelCase_								=     model(
						    lowerCAmelCase__ ,					attention_mask=lowerCAmelCase__ ,					encoder_hidden_states=lowerCAmelCase__ ,					encoder_attention_mask=lowerCAmelCase__ ,					output_hidden_states=lowerCAmelCase__ ,					)["""hidden_states"""][0]
						UpperCAmelCase_								=     model(
						    lowerCAmelCase__ ,					attention_mask=lowerCAmelCase__ ,					encoder_hidden_states=lowerCAmelCase__ ,					encoder_attention_mask=lowerCAmelCase__ ,					past_key_values=lowerCAmelCase__ ,					output_hidden_states=lowerCAmelCase__ ,					)["""hidden_states"""][0]

						# select random slice
						UpperCAmelCase_								=     ids_tensor((1,) ,					output_from_past.shape[-1]		).item()
						UpperCAmelCase_								=     output_from_no_past[:, -3:, random_slice_idx].detach()
						UpperCAmelCase_								=     output_from_past[:, :, random_slice_idx].detach()

						self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(lowerCAmelCase__ ,					lowerCAmelCase__ ,					atol=1e-3		)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.prepare_config_and_inputs()
						(
						    (
						    UpperCAmelCase_	
						)						,	(
						    UpperCAmelCase_	
						)						,	(
						    UpperCAmelCase_	
						)						,	(
						    UpperCAmelCase_	
						)						,	(
						    UpperCAmelCase_	
						)						,	(
						    UpperCAmelCase_	
						)						,	(
						    UpperCAmelCase_	
						)						,		
						)								=     config_and_inputs
						UpperCAmelCase_								=     {"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict







@require_torch
class   _UpperCamelCase  (       _snake_case						,       _snake_case						,       _snake_case						,       unittest.TestCase  ):







					'''simple docstring'''

					lowerCamelCase     :     Optional[Any]			    =       (
					    (
					        FalconModel,
					        FalconForCausalLM,
					        FalconForSequenceClassification,
					        FalconForTokenClassification,
					        FalconForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					lowerCamelCase     :     List[str]			    =       (FalconForCausalLM,) if is_torch_available() else ()
					lowerCamelCase     :     Dict			    =       (
					    {
					        'feature-extraction': FalconModel,
					        'text-classification': FalconForSequenceClassification,
					        'text-generation': FalconForCausalLM,
					        'question-answering': FalconForQuestionAnswering,
					        'token-classification': FalconForTokenClassification,
					        'zero-shot': FalconForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					lowerCamelCase     :     Any			    =       False
					lowerCamelCase     :     Any			    =       False





					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						UpperCAmelCase_								=     FalconModelTester(self		)
						UpperCAmelCase_								=     ConfigTester(self ,					config_class=lowerCAmelCase__ ,					hidden_size=37		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







						self.config_tester.run_common_tests()





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCAmelCase__		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						UpperCAmelCase_						,	*UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs()
						for alibi in [True, False]:
							UpperCAmelCase_								=     alibi
							self.model_tester.create_and_check_model(lowerCAmelCase__ ,					*lowerCAmelCase__		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase_								=     3
						UpperCAmelCase_								=     input_dict["""input_ids"""]
						UpperCAmelCase_								=     input_ids.ne(1		).to(lowerCAmelCase__		)
						UpperCAmelCase_								=     ids_tensor([self.model_tester.batch_size] ,					self.model_tester.type_sequence_label_size		)
						UpperCAmelCase_								=     FalconForSequenceClassification(lowerCAmelCase__		)
						model.to(lowerCAmelCase__		)
						model.eval()
						UpperCAmelCase_								=     model(lowerCAmelCase__ ,					attention_mask=lowerCAmelCase__ ,					labels=lowerCAmelCase__		)
						self.assertEqual(result.logits.shape ,					(self.model_tester.batch_size, self.model_tester.num_labels)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase_								=     3
						UpperCAmelCase_								=     """single_label_classification"""
						UpperCAmelCase_								=     input_dict["""input_ids"""]
						UpperCAmelCase_								=     input_ids.ne(1		).to(lowerCAmelCase__		)
						UpperCAmelCase_								=     ids_tensor([self.model_tester.batch_size] ,					self.model_tester.type_sequence_label_size		)
						UpperCAmelCase_								=     FalconForSequenceClassification(lowerCAmelCase__		)
						model.to(lowerCAmelCase__		)
						model.eval()
						UpperCAmelCase_								=     model(lowerCAmelCase__ ,					attention_mask=lowerCAmelCase__ ,					labels=lowerCAmelCase__		)
						self.assertEqual(result.logits.shape ,					(self.model_tester.batch_size, self.model_tester.num_labels)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase_								=     input_dict["""input_ids"""]
						UpperCAmelCase_								=     FalconForCausalLM(lowerCAmelCase__		)
						model.to(lowerCAmelCase__		)
						model.eval()
						UpperCAmelCase_								=     model(lowerCAmelCase__ ,					use_cache=lowerCAmelCase__		)
						UpperCAmelCase_								=     input_ids.shape[0]
						UpperCAmelCase_								=     model._convert_to_rw_cache(result.past_key_values		)
						UpperCAmelCase_								=     model._convert_cache_to_standard_format(lowerCAmelCase__ ,					lowerCAmelCase__		)
						for layer in range(len(lowerCAmelCase__		)		):
							for tensor_idx in range(2		):
								self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3		)
								self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4		)
								self.assertTrue(
								    torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]		)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase_								=     3
						UpperCAmelCase_								=     """multi_label_classification"""
						UpperCAmelCase_								=     input_dict["""input_ids"""]
						UpperCAmelCase_								=     input_ids.ne(1		).to(lowerCAmelCase__		)
						UpperCAmelCase_								=     ids_tensor(
						    [self.model_tester.batch_size, config.num_labels] ,					self.model_tester.type_sequence_label_size		).to(torch.float		)
						UpperCAmelCase_								=     FalconForSequenceClassification(lowerCAmelCase__		)
						model.to(lowerCAmelCase__		)
						model.eval()
						UpperCAmelCase_								=     model(lowerCAmelCase__ ,					attention_mask=lowerCAmelCase__ ,					labels=lowerCAmelCase__		)
						self.assertEqual(result.logits.shape ,					(self.model_tester.batch_size, self.model_tester.num_labels)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any]		):






