"""simple docstring"""



import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
				import jax.numpy as jnp

				from transformers.models.albert.modeling_flax_albert import (
				    FlaxAlbertForMaskedLM,
				    FlaxAlbertForMultipleChoice,
				    FlaxAlbertForPreTraining,
				    FlaxAlbertForQuestionAnswering,
				    FlaxAlbertForSequenceClassification,
				    FlaxAlbertForTokenClassification,
				    FlaxAlbertModel,
				)
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=13   ,      snake_case_=7   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_=99   ,      snake_case_=32   ,      snake_case_=5   ,      snake_case_=4   ,      snake_case_=37   ,      snake_case_="gelu"   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=512   ,      snake_case_=16   ,      snake_case_=2   ,      snake_case_=0.02   ,      snake_case_=4   ,      ):







										'''simple docstring'''


										A__       :     List[Any]								=			parent
										A__       :     int								=			batch_size
										A__       :     str								=			seq_length
										A__       :     Optional[int]								=			is_training
										A__       :     Dict								=			use_attention_mask
										A__       :     Any								=			use_token_type_ids
										A__       :     List[Any]								=			use_labels
										A__       :     str								=			vocab_size
										A__       :     List[Any]								=			hidden_size
										A__       :     int								=			num_hidden_layers
										A__       :     Tuple								=			num_attention_heads
										A__       :     Any								=			intermediate_size
										A__       :     Optional[int]								=			hidden_act
										A__       :     Optional[int]								=			hidden_dropout_prob
										A__       :     List[Any]								=			attention_probs_dropout_prob
										A__       :     Optional[Any]								=			max_position_embeddings
										A__       :     List[Any]								=			type_vocab_size
										A__       :     Optional[Any]								=			type_sequence_label_size
										A__       :     Any								=			initializer_range
										A__       :     Optional[Any]								=			num_choices





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size							)

										A__       :     str								=			None
										if self.use_attention_mask:
																	A__       :     Union[str, Any]								=			random_attention_mask([self.batch_size, self.seq_length]							)

										A__       :     Tuple								=			None
										if self.use_token_type_ids:
																	A__       :     str								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.type_vocab_size							)

										A__       :     Optional[int]								=			AlbertConfig(
										    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=snake_case_   ,      initializer_range=self.initializer_range   ,      )

										return config, input_ids, token_type_ids, attention_mask





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Any								=			self.prepare_config_and_inputs()
										A__				,							A__				,							A__				,							A__       :     str								=			config_and_inputs
										A__       :     Optional[int]								=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
										return config, inputs_dict





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







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				(
			    (
			        FlaxAlbertModel,
			        FlaxAlbertForPreTraining,
			        FlaxAlbertForMaskedLM,
			        FlaxAlbertForMultipleChoice,
			        FlaxAlbertForQuestionAnswering,
			        FlaxAlbertForSequenceClassification,
			        FlaxAlbertForTokenClassification,
			        FlaxAlbertForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Tuple								=			FlaxAlbertModelTester(self							)





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										for model_class_name in self.all_model_classes:
																	A__       :     List[str]								=			model_class_name.from_pretrained("""albert-base-v2"""							)
																	A__       :     str								=			model(np.ones((1, 1)							)							)
																	self.assertIsNotNone(snake_case_							)





@require_flax
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			FlaxAlbertModel.from_pretrained("""albert-base-v2"""							)
										A__       :     Tuple								=			np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]							)
										A__       :     Dict								=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
										A__       :     Optional[Any]								=			model(snake_case_   ,      attention_mask=snake_case_							)[0]
										A__       :     List[Any]								=			(1, 11, 768)
										self.assertEqual(output.shape   ,      snake_case_							)
										A__       :     Optional[int]								=			np.array(
										    [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]							)

										self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]   ,      snake_case_   ,      atol=1E-4							)							)






"""simple docstring"""



import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=1024							):
							A__				,							A__       :     Any								=			[], []

							A__       :     List[Any]								=			list(zip(lowerCAmelCase			,  lowerCAmelCase							)							)
							A__				,							A__       :     Optional[int]								=			sorted_examples[0]

							def is_too_big(lowerCAmelCase							):
														return tok(lowerCAmelCase			,  return_tensors="""pt"""							).input_ids.shape[1] > max_tokens

							for src, tgt in tqdm(sorted_examples[1:]							):
														A__       :     List[Any]								=			new_src + """ """ + src
														A__       :     List[str]								=			new_tgt + """ """ + tgt
														if is_too_big(lowerCAmelCase							) or is_too_big(lowerCAmelCase							):  # cant fit, finalize example
																					finished_src.append(lowerCAmelCase							)
																					finished_tgt.append(lowerCAmelCase							)
																					A__				,							A__       :     str								=			src, tgt
														else:  # can fit, keep adding
																					A__				,							A__       :     Union[str, Any]								=			cand_src, cand_tgt

    # cleanup
							if new_src:
														assert new_tgt
														finished_src.append(lowerCAmelCase							)
														finished_tgt.append(lowerCAmelCase							)
							return finished_src, finished_tgt

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[int]								=			Path(lowerCAmelCase							)
							save_path.mkdir(exist_ok=lowerCAmelCase							)
							for split in ["train"]:
														A__				,							A__       :     List[Any]								=			data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
														A__       :     Tuple								=			[x.rstrip() for x in Path(lowerCAmelCase							).open().readlines()]
														A__       :     Union[str, Any]								=			[x.rstrip() for x in Path(lowerCAmelCase							).open().readlines()]
														A__				,							A__       :     Tuple								=			pack_examples(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
														print(F'''packed {split} split from {len(lowerCAmelCase							)} examples -> {len(lowerCAmelCase							)}.'''							)
														Path(save_path / F'''{split}.source'''							).open("""w"""							).write("""\n""".join(lowerCAmelCase							)							)
														Path(save_path / F'''{split}.target'''							).open("""w"""							).write("""\n""".join(lowerCAmelCase							)							)
							for split in ["val", "test"]:
														A__				,							A__       :     Dict								=			data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
														shutil.copyfile(lowerCAmelCase			,  save_path / F'''{split}.source'''							)
														shutil.copyfile(lowerCAmelCase			,  save_path / F'''{split}.target'''							)

def  _A(		):
							A__       :     str								=			argparse.ArgumentParser()
							parser.add_argument("""--tok_name"""			,  type=lowerCAmelCase			,  help="""like facebook/bart-large-cnn,t5-base, etc."""							)
							parser.add_argument("""--max_seq_len"""			,  type=lowerCAmelCase			,  default=128							)
							parser.add_argument("""--data_dir"""			,  type=lowerCAmelCase							)
							parser.add_argument("""--save_path"""			,  type=lowerCAmelCase							)
							A__       :     str								=			parser.parse_args()
							A__       :     Optional[Any]								=			AutoTokenizer.from_pretrained(args.tok_name							)
							return pack_data_dir(lowerCAmelCase			,  Path(args.data_dir							)			,  args.max_seq_len			,  args.save_path							)


if __name__ == "__main__":
				packer_cli()