						'''simple docstring'''







						for model_class in self.all_generative_model_classes:
							UpperCAmelCase_						,	UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs_for_common()

							# If it doesn't support cache, pass the test
							if not hasattr(lowerCAmelCase__ ,					"""use_cache"""		):
								return

							UpperCAmelCase_								=     model_class(lowerCAmelCase__		).to(lowerCAmelCase__		)
							if "use_cache" not in inputs:
								UpperCAmelCase_								=     True
							UpperCAmelCase_								=     model(**lowerCAmelCase__		)

							# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
							if "past_key_values" not in outputs:
								return

							UpperCAmelCase_								=     (
							    getattr(lowerCAmelCase__ ,					"""decoder_layers""" ,					lowerCAmelCase__		)
							    or getattr(lowerCAmelCase__ ,					"""num_decoder_layers""" ,					lowerCAmelCase__		)
							    or config.num_hidden_layers
							)
							UpperCAmelCase_								=     getattr(lowerCAmelCase__ ,					"""num_kv_heads""" ,					config.num_attention_heads		)
							UpperCAmelCase_								=     getattr(lowerCAmelCase__ ,					"""d_model""" ,					config.hidden_size		)
							UpperCAmelCase_								=     embed_dim // num_attention_heads

							UpperCAmelCase_								=     outputs["""past_key_values"""]
							self.assertEqual(len(lowerCAmelCase__		) ,					lowerCAmelCase__		)

							UpperCAmelCase_						,	UpperCAmelCase_								=     inputs["""input_ids"""].shape
							for i in range(lowerCAmelCase__		):
								if config.new_decoder_architecture:
									UpperCAmelCase_								=     config.num_attention_heads
								elif config.multi_query:
									UpperCAmelCase_								=     1
								self.assertEqual(len(past_kv[0]		) ,					2		)  # K V for the decoder = 2
								self.assertEqual(
								    past_kv[i][0].shape ,					(batch_size, num_attention_heads, seq_length, per_head_embed_dim)		)
								self.assertEqual(
								    past_kv[i][1].shape ,					(batch_size, num_attention_heads, seq_length, per_head_embed_dim)		)







@require_torch
class   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     AutoTokenizer.from_pretrained("""Rocketknight1/falcon-rw-1b"""		)
						UpperCAmelCase_								=     FalconForCausalLM.from_pretrained("""Rocketknight1/falcon-rw-1b"""		)
						model.eval()
						model.to(lowerCAmelCase__		)
						UpperCAmelCase_								=     tokenizer("""My favorite food is""" ,					return_tensors="""pt"""		).to(lowerCAmelCase__		)

						UpperCAmelCase_								=     (
						    """My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."""
						)

						UpperCAmelCase_								=     model.generate(**lowerCAmelCase__ ,					do_sample=lowerCAmelCase__ ,					max_new_tokens=19		)
						UpperCAmelCase_								=     tokenizer.batch_decode(lowerCAmelCase__		)[0]

						self.assertEqual(lowerCAmelCase__ ,					lowerCAmelCase__		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
							UpperCAmelCase_								=     AutoTokenizer.from_pretrained(lowerCAmelCase__		)
							UpperCAmelCase_								=     FalconForCausalLM.from_pretrained(lowerCAmelCase__		)
							model.eval()
							model.to(lowerCAmelCase__		)
							UpperCAmelCase_								=     tokenizer("""My favorite food is""" ,					return_tensors="""pt"""		).to(lowerCAmelCase__		)

							# We just test that these run without errors - the models are randomly initialized
							# and so the actual text outputs will be garbage
							model.generate(**lowerCAmelCase__ ,					do_sample=lowerCAmelCase__ ,					max_new_tokens=4		)
							model.generate(**lowerCAmelCase__ ,					do_sample=lowerCAmelCase__ ,					max_new_tokens=4		)
							model.generate(**lowerCAmelCase__ ,					num_beams=2 ,					max_new_tokens=4		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







						with torch.no_grad():
							for repo in [
							    "Rocketknight1/falcon-rw-1b",
							    "Rocketknight1/tiny-random-falcon-7b",
							    "Rocketknight1/tiny-random-falcon-40b",
							]:
								UpperCAmelCase_								=     AutoTokenizer.from_pretrained(lowerCAmelCase__		)
								UpperCAmelCase_								=     FalconForCausalLM.from_pretrained(lowerCAmelCase__		)
								model.eval()
								model.to(device=lowerCAmelCase__		)
								UpperCAmelCase_								=     tokenizer("""My favorite food is""" ,					return_tensors="""pt"""		).to(lowerCAmelCase__		)

								# Test results are the same with and without cache
								UpperCAmelCase_								=     model.generate(**lowerCAmelCase__ ,					do_sample=lowerCAmelCase__ ,					max_new_tokens=20 ,					use_cache=lowerCAmelCase__		)
								UpperCAmelCase_								=     model.generate(**lowerCAmelCase__ ,					do_sample=lowerCAmelCase__ ,					max_new_tokens=20 ,					use_cache=lowerCAmelCase__		)
								self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0		)









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()


